From f4af92550cb90e47a12d4625fa615dd2b1587d42 Mon Sep 17 00:00:00 2001 From: Omede Firouz Date: Sun, 3 May 2015 11:42:02 -0700 Subject: [PATCH 01/41] [SPARK-7022] [PYSPARK] [ML] Add ML.Tuning.ParamGridBuilder to PySpark Author: Omede Firouz Author: Omede Closes #5601 from oefirouz/paramgrid and squashes the following commits: c9e2481 [Omede Firouz] Make test a doctest 9a8ce22 [Omede] Fix linter issues 8b8a6d2 [Omede Firouz] [SPARK-7022][PySpark][ML] Add ML.Tuning.ParamGridBuilder to PySpark --- python/pyspark/ml/tuning.py | 94 +++++++++++++++++++++++++++++++++++++ python/run-tests | 1 + 2 files changed, 95 insertions(+) create mode 100644 python/pyspark/ml/tuning.py diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py new file mode 100644 index 0000000000000..a383bd0c0d26f --- /dev/null +++ b/python/pyspark/ml/tuning.py @@ -0,0 +1,94 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +__all__ = ['ParamGridBuilder'] + + +class ParamGridBuilder(object): + """ + Builder for a param grid used in grid search-based model selection. + + >>> from classification import LogisticRegression + >>> lr = LogisticRegression() + >>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \ + .baseOn([lr.predictionCol, 'p']) \ + .addGrid(lr.regParam, [1.0, 2.0, 3.0]) \ + .addGrid(lr.maxIter, [1, 5]) \ + .addGrid(lr.featuresCol, ['f']) \ + .build() + >>> expected = [ \ +{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}] + >>> fail_count = 0 + >>> for e in expected: + ... if e not in output: + ... fail_count += 1 + >>> if len(expected) != len(output): + ... fail_count += 1 + >>> fail_count + 0 + """ + + def __init__(self): + self._param_grid = {} + + def addGrid(self, param, values): + """ + Sets the given parameters in this grid to fixed values. + """ + self._param_grid[param] = values + + return self + + def baseOn(self, *args): + """ + Sets the given parameters in this grid to fixed values. + Accepts either a parameter dictionary or a list of (parameter, value) pairs. + """ + if isinstance(args[0], dict): + self.baseOn(*args[0].items()) + else: + for (param, value) in args: + self.addGrid(param, [value]) + + return self + + def build(self): + """ + Builds and returns all combinations of parameters specified + by the param grid. + """ + param_maps = [{}] + for (param, values) in self._param_grid.items(): + new_param_maps = [] + for value in values: + for old_map in param_maps: + copied_map = old_map.copy() + copied_map[param] = value + new_param_maps.append(copied_map) + param_maps = new_param_maps + + return param_maps + + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/python/run-tests b/python/run-tests index 88b63b84fdc27..0e0eee3564e7c 100755 --- a/python/run-tests +++ b/python/run-tests @@ -98,6 +98,7 @@ function run_ml_tests() { echo "Run ml tests ..." run_test "pyspark/ml/feature.py" run_test "pyspark/ml/classification.py" + run_test "pyspark/ml/tuning.py" run_test "pyspark/ml/tests.py" } From daa70bf135f23381f5f410aa95a1c0e5a2888568 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Sun, 3 May 2015 13:12:50 -0700 Subject: [PATCH 02/41] [SPARK-6907] [SQL] Isolated client for HiveMetastore This PR adds initial support for loading multiple versions of Hive in a single JVM and provides a common interface for extracting metadata from the `HiveMetastoreClient` for a given version. This is accomplished by creating an isolated `ClassLoader` that operates according to the following rules: - __Shared Classes__: Java, Scala, logging, and Spark classes are delegated to `baseClassLoader` allowing the results of calls to the `ClientInterface` to be visible externally. - __Hive Classes__: new instances are loaded from `execJars`. These classes are not accessible externally due to their custom loading. - __Barrier Classes__: Classes such as `ClientWrapper` are defined in Spark but must link to a specific version of Hive. As a result, the bytecode is acquired from the Spark `ClassLoader` but a new copy is created for each instance of `IsolatedClientLoader`. This new instance is able to see a specific version of hive without using reflection where ever hive is consistent across versions. Since this is a unique instance, it is not visible externally other than as a generic `ClientInterface`, unless `isolationOn` is set to `false`. In addition to the unit tests, I have also tested this locally against mysql instances of the Hive Metastore. I've also successfully ported Spark SQL to run with this client, but due to the size of the changes, that will come in a follow-up PR. By default, Hive jars are currently downloaded from Maven automatically for a given version to ease packaging and testing. However, there is also support for specifying their location manually for deployments without internet. Author: Michael Armbrust Closes #5851 from marmbrus/isolatedClient and squashes the following commits: c72f6ac [Michael Armbrust] rxins comments 1e271fa [Michael Armbrust] [SPARK-6907][SQL] Isolated client for HiveMetastore --- .../org/apache/spark/deploy/SparkSubmit.scala | 2 +- .../spark/sql/catalyst/analysis/Catalog.scala | 2 + .../spark/sql/catalyst/util/package.scala | 37 +- .../sql/hive/client/ClientInterface.scala | 149 +++++++ .../spark/sql/hive/client/ClientWrapper.scala | 395 ++++++++++++++++++ .../hive/client/IsolatedClientLoader.scala | 172 ++++++++ .../sql/hive/client/ReflectionMagic.scala | 200 +++++++++ .../spark/sql/hive/client/package.scala | 33 ++ .../spark/sql/hive/client/VersionsSuite.scala | 105 +++++ 9 files changed, 1088 insertions(+), 7 deletions(-) create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 42b5d41b7b526..8a0327984e195 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -701,7 +701,7 @@ object SparkSubmit { } /** Provides utility functions to be used inside SparkSubmit. */ -private[deploy] object SparkSubmitUtils { +private[spark] object SparkSubmitUtils { // Exposed for testing var printStream = SparkSubmit.printStream diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala index b2f8157a1a61f..18c24b651921a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala @@ -27,6 +27,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery} */ class NoSuchTableException extends Exception +class NoSuchDatabaseException extends Exception + /** * An interface for looking up relations by name. Used by an [[Analyzer]]. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala index c86214a2aa944..9d613a940ee86 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala @@ -17,12 +17,31 @@ package org.apache.spark.sql.catalyst -import java.io.{PrintWriter, ByteArrayOutputStream, FileInputStream, File} +import java.io._ import org.apache.spark.util.Utils package object util { + /** Silences output to stderr or stdout for the duration of f */ + def quietly[A](f: => A): A = { + val origErr = System.err + val origOut = System.out + try { + System.setErr(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + System.setOut(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + + f + } finally { + System.setErr(origErr) + System.setOut(origOut) + } + } + def fileToString(file: File, encoding: String = "UTF-8"): String = { val inStream = new FileInputStream(file) val outStream = new ByteArrayOutputStream @@ -42,10 +61,9 @@ package object util { new String(outStream.toByteArray, encoding) } - def resourceToString( - resource:String, - encoding: String = "UTF-8", - classLoader: ClassLoader = Utils.getSparkClassLoader): String = { + def resourceToBytes( + resource: String, + classLoader: ClassLoader = Utils.getSparkClassLoader): Array[Byte] = { val inStream = classLoader.getResourceAsStream(resource) val outStream = new ByteArrayOutputStream try { @@ -61,7 +79,14 @@ package object util { finally { inStream.close() } - new String(outStream.toByteArray, encoding) + outStream.toByteArray + } + + def resourceToString( + resource:String, + encoding: String = "UTF-8", + classLoader: ClassLoader = Utils.getSparkClassLoader): String = { + new String(resourceToBytes(resource, classLoader), encoding) } def stringToFile(file: File, str: String): File = { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala new file mode 100644 index 0000000000000..a863aa77cb7e0 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala @@ -0,0 +1,149 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException} + +case class HiveDatabase( + name: String, + location: String) + +abstract class TableType { val name: String } +case object ExternalTable extends TableType { override val name = "EXTERNAL_TABLE" } +case object IndexTable extends TableType { override val name = "INDEX_TABLE" } +case object ManagedTable extends TableType { override val name = "MANAGED_TABLE" } +case object VirtualView extends TableType { override val name = "VIRTUAL_VIEW" } + +case class HiveStorageDescriptor( + location: String, + inputFormat: String, + outputFormat: String, + serde: String) + +case class HivePartition( + values: Seq[String], + storage: HiveStorageDescriptor) + +case class HiveColumn(name: String, hiveType: String, comment: String) +case class HiveTable( + specifiedDatabase: Option[String], + name: String, + schema: Seq[HiveColumn], + partitionColumns: Seq[HiveColumn], + properties: Map[String, String], + serdeProperties: Map[String, String], + tableType: TableType, + location: Option[String] = None, + inputFormat: Option[String] = None, + outputFormat: Option[String] = None, + serde: Option[String] = None) { + + @transient + private[client] var client: ClientInterface = _ + + private[client] def withClient(ci: ClientInterface): this.type = { + client = ci + this + } + + def database: String = specifiedDatabase.getOrElse(sys.error("database not resolved")) + + def isPartitioned: Boolean = partitionColumns.nonEmpty + + def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this) + + // Hive does not support backticks when passing names to the client. + def qualifiedName: String = s"$database.$name" +} + +/** + * An externally visible interface to the Hive client. This interface is shared across both the + * internal and external classloaders for a given version of Hive and thus must expose only + * shared classes. + */ +trait ClientInterface { + /** + * Runs a HiveQL command using Hive, returning the results as a list of strings. Each row will + * result in one string. + */ + def runSqlHive(sql: String): Seq[String] + + /** Returns the names of all tables in the given database. */ + def listTables(dbName: String): Seq[String] + + /** Returns the name of the active database. */ + def currentDatabase: String + + /** Returns the metadata for specified database, throwing an exception if it doesn't exist */ + def getDatabase(name: String): HiveDatabase = { + getDatabaseOption(name).getOrElse(throw new NoSuchDatabaseException) + } + + /** Returns the metadata for a given database, or None if it doesn't exist. */ + def getDatabaseOption(name: String): Option[HiveDatabase] + + /** Returns the specified table, or throws [[NoSuchTableException]]. */ + def getTable(dbName: String, tableName: String): HiveTable = { + getTableOption(dbName, tableName).getOrElse(throw new NoSuchTableException) + } + + /** Returns the metadata for the specified table or None if it doens't exist. */ + def getTableOption(dbName: String, tableName: String): Option[HiveTable] + + /** Creates a table with the given metadata. */ + def createTable(table: HiveTable): Unit + + /** Updates the given table with new metadata. */ + def alterTable(table: HiveTable): Unit + + /** Creates a new database with the given name. */ + def createDatabase(database: HiveDatabase): Unit + + /** Returns all partitions for the given table. */ + def getAllPartitions(hTable: HiveTable): Seq[HivePartition] + + /** Loads a static partition into an existing table. */ + def loadPartition( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit + + /** Loads data into an existing table. */ + def loadTable( + loadPath: String, // TODO URI + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit + + /** Loads new dynamic partitions into an existing table. */ + def loadDynamicPartitions( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit + + /** Used for testing only. Removes all metadata from this instance of Hive. */ + def reset(): Unit +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala new file mode 100644 index 0000000000000..ea52fea037f1f --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala @@ -0,0 +1,395 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import java.io.{BufferedReader, InputStreamReader, File, PrintStream} +import java.net.URI +import java.util.{ArrayList => JArrayList} + +import scala.collection.JavaConversions._ +import scala.language.reflectiveCalls + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.metastore.api.Database +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.metastore.api +import org.apache.hadoop.hive.metastore.api.FieldSchema +import org.apache.hadoop.hive.ql.metadata +import org.apache.hadoop.hive.ql.metadata.Hive +import org.apache.hadoop.hive.ql.session.SessionState +import org.apache.hadoop.hive.ql.processors._ +import org.apache.hadoop.hive.ql.Driver + +import org.apache.spark.Logging +import org.apache.spark.sql.execution.QueryExecutionException + + +/** + * A class that wraps the HiveClient and converts its responses to externally visible classes. + * Note that this class is typically loaded with an internal classloader for each instantiation, + * allowing it to interact directly with a specific isolated version of Hive. Loading this class + * with the isolated classloader however will result in it only being visible as a ClientInterface, + * not a ClientWrapper. + * + * This class needs to interact with multiple versions of Hive, but will always be compiled with + * the 'native', execution version of Hive. Therefore, any places where hive breaks compatibility + * must use reflection after matching on `version`. + * + * @param version the version of hive used when pick function calls that are not compatible. + * @param config a collection of configuration options that will be added to the hive conf before + * opening the hive client. + */ +class ClientWrapper( + version: HiveVersion, + config: Map[String, String]) + extends ClientInterface + with Logging + with ReflectionMagic { + + private val conf = new HiveConf(classOf[SessionState]) + config.foreach { case (k, v) => + logDebug(s"Hive Config: $k=$v") + conf.set(k, v) + } + + // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. + private val outputBuffer = new java.io.OutputStream { + var pos: Int = 0 + var buffer = new Array[Int](10240) + def write(i: Int): Unit = { + buffer(pos) = i + pos = (pos + 1) % buffer.size + } + + override def toString: String = { + val (end, start) = buffer.splitAt(pos) + val input = new java.io.InputStream { + val iterator = (start ++ end).iterator + + def read(): Int = if (iterator.hasNext) iterator.next() else -1 + } + val reader = new BufferedReader(new InputStreamReader(input)) + val stringBuilder = new StringBuilder + var line = reader.readLine() + while(line != null) { + stringBuilder.append(line) + stringBuilder.append("\n") + line = reader.readLine() + } + stringBuilder.toString() + } + } + + val state = { + val original = Thread.currentThread().getContextClassLoader + Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + val ret = try { + val newState = new SessionState(conf) + SessionState.start(newState) + newState.out = new PrintStream(outputBuffer, true, "UTF-8") + newState.err = new PrintStream(outputBuffer, true, "UTF-8") + newState + } finally { + Thread.currentThread().setContextClassLoader(original) + } + ret + } + + private val client = Hive.get(conf) + + /** + * Runs `f` with ThreadLocal session state and classloaders configured for this version of hive. + */ + private def withHiveState[A](f: => A): A = synchronized { + val original = Thread.currentThread().getContextClassLoader + Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + Hive.set(client) + version match { + case hive.v12 => + classOf[SessionState] + .callStatic[SessionState, SessionState]("start", state) + case hive.v13 => + classOf[SessionState] + .callStatic[SessionState, SessionState]("setCurrentSessionState", state) + } + val ret = try f finally { + Thread.currentThread().setContextClassLoader(original) + } + ret + } + + override def currentDatabase: String = withHiveState { + state.getCurrentDatabase + } + + override def createDatabase(database: HiveDatabase): Unit = withHiveState { + client.createDatabase( + new Database( + database.name, + "", + new File(database.location).toURI.toString, + new java.util.HashMap), + true) + } + + override def getDatabaseOption(name: String): Option[HiveDatabase] = withHiveState { + Option(client.getDatabase(name)).map { d => + HiveDatabase( + name = d.getName, + location = d.getLocationUri) + } + } + + override def getTableOption( + dbName: String, + tableName: String): Option[HiveTable] = withHiveState { + + logDebug(s"Looking up $dbName.$tableName") + + val hiveTable = Option(client.getTable(dbName, tableName, false)) + val converted = hiveTable.map { h => + + HiveTable( + name = h.getTableName, + specifiedDatabase = Option(h.getDbName), + schema = h.getCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)), + partitionColumns = h.getPartCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)), + properties = h.getParameters.toMap, + serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap, + tableType = ManagedTable, // TODO + location = version match { + case hive.v12 => Option(h.call[URI]("getDataLocation")).map(_.toString) + case hive.v13 => Option(h.call[Path]("getDataLocation")).map(_.toString) + }, + inputFormat = Option(h.getInputFormatClass).map(_.getName), + outputFormat = Option(h.getOutputFormatClass).map(_.getName), + serde = Option(h.getSerializationLib)).withClient(this) + } + converted + } + + private def toInputFormat(name: String) = + Class.forName(name).asInstanceOf[Class[_ <: org.apache.hadoop.mapred.InputFormat[_, _]]] + + private def toOutputFormat(name: String) = + Class.forName(name) + .asInstanceOf[Class[_ <: org.apache.hadoop.hive.ql.io.HiveOutputFormat[_, _]]] + + private def toQlTable(table: HiveTable): metadata.Table = { + val qlTable = new metadata.Table(table.database, table.name) + + qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment))) + qlTable.setPartCols( + table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment))) + table.properties.foreach { case (k, v) => qlTable.setProperty(k, v) } + table.serdeProperties.foreach { case (k, v) => qlTable.setSerdeParam(k, v) } + version match { + case hive.v12 => + table.location.map(new URI(_)).foreach(u => qlTable.call[URI, Unit]("setDataLocation", u)) + case hive.v13 => + table.location + .map(new org.apache.hadoop.fs.Path(_)) + .foreach(qlTable.call[Path, Unit]("setDataLocation", _)) + } + table.inputFormat.map(toInputFormat).foreach(qlTable.setInputFormatClass) + table.outputFormat.map(toOutputFormat).foreach(qlTable.setOutputFormatClass) + table.serde.foreach(qlTable.setSerializationLib) + + qlTable + } + + override def createTable(table: HiveTable): Unit = withHiveState { + val qlTable = toQlTable(table) + client.createTable(qlTable) + } + + override def alterTable(table: HiveTable): Unit = withHiveState { + val qlTable = toQlTable(table) + client.alterTable(table.qualifiedName, qlTable) + } + + override def getAllPartitions(hTable: HiveTable): Seq[HivePartition] = withHiveState { + val qlTable = toQlTable(hTable) + val qlPartitions = version match { + case hive.v12 => + client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsForPruner", qlTable) + case hive.v13 => + client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsOf", qlTable) + } + qlPartitions.map(_.getTPartition).map { p => + HivePartition( + values = Option(p.getValues).map(_.toSeq).getOrElse(Seq.empty), + storage = HiveStorageDescriptor( + location = p.getSd.getLocation, + inputFormat = p.getSd.getInputFormat, + outputFormat = p.getSd.getOutputFormat, + serde = p.getSd.getSerdeInfo.getSerializationLib)) + }.toSeq + } + + override def listTables(dbName: String): Seq[String] = withHiveState { + client.getAllTables + } + + /** + * Runs the specified SQL query using Hive. + */ + override def runSqlHive(sql: String): Seq[String] = { + val maxResults = 100000 + val results = runHive(sql, maxResults) + // It is very confusing when you only get back some of the results... + if (results.size == maxResults) sys.error("RESULTS POSSIBLY TRUNCATED") + results + } + + /** + * Execute the command using Hive and return the results as a sequence. Each element + * in the sequence is one row. + */ + protected def runHive(cmd: String, maxRows: Int = 1000): Seq[String] = withHiveState { + logDebug(s"Running hiveql '$cmd'") + if (cmd.toLowerCase.startsWith("set")) { logDebug(s"Changing config: $cmd") } + try { + val cmd_trimmed: String = cmd.trim() + val tokens: Array[String] = cmd_trimmed.split("\\s+") + val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim() + val proc: CommandProcessor = version match { + case hive.v12 => + classOf[CommandProcessorFactory] + .callStatic[String, HiveConf, CommandProcessor]("get", cmd_1, conf) + case hive.v13 => + classOf[CommandProcessorFactory] + .callStatic[Array[String], HiveConf, CommandProcessor]("get", Array(tokens(0)), conf) + } + + proc match { + case driver: Driver => + val response: CommandProcessorResponse = driver.run(cmd) + // Throw an exception if there is an error in query processing. + if (response.getResponseCode != 0) { + driver.close() + throw new QueryExecutionException(response.getErrorMessage) + } + driver.setMaxRows(maxRows) + + val results = version match { + case hive.v12 => + val res = new JArrayList[String] + driver.call[JArrayList[String], Boolean]("getResults", res) + res.toSeq + case hive.v13 => + val res = new JArrayList[Object] + driver.call[JArrayList[Object], Boolean]("getResults", res) + res.map { r => + r match { + case s: String => s + case a: Array[Object] => a(0).asInstanceOf[String] + } + } + } + driver.close() + results + + case _ => + if (state.out != null) { + state.out.println(tokens(0) + " " + cmd_1) + } + Seq(proc.run(cmd_1).getResponseCode.toString) + } + } catch { + case e: Exception => + logError( + s""" + |====================== + |HIVE FAILURE OUTPUT + |====================== + |${outputBuffer.toString} + |====================== + |END HIVE FAILURE OUTPUT + |====================== + """.stripMargin) + throw e + } + } + + def loadPartition( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit = withHiveState { + + client.loadPartition( + new Path(loadPath), // TODO: Use URI + tableName, + partSpec, + replace, + holdDDLTime, + inheritTableSpecs, + isSkewedStoreAsSubdir) + } + + def loadTable( + loadPath: String, // TODO URI + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit = withHiveState { + client.loadTable( + new Path(loadPath), + tableName, + replace, + holdDDLTime) + } + + def loadDynamicPartitions( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit = withHiveState { + client.loadDynamicPartitions( + new Path(loadPath), + tableName, + partSpec, + replace, + numDP, + holdDDLTime, + listBucketingEnabled) + } + + def reset(): Unit = withHiveState { + client.getAllTables("default").foreach { t => + logDebug(s"Deleting table $t") + val table = client.getTable("default", t) + client.getIndexes("default", t, 255).foreach { index => + client.dropIndex("default", t, index.getIndexName, true) + } + if (!table.isIndexTable) { + client.dropTable("default", t) + } + } + client.getAllDatabases.filterNot(_ == "default").foreach { db => + logDebug(s"Dropping Database: $db") + client.dropDatabase(db, true, false, true) + } + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala new file mode 100644 index 0000000000000..710dbca6e3c66 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala @@ -0,0 +1,172 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import java.io.File +import java.net.URLClassLoader +import java.util + +import scala.language.reflectiveCalls +import scala.util.Try + +import org.apache.commons.io.{FileUtils, IOUtils} + +import org.apache.spark.Logging +import org.apache.spark.deploy.SparkSubmitUtils + +import org.apache.spark.sql.catalyst.util.quietly + +/** Factory for `IsolatedClientLoader` with specific versions of hive. */ +object IsolatedClientLoader { + /** + * Creates isolated Hive client loaders by downloading the requested version from maven. + */ + def forVersion( + version: String, + config: Map[String, String] = Map.empty): IsolatedClientLoader = synchronized { + val resolvedVersion = hiveVersion(version) + val files = resolvedVersions.getOrElseUpdate(resolvedVersion, downloadVersion(resolvedVersion)) + new IsolatedClientLoader(hiveVersion(version), files, config) + } + + def hiveVersion(version: String): HiveVersion = version match { + case "12" | "0.12" | "0.12.0" => hive.v12 + case "13" | "0.13" | "0.13.0" | "0.13.1" => hive.v13 + } + + private def downloadVersion(version: HiveVersion): Seq[File] = { + val hiveArtifacts = + (Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde") ++ + (if (version.hasBuiltinsJar) "hive-builtins" :: Nil else Nil)) + .map(a => s"org.apache.hive:$a:${version.fullVersion}") :+ + "com.google.guava:guava:14.0.1" :+ + "org.apache.hadoop:hadoop-client:2.4.0" :+ + "mysql:mysql-connector-java:5.1.12" + + val classpath = quietly { + SparkSubmitUtils.resolveMavenCoordinates( + hiveArtifacts.mkString(","), + Some("http://www.datanucleus.org/downloads/maven2"), + None) + } + val allFiles = classpath.split(",").map(new File(_)).toSet + + // TODO: Remove copy logic. + val tempDir = File.createTempFile("hive", "v" + version.toString) + tempDir.delete() + tempDir.mkdir() + + allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir)) + tempDir.listFiles() + } + + private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[File]] +} + +/** + * Creates a Hive `ClientInterface` using a classloader that works according to the following rules: + * - Shared classes: Java, Scala, logging, and Spark classes are delegated to `baseClassLoader` + * allowing the results of calls to the `ClientInterface` to be visible externally. + * - Hive classes: new instances are loaded from `execJars`. These classes are not + * accessible externally due to their custom loading. + * - ClientWrapper: a new copy is created for each instance of `IsolatedClassLoader`. + * This new instance is able to see a specific version of hive without using reflection. Since + * this is a unique instance, it is not visible externally other than as a generic + * `ClientInterface`, unless `isolationOn` is set to `false`. + * + * @param version The version of hive on the classpath. used to pick specific function signatures + * that are not compatibile accross versions. + * @param execJars A collection of jar files that must include hive and hadoop. + * @param config A set of options that will be added to the HiveConf of the constructed client. + * @param isolationOn When true, custom versions of barrier classes will be constructed. Must be + * true unless loading the version of hive that is on Sparks classloader. + * @param rootClassLoader The system root classloader. Must not know about hive classes. + * @param baseClassLoader The spark classloader that is used to load shared classes. + * + */ +class IsolatedClientLoader( + val version: HiveVersion, + val execJars: Seq[File] = Seq.empty, + val config: Map[String, String] = Map.empty, + val isolationOn: Boolean = true, + val rootClassLoader: ClassLoader = ClassLoader.getSystemClassLoader.getParent.getParent, + val baseClassLoader: ClassLoader = Thread.currentThread().getContextClassLoader) + extends Logging { + + // Check to make sure that the root classloader does not know about Hive. + assert(Try(baseClassLoader.loadClass("org.apache.hive.HiveConf")).isFailure) + + /** All jars used by the hive specific classloader. */ + protected def allJars = execJars.map(_.toURI.toURL).toArray + + protected def isSharedClass(name: String): Boolean = + name.contains("slf4j") || + name.contains("log4j") || + name.startsWith("org.apache.spark.") || + name.startsWith("scala.") || + name.startsWith("com.google") || + name.startsWith("java.lang.") || + name.startsWith("java.net") + + /** True if `name` refers to a spark class that must see specific version of Hive. */ + protected def isBarrierClass(name: String): Boolean = + name.startsWith("org.apache.spark.sql.hive.execution.PairSerDe") || + name.startsWith(classOf[ClientWrapper].getName) || + name.startsWith(classOf[ReflectionMagic].getName) + + protected def classToPath(name: String): String = + name.replaceAll("\\.", "/") + ".class" + + /** The classloader that is used to load an isolated version of Hive. */ + protected val classLoader: ClassLoader = new URLClassLoader(allJars, rootClassLoader) { + override def loadClass(name: String, resolve: Boolean): Class[_] = { + val loaded = findLoadedClass(name) + if (loaded == null) doLoadClass(name, resolve) else loaded + } + + def doLoadClass(name: String, resolve: Boolean): Class[_] = { + val classFileName = name.replaceAll("\\.", "/") + ".class" + if (isBarrierClass(name) && isolationOn) { + val bytes = IOUtils.toByteArray(baseClassLoader.getResourceAsStream(classFileName)) + logDebug(s"custom defining: $name - ${util.Arrays.hashCode(bytes)}") + defineClass(name, bytes, 0, bytes.length) + } else if (!isSharedClass(name)) { + logDebug(s"hive class: $name - ${getResource(classToPath(name))}") + super.loadClass(name, resolve) + } else { + logDebug(s"shared class: $name") + baseClassLoader.loadClass(name) + } + } + } + + // Pre-reflective instantiation setup. + logDebug("Initializing the logger to avoid disaster...") + Thread.currentThread.setContextClassLoader(classLoader) + + /** The isolated client interface to Hive. */ + val client: ClientInterface = try { + classLoader + .loadClass(classOf[ClientWrapper].getName) + .getConstructors.head + .newInstance(version, config) + .asInstanceOf[ClientInterface] + } finally { + Thread.currentThread.setContextClassLoader(baseClassLoader) + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala new file mode 100644 index 0000000000000..90d03049356b5 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala @@ -0,0 +1,200 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import scala.reflect._ + +/** + * Provides implicit functions on any object for calling methods reflectively. + */ +protected trait ReflectionMagic { + /** code for InstanceMagic + println( + (1 to 22).map { n => + def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") + val types = repeat(n => s"A$n <: AnyRef : ClassTag") + val inArgs = repeat(n => s"a$n: A$n") + val erasure = repeat(n => s"classTag[A$n].erasure") + val outArgs = repeat(n => s"a$n") + s"""|def call[$types, R](name: String, $inArgs): R = { + | clazz.getMethod(name, $erasure).invoke(a, $outArgs).asInstanceOf[R] + |}""".stripMargin + }.mkString("\n") + ) + */ + + // scalastyle:off + protected implicit class InstanceMagic(a: Any) { + private val clazz = a.getClass + + def call[R](name: String): R = { + clazz.getMethod(name).invoke(a).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { + clazz.getMethod(name, classTag[A1].erasure).invoke(a, a1).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(a, a1, a2).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(a, a1, a2, a3).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(a, a1, a2, a3, a4).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(a, a1, a2, a3, a4, a5).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(a, a1, a2, a3, a4, a5, a6).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] + } + } + + /** code for StaticMagic + println( + (1 to 22).map { n => + def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") + val types = repeat(n => s"A$n <: AnyRef : ClassTag") + val inArgs = repeat(n => s"a$n: A$n") + val erasure = repeat(n => s"classTag[A$n].erasure") + val outArgs = repeat(n => s"a$n") + s"""|def callStatic[$types, R](name: String, $inArgs): R = { + | c.getDeclaredMethod(name, $erasure).invoke(c, $outArgs).asInstanceOf[R] + |}""".stripMargin + }.mkString("\n") + ) + */ + + protected implicit class StaticMagic(c: Class[_]) { + def callStatic[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { + c.getDeclaredMethod(name, classTag[A1].erasure).invoke(c, a1).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(c, a1, a2).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(c, a1, a2, a3).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(c, a1, a2, a3, a4).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(c, a1, a2, a3, a4, a5).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(c, a1, a2, a3, a4, a5, a6).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] + } + } + // scalastyle:on +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala new file mode 100644 index 0000000000000..7db9200d47440 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +/** Support for interacting with different versions of the HiveMetastoreClient */ +package object client { + private[client] abstract class HiveVersion(val fullVersion: String, val hasBuiltinsJar: Boolean) + + // scalastyle:off + private[client] object hive { + case object v10 extends HiveVersion("0.10.0", true) + case object v11 extends HiveVersion("0.11.0", false) + case object v12 extends HiveVersion("0.12.0", false) + case object v13 extends HiveVersion("0.13.1", false) + } + // scalastyle:on + +} \ No newline at end of file diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala new file mode 100644 index 0000000000000..81e77ba257bf1 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import org.apache.spark.Logging +import org.apache.spark.sql.catalyst.util.quietly +import org.apache.spark.util.Utils +import org.scalatest.FunSuite + +class VersionsSuite extends FunSuite with Logging { + val testType = "derby" + + private def buildConf() = { + lazy val warehousePath = Utils.createTempDir() + lazy val metastorePath = Utils.createTempDir() + metastorePath.delete() + Map( + "javax.jdo.option.ConnectionURL" -> s"jdbc:derby:;databaseName=$metastorePath;create=true", + "hive.metastore.warehouse.dir" -> warehousePath.toString) + } + + test("success sanity check") { + val badClient = IsolatedClientLoader.forVersion("13", buildConf()).client + val db = new HiveDatabase("default", "") + badClient.createDatabase(db) + } + + private def getNestedMessages(e: Throwable): String = { + var causes = "" + var lastException = e + while (lastException != null) { + causes += lastException.toString + "\n" + lastException = lastException.getCause + } + causes + } + + // Its actually pretty easy to mess things up and have all of your tests "pass" by accidentally + // connecting to an auto-populated, in-process metastore. Let's make sure we are getting the + // versions right by forcing a known compatibility failure. + // TODO: currently only works on mysql where we manually create the schema... + ignore("failure sanity check") { + val e = intercept[Throwable] { + val badClient = quietly { IsolatedClientLoader.forVersion("13", buildConf()).client } + } + assert(getNestedMessages(e) contains "Unknown column 'A0.OWNER_NAME' in 'field list'") + } + + private val versions = Seq("12", "13") + + private var client: ClientInterface = null + + versions.foreach { version => + test(s"$version: listTables") { + client = null + client = IsolatedClientLoader.forVersion(version, buildConf()).client + client.listTables("default") + } + + test(s"$version: createDatabase") { + val db = HiveDatabase("default", "") + client.createDatabase(db) + } + + test(s"$version: createTable") { + val table = + HiveTable( + specifiedDatabase = Option("default"), + name = "src", + schema = Seq(HiveColumn("key", "int", "")), + partitionColumns = Seq.empty, + properties = Map.empty, + serdeProperties = Map.empty, + tableType = ManagedTable, + location = None, + inputFormat = + Some(classOf[org.apache.hadoop.mapred.TextInputFormat].getName), + outputFormat = + Some(classOf[org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat[_, _]].getName), + serde = + Some(classOf[org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe].getName())) + + client.createTable(table) + } + + test(s"$version: getTable") { + client.getTable("default", "src") + } + } +} From 9e25b09f8809378777ae8bbe75dca12d2c45ff4c Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Sun, 3 May 2015 21:22:31 +0100 Subject: [PATCH 03/41] [SPARK-7302] [DOCS] SPARK building documentation still mentions building for yarn 0.23 Remove references to Hadoop 0.23 CC tgravescs Is this what you had in mind? basically all refs to 0.23? We don't support YARN 0.23, but also don't support Hadoop 0.23 anymore AFAICT. There are no builds or releases for it. In fact, on a related note, refs to CDH3 (Hadoop 0.20.2) should be removed as this certainly isn't supported either. Author: Sean Owen Closes #5863 from srowen/SPARK-7302 and squashes the following commits: 42f5d1e [Sean Owen] Remove CDH3 (Hadoop 0.20.2) refs too dad02e3 [Sean Owen] Remove references to Hadoop 0.23 --- docs/building-spark.md | 4 ---- docs/hadoop-third-party-distributions.md | 3 --- make-distribution.sh | 2 +- pom.xml | 14 -------------- 4 files changed, 1 insertion(+), 22 deletions(-) diff --git a/docs/building-spark.md b/docs/building-spark.md index ea79c5bc276d3..287fcd3c4034f 100644 --- a/docs/building-spark.md +++ b/docs/building-spark.md @@ -66,7 +66,6 @@ Because HDFS is not protocol-compatible across versions, if you want to read fro Hadoop versionProfile required - 0.23.xhadoop-0.23 1.x to 2.1.x(none) 2.2.xhadoop-2.2 2.3.xhadoop-2.3 @@ -82,9 +81,6 @@ mvn -Dhadoop.version=1.2.1 -DskipTests clean package # Cloudera CDH 4.2.0 with MapReduce v1 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package - -# Apache Hadoop 0.23.x -mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package {% endhighlight %} You can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". Spark only supports YARN versions 2.2.0 and later. diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md index 87dcc58feb494..96bd69ca3b33b 100644 --- a/docs/hadoop-third-party-distributions.md +++ b/docs/hadoop-third-party-distributions.md @@ -29,9 +29,6 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors. ReleaseVersion code CDH 4.X.X (YARN mode)2.0.0-cdh4.X.X CDH 4.X.X2.0.0-mr1-cdh4.X.X - CDH 3u60.20.2-cdh3u6 - CDH 3u50.20.2-cdh3u5 - CDH 3u40.20.2-cdh3u4 diff --git a/make-distribution.sh b/make-distribution.sh index 92177e19fe6be..1bfa9acb1fe6e 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -58,7 +58,7 @@ while (( "$#" )); do --hadoop) echo "Error: '--hadoop' is no longer supported:" echo "Error: use Maven profiles and options -Dhadoop.version and -Dyarn.version instead." - echo "Error: Related profiles include hadoop-0.23, hdaoop-2.2, hadoop-2.3 and hadoop-2.4." + echo "Error: Related profiles include hadoop-2.2, hadoop-2.3 and hadoop-2.4." exit_with_usage ;; --with-yarn) diff --git a/pom.xml b/pom.xml index 4313f940036c8..de18741feae3a 100644 --- a/pom.xml +++ b/pom.xml @@ -1614,20 +1614,6 @@ http://hadoop.apache.org/docs/ra.b.c/hadoop-project-dist/hadoop-common/dependency-analysis.html --> - - hadoop-0.23 - - - - org.apache.avro - avro - - - - 0.23.10 - - - hadoop-2.2 From 1ffa8cb91f8badf12a8aa190dc25920715a00db7 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Sun, 3 May 2015 18:06:48 -0700 Subject: [PATCH 04/41] [SPARK-7329] [MLLIB] simplify ParamGridBuilder impl as suggested by justinuang on #5601. Author: Xiangrui Meng Closes #5873 from mengxr/SPARK-7329 and squashes the following commits: d08f9cf [Xiangrui Meng] simplify tests b7a7b9b [Xiangrui Meng] simplify grid build --- python/pyspark/ml/tuning.py | 28 +++++++++------------------- 1 file changed, 9 insertions(+), 19 deletions(-) diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index a383bd0c0d26f..1773ab5bdcdb1 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -15,6 +15,8 @@ # limitations under the License. # +import itertools + __all__ = ['ParamGridBuilder'] @@ -37,14 +39,10 @@ class ParamGridBuilder(object): {lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ {lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ {lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}] - >>> fail_count = 0 - >>> for e in expected: - ... if e not in output: - ... fail_count += 1 - >>> if len(expected) != len(output): - ... fail_count += 1 - >>> fail_count - 0 + >>> len(output) == len(expected) + True + >>> all([m in expected for m in output]) + True """ def __init__(self): @@ -76,17 +74,9 @@ def build(self): Builds and returns all combinations of parameters specified by the param grid. """ - param_maps = [{}] - for (param, values) in self._param_grid.items(): - new_param_maps = [] - for value in values: - for old_map in param_maps: - copied_map = old_map.copy() - copied_map[param] = value - new_param_maps.append(copied_map) - param_maps = new_param_maps - - return param_maps + keys = self._param_grid.keys() + grid_values = self._param_grid.values() + return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)] if __name__ == "__main__": From 9646018bb4466433521b4e602b808f16e8d0ffdb Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Sun, 3 May 2015 21:44:39 -0700 Subject: [PATCH 05/41] [SPARK-7241] Pearson correlation for DataFrames submitting this PR from a phone, excuse the brevity. adds Pearson correlation to Dataframes, reusing the covariance calculation code cc mengxr rxin Author: Burak Yavuz Closes #5858 from brkyvz/df-corr and squashes the following commits: 285b838 [Burak Yavuz] addressed comments v2.0 d10babb [Burak Yavuz] addressed comments v0.2 4b74b24 [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into df-corr 4fe693b [Burak Yavuz] addressed comments v0.1 a682d06 [Burak Yavuz] ready for PR --- python/pyspark/sql/dataframe.py | 26 +++++++++ python/pyspark/sql/tests.py | 6 ++ .../spark/sql/DataFrameStatFunctions.scala | 26 +++++++++ .../sql/execution/stat/StatFunctions.scala | 58 ++++++++++++------- .../apache/spark/sql/JavaDataFrameSuite.java | 7 +++ .../apache/spark/sql/DataFrameStatSuite.scala | 33 +++++++++-- 6 files changed, 130 insertions(+), 26 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 8ddcff8fcdf98..aac5b8c4c5770 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -875,6 +875,27 @@ def fillna(self, value, subset=None): return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) + def corr(self, col1, col2, method=None): + """ + Calculates the correlation of two columns of a DataFrame as a double value. Currently only + supports the Pearson Correlation Coefficient. + :func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases. + + :param col1: The name of the first column + :param col2: The name of the second column + :param method: The correlation method. Currently only supports "pearson" + """ + if not isinstance(col1, str): + raise ValueError("col1 should be a string.") + if not isinstance(col2, str): + raise ValueError("col2 should be a string.") + if not method: + method = "pearson" + if not method == "pearson": + raise ValueError("Currently only the calculation of the Pearson Correlation " + + "coefficient is supported.") + return self._jdf.stat().corr(col1, col2, method) + def cov(self, col1, col2): """ Calculate the sample covariance for the given columns, specified by their names, as a @@ -1359,6 +1380,11 @@ class DataFrameStatFunctions(object): def __init__(self, df): self.df = df + def corr(self, col1, col2, method=None): + return self.df.corr(col1, col2, method) + + corr.__doc__ = DataFrame.corr.__doc__ + def cov(self, col1, col2): return self.df.cov(col1, col2) diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 613efc0ac029d..d652c302a54ba 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -394,6 +394,12 @@ def test_aggregator(self): self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0]) self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0]) + def test_corr(self): + import math + df = self.sc.parallelize([Row(a=i, b=math.sqrt(i)) for i in range(10)]).toDF() + corr = df.stat.corr("a", "b") + self.assertTrue(abs(corr - 0.95734012) < 1e-6) + def test_cov(self): df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF() cov = df.stat.cov("a", "b") diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala index e8fa82947759b..903532105284e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -27,6 +27,32 @@ import org.apache.spark.sql.execution.stat._ @Experimental final class DataFrameStatFunctions private[sql](df: DataFrame) { + /** + * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson + * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in + * MLlib's Statistics. + * + * @param col1 the name of the column + * @param col2 the name of the column to calculate the correlation against + * @return The Pearson Correlation Coefficient as a Double. + */ + def corr(col1: String, col2: String, method: String): Double = { + require(method == "pearson", "Currently only the calculation of the Pearson Correlation " + + "coefficient is supported.") + StatFunctions.pearsonCorrelation(df, Seq(col1, col2)) + } + + /** + * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame. + * + * @param col1 the name of the column + * @param col2 the name of the column to calculate the correlation against + * @return The Pearson Correlation Coefficient as a Double. + */ + def corr(col1: String, col2: String): Double = { + corr(col1, col2, "pearson") + } + /** * Finding frequent items for columns, possibly with false positives. Using the * frequent element count algorithm described in diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala index d4a94c24d9866..67b48e58b17ab 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala @@ -23,29 +23,43 @@ import org.apache.spark.sql.types.{DoubleType, NumericType} private[sql] object StatFunctions { + /** Calculate the Pearson Correlation Coefficient for the given columns */ + private[sql] def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double = { + val counts = collectStatisticalData(df, cols) + counts.Ck / math.sqrt(counts.MkX * counts.MkY) + } + /** Helper class to simplify tracking and merging counts. */ private class CovarianceCounter extends Serializable { - var xAvg = 0.0 - var yAvg = 0.0 - var Ck = 0.0 - var count = 0L + var xAvg = 0.0 // the mean of all examples seen so far in col1 + var yAvg = 0.0 // the mean of all examples seen so far in col2 + var Ck = 0.0 // the co-moment after k examples + var MkX = 0.0 // sum of squares of differences from the (current) mean for col1 + var MkY = 0.0 // sum of squares of differences from the (current) mean for col1 + var count = 0L // count of observed examples // add an example to the calculation def add(x: Double, y: Double): this.type = { - val oldX = xAvg + val deltaX = x - xAvg + val deltaY = y - yAvg count += 1 - xAvg += (x - xAvg) / count - yAvg += (y - yAvg) / count - Ck += (y - yAvg) * (x - oldX) + xAvg += deltaX / count + yAvg += deltaY / count + Ck += deltaX * (y - yAvg) + MkX += deltaX * (x - xAvg) + MkY += deltaY * (y - yAvg) this } // merge counters from other partitions. Formula can be found at: - // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance + // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance def merge(other: CovarianceCounter): this.type = { val totalCount = count + other.count - Ck += other.Ck + - (xAvg - other.xAvg) * (yAvg - other.yAvg) * count / totalCount * other.count + val deltaX = xAvg - other.xAvg + val deltaY = yAvg - other.yAvg + Ck += other.Ck + deltaX * deltaY * count / totalCount * other.count xAvg = (xAvg * count + other.xAvg * other.count) / totalCount yAvg = (yAvg * count + other.yAvg * other.count) / totalCount + MkX += other.MkX + deltaX * deltaX * count / totalCount * other.count + MkY += other.MkY + deltaY * deltaY * count / totalCount * other.count count = totalCount this } @@ -53,13 +67,7 @@ private[sql] object StatFunctions { def cov: Double = Ck / (count - 1) } - /** - * Calculate the covariance of two numerical columns of a DataFrame. - * @param df The DataFrame - * @param cols the column names - * @return the covariance of the two columns. - */ - private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = { + private def collectStatisticalData(df: DataFrame, cols: Seq[String]): CovarianceCounter = { require(cols.length == 2, "Currently cov supports calculating the covariance " + "between two columns.") cols.map(name => (name, df.schema.fields.find(_.name == name))).foreach { case (name, data) => @@ -68,13 +76,23 @@ private[sql] object StatFunctions { s"with dataType ${data.get.dataType} not supported.") } val columns = cols.map(n => Column(Cast(Column(n).expr, DoubleType))) - val counts = df.select(columns:_*).rdd.aggregate(new CovarianceCounter)( + df.select(columns: _*).rdd.aggregate(new CovarianceCounter)( seqOp = (counter, row) => { counter.add(row.getDouble(0), row.getDouble(1)) }, combOp = (baseCounter, other) => { baseCounter.merge(other) - }) + }) + } + + /** + * Calculate the covariance of two numerical columns of a DataFrame. + * @param df The DataFrame + * @param cols the column names + * @return the covariance of the two columns. + */ + private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = { + val counts = collectStatisticalData(df, cols) counts.cov } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index 96fe66d0b84a6..78e847239f405 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -187,6 +187,13 @@ public void testFrequentItems() { Assert.assertTrue(results.collect()[0].getSeq(0).contains(1)); } + @Test + public void testCorrelation() { + DataFrame df = context.table("testData2"); + Double pearsonCorr = df.stat().corr("a", "b", "pearson"); + Assert.assertTrue(Math.abs(pearsonCorr) < 1e-6); + } + @Test public void testCovariance() { DataFrame df = context.table("testData2"); diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala index 4f5a2ff696789..06764d2a122f1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -30,10 +30,10 @@ class DataFrameStatSuite extends FunSuite { def toLetter(i: Int): String = (i + 97).toChar.toString test("Frequent Items") { - val rows = Array.tabulate(1000) { i => + val rows = Seq.tabulate(1000) { i => if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0) } - val df = sqlCtx.sparkContext.parallelize(rows).toDF("numbers", "letters", "negDoubles") + val df = rows.toDF("numbers", "letters", "negDoubles") val results = df.stat.freqItems(Array("numbers", "letters"), 0.1) val items = results.collect().head @@ -43,19 +43,40 @@ class DataFrameStatSuite extends FunSuite { val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1) val items2 = singleColResults.collect().head items2.getSeq[Double](0) should contain (-1.0) + } + test("pearson correlation") { + val df = Seq.tabulate(10)(i => (i, 2 * i, i * -1.0)).toDF("a", "b", "c") + val corr1 = df.stat.corr("a", "b", "pearson") + assert(math.abs(corr1 - 1.0) < 1e-12) + val corr2 = df.stat.corr("a", "c", "pearson") + assert(math.abs(corr2 + 1.0) < 1e-12) + // non-trivial example. To reproduce in python, use: + // >>> from scipy.stats import pearsonr + // >>> import numpy as np + // >>> a = np.array(range(20)) + // >>> b = np.array([x * x - 2 * x + 3.5 for x in range(20)]) + // >>> pearsonr(a, b) + // (0.95723391394758572, 3.8902121417802199e-11) + // In R, use: + // > a <- 0:19 + // > b <- mapply(function(x) x * x - 2 * x + 3.5, a) + // > cor(a, b) + // [1] 0.957233913947585835 + val df2 = Seq.tabulate(20)(x => (x, x * x - 2 * x + 3.5)).toDF("a", "b") + val corr3 = df2.stat.corr("a", "b", "pearson") + assert(math.abs(corr3 - 0.95723391394758572) < 1e-12) } test("covariance") { - val rows = Array.tabulate(10)(i => (i, 2.0 * i, toLetter(i))) - val df = sqlCtx.sparkContext.parallelize(rows).toDF("singles", "doubles", "letters") + val df = Seq.tabulate(10)(i => (i, 2.0 * i, toLetter(i))).toDF("singles", "doubles", "letters") val results = df.stat.cov("singles", "doubles") - assert(math.abs(results - 55.0 / 3) < 1e-6) + assert(math.abs(results - 55.0 / 3) < 1e-12) intercept[IllegalArgumentException] { df.stat.cov("singles", "letters") // doesn't accept non-numerical dataTypes } val decimalRes = decimalData.stat.cov("a", "b") - assert(math.abs(decimalRes) < 1e-6) + assert(math.abs(decimalRes) < 1e-12) } } From 3539cb7d20f5f878132407ec3b854011b183b2ad Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Mon, 4 May 2015 00:06:25 -0700 Subject: [PATCH 06/41] [SPARK-5563] [MLLIB] LDA with online variational inference MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit JIRA: https://issues.apache.org/jira/browse/SPARK-5563 The PR contains the implementation for [Online LDA] (https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf) based on the research of Matt Hoffman and David M. Blei, which provides an efficient option for LDA users. Major advantages for the algorithm are the stream compatibility and economic time/memory consumption due to the corpus split. For more details, please refer to the jira. Online LDA can act as a fast option for LDA, and will be especially helpful for the users who needs a quick result or with large corpus. Correctness test. I have tested current PR with https://github.com/Blei-Lab/onlineldavb and the results are identical. I've uploaded the result and code to https://github.com/hhbyyh/LDACrossValidation. Author: Yuhao Yang Author: Joseph K. Bradley Closes #4419 from hhbyyh/ldaonline and squashes the following commits: 1045eec [Yuhao Yang] Merge pull request #2 from jkbradley/hhbyyh-ldaonline2 cf376ff [Joseph K. Bradley] For private vars needed for testing, I made them private and added accessors. Java doesn’t understand package-private tags, so this minimizes the issues Java users might encounter. 6149ca6 [Yuhao Yang] fix for setOptimizer cf0007d [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 54cf8da [Yuhao Yang] some style change 68c2318 [Yuhao Yang] add a java ut 4041723 [Yuhao Yang] add ut 138bfed [Yuhao Yang] Merge pull request #1 from jkbradley/hhbyyh-ldaonline-update 9e910d9 [Joseph K. Bradley] small fix 61d60df [Joseph K. Bradley] Minor cleanups: * Update *Concentration parameter documentation * EM Optimizer: createVertices() does not need to be a function * OnlineLDAOptimizer: typos in doc * Clean up the core code for online LDA (Scala style) a996a82 [Yuhao Yang] respond to comments b1178cf [Yuhao Yang] fit into the optimizer framework dbe3cff [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 15be071 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline b29193b [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline d19ef55 [Yuhao Yang] change OnlineLDA to class 97b9e1a [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline e7bf3b0 [Yuhao Yang] move to seperate file f367cc9 [Yuhao Yang] change to optimization 8cb16a6 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 62405cc [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 02d0373 [Yuhao Yang] fix style in comment f6d47ca [Yuhao Yang] Merge branch 'ldaonline' of https://github.com/hhbyyh/spark into ldaonline d86cdec [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline a570c9a [Yuhao Yang] use sample to pick up batch 4a3f27e [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline e271eb1 [Yuhao Yang] remove non ascii 581c623 [Yuhao Yang] seperate API and adjust batch split 37af91a [Yuhao Yang] iMerge remote-tracking branch 'upstream/master' into ldaonline 20328d1 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline i aa365d1 [Yuhao Yang] merge upstream master 3a06526 [Yuhao Yang] merge with new example 0dd3947 [Yuhao Yang] kMerge remote-tracking branch 'upstream/master' into ldaonline 0d0f3ee [Yuhao Yang] replace random split with sliding fa408a8 [Yuhao Yang] ssMerge remote-tracking branch 'upstream/master' into ldaonline 45884ab [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline s f41c5ca [Yuhao Yang] style fix 26dca1b [Yuhao Yang] style fix and make class private 043e786 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline s Conflicts: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala d640d9c [Yuhao Yang] online lda initial checkin --- .../apache/spark/mllib/clustering/LDA.scala | 65 ++-- .../spark/mllib/clustering/LDAOptimizer.scala | 320 ++++++++++++++++-- .../spark/mllib/clustering/JavaLDASuite.java | 38 ++- .../spark/mllib/clustering/LDASuite.scala | 89 ++++- 4 files changed, 438 insertions(+), 74 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index 37bf88b73b911..c8daa2388e868 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -78,35 +78,29 @@ class LDA private ( * * This is the parameter to a symmetric Dirichlet distribution. */ - def getDocConcentration: Double = { - if (this.docConcentration == -1) { - (50.0 / k) + 1.0 - } else { - this.docConcentration - } - } + def getDocConcentration: Double = this.docConcentration /** * Concentration parameter (commonly named "alpha") for the prior placed on documents' * distributions over topics ("theta"). * - * This is the parameter to a symmetric Dirichlet distribution. + * This is the parameter to a symmetric Dirichlet distribution, where larger values + * mean more smoothing (more regularization). * - * This value should be > 1.0, where larger values mean more smoothing (more regularization). * If set to -1, then docConcentration is set automatically. * (default = -1 = automatic) * - * Automatic setting of parameter: - * - For EM: default = (50 / k) + 1. - * - The 50/k is common in LDA libraries. - * - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM. - * - * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), - * but values in (0,1) are not yet supported. + * Optimizer-specific parameter settings: + * - EM + * - Value should be > 1.0 + * - default = (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows + * Asuncion et al. (2009), who recommend a +1 adjustment for EM. + * - Online + * - Value should be >= 0 + * - default = (1.0 / k), following the implementation from + * [[https://github.com/Blei-Lab/onlineldavb]]. */ def setDocConcentration(docConcentration: Double): this.type = { - require(docConcentration > 1.0 || docConcentration == -1.0, - s"LDA docConcentration must be > 1.0 (or -1 for auto), but was set to $docConcentration") this.docConcentration = docConcentration this } @@ -126,13 +120,7 @@ class LDA private ( * Note: The topics' distributions over terms are called "beta" in the original LDA paper * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009. */ - def getTopicConcentration: Double = { - if (this.topicConcentration == -1) { - 1.1 - } else { - this.topicConcentration - } - } + def getTopicConcentration: Double = this.topicConcentration /** * Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' @@ -143,21 +131,20 @@ class LDA private ( * Note: The topics' distributions over terms are called "beta" in the original LDA paper * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009. * - * This value should be > 0.0. * If set to -1, then topicConcentration is set automatically. * (default = -1 = automatic) * - * Automatic setting of parameter: - * - For EM: default = 0.1 + 1. - * - The 0.1 gives a small amount of smoothing. - * - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM. - * - * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), - * but values in (0,1) are not yet supported. + * Optimizer-specific parameter settings: + * - EM + * - Value should be > 1.0 + * - default = 0.1 + 1, where 0.1 gives a small amount of smoothing and +1 follows + * Asuncion et al. (2009), who recommend a +1 adjustment for EM. + * - Online + * - Value should be >= 0 + * - default = (1.0 / k), following the implementation from + * [[https://github.com/Blei-Lab/onlineldavb]]. */ def setTopicConcentration(topicConcentration: Double): this.type = { - require(topicConcentration > 1.0 || topicConcentration == -1.0, - s"LDA topicConcentration must be > 1.0 (or -1 for auto), but was set to $topicConcentration") this.topicConcentration = topicConcentration this } @@ -223,14 +210,15 @@ class LDA private ( /** * Set the LDAOptimizer used to perform the actual calculation by algorithm name. - * Currently "em" is supported. + * Currently "em", "online" is supported. */ def setOptimizer(optimizerName: String): this.type = { this.ldaOptimizer = optimizerName.toLowerCase match { case "em" => new EMLDAOptimizer + case "online" => new OnlineLDAOptimizer case other => - throw new IllegalArgumentException(s"Only em is supported but got $other.") + throw new IllegalArgumentException(s"Only em, online are supported but got $other.") } this } @@ -245,8 +233,7 @@ class LDA private ( * @return Inferred LDA model */ def run(documents: RDD[(Long, Vector)]): LDAModel = { - val state = ldaOptimizer.initialState(documents, k, getDocConcentration, getTopicConcentration, - seed, checkpointInterval) + val state = ldaOptimizer.initialize(documents, this) var iter = 0 val iterationTimes = Array.fill[Double](maxIterations)(0) while (iter < maxIterations) { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala index ffd72a294c6c6..093aa0f315ab2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala @@ -19,13 +19,15 @@ package org.apache.spark.mllib.clustering import java.util.Random -import breeze.linalg.{DenseVector => BDV, normalize} +import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, sum, normalize, kron} +import breeze.numerics.{digamma, exp, abs} +import breeze.stats.distributions.{Gamma, RandBasis} import org.apache.spark.annotation.Experimental import org.apache.spark.graphx._ import org.apache.spark.graphx.impl.GraphImpl import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer -import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.linalg.{Matrices, SparseVector, DenseVector, Vector} import org.apache.spark.rdd.RDD /** @@ -35,7 +37,7 @@ import org.apache.spark.rdd.RDD * hold optimizer-specific parameters for users to set. */ @Experimental -trait LDAOptimizer{ +trait LDAOptimizer { /* DEVELOPERS NOTE: @@ -49,13 +51,7 @@ trait LDAOptimizer{ * Initializer for the optimizer. LDA passes the common parameters to the optimizer and * the internal structure can be initialized properly. */ - private[clustering] def initialState( - docs: RDD[(Long, Vector)], - k: Int, - docConcentration: Double, - topicConcentration: Double, - randomSeed: Long, - checkpointInterval: Int): LDAOptimizer + private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer private[clustering] def next(): LDAOptimizer @@ -80,12 +76,12 @@ trait LDAOptimizer{ * */ @Experimental -class EMLDAOptimizer extends LDAOptimizer{ +class EMLDAOptimizer extends LDAOptimizer { import LDA._ /** - * Following fields will only be initialized through initialState method + * The following fields will only be initialized through the initialize() method */ private[clustering] var graph: Graph[TopicCounts, TokenCount] = null private[clustering] var k: Int = 0 @@ -98,13 +94,23 @@ class EMLDAOptimizer extends LDAOptimizer{ /** * Compute bipartite term/doc graph. */ - private[clustering] override def initialState( - docs: RDD[(Long, Vector)], - k: Int, - docConcentration: Double, - topicConcentration: Double, - randomSeed: Long, - checkpointInterval: Int): LDAOptimizer = { + override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer = { + + val docConcentration = lda.getDocConcentration + val topicConcentration = lda.getTopicConcentration + val k = lda.getK + + // Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), + // but values in (0,1) are not yet supported. + require(docConcentration > 1.0 || docConcentration == -1.0, s"LDA docConcentration must be" + + s" > 1.0 (or -1 for auto) for EM Optimizer, but was set to $docConcentration") + require(topicConcentration > 1.0 || topicConcentration == -1.0, s"LDA topicConcentration " + + s"must be > 1.0 (or -1 for auto) for EM Optimizer, but was set to $topicConcentration") + + this.docConcentration = if (docConcentration == -1) (50.0 / k) + 1.0 else docConcentration + this.topicConcentration = if (topicConcentration == -1) 1.1 else topicConcentration + val randomSeed = lda.getSeed + // For each document, create an edge (Document -> Term) for each unique term in the document. val edges: RDD[Edge[TokenCount]] = docs.flatMap { case (docID: Long, termCounts: Vector) => // Add edges for terms with non-zero counts. @@ -113,11 +119,9 @@ class EMLDAOptimizer extends LDAOptimizer{ } } - val vocabSize = docs.take(1).head._2.size - // Create vertices. // Initially, we use random soft assignments of tokens to topics (random gamma). - def createVertices(): RDD[(VertexId, TopicCounts)] = { + val docTermVertices: RDD[(VertexId, TopicCounts)] = { val verticesTMP: RDD[(VertexId, TopicCounts)] = edges.mapPartitionsWithIndex { case (partIndex, partEdges) => val random = new Random(partIndex + randomSeed) @@ -130,22 +134,18 @@ class EMLDAOptimizer extends LDAOptimizer{ verticesTMP.reduceByKey(_ + _) } - val docTermVertices = createVertices() - // Partition such that edges are grouped by document this.graph = Graph(docTermVertices, edges).partitionBy(PartitionStrategy.EdgePartition1D) this.k = k - this.vocabSize = vocabSize - this.docConcentration = docConcentration - this.topicConcentration = topicConcentration - this.checkpointInterval = checkpointInterval + this.vocabSize = docs.take(1).head._2.size + this.checkpointInterval = lda.getCheckpointInterval this.graphCheckpointer = new PeriodicGraphCheckpointer[TopicCounts, TokenCount](graph, checkpointInterval) this.globalTopicTotals = computeGlobalTopicTotals() this } - private[clustering] override def next(): EMLDAOptimizer = { + override private[clustering] def next(): EMLDAOptimizer = { require(graph != null, "graph is null, EMLDAOptimizer not initialized.") val eta = topicConcentration @@ -202,9 +202,269 @@ class EMLDAOptimizer extends LDAOptimizer{ graph.vertices.filter(isTermVertex).values.fold(BDV.zeros[Double](numTopics))(_ += _) } - private[clustering] override def getLDAModel(iterationTimes: Array[Double]): LDAModel = { + override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = { require(graph != null, "graph is null, EMLDAOptimizer not initialized.") this.graphCheckpointer.deleteAllCheckpoints() new DistributedLDAModel(this, iterationTimes) } } + + +/** + * :: Experimental :: + * + * An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA + * algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic + * distribution adaptively. + * + * Original Online LDA paper: + * Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010. + */ +@Experimental +class OnlineLDAOptimizer extends LDAOptimizer { + + // LDA common parameters + private var k: Int = 0 + private var corpusSize: Long = 0 + private var vocabSize: Int = 0 + + /** alias for docConcentration */ + private var alpha: Double = 0 + + /** (private[clustering] for debugging) Get docConcentration */ + private[clustering] def getAlpha: Double = alpha + + /** alias for topicConcentration */ + private var eta: Double = 0 + + /** (private[clustering] for debugging) Get topicConcentration */ + private[clustering] def getEta: Double = eta + + private var randomGenerator: java.util.Random = null + + // Online LDA specific parameters + // Learning rate is: (tau_0 + t)^{-kappa} + private var tau_0: Double = 1024 + private var kappa: Double = 0.51 + private var miniBatchFraction: Double = 0.05 + + // internal data structure + private var docs: RDD[(Long, Vector)] = null + + /** Dirichlet parameter for the posterior over topics */ + private var lambda: BDM[Double] = null + + /** (private[clustering] for debugging) Get parameter for topics */ + private[clustering] def getLambda: BDM[Double] = lambda + + /** Current iteration (count of invocations of [[next()]]) */ + private var iteration: Int = 0 + private var gammaShape: Double = 100 + + /** + * A (positive) learning parameter that downweights early iterations. Larger values make early + * iterations count less. + */ + def getTau_0: Double = this.tau_0 + + /** + * A (positive) learning parameter that downweights early iterations. Larger values make early + * iterations count less. + * Default: 1024, following the original Online LDA paper. + */ + def setTau_0(tau_0: Double): this.type = { + require(tau_0 > 0, s"LDA tau_0 must be positive, but was set to $tau_0") + this.tau_0 = tau_0 + this + } + + /** + * Learning rate: exponential decay rate + */ + def getKappa: Double = this.kappa + + /** + * Learning rate: exponential decay rate---should be between + * (0.5, 1.0] to guarantee asymptotic convergence. + * Default: 0.51, based on the original Online LDA paper. + */ + def setKappa(kappa: Double): this.type = { + require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa") + this.kappa = kappa + this + } + + /** + * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration + */ + def getMiniBatchFraction: Double = this.miniBatchFraction + + /** + * Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in + * each iteration. + * + * Note that this should be adjusted in synch with [[LDA.setMaxIterations()]] + * so the entire corpus is used. Specifically, set both so that + * maxIterations * miniBatchFraction >= 1. + * + * Default: 0.05, i.e., 5% of total documents. + */ + def setMiniBatchFraction(miniBatchFraction: Double): this.type = { + require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0, + s"Online LDA miniBatchFraction must be in range (0,1], but was set to $miniBatchFraction") + this.miniBatchFraction = miniBatchFraction + this + } + + /** + * (private[clustering]) + * Set the Dirichlet parameter for the posterior over topics. + * This is only used for testing now. In the future, it can help support training stop/resume. + */ + private[clustering] def setLambda(lambda: BDM[Double]): this.type = { + this.lambda = lambda + this + } + + /** + * (private[clustering]) + * Used for random initialization of the variational parameters. + * Larger value produces values closer to 1.0. + * This is only used for testing currently. + */ + private[clustering] def setGammaShape(shape: Double): this.type = { + this.gammaShape = shape + this + } + + override private[clustering] def initialize( + docs: RDD[(Long, Vector)], + lda: LDA): OnlineLDAOptimizer = { + this.k = lda.getK + this.corpusSize = docs.count() + this.vocabSize = docs.first()._2.size + this.alpha = if (lda.getDocConcentration == -1) 1.0 / k else lda.getDocConcentration + this.eta = if (lda.getTopicConcentration == -1) 1.0 / k else lda.getTopicConcentration + this.randomGenerator = new Random(lda.getSeed) + + this.docs = docs + + // Initialize the variational distribution q(beta|lambda) + this.lambda = getGammaMatrix(k, vocabSize) + this.iteration = 0 + this + } + + override private[clustering] def next(): OnlineLDAOptimizer = { + val batch = docs.sample(withReplacement = true, miniBatchFraction, randomGenerator.nextLong()) + if (batch.isEmpty()) return this + submitMiniBatch(batch) + } + + /** + * Submit a subset (like 1%, decide by the miniBatchFraction) of the corpus to the Online LDA + * model, and it will update the topic distribution adaptively for the terms appearing in the + * subset. + */ + private[clustering] def submitMiniBatch(batch: RDD[(Long, Vector)]): OnlineLDAOptimizer = { + iteration += 1 + val k = this.k + val vocabSize = this.vocabSize + val Elogbeta = dirichletExpectation(lambda) + val expElogbeta = exp(Elogbeta) + val alpha = this.alpha + val gammaShape = this.gammaShape + + val stats: RDD[BDM[Double]] = batch.mapPartitions { docs => + val stat = BDM.zeros[Double](k, vocabSize) + docs.foreach { doc => + val termCounts = doc._2 + val (ids: List[Int], cts: Array[Double]) = termCounts match { + case v: DenseVector => ((0 until v.size).toList, v.values) + case v: SparseVector => (v.indices.toList, v.values) + case v => throw new IllegalArgumentException("Online LDA does not support vector type " + + v.getClass) + } + + // Initialize the variational distribution q(theta|gamma) for the mini-batch + var gammad = new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k).t // 1 * K + var Elogthetad = digamma(gammad) - digamma(sum(gammad)) // 1 * K + var expElogthetad = exp(Elogthetad) // 1 * K + val expElogbetad = expElogbeta(::, ids).toDenseMatrix // K * ids + + var phinorm = expElogthetad * expElogbetad + 1e-100 // 1 * ids + var meanchange = 1D + val ctsVector = new BDV[Double](cts).t // 1 * ids + + // Iterate between gamma and phi until convergence + while (meanchange > 1e-3) { + val lastgamma = gammad + // 1*K 1 * ids ids * k + gammad = (expElogthetad :* ((ctsVector / phinorm) * expElogbetad.t)) + alpha + Elogthetad = digamma(gammad) - digamma(sum(gammad)) + expElogthetad = exp(Elogthetad) + phinorm = expElogthetad * expElogbetad + 1e-100 + meanchange = sum(abs(gammad - lastgamma)) / k + } + + val m1 = expElogthetad.t + val m2 = (ctsVector / phinorm).t.toDenseVector + var i = 0 + while (i < ids.size) { + stat(::, ids(i)) := stat(::, ids(i)) + m1 * m2(i) + i += 1 + } + } + Iterator(stat) + } + + val statsSum: BDM[Double] = stats.reduce(_ += _) + val batchResult = statsSum :* expElogbeta + + // Note that this is an optimization to avoid batch.count + update(batchResult, iteration, (miniBatchFraction * corpusSize).ceil.toInt) + this + } + + override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = { + new LocalLDAModel(Matrices.fromBreeze(lambda).transpose) + } + + /** + * Update lambda based on the batch submitted. batchSize can be different for each iteration. + */ + private[clustering] def update(stat: BDM[Double], iter: Int, batchSize: Int): Unit = { + val tau_0 = this.getTau_0 + val kappa = this.getKappa + + // weight of the mini-batch. + val weight = math.pow(tau_0 + iter, -kappa) + + // Update lambda based on documents. + lambda = lambda * (1 - weight) + + (stat * (corpusSize.toDouble / batchSize.toDouble) + eta) * weight + } + + /** + * Get a random matrix to initialize lambda + */ + private def getGammaMatrix(row: Int, col: Int): BDM[Double] = { + val randBasis = new RandBasis(new org.apache.commons.math3.random.MersenneTwister( + randomGenerator.nextLong())) + val gammaRandomGenerator = new Gamma(gammaShape, 1.0 / gammaShape)(randBasis) + val temp = gammaRandomGenerator.sample(row * col).toArray + new BDM[Double](col, row, temp).t + } + + /** + * For theta ~ Dir(alpha), computes E[log(theta)] given alpha. Currently the implementation + * uses digamma which is accurate but expensive. + */ + private def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = { + val rowSum = sum(alpha(breeze.linalg.*, ::)) + val digAlpha = digamma(alpha) + val digRowSum = digamma(rowSum) + val result = digAlpha(::, breeze.linalg.*) - digRowSum + result + } +} diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java index fbe171b4b1ab1..f394d903966de 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java @@ -20,7 +20,6 @@ import java.io.Serializable; import java.util.ArrayList; -import org.apache.spark.api.java.JavaRDD; import scala.Tuple2; import org.junit.After; @@ -30,6 +29,7 @@ import org.junit.Test; import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.mllib.linalg.Matrix; import org.apache.spark.mllib.linalg.Vector; @@ -109,11 +109,45 @@ public void distributedLDAModel() { assert(model.logPrior() < 0.0); } + @Test + public void OnlineOptimizerCompatibility() { + int k = 3; + double topicSmoothing = 1.2; + double termSmoothing = 1.2; + + // Train a model + OnlineLDAOptimizer op = new OnlineLDAOptimizer() + .setTau_0(1024) + .setKappa(0.51) + .setGammaShape(1e40) + .setMiniBatchFraction(0.5); + + LDA lda = new LDA(); + lda.setK(k) + .setDocConcentration(topicSmoothing) + .setTopicConcentration(termSmoothing) + .setMaxIterations(5) + .setSeed(12345) + .setOptimizer(op); + + LDAModel model = lda.run(corpus); + + // Check: basic parameters + assertEquals(model.k(), k); + assertEquals(model.vocabSize(), tinyVocabSize); + + // Check: topic summaries + Tuple2[] roundedTopicSummary = model.describeTopics(); + assertEquals(roundedTopicSummary.length, k); + Tuple2[] roundedLocalTopicSummary = model.describeTopics(); + assertEquals(roundedLocalTopicSummary.length, k); + } + private static int tinyK = LDASuite$.MODULE$.tinyK(); private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize(); private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics(); private static Tuple2[] tinyTopicDescription = LDASuite$.MODULE$.tinyTopicDescription(); - JavaPairRDD corpus; + private JavaPairRDD corpus; } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index 41ec794146c69..2dcc881f5abd2 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.mllib.clustering +import breeze.linalg.{DenseMatrix => BDM} + import org.scalatest.FunSuite import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors} @@ -37,7 +39,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { // Check: describeTopics() with all terms val fullTopicSummary = model.describeTopics() - assert(fullTopicSummary.size === tinyK) + assert(fullTopicSummary.length === tinyK) fullTopicSummary.zip(tinyTopicDescription).foreach { case ((algTerms, algTermWeights), (terms, termWeights)) => assert(algTerms === terms) @@ -54,7 +56,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { } } - test("running and DistributedLDAModel") { + test("running and DistributedLDAModel with default Optimizer (EM)") { val k = 3 val topicSmoothing = 1.2 val termSmoothing = 1.2 @@ -99,7 +101,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { // Check: per-doc topic distributions val topicDistributions = model.topicDistributions.collect() // Ensure all documents are covered. - assert(topicDistributions.size === tinyCorpus.size) + assert(topicDistributions.length === tinyCorpus.length) assert(tinyCorpus.map(_._1).toSet === topicDistributions.map(_._1).toSet) // Ensure we have proper distributions topicDistributions.foreach { case (docId, topicDistribution) => @@ -131,6 +133,87 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { assert(lda.getBeta === 3.0) assert(lda.getTopicConcentration === 3.0) } + + test("OnlineLDAOptimizer initialization") { + val lda = new LDA().setK(2) + val corpus = sc.parallelize(tinyCorpus, 2) + val op = new OnlineLDAOptimizer().initialize(corpus, lda) + op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau_0(567) + assert(op.getAlpha == 0.5) // default 1.0 / k + assert(op.getEta == 0.5) // default 1.0 / k + assert(op.getKappa == 0.9876) + assert(op.getMiniBatchFraction == 0.123) + assert(op.getTau_0 == 567) + } + + test("OnlineLDAOptimizer one iteration") { + // run OnlineLDAOptimizer for 1 iteration to verify it's consistency with Blei-lab, + // [[https://github.com/Blei-Lab/onlineldavb]] + val k = 2 + val vocabSize = 6 + + def docs: Array[(Long, Vector)] = Array( + Vectors.sparse(vocabSize, Array(0, 1, 2), Array(1, 1, 1)), // apple, orange, banana + Vectors.sparse(vocabSize, Array(3, 4, 5), Array(1, 1, 1)) // tiger, cat, dog + ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) } + val corpus = sc.parallelize(docs, 2) + + // Set GammaShape large to avoid the stochastic impact. + val op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51).setGammaShape(1e40) + .setMiniBatchFraction(1) + val lda = new LDA().setK(k).setMaxIterations(1).setOptimizer(op).setSeed(12345) + + val state = op.initialize(corpus, lda) + // override lambda to simulate an intermediate state + // [[ 1.1 1.2 1.3 0.9 0.8 0.7] + // [ 0.9 0.8 0.7 1.1 1.2 1.3]] + op.setLambda(new BDM[Double](k, vocabSize, + Array(1.1, 0.9, 1.2, 0.8, 1.3, 0.7, 0.9, 1.1, 0.8, 1.2, 0.7, 1.3))) + + // run for one iteration + state.submitMiniBatch(corpus) + + // verify the result, Note this generate the identical result as + // [[https://github.com/Blei-Lab/onlineldavb]] + val topic1 = op.getLambda(0, ::).inner.toArray.map("%.4f".format(_)).mkString(", ") + val topic2 = op.getLambda(1, ::).inner.toArray.map("%.4f".format(_)).mkString(", ") + assert("1.1101, 1.2076, 1.3050, 0.8899, 0.7924, 0.6950" == topic1) + assert("0.8899, 0.7924, 0.6950, 1.1101, 1.2076, 1.3050" == topic2) + } + + test("OnlineLDAOptimizer with toy data") { + def toydata: Array[(Long, Vector)] = Array( + Vectors.sparse(6, Array(0, 1), Array(1, 1)), + Vectors.sparse(6, Array(1, 2), Array(1, 1)), + Vectors.sparse(6, Array(0, 2), Array(1, 1)), + Vectors.sparse(6, Array(3, 4), Array(1, 1)), + Vectors.sparse(6, Array(3, 5), Array(1, 1)), + Vectors.sparse(6, Array(4, 5), Array(1, 1)) + ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) } + + val docs = sc.parallelize(toydata) + val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau_0(1024).setKappa(0.51) + .setGammaShape(1e10) + val lda = new LDA().setK(2) + .setDocConcentration(0.01) + .setTopicConcentration(0.01) + .setMaxIterations(100) + .setOptimizer(op) + .setSeed(12345) + + val ldaModel = lda.run(docs) + val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 10) + val topics = topicIndices.map { case (terms, termWeights) => + terms.zip(termWeights) + } + + // check distribution for each topic, typical distribution is (0.3, 0.3, 0.3, 0.02, 0.02, 0.02) + topics.foreach { topic => + val smalls = topic.filter(t => t._2 < 0.1).map(_._2) + assert(smalls.length == 3 && smalls.sum < 0.2) + } + } + } private[clustering] object LDASuite { From 343d3bfafd449a0371feb6a88f78e07302fa7143 Mon Sep 17 00:00:00 2001 From: tianyi Date: Mon, 4 May 2015 16:59:34 +0800 Subject: [PATCH 07/41] [SPARK-5100] [SQL] add webui for thriftserver This PR is a rebased version of #3946 , and mainly focused on creating an independent tab for the thrift server in spark web UI. Features: 1. Session related statistics ( username and IP are only supported in hive-0.13.1 ) 2. List all the SQL executing or executed on this server 3. Provide links to the job generated by SQL 4. Provide link to show all SQL executing or executed in a specified session Prototype snapshots: This is the main page for thrift server ![image](https://cloud.githubusercontent.com/assets/1411869/7361379/df7dcc64-ed89-11e4-9964-4df0b32f475e.png) Author: tianyi Closes #5730 from tianyi/SPARK-5100 and squashes the following commits: cfd14c7 [tianyi] style fix 0efe3d5 [tianyi] revert part of pom change c0f2fa0 [tianyi] extends HiveThriftJdbcTest to start/stop thriftserver for UI test aa20408 [tianyi] fix style problem c9df6f9 [tianyi] add testsuite for thriftserver ui and fix some style issue 9830199 [tianyi] add webui for thriftserver --- .../scala/org/apache/spark/sql/SQLConf.scala | 2 + sql/hive-thriftserver/pom.xml | 12 ++ .../hive/thriftserver/HiveThriftServer2.scala | 161 +++++++++++++- .../thriftserver/ui/ThriftServerPage.scala | 190 +++++++++++++++++ .../ui/ThriftServerSessionPage.scala | 197 ++++++++++++++++++ .../thriftserver/ui/ThriftServerTab.scala | 50 +++++ .../HiveThriftServer2Suites.scala | 12 +- .../hive/thriftserver/UISeleniumSuite.scala | 105 ++++++++++ .../spark/sql/hive/thriftserver/Shim12.scala | 18 +- .../spark/sql/hive/thriftserver/Shim13.scala | 26 ++- 10 files changed, 751 insertions(+), 22 deletions(-) create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index 2fa602a6082dc..99db959a8741c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -52,6 +52,8 @@ private[spark] object SQLConf { // This is only used for the thriftserver val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool" + val THRIFTSERVER_UI_STATEMENT_LIMIT = "spark.sql.thriftserver.ui.retainedStatements" + val THRIFTSERVER_UI_SESSION_LIMIT = "spark.sql.thriftserver.ui.retainedSessions" // This is used to set the default data source val DEFAULT_DATA_SOURCE_NAME = "spark.sql.sources.default" diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml index f38c796241df1..437f697d25bf3 100644 --- a/sql/hive-thriftserver/pom.xml +++ b/sql/hive-thriftserver/pom.xml @@ -57,6 +57,18 @@ ${hive.group} hive-beeline + + + org.seleniumhq.selenium + selenium-java + test + + + io.netty + netty + + + target/scala-${scala.binary.version}/classes diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala index 832596fc8bee5..0be5a92c2546c 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala @@ -22,20 +22,27 @@ import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.hive.service.cli.thrift.{ThriftBinaryCLIService, ThriftHttpCLIService} import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor} +import org.apache.spark.sql.SQLConf -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ -import org.apache.spark.scheduler.{SparkListenerApplicationEnd, SparkListener} +import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListenerApplicationEnd, SparkListener} +import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab import org.apache.spark.util.Utils +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer + /** * The main entry point for the Spark SQL port of HiveServer2. Starts up a `SparkSQLContext` and a * `HiveThriftServer2` thrift server. */ object HiveThriftServer2 extends Logging { var LOG = LogFactory.getLog(classOf[HiveServer2]) + var uiTab: Option[ThriftServerTab] = _ + var listener: HiveThriftServer2Listener = _ /** * :: DeveloperApi :: @@ -46,7 +53,13 @@ object HiveThriftServer2 extends Logging { val server = new HiveThriftServer2(sqlContext) server.init(sqlContext.hiveconf) server.start() - sqlContext.sparkContext.addSparkListener(new HiveThriftServer2Listener(server)) + listener = new HiveThriftServer2Listener(server, sqlContext.conf) + sqlContext.sparkContext.addSparkListener(listener) + uiTab = if (sqlContext.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) { + Some(new ThriftServerTab(sqlContext.sparkContext)) + } else { + None + } } def main(args: Array[String]) { @@ -58,14 +71,23 @@ object HiveThriftServer2 extends Logging { logInfo("Starting SparkContext") SparkSQLEnv.init() - Utils.addShutdownHook { () => SparkSQLEnv.stop() } + Utils.addShutdownHook { () => + SparkSQLEnv.stop() + uiTab.foreach(_.detach()) + } try { val server = new HiveThriftServer2(SparkSQLEnv.hiveContext) server.init(SparkSQLEnv.hiveContext.hiveconf) server.start() logInfo("HiveThriftServer2 started") - SparkSQLEnv.sparkContext.addSparkListener(new HiveThriftServer2Listener(server)) + listener = new HiveThriftServer2Listener(server, SparkSQLEnv.hiveContext.conf) + SparkSQLEnv.sparkContext.addSparkListener(listener) + uiTab = if (SparkSQLEnv.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) { + Some(new ThriftServerTab(SparkSQLEnv.sparkContext)) + } else { + None + } } catch { case e: Exception => logError("Error starting HiveThriftServer2", e) @@ -73,15 +95,140 @@ object HiveThriftServer2 extends Logging { } } + private[thriftserver] class SessionInfo( + val sessionId: String, + val startTimestamp: Long, + val ip: String, + val userName: String) { + var finishTimestamp: Long = 0L + var totalExecution: Int = 0 + def totalTime: Long = { + if (finishTimestamp == 0L) { + System.currentTimeMillis - startTimestamp + } else { + finishTimestamp - startTimestamp + } + } + } + + private[thriftserver] object ExecutionState extends Enumeration { + val STARTED, COMPILED, FAILED, FINISHED = Value + type ExecutionState = Value + } + + private[thriftserver] class ExecutionInfo( + val statement: String, + val sessionId: String, + val startTimestamp: Long, + val userName: String) { + var finishTimestamp: Long = 0L + var executePlan: String = "" + var detail: String = "" + var state: ExecutionState.Value = ExecutionState.STARTED + val jobId: ArrayBuffer[String] = ArrayBuffer[String]() + var groupId: String = "" + def totalTime: Long = { + if (finishTimestamp == 0L) { + System.currentTimeMillis - startTimestamp + } else { + finishTimestamp - startTimestamp + } + } + } + + /** * A inner sparkListener called in sc.stop to clean up the HiveThriftServer2 */ - class HiveThriftServer2Listener(val server: HiveServer2) extends SparkListener { + private[thriftserver] class HiveThriftServer2Listener( + val server: HiveServer2, + val conf: SQLConf) extends SparkListener { + override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = { server.stop() } - } + val sessionList = new mutable.LinkedHashMap[String, SessionInfo] + val executionList = new mutable.LinkedHashMap[String, ExecutionInfo] + val retainedStatements = + conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT, "200").toInt + val retainedSessions = + conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT, "200").toInt + var totalRunning = 0 + + override def onJobStart(jobStart: SparkListenerJobStart): Unit = { + for { + props <- Option(jobStart.properties) + groupId <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID)) + (_, info) <- executionList if info.groupId == groupId + } { + info.jobId += jobStart.jobId.toString + info.groupId = groupId + } + } + + def onSessionCreated(ip: String, sessionId: String, userName: String = "UNKNOWN"): Unit = { + val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName) + sessionList.put(sessionId, info) + trimSessionIfNecessary() + } + + def onSessionClosed(sessionId: String): Unit = { + sessionList(sessionId).finishTimestamp = System.currentTimeMillis + } + + def onStatementStart( + id: String, + sessionId: String, + statement: String, + groupId: String, + userName: String = "UNKNOWN"): Unit = { + val info = new ExecutionInfo(statement, sessionId, System.currentTimeMillis, userName) + info.state = ExecutionState.STARTED + executionList.put(id, info) + trimExecutionIfNecessary() + sessionList(sessionId).totalExecution += 1 + executionList(id).groupId = groupId + totalRunning += 1 + } + + def onStatementParsed(id: String, executionPlan: String): Unit = { + executionList(id).executePlan = executionPlan + executionList(id).state = ExecutionState.COMPILED + } + + def onStatementError(id: String, errorMessage: String, errorTrace: String): Unit = { + executionList(id).finishTimestamp = System.currentTimeMillis + executionList(id).detail = errorMessage + executionList(id).state = ExecutionState.FAILED + totalRunning -= 1 + } + + def onStatementFinish(id: String): Unit = { + executionList(id).finishTimestamp = System.currentTimeMillis + executionList(id).state = ExecutionState.FINISHED + totalRunning -= 1 + } + + private def trimExecutionIfNecessary() = synchronized { + if (executionList.size > retainedStatements) { + val toRemove = math.max(retainedStatements / 10, 1) + executionList.take(toRemove).foreach { s => + executionList.remove(s._1) + } + } + } + + private def trimSessionIfNecessary() = synchronized { + if (sessionList.size > retainedSessions) { + val toRemove = math.max(retainedSessions / 10, 1) + sessionList.take(toRemove).foreach { s => + sessionList.remove(s._1) + } + } + + } + } } private[hive] class HiveThriftServer2(hiveContext: HiveContext) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala new file mode 100644 index 0000000000000..71b16b6bebffb --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala @@ -0,0 +1,190 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import java.util.Calendar +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.commons.lang3.StringEscapeUtils +import org.apache.spark.Logging +import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{SessionInfo, ExecutionState, ExecutionInfo} +import org.apache.spark.ui.UIUtils._ +import org.apache.spark.ui._ + + +/** Page for Spark Web UI that shows statistics of a streaming job */ +private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("") with Logging { + + private val listener = parent.listener + private val startTime = Calendar.getInstance().getTime() + private val emptyCell = "-" + + /** Render the page */ + def render(request: HttpServletRequest): Seq[Node] = { + val content = + generateBasicStats() ++ +
++ +

+ {listener.sessionList.size} session(s) are online, + running {listener.totalRunning} SQL statement(s) +

++ + generateSessionStatsTable() ++ + generateSQLStatsTable() + UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + } + + /** Generate basic stats of the streaming program */ + private def generateBasicStats(): Seq[Node] = { + val timeSinceStart = System.currentTimeMillis() - startTime.getTime +
    +
  • + Started at: {startTime.toString} +
    • +
  • + Time since start: {formatDurationVerbose(timeSinceStart)} +
    • +
+ } + + /** Generate stats of batch statements of the thrift server program */ + private def generateSQLStatsTable(): Seq[Node] = { + val numStatement = listener.executionList.size + val table = if (numStatement > 0) { + val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration", + "Statement", "State", "Detail") + val dataRows = listener.executionList.values + + def generateDataRow(info: ExecutionInfo): Seq[Node] = { + val jobLink = info.jobId.map { id: String => + + [{id}] + + } + val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + + {info.userName} + + {jobLink} + + {info.groupId} + {formatDate(info.startTimestamp)} + {if(info.finishTimestamp > 0) formatDate(info.finishTimestamp)} + {formatDurationOption(Some(info.totalTime))} + {info.statement} + {info.state} + {errorMessageCell(detail)} + + } + + Some(UIUtils.listingTable(headerRow, generateDataRow, + dataRows, false, None, Seq(null), false)) + } else { + None + } + + val content = +
SQL Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + private def errorMessageCell(errorMessage: String): Seq[Node] = { + val isMultiline = errorMessage.indexOf('\n') >= 0 + val errorSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + errorMessage.substring(0, errorMessage.indexOf('\n')) + } else { + errorMessage + }) + val details = if (isMultiline) { + // scalastyle:off + + + details + ++ + + // scalastyle:on + } else { + "" + } + {errorSummary}{details} + } + + /** Generate stats of batch sessions of the thrift server program */ + private def generateSessionStatsTable(): Seq[Node] = { + val numBatches = listener.sessionList.size + val table = if (numBatches > 0) { + val dataRows = + listener.sessionList.values + val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration", + "Total Execute") + def generateDataRow(session: SessionInfo): Seq[Node] = { + val sessionLink = "%s/ThriftServer/session?id=%s" + .format(UIUtils.prependBaseUri(parent.basePath), session.sessionId) + + {session.userName} + {session.ip} + {session.sessionId} , + {formatDate(session.startTimestamp)} + {if(session.finishTimestamp > 0) formatDate(session.finishTimestamp)} + {formatDurationOption(Some(session.totalTime))} + {session.totalExecution.toString} + + } + Some(UIUtils.listingTable(headerRow, generateDataRow, dataRows, true, None, Seq(null), false)) + } else { + None + } + + val content = +
Session Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + + /** + * Returns a human-readable string representing a duration such as "5 second 35 ms" + */ + private def formatDurationOption(msOption: Option[Long]): String = { + msOption.map(formatDurationVerbose).getOrElse(emptyCell) + } + + /** Generate HTML table from string data */ + private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = { + def generateDataRow(data: Seq[String]): Seq[Node] = { + {data.map(d => {d})} + } + UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) + } +} + diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala new file mode 100644 index 0000000000000..33ba038ecce73 --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala @@ -0,0 +1,197 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import java.util.Calendar +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.commons.lang3.StringEscapeUtils +import org.apache.spark.Logging +import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{ExecutionInfo, ExecutionState} +import org.apache.spark.ui.UIUtils._ +import org.apache.spark.ui._ + +/** Page for Spark Web UI that shows statistics of a streaming job */ +private[ui] class ThriftServerSessionPage(parent: ThriftServerTab) + extends WebUIPage("session") with Logging { + + private val listener = parent.listener + private val startTime = Calendar.getInstance().getTime() + private val emptyCell = "-" + + /** Render the page */ + def render(request: HttpServletRequest): Seq[Node] = { + val parameterId = request.getParameter("id") + require(parameterId != null && parameterId.nonEmpty, "Missing id parameter") + val sessionStat = listener.sessionList.find(stat => { + stat._1 == parameterId + }).getOrElse(null) + require(sessionStat != null, "Invalid sessionID[" + parameterId + "]") + + val content = + generateBasicStats() ++ +
++ +

+ User {sessionStat._2.userName}, + IP {sessionStat._2.ip}, + Session created at {formatDate(sessionStat._2.startTimestamp)}, + Total run {sessionStat._2.totalExecution} SQL +

++ + generateSQLStatsTable(sessionStat._2.sessionId) + UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + } + + /** Generate basic stats of the streaming program */ + private def generateBasicStats(): Seq[Node] = { + val timeSinceStart = System.currentTimeMillis() - startTime.getTime +
    +
  • + Started at: {startTime.toString} +
    • +
  • + Time since start: {formatDurationVerbose(timeSinceStart)} +
    • +
+ } + + /** Generate stats of batch statements of the thrift server program */ + private def generateSQLStatsTable(sessionID: String): Seq[Node] = { + val executionList = listener.executionList + .filter(_._2.sessionId == sessionID) + val numStatement = executionList.size + val table = if (numStatement > 0) { + val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration", + "Statement", "State", "Detail") + val dataRows = executionList.values.toSeq.sortBy(_.startTimestamp).reverse + + def generateDataRow(info: ExecutionInfo): Seq[Node] = { + val jobLink = info.jobId.map { id: String => + + [{id}] + + } + val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + + {info.userName} + + {jobLink} + + {info.groupId} + {formatDate(info.startTimestamp)} + {formatDate(info.finishTimestamp)} + {formatDurationOption(Some(info.totalTime))} + {info.statement} + {info.state} + {errorMessageCell(detail)} + + } + + Some(UIUtils.listingTable(headerRow, generateDataRow, + dataRows, false, None, Seq(null), false)) + } else { + None + } + + val content = +
SQL Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + private def errorMessageCell(errorMessage: String): Seq[Node] = { + val isMultiline = errorMessage.indexOf('\n') >= 0 + val errorSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + errorMessage.substring(0, errorMessage.indexOf('\n')) + } else { + errorMessage + }) + val details = if (isMultiline) { + // scalastyle:off + + + details + ++ + + // scalastyle:on + } else { + "" + } + {errorSummary}{details} + } + + /** Generate stats of batch sessions of the thrift server program */ + private def generateSessionStatsTable(): Seq[Node] = { + val numBatches = listener.sessionList.size + val table = if (numBatches > 0) { + val dataRows = + listener.sessionList.values.toSeq.sortBy(_.startTimestamp).reverse.map ( session => + Seq( + session.userName, + session.ip, + session.sessionId, + formatDate(session.startTimestamp), + formatDate(session.finishTimestamp), + formatDurationOption(Some(session.totalTime)), + session.totalExecution.toString + ) + ).toSeq + val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration", + "Total Execute") + Some(listingTable(headerRow, dataRows)) + } else { + None + } + + val content = +
Session Statistics
++ +
+
    + {table.getOrElse("No statistics have been generated yet.")} +
+
+ + content + } + + + /** + * Returns a human-readable string representing a duration such as "5 second 35 ms" + */ + private def formatDurationOption(msOption: Option[Long]): String = { + msOption.map(formatDurationVerbose).getOrElse(emptyCell) + } + + /** Generate HTML table from string data */ + private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = { + def generateDataRow(data: Seq[String]): Seq[Node] = { + {data.map(d => {d})} + } + UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) + } +} + diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala new file mode 100644 index 0000000000000..343031f10c75c --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import org.apache.spark.sql.hive.thriftserver.{HiveThriftServer2, SparkSQLEnv} +import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab._ +import org.apache.spark.ui.{SparkUI, SparkUITab} +import org.apache.spark.{SparkContext, Logging, SparkException} + +/** + * Spark Web UI tab that shows statistics of a streaming job. + * This assumes the given SparkContext has enabled its SparkUI. + */ +private[thriftserver] class ThriftServerTab(sparkContext: SparkContext) + extends SparkUITab(getSparkUI(sparkContext), "ThriftServer") with Logging { + + val parent = getSparkUI(sparkContext) + val listener = HiveThriftServer2.listener + + attachPage(new ThriftServerPage(this)) + attachPage(new ThriftServerSessionPage(this)) + parent.attachTab(this) + + def detach() { + getSparkUI(sparkContext).detachTab(this) + } +} + +private[thriftserver] object ThriftServerTab { + def getSparkUI(sparkContext: SparkContext): SparkUI = { + sparkContext.ui.getOrElse { + throw new SparkException("Parent SparkUI to attach this tab to not found!") + } + } +} diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala index 4cf95e7bdfb2b..1fadea97fd07f 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala @@ -409,24 +409,24 @@ abstract class HiveThriftServer2Test extends FunSuite with BeforeAndAfterAll wit private val CLASS_NAME = HiveThriftServer2.getClass.getCanonicalName.stripSuffix("$") private val LOG_FILE_MARK = s"starting $CLASS_NAME, logging to " - private val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) - private val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) + protected val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) + protected val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) private var listeningPort: Int = _ protected def serverPort: Int = listeningPort protected def user = System.getProperty("user.name") - private var warehousePath: File = _ - private var metastorePath: File = _ - private def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" + protected var warehousePath: File = _ + protected var metastorePath: File = _ + protected def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" private val pidDir: File = Utils.createTempDir("thriftserver-pid") private var logPath: File = _ private var logTailingProcess: Process = _ private var diagnosisBuffer: ArrayBuffer[String] = ArrayBuffer.empty[String] - private def serverStartCommand(port: Int) = { + protected def serverStartCommand(port: Int) = { val portConf = if (mode == ServerMode.binary) { ConfVars.HIVE_SERVER2_THRIFT_PORT } else { diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala new file mode 100644 index 0000000000000..47541015a3611 --- /dev/null +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver + + + +import scala.util.Random + +import org.openqa.selenium.WebDriver +import org.openqa.selenium.htmlunit.HtmlUnitDriver +import org.scalatest.{Matchers, BeforeAndAfterAll} +import org.scalatest.concurrent.Eventually._ +import org.scalatest.selenium.WebBrowser +import org.scalatest.time.SpanSugar._ + +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.spark.sql.hive.HiveContext + + +class UISeleniumSuite + extends HiveThriftJdbcTest + with WebBrowser with Matchers with BeforeAndAfterAll { + + implicit var webDriver: WebDriver = _ + var server: HiveThriftServer2 = _ + var hc: HiveContext = _ + val uiPort = 20000 + Random.nextInt(10000) + override def mode: ServerMode.Value = ServerMode.binary + + override def beforeAll(): Unit = { + webDriver = new HtmlUnitDriver + super.beforeAll() + } + + override def afterAll(): Unit = { + if (webDriver != null) { + webDriver.quit() + } + super.afterAll() + } + + override protected def serverStartCommand(port: Int) = { + val portConf = if (mode == ServerMode.binary) { + ConfVars.HIVE_SERVER2_THRIFT_PORT + } else { + ConfVars.HIVE_SERVER2_THRIFT_HTTP_PORT + } + + s"""$startScript + | --master local + | --hiveconf hive.root.logger=INFO,console + | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri + | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath + | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost + | --hiveconf ${ConfVars.HIVE_SERVER2_TRANSPORT_MODE}=$mode + | --hiveconf $portConf=$port + | --driver-class-path ${sys.props("java.class.path")} + | --conf spark.ui.enabled=true + | --conf spark.ui.port=$uiPort + """.stripMargin.split("\\s+").toSeq + } + + test("thrift server ui test") { + withJdbcStatement(statement =>{ + val baseURL = s"http://localhost:${uiPort}" + + val queries = Seq( + "CREATE TABLE test_map(key INT, value STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map") + + queries.foreach(statement.execute) + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (baseURL) + find(cssSelector("""ul li a[href*="ThriftServer"]""")) should not be(None) + } + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (baseURL + "/ThriftServer") + find(id("sessionstat")) should not be(None) + find(id("sqlstat")) should not be(None) + + // check whether statements exists + queries.foreach { line => + findAll(cssSelector("""ul table tbody tr td""")).map(_.text).toList should contain (line) + } + } + }) + } +} diff --git a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala index 95a6e86d0546d..b3a79ba1c7d6b 100644 --- a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala +++ b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, Map => JMap} +import java.util.{ArrayList => JArrayList, Map => JMap, UUID} import org.apache.commons.logging.Log import org.apache.hadoop.hive.conf.HiveConf @@ -190,9 +190,12 @@ private[hive] class SparkExecuteStatementOperation( } def run(): Unit = { + val statementId = UUID.randomUUID().toString logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) - hiveContext.sparkContext.setJobDescription(statement) + HiveThriftServer2.listener.onStatementStart( + statementId, parentSession.getSessionHandle.getSessionId.toString, statement, statementId) + hiveContext.sparkContext.setJobGroup(statementId, statement) sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) } @@ -205,6 +208,7 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } + HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString()) iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -221,10 +225,13 @@ private[hive] class SparkExecuteStatementOperation( // HiveServer will silently swallow them. case e: Throwable => setState(OperationState.ERROR) + HiveThriftServer2.listener.onStatementError( + statementId, e.getMessage, e.getStackTraceString) logError("Error executing query:",e) throw new HiveSQLException(e.toString) } setState(OperationState.FINISHED) + HiveThriftServer2.listener.onStatementFinish(statementId) } } @@ -255,11 +262,14 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) withImpersonation: Boolean, delegationToken: String): SessionHandle = { hiveContext.openSession() - - super.openSession(username, passwd, sessionConf, withImpersonation, delegationToken) + val sessionHandle = super.openSession( + username, passwd, sessionConf, withImpersonation, delegationToken) + HiveThriftServer2.listener.onSessionCreated("UNKNOWN", sessionHandle.getSessionId.toString) + sessionHandle } override def closeSession(sessionHandle: SessionHandle) { + HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) super.closeSession(sessionHandle) sparkSqlOperationManager.sessionToActivePool -= sessionHandle diff --git a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala index 178eb1af7cdcd..b9d4f1c58c982 100644 --- a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala +++ b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} +import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, UUID} import org.apache.commons.logging.Log import org.apache.hadoop.hive.conf.HiveConf @@ -36,7 +36,7 @@ import org.apache.hive.service.cli._ import org.apache.hive.service.cli.operation.ExecuteStatementOperation import org.apache.hive.service.cli.session.{SessionManager, HiveSession} -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, Logging} import org.apache.spark.sql.{DataFrame, Row => SparkRow, SQLConf} import org.apache.spark.sql.execution.SetCommand import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ @@ -161,9 +161,16 @@ private[hive] class SparkExecuteStatementOperation( } def run(): Unit = { + val statementId = UUID.randomUUID().toString logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) - hiveContext.sparkContext.setJobDescription(statement) + HiveThriftServer2.listener.onStatementStart( + statementId, + parentSession.getSessionHandle.getSessionId.toString, + statement, + statementId, + parentSession.getUsername) + hiveContext.sparkContext.setJobGroup(statementId, statement) sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) } @@ -176,6 +183,7 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } + HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString()) iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -192,10 +200,13 @@ private[hive] class SparkExecuteStatementOperation( // HiveServer will silently swallow them. case e: Throwable => setState(OperationState.ERROR) + HiveThriftServer2.listener.onStatementError( + statementId, e.getMessage, e.getStackTraceString) logError("Error executing query:", e) throw new HiveSQLException(e.toString) } setState(OperationState.FINISHED) + HiveThriftServer2.listener.onStatementFinish(statementId) } } @@ -227,11 +238,16 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) withImpersonation: Boolean, delegationToken: String): SessionHandle = { hiveContext.openSession() - - super.openSession(protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + val sessionHandle = super.openSession( + protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + val session = super.getSession(sessionHandle) + HiveThriftServer2.listener.onSessionCreated( + session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername) + sessionHandle } override def closeSession(sessionHandle: SessionHandle) { + HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) super.closeSession(sessionHandle) sparkSqlOperationManager.sessionToActivePool -= sessionHandle From 5a1a1075a607be683f008ef92fa227803370c45f Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Mon, 4 May 2015 17:17:55 +0100 Subject: [PATCH 08/41] [MINOR] Fix python test typo? I suspect haven't been using anaconda in tests in a while. I wonder if this change actually does anything but this line as it stands looks strictly less correct. Author: Andrew Or Closes #5883 from andrewor14/fix-run-tests-typo and squashes the following commits: a3ad720 [Andrew Or] Fix typo? --- dev/run-tests | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dev/run-tests b/dev/run-tests index 861d1671182c2..05c63bce4d40d 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -236,7 +236,7 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS # add path for python 3 in jenkins -export PATH="${PATH}:/home/anaonda/envs/py3k/bin" +export PATH="${PATH}:/home/anaconda/envs/py3k/bin" ./python/run-tests echo "" From e0833c5958bbd73ff27cfe6865648d7b6e5a99bc Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Mon, 4 May 2015 11:28:59 -0700 Subject: [PATCH 09/41] [SPARK-5956] [MLLIB] Pipeline components should be copyable. This PR added `copy(extra: ParamMap): Params` to `Params`, which makes a copy of the current instance with a randomly generated uid and some extra param values. With this change, we only need to implement `fit` and `transform` without extra param values given the default implementation of `fit(dataset, extra)`: ~~~scala def fit(dataset: DataFrame, extra: ParamMap): Model = { copy(extra).fit(dataset) } ~~~ Inside `fit` and `transform`, since only the embedded values are used, I added `$` as an alias for `getOrDefault` to make the code easier to read. For example, in `LinearRegression.fit` we have: ~~~scala val effectiveRegParam = $(regParam) / yStd val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam ~~~ Meta-algorithm like `Pipeline` implements its own `copy(extra)`. So the fitted pipeline model stored all copied stages (no matter whether it is a transformer or a model). Other changes: * `Params$.inheritValues` is moved to `Params!.copyValues` and returns the target instance. * `fittingParamMap` was removed because the `parent` carries this information. * `validate` was renamed to `validateParams` to be more precise. TODOs: * [x] add tests for newly added methods * [ ] update documentation jkbradley dbtsai Author: Xiangrui Meng Closes #5820 from mengxr/SPARK-5956 and squashes the following commits: 7bef88d [Xiangrui Meng] address comments 05229c3 [Xiangrui Meng] assert -> assertEquals b2927b1 [Xiangrui Meng] organize imports f14456b [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5956 93e7924 [Xiangrui Meng] add tests for hasParam & copy 463ecae [Xiangrui Meng] merge master 2b954c3 [Xiangrui Meng] update Binarizer 465dd12 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5956 282a1a8 [Xiangrui Meng] fix test 819dd2d [Xiangrui Meng] merge master b642872 [Xiangrui Meng] example code runs 5a67779 [Xiangrui Meng] examples compile c76b4d1 [Xiangrui Meng] fix all unit tests 0f4fd64 [Xiangrui Meng] fix some tests 9286a22 [Xiangrui Meng] copyValues to trained models 53e0973 [Xiangrui Meng] move inheritValues to Params and rename it to copyValues 9ee004e [Xiangrui Meng] merge copy and copyWith; rename validate to validateParams d882afc [Xiangrui Meng] test compile f082a31 [Xiangrui Meng] make Params copyable and simply handling of extra params in all spark.ml components --- .../examples/ml/JavaDeveloperApiExample.java | 24 ++-- .../examples/ml/JavaSimpleParamsExample.java | 4 +- .../examples/ml/DecisionTreeExample.scala | 6 +- .../examples/ml/DeveloperApiExample.scala | 22 ++-- .../apache/spark/examples/ml/GBTExample.scala | 4 +- .../examples/ml/RandomForestExample.scala | 6 +- .../examples/ml/SimpleParamsExample.scala | 4 +- .../scala/org/apache/spark/ml/Estimator.scala | 26 ++++- .../scala/org/apache/spark/ml/Evaluator.scala | 20 +++- .../scala/org/apache/spark/ml/Model.scala | 9 +- .../scala/org/apache/spark/ml/Pipeline.scala | 106 ++++++++---------- .../org/apache/spark/ml/Transformer.scala | 46 +++++--- .../spark/ml/classification/Classifier.scala | 49 +++----- .../DecisionTreeClassifier.scala | 29 ++--- .../ml/classification/GBTClassifier.scala | 33 +++--- .../classification/LogisticRegression.scala | 58 +++++----- .../ProbabilisticClassifier.scala | 33 ++---- .../RandomForestClassifier.scala | 31 ++--- .../BinaryClassificationEvaluator.scala | 17 ++- .../apache/spark/ml/feature/Binarizer.scala | 20 ++-- .../apache/spark/ml/feature/HashingTF.scala | 10 +- .../org/apache/spark/ml/feature/IDF.scala | 38 +++---- .../apache/spark/ml/feature/Normalizer.scala | 10 +- .../ml/feature/PolynomialExpansion.scala | 9 +- .../spark/ml/feature/StandardScaler.scala | 49 ++++---- .../spark/ml/feature/StringIndexer.scala | 34 +++--- .../apache/spark/ml/feature/Tokenizer.scala | 18 +-- .../spark/ml/feature/VectorAssembler.scala | 15 +-- .../spark/ml/feature/VectorIndexer.scala | 74 ++++++------ .../apache/spark/ml/feature/Word2Vec.scala | 62 +++++----- .../spark/ml/impl/estimator/Predictor.scala | 72 ++++-------- .../spark/ml/impl/tree/treeParams.scala | 35 +++--- .../org/apache/spark/ml/param/params.scala | 75 ++++++++----- .../ml/param/shared/SharedParamsCodeGen.scala | 5 +- .../spark/ml/param/shared/sharedParams.scala | 35 +++--- .../apache/spark/ml/recommendation/ALS.scala | 73 ++++++------ .../ml/regression/DecisionTreeRegressor.scala | 23 ++-- .../spark/ml/regression/GBTRegressor.scala | 30 ++--- .../ml/regression/LinearRegression.scala | 41 ++++--- .../ml/regression/RandomForestRegressor.scala | 27 ++--- .../spark/ml/regression/Regressor.scala | 2 +- .../spark/ml/tuning/CrossValidator.scala | 51 ++++----- .../JavaLogisticRegressionSuite.java | 14 ++- .../regression/JavaLinearRegressionSuite.java | 21 ++-- .../ml/tuning/JavaCrossValidatorSuite.java | 6 +- .../org/apache/spark/ml/PipelineSuite.scala | 26 +++-- .../DecisionTreeClassifierSuite.scala | 4 +- .../classification/GBTClassifierSuite.scala | 4 +- .../LogisticRegressionSuite.scala | 18 +-- .../RandomForestClassifierSuite.scala | 4 +- .../apache/spark/ml/param/ParamsSuite.scala | 13 ++- .../apache/spark/ml/param/TestParams.scala | 14 ++- .../DecisionTreeRegressorSuite.scala | 4 +- .../ml/regression/GBTRegressorSuite.scala | 3 +- .../RandomForestRegressorSuite.scala | 4 +- .../spark/ml/tuning/CrossValidatorSuite.scala | 6 +- 56 files changed, 671 insertions(+), 805 deletions(-) diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index 46377a99c4857..eac4f898a475d 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -28,7 +28,6 @@ import org.apache.spark.ml.classification.ClassificationModel; import org.apache.spark.ml.param.IntParam; import org.apache.spark.ml.param.ParamMap; -import org.apache.spark.ml.param.Params$; import org.apache.spark.mllib.linalg.BLAS; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; @@ -129,16 +128,16 @@ MyJavaLogisticRegression setMaxIter(int value) { // This method is used by fit(). // In Java, we have to make it public since Java does not understand Scala's protected modifier. - public MyJavaLogisticRegressionModel train(DataFrame dataset, ParamMap paramMap) { + public MyJavaLogisticRegressionModel train(DataFrame dataset) { // Extract columns from data using helper method. - JavaRDD oldDataset = extractLabeledPoints(dataset, paramMap).toJavaRDD(); + JavaRDD oldDataset = extractLabeledPoints(dataset).toJavaRDD(); // Do learning to estimate the weight vector. int numFeatures = oldDataset.take(1).get(0).features().size(); Vector weights = Vectors.zeros(numFeatures); // Learning would happen here. // Create a model, and return it. - return new MyJavaLogisticRegressionModel(this, paramMap, weights); + return new MyJavaLogisticRegressionModel(this, weights); } } @@ -155,18 +154,11 @@ class MyJavaLogisticRegressionModel private MyJavaLogisticRegression parent_; public MyJavaLogisticRegression parent() { return parent_; } - private ParamMap fittingParamMap_; - public ParamMap fittingParamMap() { return fittingParamMap_; } - private Vector weights_; public Vector weights() { return weights_; } - public MyJavaLogisticRegressionModel( - MyJavaLogisticRegression parent_, - ParamMap fittingParamMap_, - Vector weights_) { + public MyJavaLogisticRegressionModel(MyJavaLogisticRegression parent_, Vector weights_) { this.parent_ = parent_; - this.fittingParamMap_ = fittingParamMap_; this.weights_ = weights_; } @@ -210,10 +202,8 @@ public Vector predictRaw(Vector features) { * In Java, we have to make this method public since Java does not understand Scala's protected * modifier. */ - public MyJavaLogisticRegressionModel copy() { - MyJavaLogisticRegressionModel m = - new MyJavaLogisticRegressionModel(parent_, fittingParamMap_, weights_); - Params$.MODULE$.inheritValues(this.extractParamMap(), this, m); - return m; + @Override + public MyJavaLogisticRegressionModel copy(ParamMap extra) { + return copyValues(new MyJavaLogisticRegressionModel(parent_, weights_), extra); } } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index 4e02acce696e6..29158d5c85651 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -71,7 +71,7 @@ public static void main(String[] args) { // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this // LogisticRegression instance. - System.out.println("Model 1 was fit using parameters: " + model1.fittingParamMap()); + System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap()); // We may alternatively specify parameters using a ParamMap. ParamMap paramMap = new ParamMap(); @@ -87,7 +87,7 @@ public static void main(String[] args) { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. LogisticRegressionModel model2 = lr.fit(training, paramMapCombined); - System.out.println("Model 2 was fit using parameters: " + model2.fittingParamMap()); + System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap()); // Prepare test documents. List localTest = Lists.newArrayList( diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala index 9002e99d82ad3..8340d91101ab3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -276,16 +276,14 @@ object DecisionTreeExample { // Get the trained Decision Tree from the fitted PipelineModel algo match { case "classification" => - val treeModel = pipelineModel.getModel[DecisionTreeClassificationModel]( - dt.asInstanceOf[DecisionTreeClassifier]) + val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeClassificationModel] if (treeModel.numNodes < 20) { println(treeModel.toDebugString) // Print full model. } else { println(treeModel) // Print model summary. } case "regression" => - val treeModel = pipelineModel.getModel[DecisionTreeRegressionModel]( - dt.asInstanceOf[DecisionTreeRegressor]) + val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeRegressionModel] if (treeModel.numNodes < 20) { println(treeModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala index 2245fa429fda3..2a2d0677272a0 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala @@ -18,13 +18,12 @@ package org.apache.spark.examples.ml import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.ml.classification.{Classifier, ClassifierParams, ClassificationModel} -import org.apache.spark.ml.param.{Params, IntParam, ParamMap} +import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams} +import org.apache.spark.ml.param.{IntParam, ParamMap} import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.sql.{DataFrame, Row, SQLContext} - /** * A simple example demonstrating how to write your own learning algorithm using Estimator, * Transformer, and other abstractions. @@ -99,7 +98,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams { * class since the maxIter parameter is only used during training (not in the Model). */ val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") - def getMaxIter: Int = getOrDefault(maxIter) + def getMaxIter: Int = $(maxIter) } /** @@ -117,18 +116,16 @@ private class MyLogisticRegression def setMaxIter(value: Int): this.type = set(maxIter, value) // This method is used by fit() - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): MyLogisticRegressionModel = { + override protected def train(dataset: DataFrame): MyLogisticRegressionModel = { // Extract columns from data using helper method. - val oldDataset = extractLabeledPoints(dataset, paramMap) + val oldDataset = extractLabeledPoints(dataset) // Do learning to estimate the weight vector. val numFeatures = oldDataset.take(1)(0).features.size val weights = Vectors.zeros(numFeatures) // Learning would happen here. // Create a model, and return it. - new MyLogisticRegressionModel(this, paramMap, weights) + new MyLogisticRegressionModel(this, weights) } } @@ -139,7 +136,6 @@ private class MyLogisticRegression */ private class MyLogisticRegressionModel( override val parent: MyLogisticRegression, - override val fittingParamMap: ParamMap, val weights: Vector) extends ClassificationModel[Vector, MyLogisticRegressionModel] with MyLogisticRegressionParams { @@ -176,9 +172,7 @@ private class MyLogisticRegressionModel( * * This is used for the default implementation of [[transform()]]. */ - override protected def copy(): MyLogisticRegressionModel = { - val m = new MyLogisticRegressionModel(parent, fittingParamMap, weights) - Params.inheritValues(extractParamMap(), this, m) - m + override def copy(extra: ParamMap): MyLogisticRegressionModel = { + copyValues(new MyLogisticRegressionModel(parent, weights), extra) } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala index 5fccb142d4c3d..c5899b6683c79 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala @@ -201,14 +201,14 @@ object GBTExample { // Get the trained GBT from the fitted PipelineModel algo match { case "classification" => - val rfModel = pipelineModel.getModel[GBTClassificationModel](dt.asInstanceOf[GBTClassifier]) + val rfModel = pipelineModel.stages.last.asInstanceOf[GBTClassificationModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { println(rfModel) // Print model summary. } case "regression" => - val rfModel = pipelineModel.getModel[GBTRegressionModel](dt.asInstanceOf[GBTRegressor]) + val rfModel = pipelineModel.stages.last.asInstanceOf[GBTRegressionModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala index 9b909324ec82a..7f88d2681bcaa 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala @@ -209,16 +209,14 @@ object RandomForestExample { // Get the trained Random Forest from the fitted PipelineModel algo match { case "classification" => - val rfModel = pipelineModel.getModel[RandomForestClassificationModel]( - dt.asInstanceOf[RandomForestClassifier]) + val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestClassificationModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { println(rfModel) // Print model summary. } case "regression" => - val rfModel = pipelineModel.getModel[RandomForestRegressionModel]( - dt.asInstanceOf[RandomForestRegressor]) + val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestRegressionModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala index bf805149d0af6..e8a991f50e338 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala @@ -63,7 +63,7 @@ object SimpleParamsExample { // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this // LogisticRegression instance. - println("Model 1 was fit using parameters: " + model1.fittingParamMap) + println("Model 1 was fit using parameters: " + model1.parent.extractParamMap()) // We may alternatively specify parameters using a ParamMap, // which supports several methods for specifying parameters. @@ -78,7 +78,7 @@ object SimpleParamsExample { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. val model2 = lr.fit(training.toDF(), paramMapCombined) - println("Model 2 was fit using parameters: " + model2.fittingParamMap) + println("Model 2 was fit using parameters: " + model2.parent.extractParamMap()) // Prepare test data. val test = sc.parallelize(Seq( diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala index d6b3503ebdd9a..7f3f3262a644f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala @@ -34,13 +34,16 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { * Fits a single model to the input data with optional parameters. * * @param dataset input dataset - * @param paramPairs Optional list of param pairs. - * These values override any specified in this Estimator's embedded ParamMap. + * @param firstParamPair the first param pair, overrides embedded params + * @param otherParamPairs other param pairs. These values override any specified in this + * Estimator's embedded ParamMap. * @return fitted model */ @varargs - def fit(dataset: DataFrame, paramPairs: ParamPair[_]*): M = { - val map = ParamMap(paramPairs: _*) + def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M = { + val map = new ParamMap() + .put(firstParamPair) + .put(otherParamPairs: _*) fit(dataset, map) } @@ -52,12 +55,19 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { * These values override any specified in this Estimator's embedded ParamMap. * @return fitted model */ - def fit(dataset: DataFrame, paramMap: ParamMap): M + def fit(dataset: DataFrame, paramMap: ParamMap): M = { + copy(paramMap).fit(dataset) + } + + /** + * Fits a model to the input data. + */ + def fit(dataset: DataFrame): M /** * Fits multiple models to the input data with multiple sets of parameters. * The default implementation uses a for loop on each parameter map. - * Subclasses could overwrite this to optimize multi-model training. + * Subclasses could override this to optimize multi-model training. * * @param dataset input dataset * @param paramMaps An array of parameter maps. @@ -67,4 +77,8 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[M] = { paramMaps.map(fit(dataset, _)) } + + override def copy(extra: ParamMap): Estimator[M] = { + super.copy(extra).asInstanceOf[Estimator[M]] + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala index 8b4b5fd8af986..5f2f8c94e9ff7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala @@ -18,8 +18,7 @@ package org.apache.spark.ml import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.util.Identifiable +import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.sql.DataFrame /** @@ -27,7 +26,7 @@ import org.apache.spark.sql.DataFrame * Abstract class for evaluators that compute metrics from predictions. */ @AlphaComponent -abstract class Evaluator extends Identifiable { +abstract class Evaluator extends Params { /** * Evaluates the output. @@ -36,5 +35,18 @@ abstract class Evaluator extends Identifiable { * @param paramMap parameter map that specifies the input columns and output metrics * @return metric */ - def evaluate(dataset: DataFrame, paramMap: ParamMap): Double + def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { + this.copy(paramMap).evaluate(dataset) + } + + /** + * Evaluates the output. + * @param dataset a dataset that contains labels/observations and predictions. + * @return metric + */ + def evaluate(dataset: DataFrame): Double + + override def copy(extra: ParamMap): Evaluator = { + super.copy(extra).asInstanceOf[Evaluator] + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala index a491bc7ee8295..9974efe7b1d25 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Model.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Model.scala @@ -34,9 +34,8 @@ abstract class Model[M <: Model[M]] extends Transformer { */ val parent: Estimator[M] - /** - * Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model. - * Note: For ensembles' component Models, this value can be null. - */ - val fittingParamMap: ParamMap + override def copy(extra: ParamMap): M = { + // The default implementation of Params.copy doesn't work for models. + throw new NotImplementedError(s"${this.getClass} doesn't implement copy(extra: ParamMap)") + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 6bfeecd764d75..33d430f5671ee 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -21,7 +21,7 @@ import scala.collection.mutable.ListBuffer import org.apache.spark.Logging import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{Params, Param, ParamMap} +import org.apache.spark.ml.param.{Param, ParamMap, Params} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -30,40 +30,41 @@ import org.apache.spark.sql.types.StructType * A stage in a pipeline, either an [[Estimator]] or a [[Transformer]]. */ @AlphaComponent -abstract class PipelineStage extends Serializable with Logging { +abstract class PipelineStage extends Params with Logging { /** * :: DeveloperApi :: * - * Derives the output schema from the input schema and parameters. - * The schema describes the columns and types of the data. - * - * @param schema Input schema to this stage - * @param paramMap Parameters passed to this stage - * @return Output schema from this stage + * Derives the output schema from the input schema. */ @DeveloperApi - def transformSchema(schema: StructType, paramMap: ParamMap): StructType + def transformSchema(schema: StructType): StructType /** + * :: DeveloperApi :: + * * Derives the output schema from the input schema and parameters, optionally with logging. * * This should be optimistic. If it is unclear whether the schema will be valid, then it should * be assumed valid until proven otherwise. */ + @DeveloperApi protected def transformSchema( schema: StructType, - paramMap: ParamMap, logging: Boolean): StructType = { if (logging) { logDebug(s"Input schema: ${schema.json}") } - val outputSchema = transformSchema(schema, paramMap) + val outputSchema = transformSchema(schema) if (logging) { logDebug(s"Expected output schema: ${outputSchema.json}") } outputSchema } + + override def copy(extra: ParamMap): PipelineStage = { + super.copy(extra).asInstanceOf[PipelineStage] + } } /** @@ -81,15 +82,22 @@ abstract class PipelineStage extends Serializable with Logging { @AlphaComponent class Pipeline extends Estimator[PipelineModel] { - /** param for pipeline stages */ + /** + * param for pipeline stages + * @group param + */ val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline") + + /** @group setParam */ def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this } - def getStages: Array[PipelineStage] = getOrDefault(stages) - override def validate(paramMap: ParamMap): Unit = { + /** @group getParam */ + def getStages: Array[PipelineStage] = $(stages).clone() + + override def validateParams(paramMap: ParamMap): Unit = { val map = extractParamMap(paramMap) getStages.foreach { - case pStage: Params => pStage.validate(map) + case pStage: Params => pStage.validateParams(map) case _ => } } @@ -104,13 +112,11 @@ class Pipeline extends Estimator[PipelineModel] { * pipeline stages. If there are no stages, the output model acts as an identity transformer. * * @param dataset input dataset - * @param paramMap parameter map * @return fitted pipeline */ - override def fit(dataset: DataFrame, paramMap: ParamMap): PipelineModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val theStages = map(stages) + override def fit(dataset: DataFrame): PipelineModel = { + transformSchema(dataset.schema, logging = true) + val theStages = $(stages) // Search for the last estimator. var indexOfLastEstimator = -1 theStages.view.zipWithIndex.foreach { case (stage, index) => @@ -126,7 +132,7 @@ class Pipeline extends Estimator[PipelineModel] { if (index <= indexOfLastEstimator) { val transformer = stage match { case estimator: Estimator[_] => - estimator.fit(curDataset, paramMap) + estimator.fit(curDataset) case t: Transformer => t case _ => @@ -134,7 +140,7 @@ class Pipeline extends Estimator[PipelineModel] { s"Do not support stage $stage of type ${stage.getClass}") } if (index < indexOfLastEstimator) { - curDataset = transformer.transform(curDataset, paramMap) + curDataset = transformer.transform(curDataset) } transformers += transformer } else { @@ -142,15 +148,20 @@ class Pipeline extends Estimator[PipelineModel] { } } - new PipelineModel(this, map, transformers.toArray) + new PipelineModel(this, transformers.toArray) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val theStages = map(stages) + override def copy(extra: ParamMap): Pipeline = { + val map = extractParamMap(extra) + val newStages = map(stages).map(_.copy(extra)) + new Pipeline().setStages(newStages) + } + + override def transformSchema(schema: StructType): StructType = { + val theStages = $(stages) require(theStages.toSet.size == theStages.length, "Cannot have duplicate components in a pipeline.") - theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur, paramMap)) + theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur)) } } @@ -161,43 +172,24 @@ class Pipeline extends Estimator[PipelineModel] { @AlphaComponent class PipelineModel private[ml] ( override val parent: Pipeline, - override val fittingParamMap: ParamMap, - private[ml] val stages: Array[Transformer]) + val stages: Array[Transformer]) extends Model[PipelineModel] with Logging { - override def validate(paramMap: ParamMap): Unit = { - val map = fittingParamMap ++ extractParamMap(paramMap) - stages.foreach(_.validate(map)) + override def validateParams(): Unit = { + super.validateParams() + stages.foreach(_.validateParams()) } - /** - * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input - * estimator does not exist in the pipeline. - */ - def getModel[M <: Model[M]](stage: Estimator[M]): M = { - val matched = stages.filter { - case m: Model[_] => m.parent.eq(stage) - case _ => false - } - if (matched.isEmpty) { - throw new NoSuchElementException(s"Cannot find stage $stage from the pipeline.") - } else if (matched.length > 1) { - throw new IllegalStateException(s"Cannot have duplicate estimators in the sample pipeline.") - } else { - matched.head.asInstanceOf[M] - } + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur)) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = fittingParamMap ++ extractParamMap(paramMap) - transformSchema(dataset.schema, map, logging = true) - stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map)) + override def transformSchema(schema: StructType): StructType = { + stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = fittingParamMap ++ extractParamMap(paramMap) - stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map)) + override def copy(extra: ParamMap): PipelineModel = { + new PipelineModel(parent, stages) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index 0acda71ec6045..d96b54e511e9c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -37,13 +37,18 @@ abstract class Transformer extends PipelineStage with Params { /** * Transforms the dataset with optional parameters * @param dataset input dataset - * @param paramPairs optional list of param pairs, overwrite embedded params + * @param firstParamPair the first param pair, overwrite embedded params + * @param otherParamPairs other param pairs, overwrite embedded params * @return transformed dataset */ @varargs - def transform(dataset: DataFrame, paramPairs: ParamPair[_]*): DataFrame = { + def transform( + dataset: DataFrame, + firstParamPair: ParamPair[_], + otherParamPairs: ParamPair[_]*): DataFrame = { val map = new ParamMap() - paramPairs.foreach(map.put(_)) + .put(firstParamPair) + .put(otherParamPairs: _*) transform(dataset, map) } @@ -53,7 +58,18 @@ abstract class Transformer extends PipelineStage with Params { * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame + def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + this.copy(paramMap).transform(dataset) + } + + /** + * Transforms the input dataset. + */ + def transform(dataset: DataFrame): DataFrame + + override def copy(extra: ParamMap): Transformer = { + super.copy(extra).asInstanceOf[Transformer] + } } /** @@ -74,7 +90,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O * account of the embedded param map. So the param values should be determined solely by the input * param map. */ - protected def createTransformFunc(paramMap: ParamMap): IN => OUT + protected def createTransformFunc: IN => OUT /** * Returns the data type of the output column. @@ -86,22 +102,20 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O */ protected def validateInputType(inputType: DataType): Unit = {} - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType validateInputType(inputType) - if (schema.fieldNames.contains(map(outputCol))) { - throw new IllegalArgumentException(s"Output column ${map(outputCol)} already exists.") + if (schema.fieldNames.contains($(outputCol))) { + throw new IllegalArgumentException(s"Output column ${$(outputCol)} already exists.") } val outputFields = schema.fields :+ - StructField(map(outputCol), outputDataType, nullable = false) + StructField($(outputCol), outputDataType, nullable = false) StructType(outputFields) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - dataset.withColumn(map(outputCol), - callUDF(this.createTransformFunc(map), outputDataType, dataset(map(inputCol)))) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + dataset.withColumn($(outputCol), + callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol)))) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala index 29339c98f51cf..d3361e24705c8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala @@ -19,7 +19,6 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} -import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.ml.param.shared.HasRawPredictionCol import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} @@ -27,7 +26,6 @@ import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} - /** * :: DeveloperApi :: * Params for classification. @@ -40,12 +38,10 @@ private[spark] trait ClassifierParams extends PredictorParams override protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = extractParamMap(paramMap) - SchemaUtils.appendColumn(parentSchema, map(rawPredictionCol), new VectorUDT) + val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType) + SchemaUtils.appendColumn(parentSchema, $(rawPredictionCol), new VectorUDT) } } @@ -102,27 +98,16 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur * - raw predictions (confidences) as [[rawPredictionCol]] of type [[Vector]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This default implementation should be overridden as needed. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } + transformSchema(dataset.schema, logging = true) val (numColsOutput, outputData) = - ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map) + ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this) if (numColsOutput == 0) { logWarning(s"$uid: ClassificationModel.transform() was called as NOOP" + " since no output columns were set.") @@ -158,7 +143,6 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur */ @DeveloperApi protected def predictRaw(features: FeaturesType): Vector - } private[ml] object ClassificationModel { @@ -167,38 +151,35 @@ private[ml] object ClassificationModel { * Added prediction column(s). This is separated from [[ClassificationModel.transform()]] * since it is used by [[org.apache.spark.ml.classification.ProbabilisticClassificationModel]]. * @param dataset Input dataset - * @param map Parameter map. This will NOT be merged with the embedded paramMap; the merge - * should already be done. * @return (number of columns added, transformed dataset) */ def transformColumnsImpl[FeaturesType]( dataset: DataFrame, - model: ClassificationModel[FeaturesType, _], - map: ParamMap): (Int, DataFrame) = { + model: ClassificationModel[FeaturesType, _]): (Int, DataFrame) = { // Output selected columns only. // This is a bit complicated since it tries to avoid repeated computation. var tmpData = dataset var numColsOutput = 0 - if (map(model.rawPredictionCol) != "") { + if (model.getRawPredictionCol != "") { // output raw prediction val features2raw: FeaturesType => Vector = model.predictRaw - tmpData = tmpData.withColumn(map(model.rawPredictionCol), - callUDF(features2raw, new VectorUDT, col(map(model.featuresCol)))) + tmpData = tmpData.withColumn(model.getRawPredictionCol, + callUDF(features2raw, new VectorUDT, col(model.getFeaturesCol))) numColsOutput += 1 - if (map(model.predictionCol) != "") { + if (model.getPredictionCol != "") { val raw2pred: Vector => Double = (rawPred) => { rawPred.toArray.zipWithIndex.maxBy(_._1)._2 } - tmpData = tmpData.withColumn(map(model.predictionCol), - callUDF(raw2pred, DoubleType, col(map(model.rawPredictionCol)))) + tmpData = tmpData.withColumn(model.getPredictionCol, + callUDF(raw2pred, DoubleType, col(model.getRawPredictionCol))) numColsOutput += 1 } - } else if (map(model.predictionCol) != "") { + } else if (model.getPredictionCol != "") { // output prediction val features2pred: FeaturesType => Double = model.predict - tmpData = tmpData.withColumn(map(model.predictionCol), - callUDF(features2pred, DoubleType, col(map(model.featuresCol)))) + tmpData = tmpData.withColumn(model.getPredictionCol, + callUDF(features2pred, DoubleType, col(model.getFeaturesCol))) numColsOutput += 1 } (numColsOutput, tmpData) diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala index ee2a8dc6db171..419e5ba05d38a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala @@ -18,9 +18,9 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{Predictor, PredictionModel} +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, Node} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector @@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -64,22 +63,20 @@ final class DecisionTreeClassifier override def setImpurity(value: String): this.type = super.setImpurity(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): DecisionTreeClassificationModel = { + override protected def train(dataset: DataFrame): DecisionTreeClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = getOldStrategy(categoricalFeatures, numClasses) val oldModel = OldDecisionTree.train(oldDataset, strategy) - DecisionTreeClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + DecisionTreeClassificationModel.fromOld(oldModel, this, categoricalFeatures) } /** (private[ml]) Create a Strategy instance to use with the old API. */ @@ -106,7 +103,6 @@ object DecisionTreeClassifier { @AlphaComponent final class DecisionTreeClassificationModel private[ml] ( override val parent: DecisionTreeClassifier, - override val fittingParamMap: ParamMap, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeClassificationModel] with DecisionTreeModel with Serializable { @@ -118,10 +114,8 @@ final class DecisionTreeClassificationModel private[ml] ( rootNode.predict(features) } - override protected def copy(): DecisionTreeClassificationModel = { - val m = new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): DecisionTreeClassificationModel = { + copyValues(new DecisionTreeClassificationModel(parent, rootNode), extra) } override def toString: String = { @@ -140,12 +134,11 @@ private[ml] object DecisionTreeClassificationModel { def fromOld( oldModel: OldDecisionTreeModel, parent: DecisionTreeClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): DecisionTreeClassificationModel = { require(oldModel.algo == OldAlgo.Classification, s"Cannot convert non-classification DecisionTreeModel (old API) to" + s" DecisionTreeClassificationModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) + new DecisionTreeClassificationModel(parent, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala index 3d849867d4c47..534ea95b1c538 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala @@ -23,7 +23,7 @@ import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Param, Params, ParamMap} +import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.ml.regression.DecisionTreeRegressionModel import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils @@ -31,12 +31,11 @@ import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} -import org.apache.spark.mllib.tree.loss.{Loss => OldLoss, LogLoss => OldLogLoss} +import org.apache.spark.mllib.tree.loss.{LogLoss => OldLogLoss, Loss => OldLoss} import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -112,7 +111,7 @@ final class GBTClassifier def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType).toLowerCase + def getLossType: String = $(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { @@ -124,25 +123,23 @@ final class GBTClassifier } } - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): GBTClassificationModel = { + override protected def train(dataset: DataFrame): GBTClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("GBTClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } require(numClasses == 2, s"GBTClassifier only supports binary classification but was given numClasses = $numClasses") - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification) val oldGBT = new OldGBT(boostingStrategy) val oldModel = oldGBT.run(oldDataset) - GBTClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -165,7 +162,6 @@ object GBTClassifier { @AlphaComponent final class GBTClassificationModel( override val parent: GBTClassifier, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTClassificationModel] @@ -188,10 +184,8 @@ final class GBTClassificationModel( if (prediction > 0.0) 1.0 else 0.0 } - override protected def copy(): GBTClassificationModel = { - val m = new GBTClassificationModel(parent, fittingParamMap, _trees, _treeWeights) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): GBTClassificationModel = { + copyValues(new GBTClassificationModel(parent, _trees, _treeWeights), extra) } override def toString: String = { @@ -210,14 +204,13 @@ private[ml] object GBTClassificationModel { def fromOld( oldModel: OldGBTModel, parent: GBTClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): GBTClassificationModel = { require(oldModel.algo == OldAlgo.Classification, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTClassificationModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + new GBTClassificationModel(parent, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index cc8b0721cf2b6..b73be035e29b5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -21,12 +21,11 @@ import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS -import org.apache.spark.mllib.linalg.{VectorUDT, BLAS, Vector, Vectors} +import org.apache.spark.mllib.linalg.{BLAS, Vector, VectorUDT, Vectors} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.storage.StorageLevel - /** * Params for logistic regression. */ @@ -59,9 +58,9 @@ class LogisticRegression /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) - override protected def train(dataset: DataFrame, paramMap: ParamMap): LogisticRegressionModel = { + override protected def train(dataset: DataFrame): LogisticRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist oldDataset. - val oldDataset = extractLabeledPoints(dataset, paramMap) + val oldDataset = extractLabeledPoints(dataset) val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE if (handlePersistence) { oldDataset.persist(StorageLevel.MEMORY_AND_DISK) @@ -69,17 +68,17 @@ class LogisticRegression // Train model val lr = new LogisticRegressionWithLBFGS() - .setIntercept(paramMap(fitIntercept)) + .setIntercept($(fitIntercept)) lr.optimizer - .setRegParam(paramMap(regParam)) - .setNumIterations(paramMap(maxIter)) + .setRegParam($(regParam)) + .setNumIterations($(maxIter)) val oldModel = lr.run(oldDataset) - val lrm = new LogisticRegressionModel(this, paramMap, oldModel.weights, oldModel.intercept) + val lrm = new LogisticRegressionModel(this, oldModel.weights, oldModel.intercept) if (handlePersistence) { oldDataset.unpersist() } - lrm + copyValues(lrm) } } @@ -92,7 +91,6 @@ class LogisticRegression @AlphaComponent class LogisticRegressionModel private[ml] ( override val parent: LogisticRegression, - override val fittingParamMap: ParamMap, val weights: Vector, val intercept: Double) extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] @@ -110,16 +108,14 @@ class LogisticRegressionModel private[ml] ( 1.0 / (1.0 + math.exp(-m)) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This is overridden (a) to be more efficient (avoiding re-computing values when creating // multiple output columns) and (b) to handle threshold, which the abstractions do not use. // TODO: We should abstract away the steps defined by UDFs below so that the abstractions // can call whichever UDFs are needed to create the output columns. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - - val map = extractParamMap(paramMap) + transformSchema(dataset.schema, logging = true) // Output selected columns only. // This is a bit complicated since it tries to avoid repeated computation. @@ -128,41 +124,41 @@ class LogisticRegressionModel private[ml] ( // prediction (max margin) var tmpData = dataset var numColsOutput = 0 - if (map(rawPredictionCol) != "") { + if ($(rawPredictionCol) != "") { val features2raw: Vector => Vector = (features) => predictRaw(features) - tmpData = tmpData.withColumn(map(rawPredictionCol), - callUDF(features2raw, new VectorUDT, col(map(featuresCol)))) + tmpData = tmpData.withColumn($(rawPredictionCol), + callUDF(features2raw, new VectorUDT, col($(featuresCol)))) numColsOutput += 1 } - if (map(probabilityCol) != "") { - if (map(rawPredictionCol) != "") { + if ($(probabilityCol) != "") { + if ($(rawPredictionCol) != "") { val raw2prob = udf { (rawPreds: Vector) => val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) Vectors.dense(1.0 - prob1, prob1): Vector } - tmpData = tmpData.withColumn(map(probabilityCol), raw2prob(col(map(rawPredictionCol)))) + tmpData = tmpData.withColumn($(probabilityCol), raw2prob(col($(rawPredictionCol)))) } else { val features2prob = udf { (features: Vector) => predictProbabilities(features) : Vector } - tmpData = tmpData.withColumn(map(probabilityCol), features2prob(col(map(featuresCol)))) + tmpData = tmpData.withColumn($(probabilityCol), features2prob(col($(featuresCol)))) } numColsOutput += 1 } - if (map(predictionCol) != "") { - val t = map(threshold) - if (map(probabilityCol) != "") { + if ($(predictionCol) != "") { + val t = $(threshold) + if ($(probabilityCol) != "") { val predict = udf { probs: Vector => if (probs(1) > t) 1.0 else 0.0 } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(probabilityCol)))) - } else if (map(rawPredictionCol) != "") { + tmpData = tmpData.withColumn($(predictionCol), predict(col($(probabilityCol)))) + } else if ($(rawPredictionCol) != "") { val predict = udf { rawPreds: Vector => val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) if (prob1 > t) 1.0 else 0.0 } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(rawPredictionCol)))) + tmpData = tmpData.withColumn($(predictionCol), predict(col($(rawPredictionCol)))) } else { val predict = udf { features: Vector => this.predict(features) } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(featuresCol)))) + tmpData = tmpData.withColumn($(predictionCol), predict(col($(featuresCol)))) } numColsOutput += 1 } @@ -193,9 +189,7 @@ class LogisticRegressionModel private[ml] ( Vectors.dense(0.0, m) } - override protected def copy(): LogisticRegressionModel = { - val m = new LogisticRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): LogisticRegressionModel = { + copyValues(new LogisticRegressionModel(parent, weights, intercept), extra) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala index 10404548ccfde..8519841c5c26c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala @@ -18,7 +18,6 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} @@ -34,12 +33,10 @@ private[classification] trait ProbabilisticClassifierParams override protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = extractParamMap(paramMap) - SchemaUtils.appendColumn(parentSchema, map(probabilityCol), new VectorUDT) + val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType) + SchemaUtils.appendColumn(parentSchema, $(probabilityCol), new VectorUDT) } } @@ -95,36 +92,22 @@ private[spark] abstract class ProbabilisticClassificationModel[ * - probability of each class as [[probabilityCol]] of type [[Vector]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This default implementation should be overridden as needed. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } + transformSchema(dataset.schema, logging = true) val (numColsOutput, outputData) = - ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map) + ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this) // Output selected columns only. - if (map(probabilityCol) != "") { + if ($(probabilityCol) != "") { // output probabilities - val features2probs: FeaturesType => Vector = (features) => { - tmpModel.predictProbabilities(features) - } - outputData.withColumn(map(probabilityCol), - callUDF(features2probs, new VectorUDT, col(map(featuresCol)))) + outputData.withColumn($(probabilityCol), + callUDF(predictProbabilities _, new VectorUDT, col($(featuresCol)))) } else { if (numColsOutput == 0) { this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" + diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala index cfd6508fce890..17f59bb42e129 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala @@ -22,18 +22,17 @@ import scala.collection.mutable import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -81,24 +80,22 @@ final class RandomForestClassifier override def setFeatureSubsetStrategy(value: String): this.type = super.setFeatureSubsetStrategy(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): RandomForestClassificationModel = { + override protected def train(dataset: DataFrame): RandomForestClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("RandomForestClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity) val oldModel = OldRandomForest.trainClassifier( oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) - RandomForestClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + RandomForestClassificationModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -123,7 +120,6 @@ object RandomForestClassifier { @AlphaComponent final class RandomForestClassificationModel private[ml] ( override val parent: RandomForestClassifier, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeClassificationModel]) extends PredictionModel[Vector, RandomForestClassificationModel] with TreeEnsembleModel with Serializable { @@ -150,10 +146,8 @@ final class RandomForestClassificationModel private[ml] ( votes.maxBy(_._2)._1 } - override protected def copy(): RandomForestClassificationModel = { - val m = new RandomForestClassificationModel(parent, fittingParamMap, _trees) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): RandomForestClassificationModel = { + copyValues(new RandomForestClassificationModel(parent, _trees), extra) } override def toString: String = { @@ -172,14 +166,13 @@ private[ml] object RandomForestClassificationModel { def fromOld( oldModel: OldRandomForestModel, parent: RandomForestClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): RandomForestClassificationModel = { require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeClassificationModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestClassificationModel(parent, fittingParamMap, newTrees) + new RandomForestClassificationModel(parent, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index c865eb9fe092d..e5a73c6087a11 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -33,8 +33,7 @@ import org.apache.spark.sql.types.DoubleType * Evaluator for binary classification, which expects two input columns: score and label. */ @AlphaComponent -class BinaryClassificationEvaluator extends Evaluator with Params - with HasRawPredictionCol with HasLabelCol { +class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol { /** * param for metric name in evaluation @@ -44,7 +43,7 @@ class BinaryClassificationEvaluator extends Evaluator with Params "metric name in evaluation (areaUnderROC|areaUnderPR)") /** @group getParam */ - def getMetricName: String = getOrDefault(metricName) + def getMetricName: String = $(metricName) /** @group setParam */ def setMetricName(value: String): this.type = set(metricName, value) @@ -57,20 +56,18 @@ class BinaryClassificationEvaluator extends Evaluator with Params setDefault(metricName -> "areaUnderROC") - override def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { - val map = extractParamMap(paramMap) - + override def evaluate(dataset: DataFrame): Double = { val schema = dataset.schema - SchemaUtils.checkColumnType(schema, map(rawPredictionCol), new VectorUDT) - SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, $(rawPredictionCol), new VectorUDT) + SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType) // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2. - val scoreAndLabels = dataset.select(map(rawPredictionCol), map(labelCol)) + val scoreAndLabels = dataset.select($(rawPredictionCol), $(labelCol)) .map { case Row(rawPrediction: Vector, label: Double) => (rawPrediction(1), label) } val metrics = new BinaryClassificationMetrics(scoreAndLabels) - val metric = map(metricName) match { + val metric = $(metricName) match { case "areaUnderROC" => metrics.areaUnderROC() case "areaUnderPR" => diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala index f3ce6dfca2c1c..6eb1db6971111 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala @@ -44,7 +44,7 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol { new DoubleParam(this, "threshold", "threshold used to binarize continuous features") /** @group getParam */ - def getThreshold: Double = getOrDefault(threshold) + def getThreshold: Double = $(threshold) /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) @@ -57,23 +57,21 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val td = map(threshold) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val td = $(threshold) val binarizer = udf { in: Double => if (in > td) 1.0 else 0.0 } - val outputColName = map(outputCol) + val outputColName = $(outputCol) val metadata = BinaryAttribute.defaultAttr.withName(outputColName).toMetadata() dataset.select(col("*"), - binarizer(col(map(inputCol))).as(outputColName, metadata)) + binarizer(col($(inputCol))).as(outputColName, metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), DoubleType) + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) val inputFields = schema.fields - val outputColName = map(outputCol) + val outputColName = $(outputCol) require(inputFields.forall(_.name != outputColName), s"Output column $outputColName already exists.") diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index 0b3128f9ee8cd..c305a819a8966 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -19,9 +19,9 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap} +import org.apache.spark.ml.param.{IntParam, ParamValidators} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** @@ -42,13 +42,13 @@ class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { setDefault(numFeatures -> (1 << 18)) /** @group getParam */ - def getNumFeatures: Int = getOrDefault(numFeatures) + def getNumFeatures: Int = $(numFeatures) /** @group setParam */ def setNumFeatures(value: Int): this.type = set(numFeatures, value) - override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = { - val hashingTF = new feature.HashingTF(paramMap(numFeatures)) + override protected def createTransformFunc: Iterable[_] => Vector = { + val hashingTF = new feature.HashingTF($(numFeatures)) hashingTF.transform } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala index e6a62d998bb97..d901a20aed002 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala @@ -43,7 +43,7 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol setDefault(minDocFreq -> 0) /** @group getParam */ - def getMinDocFreq: Int = getOrDefault(minDocFreq) + def getMinDocFreq: Int = $(minDocFreq) /** @group setParam */ def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) @@ -51,10 +51,9 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol /** * Validate and transform the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), new VectorUDT) - SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT) + protected def validateAndTransformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT) + SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) } } @@ -71,18 +70,15 @@ final class IDF extends Estimator[IDFModel] with IDFBase { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): IDFModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val idf = new feature.IDF(map(minDocFreq)).fit(input) - val model = new IDFModel(this, map, idf) - Params.inheritValues(map, this, model) - model + override def fit(dataset: DataFrame): IDFModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val idf = new feature.IDF($(minDocFreq)).fit(input) + copyValues(new IDFModel(this, idf)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -93,7 +89,6 @@ final class IDF extends Estimator[IDFModel] with IDFBase { @AlphaComponent class IDFModel private[ml] ( override val parent: IDF, - override val fittingParamMap: ParamMap, idfModel: feature.IDFModel) extends Model[IDFModel] with IDFBase { @@ -103,14 +98,13 @@ class IDFModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) val idf = udf { vec: Vector => idfModel.transform(vec) } - dataset.withColumn(map(outputCol), idf(col(map(inputCol)))) + dataset.withColumn($(outputCol), idf(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala index bd2b5f6067e2d..755b46a64c7f1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -19,9 +19,9 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamValidators, DoubleParam, ParamMap} +import org.apache.spark.ml.param.{DoubleParam, ParamValidators} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** @@ -41,13 +41,13 @@ class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { setDefault(p -> 2.0) /** @group getParam */ - def getP: Double = getOrDefault(p) + def getP: Double = $(p) /** @group setParam */ def setP(value: Double): this.type = set(p, value) - override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { - val normalizer = new feature.Normalizer(paramMap(p)) + override protected def createTransformFunc: Vector => Vector = { + val normalizer = new feature.Normalizer($(p)) normalizer.transform } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala index 1b7c939c2dffe..63e190c8aae53 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala @@ -21,7 +21,7 @@ import scala.collection.mutable import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap} +import org.apache.spark.ml.param.{IntParam, ParamValidators} import org.apache.spark.mllib.linalg._ import org.apache.spark.sql.types.DataType @@ -47,14 +47,13 @@ class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExp setDefault(degree -> 2) /** @group getParam */ - def getDegree: Int = getOrDefault(degree) + def getDegree: Int = $(degree) /** @group setParam */ def setDegree(value: Int): this.type = set(degree, value) - override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { v => - val d = paramMap(degree) - PolynomialExpansion.expand(v, d) + override protected def createTransformFunc: Vector => Vector = { v => + PolynomialExpansion.expand(v, $(degree)) } override protected def outputDataType: DataType = new VectorUDT() diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index a0e9ed32e0e4c..7cad59ff3fa37 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -71,25 +71,21 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP /** @group setParam */ def setWithStd(value: Boolean): this.type = set(withStd, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): StandardScalerModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val scaler = new feature.StandardScaler(withMean = map(withMean), withStd = map(withStd)) + override def fit(dataset: DataFrame): StandardScalerModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val scaler = new feature.StandardScaler(withMean = $(withMean), withStd = $(withStd)) val scalerModel = scaler.fit(input) - val model = new StandardScalerModel(this, map, scalerModel) - Params.inheritValues(map, this, model) - model + copyValues(new StandardScalerModel(this, scalerModel)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], - s"Input column ${map(inputCol)} must be a vector column") - require(!schema.fieldNames.contains(map(outputCol)), - s"Output column ${map(outputCol)} already exists.") - val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false) + s"Input column ${$(inputCol)} must be a vector column") + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } } @@ -101,7 +97,6 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP @AlphaComponent class StandardScalerModel private[ml] ( override val parent: StandardScaler, - override val fittingParamMap: ParamMap, scaler: feature.StandardScalerModel) extends Model[StandardScalerModel] with StandardScalerParams { @@ -111,21 +106,19 @@ class StandardScalerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val scale = udf((v: Vector) => { scaler.transform(v) } : Vector) - dataset.withColumn(map(outputCol), scale(col(map(inputCol)))) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val scale = udf { scaler.transform _ } + dataset.withColumn($(outputCol), scale(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], - s"Input column ${map(inputCol)} must be a vector column") - require(!schema.fieldNames.contains(map(outputCol)), - s"Output column ${map(outputCol)} already exists.") - val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false) + s"Input column ${$(inputCol)} must be a vector column") + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala index 9db3b29e10d69..3d78537ad84cb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -34,18 +34,17 @@ import org.apache.spark.util.collection.OpenHashMap private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol { /** Validates and transforms the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputColName = map(inputCol) + protected def validateAndTransformSchema(schema: StructType): StructType = { + val inputColName = $(inputCol) val inputDataType = schema(inputColName).dataType require(inputDataType == StringType || inputDataType.isInstanceOf[NumericType], s"The input column $inputColName must be either string type or numeric type, " + s"but got $inputDataType.") val inputFields = schema.fields - val outputColName = map(outputCol) + val outputColName = $(outputCol) require(inputFields.forall(_.name != outputColName), s"Output column $outputColName already exists.") - val attr = NominalAttribute.defaultAttr.withName(map(outputCol)) + val attr = NominalAttribute.defaultAttr.withName($(outputCol)) val outputFields = inputFields :+ attr.toStructField() StructType(outputFields) } @@ -69,19 +68,16 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase // TODO: handle unseen labels - override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = { - val map = extractParamMap(paramMap) - val counts = dataset.select(col(map(inputCol)).cast(StringType)) + override def fit(dataset: DataFrame): StringIndexerModel = { + val counts = dataset.select(col($(inputCol)).cast(StringType)) .map(_.getString(0)) .countByValue() val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray - val model = new StringIndexerModel(this, map, labels) - Params.inheritValues(map, this, model) - model + copyValues(new StringIndexerModel(this, labels)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -92,7 +88,6 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase @AlphaComponent class StringIndexerModel private[ml] ( override val parent: StringIndexer, - override val fittingParamMap: ParamMap, labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase { private val labelToIndex: OpenHashMap[String, Double] = { @@ -112,8 +107,7 @@ class StringIndexerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { val indexer = udf { label: String => if (labelToIndex.contains(label)) { labelToIndex(label) @@ -122,14 +116,14 @@ class StringIndexerModel private[ml] ( throw new SparkException(s"Unseen label: $label.") } } - val outputColName = map(outputCol) + val outputColName = $(outputCol) val metadata = NominalAttribute.defaultAttr .withName(outputColName).withValues(labels).toMetadata() dataset.select(col("*"), - indexer(dataset(map(inputCol)).cast(StringType)).as(outputColName, metadata)) + indexer(dataset($(inputCol)).cast(StringType)).as(outputColName, metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 01752ba482d0c..2863b7621526e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer import org.apache.spark.ml.param._ -import org.apache.spark.sql.types.{DataType, StringType, ArrayType} +import org.apache.spark.sql.types.{ArrayType, DataType, StringType} /** * :: AlphaComponent :: @@ -29,7 +29,7 @@ import org.apache.spark.sql.types.{DataType, StringType, ArrayType} @AlphaComponent class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { - override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { + override protected def createTransformFunc: String => Seq[String] = { _.toLowerCase.split("\\s") } @@ -62,7 +62,7 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setMinTokenLength(value: Int): this.type = set(minTokenLength, value) /** @group getParam */ - def getMinTokenLength: Int = getOrDefault(minTokenLength) + def getMinTokenLength: Int = $(minTokenLength) /** * Indicates whether regex splits on gaps (true) or matching tokens (false). @@ -75,7 +75,7 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setGaps(value: Boolean): this.type = set(gaps, value) /** @group getParam */ - def getGaps: Boolean = getOrDefault(gaps) + def getGaps: Boolean = $(gaps) /** * Regex pattern used by tokenizer. @@ -88,14 +88,14 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setPattern(value: String): this.type = set(pattern, value) /** @group getParam */ - def getPattern: String = getOrDefault(pattern) + def getPattern: String = $(pattern) setDefault(minTokenLength -> 1, gaps -> false, pattern -> "\\p{L}+|[^\\p{L}\\s]+") - override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { str => - val re = paramMap(pattern).r - val tokens = if (paramMap(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq - val minLength = paramMap(minTokenLength) + override protected def createTransformFunc: String => Seq[String] = { str => + val re = $(pattern).r + val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq + val minLength = $(minTokenLength) tokens.filter(_.length >= minLength) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index 5e781a326d98c..8f2e62a8e2081 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -22,7 +22,6 @@ import scala.collection.mutable.ArrayBuilder import org.apache.spark.SparkException import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.Transformer -import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} import org.apache.spark.sql.{DataFrame, Row} @@ -42,13 +41,12 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { val assembleFunc = udf { r: Row => VectorAssembler.assemble(r.toSeq: _*) } val schema = dataset.schema - val inputColNames = map(inputCols) + val inputColNames = $(inputCols) val args = inputColNames.map { c => schema(c).dataType match { case DoubleType => dataset(c) @@ -56,13 +54,12 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { case _: NumericType | BooleanType => dataset(c).cast(DoubleType).as(s"${c}_double_$uid") } } - dataset.select(col("*"), assembleFunc(struct(args : _*)).as(map(outputCol))) + dataset.select(col("*"), assembleFunc(struct(args : _*)).as($(outputCol))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputColNames = map(inputCols) - val outputColName = map(outputCol) + override def transformSchema(schema: StructType): StructType = { + val inputColNames = $(inputCols) + val outputColName = $(outputCol) val inputDataTypes = inputColNames.map(name => schema(name).dataType) inputDataTypes.foreach { case _: NumericType | BooleanType => diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala index ed833c63c7ef1..07ea579d69893 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -18,19 +18,17 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} -import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute, - Attribute, AttributeGroup} -import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap, Params} +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, NumericAttribute} +import org.apache.spark.ml.param.{IntParam, ParamValidators, Params} import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT} -import org.apache.spark.sql.{Row, DataFrame} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, VectorUDT} +import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions.callUDF import org.apache.spark.sql.types.{StructField, StructType} import org.apache.spark.util.collection.OpenHashSet - /** Private trait for params for VectorIndexer and VectorIndexerModel */ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOutputCol { @@ -49,7 +47,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu setDefault(maxCategories -> 20) /** @group getParam */ - def getMaxCategories: Int = getOrDefault(maxCategories) + def getMaxCategories: Int = $(maxCategories) } /** @@ -100,33 +98,29 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): VectorIndexerModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val firstRow = dataset.select(map(inputCol)).take(1) + override def fit(dataset: DataFrame): VectorIndexerModel = { + transformSchema(dataset.schema, logging = true) + val firstRow = dataset.select($(inputCol)).take(1) require(firstRow.length == 1, s"VectorIndexer cannot be fit on an empty dataset.") val numFeatures = firstRow(0).getAs[Vector](0).size - val vectorDataset = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val maxCats = map(maxCategories) + val vectorDataset = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val maxCats = $(maxCategories) val categoryStats: VectorIndexer.CategoryStats = vectorDataset.mapPartitions { iter => val localCatStats = new VectorIndexer.CategoryStats(numFeatures, maxCats) iter.foreach(localCatStats.addVector) Iterator(localCatStats) }.reduce((stats1, stats2) => stats1.merge(stats2)) - val model = new VectorIndexerModel(this, map, numFeatures, categoryStats.getCategoryMaps) - Params.inheritValues(map, this, model) - model + copyValues(new VectorIndexerModel(this, numFeatures, categoryStats.getCategoryMaps)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType): StructType = { // We do not transfer feature metadata since we do not know what types of features we will // produce in transform(). - val map = extractParamMap(paramMap) val dataType = new VectorUDT - require(map.contains(inputCol), s"VectorIndexer requires input column parameter: $inputCol") - require(map.contains(outputCol), s"VectorIndexer requires output column parameter: $outputCol") - SchemaUtils.checkColumnType(schema, map(inputCol), dataType) - SchemaUtils.appendColumn(schema, map(outputCol), dataType) + require(isDefined(inputCol), s"VectorIndexer requires input column parameter: $inputCol") + require(isDefined(outputCol), s"VectorIndexer requires output column parameter: $outputCol") + SchemaUtils.checkColumnType(schema, $(inputCol), dataType) + SchemaUtils.appendColumn(schema, $(outputCol), dataType) } } @@ -243,7 +237,6 @@ private object VectorIndexer { @AlphaComponent class VectorIndexerModel private[ml] ( override val parent: VectorIndexer, - override val fittingParamMap: ParamMap, val numFeatures: Int, val categoryMaps: Map[Int, Map[Double, Int]]) extends Model[VectorIndexerModel] with VectorIndexerParams { @@ -326,35 +319,33 @@ class VectorIndexerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val newField = prepOutputField(dataset.schema, map) - val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol))) - dataset.withColumn(map(outputCol), newCol.as(map(outputCol), newField.metadata)) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val newField = prepOutputField(dataset.schema) + val newCol = callUDF(transformFunc, new VectorUDT, dataset($(inputCol))) + dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) + override def transformSchema(schema: StructType): StructType = { val dataType = new VectorUDT - require(map.contains(inputCol), + require(isDefined(inputCol), s"VectorIndexerModel requires input column parameter: $inputCol") - require(map.contains(outputCol), + require(isDefined(outputCol), s"VectorIndexerModel requires output column parameter: $outputCol") - SchemaUtils.checkColumnType(schema, map(inputCol), dataType) + SchemaUtils.checkColumnType(schema, $(inputCol), dataType) // If the input metadata specifies numFeatures, compare with expected numFeatures. - val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol))) val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) { Some(origAttrGroup.attributes.get.length) } else { origAttrGroup.numAttributes } require(origNumFeatures.forall(_ == numFeatures), "VectorIndexerModel expected" + - s" $numFeatures features, but input column ${map(inputCol)} had metadata specifying" + + s" $numFeatures features, but input column ${$(inputCol)} had metadata specifying" + s" ${origAttrGroup.numAttributes.get} features.") - val newField = prepOutputField(schema, map) + val newField = prepOutputField(schema) val outputFields = schema.fields :+ newField StructType(outputFields) } @@ -362,11 +353,10 @@ class VectorIndexerModel private[ml] ( /** * Prepare the output column field, including per-feature metadata. * @param schema Input schema - * @param map Parameter map (with this class' embedded parameter map folded in) * @return Output column field. This field does not contain non-ML metadata. */ - private def prepOutputField(schema: StructType, map: ParamMap): StructField = { - val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + private def prepOutputField(schema: StructType): StructField = { + val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol))) val featureAttributes: Array[Attribute] = if (origAttrGroup.attributes.nonEmpty) { // Convert original attributes to modified attributes val origAttrs: Array[Attribute] = origAttrGroup.attributes.get @@ -389,7 +379,7 @@ class VectorIndexerModel private[ml] ( } else { partialFeatureAttributes } - val newAttributeGroup = new AttributeGroup(map(outputCol), featureAttributes) + val newAttributeGroup = new AttributeGroup($(outputCol), featureAttributes) newAttributeGroup.toStructField() } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala index 0163fa8bd8a5b..34ff92970129f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala @@ -18,16 +18,16 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util.SchemaUtils -import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.BLAS._ import org.apache.spark.mllib.linalg.{VectorUDT, Vectors} +import org.apache.spark.mllib.linalg.BLAS._ +import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.{DataFrame, Row} /** * Params for [[Word2Vec]] and [[Word2VecModel]]. @@ -43,7 +43,7 @@ private[feature] trait Word2VecBase extends Params setDefault(vectorSize -> 100) /** @group getParam */ - def getVectorSize: Int = getOrDefault(vectorSize) + def getVectorSize: Int = $(vectorSize) /** * Number of partitions for sentences of words. @@ -53,7 +53,7 @@ private[feature] trait Word2VecBase extends Params setDefault(numPartitions -> 1) /** @group getParam */ - def getNumPartitions: Int = getOrDefault(numPartitions) + def getNumPartitions: Int = $(numPartitions) /** * The minimum number of times a token must appear to be included in the word2vec model's @@ -64,7 +64,7 @@ private[feature] trait Word2VecBase extends Params setDefault(minCount -> 5) /** @group getParam */ - def getMinCount: Int = getOrDefault(minCount) + def getMinCount: Int = $(minCount) setDefault(stepSize -> 0.025) setDefault(maxIter -> 1) @@ -73,10 +73,9 @@ private[feature] trait Word2VecBase extends Params /** * Validate and transform the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), new ArrayType(StringType, true)) - SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT) + protected def validateAndTransformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true)) + SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) } } @@ -112,25 +111,22 @@ final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { /** @group setParam */ def setMinCount(value: Int): this.type = set(minCount, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): Word2VecModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Seq[String]) => v } + override def fit(dataset: DataFrame): Word2VecModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0)) val wordVectors = new feature.Word2Vec() - .setLearningRate(map(stepSize)) - .setMinCount(map(minCount)) - .setNumIterations(map(maxIter)) - .setNumPartitions(map(numPartitions)) - .setSeed(map(seed)) - .setVectorSize(map(vectorSize)) + .setLearningRate($(stepSize)) + .setMinCount($(minCount)) + .setNumIterations($(maxIter)) + .setNumPartitions($(numPartitions)) + .setSeed($(seed)) + .setVectorSize($(vectorSize)) .fit(input) - val model = new Word2VecModel(this, map, wordVectors) - Params.inheritValues(map, this, model) - model + copyValues(new Word2VecModel(this, wordVectors)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -141,7 +137,6 @@ final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { @AlphaComponent class Word2VecModel private[ml] ( override val parent: Word2Vec, - override val fittingParamMap: ParamMap, wordVectors: feature.Word2VecModel) extends Model[Word2VecModel] with Word2VecBase { @@ -155,15 +150,14 @@ class Word2VecModel private[ml] ( * Transform a sentence column to a vector column to represent the whole sentence. The transform * is performed by averaging all word vectors it contains. */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) val bWordVectors = dataset.sqlContext.sparkContext.broadcast(wordVectors) val word2Vec = udf { sentence: Seq[String] => if (sentence.size == 0) { - Vectors.sparse(map(vectorSize), Array.empty[Int], Array.empty[Double]) + Vectors.sparse($(vectorSize), Array.empty[Int], Array.empty[Double]) } else { - val cum = Vectors.zeros(map(vectorSize)) + val cum = Vectors.zeros($(vectorSize)) val model = bWordVectors.value.getVectors for (word <- sentence) { if (model.contains(word)) { @@ -176,10 +170,10 @@ class Word2VecModel private[ml] ( cum } } - dataset.withColumn(map(outputCol), word2Vec(col(map(inputCol)))) + dataset.withColumn($(outputCol), word2Vec(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala index 195333a5cc47f..e8b3628140e99 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala @@ -18,18 +18,17 @@ package org.apache.spark.ml.impl.estimator import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} - /** * :: DeveloperApi :: * @@ -44,7 +43,6 @@ private[spark] trait PredictorParams extends Params /** * Validates and transforms the input schema with the provided param map. * @param schema input schema - * @param paramMap additional parameters * @param fitting whether this is in fitting * @param featuresDataType SQL DataType for FeaturesType. * E.g., [[org.apache.spark.mllib.linalg.VectorUDT]] for vector features. @@ -52,17 +50,15 @@ private[spark] trait PredictorParams extends Params */ protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val map = extractParamMap(paramMap) // TODO: Support casting Array[Double] and Array[Float] to Vector when FeaturesType = Vector - SchemaUtils.checkColumnType(schema, map(featuresCol), featuresDataType) + SchemaUtils.checkColumnType(schema, $(featuresCol), featuresDataType) if (fitting) { // TODO: Allow other numeric types - SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType) } - SchemaUtils.appendColumn(schema, map(predictionCol), DoubleType) + SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } } @@ -96,14 +92,15 @@ private[spark] abstract class Predictor[ /** @group setParam */ def setPredictionCol(value: String): Learner = set(predictionCol, value).asInstanceOf[Learner] - override def fit(dataset: DataFrame, paramMap: ParamMap): M = { + override def fit(dataset: DataFrame): M = { // This handles a few items such as schema validation. // Developers only need to implement train(). - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val model = train(dataset, map) - Params.inheritValues(map, this, model) // copy params to model - model + transformSchema(dataset.schema, logging = true) + copyValues(train(dataset)) + } + + override def copy(extra: ParamMap): Learner = { + super.copy(extra).asInstanceOf[Learner] } /** @@ -114,12 +111,10 @@ private[spark] abstract class Predictor[ * and copying parameters into the model. * * @param dataset Training dataset - * @param paramMap Parameter map. Unlike [[fit()]]'s paramMap, this paramMap has already - * been combined with the embedded ParamMap. * @return Fitted model */ @DeveloperApi - protected def train(dataset: DataFrame, paramMap: ParamMap): M + protected def train(dataset: DataFrame): M /** * :: DeveloperApi :: @@ -134,17 +129,16 @@ private[spark] abstract class Predictor[ @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap, fitting = true, featuresDataType) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = true, featuresDataType) } /** * Extract [[labelCol]] and [[featuresCol]] from the given dataset, * and put it in an RDD with strong types. */ - protected def extractLabeledPoints(dataset: DataFrame, paramMap: ParamMap): RDD[LabeledPoint] = { - val map = extractParamMap(paramMap) - dataset.select(map(labelCol), map(featuresCol)) + protected def extractLabeledPoints(dataset: DataFrame): RDD[LabeledPoint] = { + dataset.select($(labelCol), $(featuresCol)) .map { case Row(label: Double, features: Vector) => LabeledPoint(label, features) } @@ -186,8 +180,8 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap, fitting = false, featuresDataType) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = false, featuresDataType) } /** @@ -195,30 +189,16 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel * the predictions as a new column [[predictionCol]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset with [[predictionCol]] of type [[Double]] */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This default implementation should be overridden as needed. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } + transformSchema(dataset.schema, logging = true) - if (map(predictionCol) != "") { - val pred: FeaturesType => Double = (features) => { - tmpModel.predict(features) - } - dataset.withColumn(map(predictionCol), callUDF(pred, DoubleType, col(map(featuresCol)))) + if ($(predictionCol) != "") { + dataset.withColumn($(predictionCol), callUDF(predict _, DoubleType, col($(featuresCol)))) } else { this.logWarning(s"$uid: Predictor.transform() was called as NOOP" + " since no output columns were set.") @@ -234,10 +214,4 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel */ @DeveloperApi protected def predict(features: FeaturesType): Double - - /** - * Create a copy of the model. - * The copy is shallow, except for the embedded paramMap, which gets a deep copy. - */ - protected def copy(): M } diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala index fb770622e71f0..0e225627d4ee3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala @@ -20,14 +20,11 @@ package org.apache.spark.ml.impl.tree import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.impl.estimator.PredictorParams import org.apache.spark.ml.param._ -import org.apache.spark.ml.param.shared.{HasSeed, HasMaxIter} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, - BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy} -import org.apache.spark.mllib.tree.impurity.{Gini => OldGini, Entropy => OldEntropy, - Impurity => OldImpurity, Variance => OldVariance} +import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance} import org.apache.spark.mllib.tree.loss.{Loss => OldLoss} - /** * :: DeveloperApi :: * Parameters for Decision Tree-based algorithms. @@ -123,43 +120,43 @@ private[ml] trait DecisionTreeParams extends PredictorParams { def setMaxDepth(value: Int): this.type = set(maxDepth, value) /** @group getParam */ - final def getMaxDepth: Int = getOrDefault(maxDepth) + final def getMaxDepth: Int = $(maxDepth) /** @group setParam */ def setMaxBins(value: Int): this.type = set(maxBins, value) /** @group getParam */ - final def getMaxBins: Int = getOrDefault(maxBins) + final def getMaxBins: Int = $(maxBins) /** @group setParam */ def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value) /** @group getParam */ - final def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) + final def getMinInstancesPerNode: Int = $(minInstancesPerNode) /** @group setParam */ def setMinInfoGain(value: Double): this.type = set(minInfoGain, value) /** @group getParam */ - final def getMinInfoGain: Double = getOrDefault(minInfoGain) + final def getMinInfoGain: Double = $(minInfoGain) /** @group expertSetParam */ def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value) /** @group expertGetParam */ - final def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) + final def getMaxMemoryInMB: Int = $(maxMemoryInMB) /** @group expertSetParam */ def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value) /** @group expertGetParam */ - final def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) + final def getCacheNodeIds: Boolean = $(cacheNodeIds) /** @group expertSetParam */ def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) /** @group expertGetParam */ - final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + final def getCheckpointInterval: Int = $(checkpointInterval) /** (private[ml]) Create a Strategy instance to use with the old API. */ private[ml] def getOldStrategy( @@ -206,7 +203,7 @@ private[ml] trait TreeClassifierParams extends Params { def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity).toLowerCase + final def getImpurity: String = $(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -248,7 +245,7 @@ private[ml] trait TreeRegressorParams extends Params { def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity).toLowerCase + final def getImpurity: String = $(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -291,7 +288,7 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed { def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value) /** @group getParam */ - final def getSubsamplingRate: Double = getOrDefault(subsamplingRate) + final def getSubsamplingRate: Double = $(subsamplingRate) /** @group setParam */ def setSeed(value: Long): this.type = set(seed, value) @@ -364,13 +361,13 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams { def setNumTrees(value: Int): this.type = set(numTrees, value) /** @group getParam */ - final def getNumTrees: Int = getOrDefault(numTrees) + final def getNumTrees: Int = $(numTrees) /** @group setParam */ def setFeatureSubsetStrategy(value: String): this.type = set(featureSubsetStrategy, value) /** @group getParam */ - final def getFeatureSubsetStrategy: String = getOrDefault(featureSubsetStrategy).toLowerCase + final def getFeatureSubsetStrategy: String = $(featureSubsetStrategy).toLowerCase } private[ml] object RandomForestParams { @@ -418,7 +415,7 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { def setStepSize(value: Double): this.type = set(stepSize, value) /** @group getParam */ - final def getStepSize: Double = getOrDefault(stepSize) + final def getStepSize: Double = $(stepSize) /** (private[ml]) Create a BoostingStrategy instance to use with the old API. */ private[ml] def getOldBoostingStrategy( diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index df6360dce6013..51ce19d29cd29 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -23,7 +23,7 @@ import java.util.NoSuchElementException import scala.annotation.varargs import scala.collection.mutable -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} +import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.util.Identifiable /** @@ -49,7 +49,7 @@ class Param[T] (val parent: Params, val name: String, val doc: String, val isVal * Assert that the given value is valid for this parameter. * * Note: Parameter checks involving interactions between multiple parameters should be - * implemented in [[Params.validate()]]. Checks for input/output columns should be + * implemented in [[Params.validateParams()]]. Checks for input/output columns should be * implemented in [[org.apache.spark.ml.PipelineStage.transformSchema()]]. * * DEVELOPERS: This method is only called by [[ParamPair]], which means that all parameters @@ -258,7 +258,9 @@ trait Params extends Identifiable with Serializable { * [[Param.validate()]]. This method does not handle input/output column parameters; * those are checked during schema validation. */ - def validate(paramMap: ParamMap): Unit = { } + def validateParams(paramMap: ParamMap): Unit = { + copy(paramMap).validateParams() + } /** * Validates parameter values stored internally. @@ -269,7 +271,11 @@ trait Params extends Identifiable with Serializable { * [[Param.validate()]]. This method does not handle input/output column parameters; * those are checked during schema validation. */ - def validate(): Unit = validate(ParamMap.empty) + def validateParams(): Unit = { + params.filter(isDefined _).foreach { param => + param.asInstanceOf[Param[Any]].validate($(param)) + } + } /** * Returns the documentation of all params. @@ -288,6 +294,11 @@ trait Params extends Identifiable with Serializable { defaultParamMap.contains(param) || paramMap.contains(param) } + /** Tests whether this instance contains a param with a given name. */ + def hasParam(paramName: String): Boolean = { + params.exists(_.name == paramName) + } + /** Gets a param by its name. */ def getParam(paramName: String): Param[Any] = { params.find(_.name == paramName).getOrElse { @@ -337,6 +348,9 @@ trait Params extends Identifiable with Serializable { get(param).orElse(getDefault(param)).get } + /** An alias for [[getOrDefault()]]. */ + protected final def $[T](param: Param[T]): T = getOrDefault(param) + /** * Sets a default value for a param. * @param param param to set the default value. Make sure that this param is initialized before @@ -382,19 +396,31 @@ trait Params extends Identifiable with Serializable { defaultParamMap.contains(param) } + /** + * Creates a copy of this instance with a randomly generated uid and some extra params. + * The default implementation calls the default constructor to create a new instance, then + * copies the embedded and extra parameters over and returns the new instance. + * Subclasses should override this method if the default approach is not sufficient. + */ + def copy(extra: ParamMap): Params = { + val that = this.getClass.newInstance() + copyValues(that, extra) + that + } + /** * Extracts the embedded default param values and user-supplied values, and then merges them with * extra values from input into a flat param map, where the latter value is used if there exist * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap. */ - protected final def extractParamMap(extraParamMap: ParamMap): ParamMap = { + final def extractParamMap(extraParamMap: ParamMap): ParamMap = { defaultParamMap ++ paramMap ++ extraParamMap } /** * [[extractParamMap]] with no extra values. */ - protected final def extractParamMap(): ParamMap = { + final def extractParamMap(): ParamMap = { extractParamMap(ParamMap.empty) } @@ -408,34 +434,21 @@ trait Params extends Identifiable with Serializable { private def shouldOwn(param: Param[_]): Unit = { require(param.parent.eq(this), s"Param $param does not belong to $this.") } -} -/** - * :: DeveloperApi :: - * - * Helper functionality for developers. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. - */ -@DeveloperApi -private[spark] object Params { - - /** - * Copies parameter values from the parent estimator to the child model it produced. - * @param paramMap the param map that holds parameters of the parent - * @param parent the parent estimator - * @param child the child model - */ - def inheritValues[E <: Params, M <: E]( - paramMap: ParamMap, - parent: E, - child: M): Unit = { - val childParams = child.params.map(_.name).toSet - parent.params.foreach { param => - if (paramMap.contains(param) && childParams.contains(param.name)) { - child.set(child.getParam(param.name), paramMap(param)) + /** + * Copies param values from this instance to another instance for params shared by them. + * @param to the target instance + * @param extra extra params to be copied + * @return the target instance with param values copied + */ + protected def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T = { + val map = extractParamMap(extra) + params.foreach { param => + if (map.contains(param) && to.hasParam(param.name)) { + to.set(param.name, map(param)) } } + to } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 7da4bb4b4bf25..d379172e0bf53 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -21,8 +21,6 @@ import java.io.PrintWriter import scala.reflect.ClassTag -import org.apache.spark.ml.param.ParamValidators - /** * Code generator for shared params (sharedParams.scala). Run under the Spark folder with * {{{ @@ -142,7 +140,7 @@ private[shared] object SharedParamsCodeGen { | final val $name: $Param = new $Param(this, "$name", "$doc"$isValid) |$setDefault | /** @group getParam */ - | final def get$Name: $T = getOrDefault($name) + | final def get$Name: $T = $$($name) |} |""".stripMargin } @@ -169,7 +167,6 @@ private[shared] object SharedParamsCodeGen { | |package org.apache.spark.ml.param.shared | - |import org.apache.spark.annotation.DeveloperApi |import org.apache.spark.ml.param._ |import org.apache.spark.util.Utils | diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index e1549f46a68d4..fb1874ccfc8dc 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -17,7 +17,6 @@ package org.apache.spark.ml.param.shared -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param._ import org.apache.spark.util.Utils @@ -37,7 +36,7 @@ private[ml] trait HasRegParam extends Params { final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ - final def getRegParam: Double = getOrDefault(regParam) + final def getRegParam: Double = $(regParam) } /** @@ -52,7 +51,7 @@ private[ml] trait HasMaxIter extends Params { final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ - final def getMaxIter: Int = getOrDefault(maxIter) + final def getMaxIter: Int = $(maxIter) } /** @@ -69,7 +68,7 @@ private[ml] trait HasFeaturesCol extends Params { setDefault(featuresCol, "features") /** @group getParam */ - final def getFeaturesCol: String = getOrDefault(featuresCol) + final def getFeaturesCol: String = $(featuresCol) } /** @@ -86,7 +85,7 @@ private[ml] trait HasLabelCol extends Params { setDefault(labelCol, "label") /** @group getParam */ - final def getLabelCol: String = getOrDefault(labelCol) + final def getLabelCol: String = $(labelCol) } /** @@ -103,7 +102,7 @@ private[ml] trait HasPredictionCol extends Params { setDefault(predictionCol, "prediction") /** @group getParam */ - final def getPredictionCol: String = getOrDefault(predictionCol) + final def getPredictionCol: String = $(predictionCol) } /** @@ -120,7 +119,7 @@ private[ml] trait HasRawPredictionCol extends Params { setDefault(rawPredictionCol, "rawPrediction") /** @group getParam */ - final def getRawPredictionCol: String = getOrDefault(rawPredictionCol) + final def getRawPredictionCol: String = $(rawPredictionCol) } /** @@ -137,7 +136,7 @@ private[ml] trait HasProbabilityCol extends Params { setDefault(probabilityCol, "probability") /** @group getParam */ - final def getProbabilityCol: String = getOrDefault(probabilityCol) + final def getProbabilityCol: String = $(probabilityCol) } /** @@ -152,7 +151,7 @@ private[ml] trait HasThreshold extends Params { final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1)) /** @group getParam */ - final def getThreshold: Double = getOrDefault(threshold) + final def getThreshold: Double = $(threshold) } /** @@ -167,7 +166,7 @@ private[ml] trait HasInputCol extends Params { final val inputCol: Param[String] = new Param[String](this, "inputCol", "input column name") /** @group getParam */ - final def getInputCol: String = getOrDefault(inputCol) + final def getInputCol: String = $(inputCol) } /** @@ -182,7 +181,7 @@ private[ml] trait HasInputCols extends Params { final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names") /** @group getParam */ - final def getInputCols: Array[String] = getOrDefault(inputCols) + final def getInputCols: Array[String] = $(inputCols) } /** @@ -197,7 +196,7 @@ private[ml] trait HasOutputCol extends Params { final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name") /** @group getParam */ - final def getOutputCol: String = getOrDefault(outputCol) + final def getOutputCol: String = $(outputCol) } /** @@ -212,7 +211,7 @@ private[ml] trait HasCheckpointInterval extends Params { final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1)", ParamValidators.gtEq(1)) /** @group getParam */ - final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + final def getCheckpointInterval: Int = $(checkpointInterval) } /** @@ -229,7 +228,7 @@ private[ml] trait HasFitIntercept extends Params { setDefault(fitIntercept, true) /** @group getParam */ - final def getFitIntercept: Boolean = getOrDefault(fitIntercept) + final def getFitIntercept: Boolean = $(fitIntercept) } /** @@ -246,7 +245,7 @@ private[ml] trait HasSeed extends Params { setDefault(seed, Utils.random.nextLong()) /** @group getParam */ - final def getSeed: Long = getOrDefault(seed) + final def getSeed: Long = $(seed) } /** @@ -261,7 +260,7 @@ private[ml] trait HasElasticNetParam extends Params { final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", ParamValidators.inRange(0, 1)) /** @group getParam */ - final def getElasticNetParam: Double = getOrDefault(elasticNetParam) + final def getElasticNetParam: Double = $(elasticNetParam) } /** @@ -276,7 +275,7 @@ private[ml] trait HasTol extends Params { final val tol: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms") /** @group getParam */ - final def getTol: Double = getOrDefault(tol) + final def getTol: Double = $(tol) } /** @@ -291,6 +290,6 @@ private[ml] trait HasStepSize extends Params { final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size to be used for each iteration of optimization.") /** @group getParam */ - final def getStepSize: Double = getOrDefault(stepSize) + final def getStepSize: Double = $(stepSize) } // scalastyle:on diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index f9f2b2764ddb1..6cf4b40075281 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -59,7 +59,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val rank = new IntParam(this, "rank", "rank of the factorization", ParamValidators.gtEq(1)) /** @group getParam */ - def getRank: Int = getOrDefault(rank) + def getRank: Int = $(rank) /** * Param for number of user blocks (>= 1). @@ -70,7 +70,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR ParamValidators.gtEq(1)) /** @group getParam */ - def getNumUserBlocks: Int = getOrDefault(numUserBlocks) + def getNumUserBlocks: Int = $(numUserBlocks) /** * Param for number of item blocks (>= 1). @@ -81,7 +81,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR ParamValidators.gtEq(1)) /** @group getParam */ - def getNumItemBlocks: Int = getOrDefault(numItemBlocks) + def getNumItemBlocks: Int = $(numItemBlocks) /** * Param to decide whether to use implicit preference. @@ -91,7 +91,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val implicitPrefs = new BooleanParam(this, "implicitPrefs", "whether to use implicit preference") /** @group getParam */ - def getImplicitPrefs: Boolean = getOrDefault(implicitPrefs) + def getImplicitPrefs: Boolean = $(implicitPrefs) /** * Param for the alpha parameter in the implicit preference formulation (>= 0). @@ -102,7 +102,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR ParamValidators.gtEq(0)) /** @group getParam */ - def getAlpha: Double = getOrDefault(alpha) + def getAlpha: Double = $(alpha) /** * Param for the column name for user ids. @@ -112,7 +112,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val userCol = new Param[String](this, "userCol", "column name for user ids") /** @group getParam */ - def getUserCol: String = getOrDefault(userCol) + def getUserCol: String = $(userCol) /** * Param for the column name for item ids. @@ -122,7 +122,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val itemCol = new Param[String](this, "itemCol", "column name for item ids") /** @group getParam */ - def getItemCol: String = getOrDefault(itemCol) + def getItemCol: String = $(itemCol) /** * Param for the column name for ratings. @@ -132,7 +132,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val ratingCol = new Param[String](this, "ratingCol", "column name for ratings") /** @group getParam */ - def getRatingCol: String = getOrDefault(ratingCol) + def getRatingCol: String = $(ratingCol) /** * Param for whether to apply nonnegativity constraints. @@ -143,7 +143,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR this, "nonnegative", "whether to use nonnegative constraint for least squares") /** @group getParam */ - def getNonnegative: Boolean = getOrDefault(nonnegative) + def getNonnegative: Boolean = $(nonnegative) setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10, implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item", @@ -152,19 +152,17 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR /** * Validates and transforms the input schema. * @param schema input schema - * @param paramMap extra params * @return output schema */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - assert(schema(map(userCol)).dataType == IntegerType) - assert(schema(map(itemCol)).dataType== IntegerType) - val ratingType = schema(map(ratingCol)).dataType - assert(ratingType == FloatType || ratingType == DoubleType) - val predictionColName = map(predictionCol) - assert(!schema.fieldNames.contains(predictionColName), + protected def validateAndTransformSchema(schema: StructType): StructType = { + require(schema($(userCol)).dataType == IntegerType) + require(schema($(itemCol)).dataType== IntegerType) + val ratingType = schema($(ratingCol)).dataType + require(ratingType == FloatType || ratingType == DoubleType) + val predictionColName = $(predictionCol) + require(!schema.fieldNames.contains(predictionColName), s"Prediction column $predictionColName already exists.") - val newFields = schema.fields :+ StructField(map(predictionCol), FloatType, nullable = false) + val newFields = schema.fields :+ StructField($(predictionCol), FloatType, nullable = false) StructType(newFields) } } @@ -174,7 +172,6 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR */ class ALSModel private[ml] ( override val parent: ALS, - override val fittingParamMap: ParamMap, k: Int, userFactors: RDD[(Int, Array[Float])], itemFactors: RDD[(Int, Array[Float])]) @@ -183,9 +180,8 @@ class ALSModel private[ml] ( /** @group setParam */ def setPredictionCol(value: String): this.type = set(predictionCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { import dataset.sqlContext.implicits._ - val map = extractParamMap(paramMap) val users = userFactors.toDF("id", "features") val items = itemFactors.toDF("id", "features") @@ -199,13 +195,13 @@ class ALSModel private[ml] ( } } dataset - .join(users, dataset(map(userCol)) === users("id"), "left") - .join(items, dataset(map(itemCol)) === items("id"), "left") - .select(dataset("*"), predict(users("features"), items("features")).as(map(predictionCol))) + .join(users, dataset($(userCol)) === users("id"), "left") + .join(items, dataset($(itemCol)) === items("id"), "left") + .select(dataset("*"), predict(users("features"), items("features")).as($(predictionCol))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -292,25 +288,22 @@ class ALS extends Estimator[ALSModel] with ALSParams { this } - override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = { - val map = extractParamMap(paramMap) + override def fit(dataset: DataFrame): ALSModel = { val ratings = dataset - .select(col(map(userCol)), col(map(itemCol)), col(map(ratingCol)).cast(FloatType)) + .select(col($(userCol)), col($(itemCol)), col($(ratingCol)).cast(FloatType)) .map { row => Rating(row.getInt(0), row.getInt(1), row.getFloat(2)) } - val (userFactors, itemFactors) = ALS.train(ratings, rank = map(rank), - numUserBlocks = map(numUserBlocks), numItemBlocks = map(numItemBlocks), - maxIter = map(maxIter), regParam = map(regParam), implicitPrefs = map(implicitPrefs), - alpha = map(alpha), nonnegative = map(nonnegative), - checkpointInterval = map(checkpointInterval)) - val model = new ALSModel(this, map, map(rank), userFactors, itemFactors) - Params.inheritValues(map, this, model) - model + val (userFactors, itemFactors) = ALS.train(ratings, rank = $(rank), + numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks), + maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs), + alpha = $(alpha), nonnegative = $(nonnegative), + checkpointInterval = $(checkpointInterval)) + copyValues(new ALSModel(this, $(rank), userFactors, itemFactors)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala index 756725a64b0f3..b07c26fe79b36 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, Node} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector @@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -63,15 +62,13 @@ final class DecisionTreeRegressor override def setImpurity(value: String): this.type = super.setImpurity(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): DecisionTreeRegressionModel = { + override protected def train(dataset: DataFrame): DecisionTreeRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = getOldStrategy(categoricalFeatures) val oldModel = OldDecisionTree.train(oldDataset, strategy) - DecisionTreeRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(oldModel, this, categoricalFeatures) } /** (private[ml]) Create a Strategy instance to use with the old API. */ @@ -96,7 +93,6 @@ object DecisionTreeRegressor { @AlphaComponent final class DecisionTreeRegressionModel private[ml] ( override val parent: DecisionTreeRegressor, - override val fittingParamMap: ParamMap, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with Serializable { @@ -108,10 +104,8 @@ final class DecisionTreeRegressionModel private[ml] ( rootNode.predict(features) } - override protected def copy(): DecisionTreeRegressionModel = { - val m = new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): DecisionTreeRegressionModel = { + copyValues(new DecisionTreeRegressionModel(parent, rootNode), extra) } override def toString: String = { @@ -130,12 +124,11 @@ private[ml] object DecisionTreeRegressionModel { def fromOld( oldModel: OldDecisionTreeModel, parent: DecisionTreeRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): DecisionTreeRegressionModel = { require(oldModel.algo == OldAlgo.Regression, s"Cannot convert non-regression DecisionTreeModel (old API) to" + s" DecisionTreeRegressionModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) + new DecisionTreeRegressionModel(parent, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala index 76c98376930c5..bc796958e4545 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala @@ -23,20 +23,18 @@ import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap, Param} +import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} -import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, - SquaredError => OldSquaredError} +import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, SquaredError => OldSquaredError} import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -111,7 +109,7 @@ final class GBTRegressor def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType).toLowerCase + def getLossType: String = $(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { @@ -124,16 +122,14 @@ final class GBTRegressor } } - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): GBTRegressionModel = { + override protected def train(dataset: DataFrame): GBTRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression) val oldGBT = new OldGBT(boostingStrategy) val oldModel = oldGBT.run(oldDataset) - GBTRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -155,7 +151,6 @@ object GBTRegressor { @AlphaComponent final class GBTRegressionModel( override val parent: GBTRegressor, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTRegressionModel] @@ -178,10 +173,8 @@ final class GBTRegressionModel( if (prediction > 0.0) 1.0 else 0.0 } - override protected def copy(): GBTRegressionModel = { - val m = new GBTRegressionModel(parent, fittingParamMap, _trees, _treeWeights) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): GBTRegressionModel = { + copyValues(new GBTRegressionModel(parent, _trees, _treeWeights), extra) } override def toString: String = { @@ -200,14 +193,13 @@ private[ml] object GBTRegressionModel { def fromOld( oldModel: OldGBTModel, parent: GBTRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): GBTRegressionModel = { require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTRegressionModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + new GBTRegressionModel(parent, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index 0b81c48466be9..66c475f2d9840 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -19,22 +19,22 @@ package org.apache.spark.ml.regression import scala.collection.mutable -import breeze.linalg.{norm => brzNorm, DenseVector => BDV} -import breeze.optimize.{LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} -import breeze.optimize.{CachedDiffFunction, DiffFunction} +import breeze.linalg.{DenseVector => BDV, norm => brzNorm} +import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, + OWLQN => BreezeOWLQN} +import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter, HasRegParam} -import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol} import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.linalg.BLAS._ import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel import org.apache.spark.util.StatCounter -import org.apache.spark.Logging /** * Params for linear regression. @@ -96,9 +96,9 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress def setTol(value: Double): this.type = set(tol, value) setDefault(tol -> 1E-6) - override protected def train(dataset: DataFrame, paramMap: ParamMap): LinearRegressionModel = { + override protected def train(dataset: DataFrame): LinearRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist instances. - val instances = extractLabeledPoints(dataset, paramMap).map { + val instances = extractLabeledPoints(dataset).map { case LabeledPoint(label: Double, features: Vector) => (label, features) } val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE @@ -125,7 +125,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " + s"and the intercept will be the mean of the label; as a result, training is not needed.") if (handlePersistence) instances.unpersist() - return new LinearRegressionModel(this, paramMap, Vectors.sparse(numFeatures, Seq()), yMean) + return new LinearRegressionModel(this, Vectors.sparse(numFeatures, Seq()), yMean) } val featuresMean = summarizer.mean.toArray @@ -133,17 +133,17 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress // Since we implicitly do the feature scaling when we compute the cost function // to improve the convergence, the effective regParam will be changed. - val effectiveRegParam = paramMap(regParam) / yStd - val effectiveL1RegParam = paramMap(elasticNetParam) * effectiveRegParam - val effectiveL2RegParam = (1.0 - paramMap(elasticNetParam)) * effectiveRegParam + val effectiveRegParam = $(regParam) / yStd + val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam + val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam val costFun = new LeastSquaresCostFun(instances, yStd, yMean, featuresStd, featuresMean, effectiveL2RegParam) - val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { - new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol)) + val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { + new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol)) } else { - new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol)) + new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegParam, $(tol)) } val initialWeights = Vectors.zeros(numFeatures) @@ -178,7 +178,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress if (handlePersistence) instances.unpersist() // TODO: Converts to sparse format based on the storage, but may base on the scoring speed. - new LinearRegressionModel(this, paramMap, weights.compressed, intercept) + new LinearRegressionModel(this, weights.compressed, intercept) } } @@ -190,7 +190,6 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress @AlphaComponent class LinearRegressionModel private[ml] ( override val parent: LinearRegression, - override val fittingParamMap: ParamMap, val weights: Vector, val intercept: Double) extends RegressionModel[Vector, LinearRegressionModel] @@ -200,10 +199,8 @@ class LinearRegressionModel private[ml] ( dot(features, weights) + intercept } - override protected def copy(): LinearRegressionModel = { - val m = new LinearRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(extractParamMap(), this, m) - m + override def copy(extra: ParamMap): LinearRegressionModel = { + copyValues(new LinearRegressionModel(parent, weights, intercept), extra) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala index 2171ef3d32c26..0468a1be1ba74 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala @@ -20,18 +20,17 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree.{RandomForestParams, TreeRegressorParams} -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -77,17 +76,15 @@ final class RandomForestRegressor override def setFeatureSubsetStrategy(value: String): this.type = super.setFeatureSubsetStrategy(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): RandomForestRegressionModel = { + override protected def train(dataset: DataFrame): RandomForestRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity) val oldModel = OldRandomForest.trainRegressor( oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) - RandomForestRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + RandomForestRegressionModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -110,7 +107,6 @@ object RandomForestRegressor { @AlphaComponent final class RandomForestRegressionModel private[ml] ( override val parent: RandomForestRegressor, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel]) extends PredictionModel[Vector, RandomForestRegressionModel] with TreeEnsembleModel with Serializable { @@ -132,10 +128,8 @@ final class RandomForestRegressionModel private[ml] ( _trees.map(_.rootNode.predict(features)).sum / numTrees } - override protected def copy(): RandomForestRegressionModel = { - val m = new RandomForestRegressionModel(parent, fittingParamMap, _trees) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): RandomForestRegressionModel = { + copyValues(new RandomForestRegressionModel(parent, _trees), extra) } override def toString: String = { @@ -154,14 +148,13 @@ private[ml] object RandomForestRegressionModel { def fromOld( oldModel: OldRandomForestModel, parent: RandomForestRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): RandomForestRegressionModel = { require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestRegressionModel(parent, fittingParamMap, newTrees) + new RandomForestRegressionModel(parent, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala index d679085eeafe1..c6b3327db6ad3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala @@ -17,7 +17,7 @@ package org.apache.spark.ml.regression -import org.apache.spark.annotation.{DeveloperApi, AlphaComponent} +import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} /** diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index d1ad0893cd044..cee2aa6e85523 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -39,7 +39,7 @@ private[ml] trait CrossValidatorParams extends Params { val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection") /** @group getParam */ - def getEstimator: Estimator[_] = getOrDefault(estimator) + def getEstimator: Estimator[_] = $(estimator) /** * param for estimator param maps @@ -49,7 +49,7 @@ private[ml] trait CrossValidatorParams extends Params { new Param(this, "estimatorParamMaps", "param maps for the estimator") /** @group getParam */ - def getEstimatorParamMaps: Array[ParamMap] = getOrDefault(estimatorParamMaps) + def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps) /** * param for the evaluator for selection @@ -58,7 +58,7 @@ private[ml] trait CrossValidatorParams extends Params { val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection") /** @group getParam */ - def getEvaluator: Evaluator = getOrDefault(evaluator) + def getEvaluator: Evaluator = $(evaluator) /** * Param for number of folds for cross validation. Must be >= 2. @@ -69,7 +69,7 @@ private[ml] trait CrossValidatorParams extends Params { "number of folds for cross validation (>= 2)", ParamValidators.gtEq(2)) /** @group getParam */ - def getNumFolds: Int = getOrDefault(numFolds) + def getNumFolds: Int = $(numFolds) setDefault(numFolds -> 3) } @@ -95,23 +95,22 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP /** @group setParam */ def setNumFolds(value: Int): this.type = set(numFolds, value) - override def validate(paramMap: ParamMap): Unit = { + override def validateParams(paramMap: ParamMap): Unit = { getEstimatorParamMaps.foreach { eMap => - getEstimator.validate(eMap ++ paramMap) + getEstimator.validateParams(eMap ++ paramMap) } } - override def fit(dataset: DataFrame, paramMap: ParamMap): CrossValidatorModel = { - val map = extractParamMap(paramMap) + override def fit(dataset: DataFrame): CrossValidatorModel = { val schema = dataset.schema - transformSchema(dataset.schema, paramMap, logging = true) + transformSchema(dataset.schema, logging = true) val sqlCtx = dataset.sqlContext - val est = map(estimator) - val eval = map(evaluator) - val epm = map(estimatorParamMaps) + val est = $(estimator) + val eval = $(evaluator) + val epm = $(estimatorParamMaps) val numModels = epm.length val metrics = new Array[Double](epm.length) - val splits = MLUtils.kFold(dataset.rdd, map(numFolds), 0) + val splits = MLUtils.kFold(dataset.rdd, $(numFolds), 0) splits.zipWithIndex.foreach { case ((training, validation), splitIndex) => val trainingDataset = sqlCtx.createDataFrame(training, schema).cache() val validationDataset = sqlCtx.createDataFrame(validation, schema).cache() @@ -121,27 +120,24 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP trainingDataset.unpersist() var i = 0 while (i < numModels) { - val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) + val metric = eval.evaluate(models(i).transform(validationDataset, epm(i))) logDebug(s"Got metric $metric for model trained with ${epm(i)}.") metrics(i) += metric i += 1 } validationDataset.unpersist() } - f2jBLAS.dscal(numModels, 1.0 / map(numFolds), metrics, 1) + f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1) logInfo(s"Average cross-validation metrics: ${metrics.toSeq}") val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1) logInfo(s"Best set of parameters:\n${epm(bestIndex)}") logInfo(s"Best cross-validation metric: $bestMetric.") val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]] - val cvModel = new CrossValidatorModel(this, map, bestModel) - Params.inheritValues(map, this, cvModel) - cvModel + copyValues(new CrossValidatorModel(this, bestModel)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - map(estimator).transformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + $(estimator).transformSchema(schema) } } @@ -152,19 +148,18 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP @AlphaComponent class CrossValidatorModel private[ml] ( override val parent: CrossValidator, - override val fittingParamMap: ParamMap, val bestModel: Model[_]) extends Model[CrossValidatorModel] with CrossValidatorParams { - override def validate(paramMap: ParamMap): Unit = { - bestModel.validate(paramMap) + override def validateParams(paramMap: ParamMap): Unit = { + bestModel.validateParams(paramMap) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - bestModel.transform(dataset, paramMap) + override def transform(dataset: DataFrame): DataFrame = { + bestModel.transform(dataset) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - bestModel.transformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + bestModel.transformSchema(schema) } } diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index 3f8e59de0f05c..7e7189a2b1d53 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -84,9 +84,10 @@ public void logisticRegressionWithSetters() { .setThreshold(0.6) .setProbabilityCol("myProbability"); LogisticRegressionModel model = lr.fit(dataset); - assert(model.fittingParamMap().apply(lr.maxIter()).equals(10)); - assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); - assert(model.fittingParamMap().apply(lr.threshold()).equals(0.6)); + LogisticRegression parent = model.parent(); + assert(parent.getMaxIter() == 10); + assert(parent.getRegParam() == 1.0); + assert(parent.getThreshold() == 0.6); assert(model.getThreshold() == 0.6); // Modify model params, and check that the params worked. @@ -109,9 +110,10 @@ public void logisticRegressionWithSetters() { // Call fit() with new params, and check as many params as we can. LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.threshold().w(0.4), lr.probabilityCol().w("theProb")); - assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5)); - assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); - assert(model2.fittingParamMap().apply(lr.threshold()).equals(0.4)); + LogisticRegression parent2 = model2.parent(); + assert(parent2.getMaxIter() == 5); + assert(parent2.getRegParam() == 0.1); + assert(parent2.getThreshold() == 0.4); assert(model2.getThreshold() == 0.4); assert(model2.getProbabilityCol().equals("theProb")); } diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java index 0cc36c8d56d70..a82b86d560b6e 100644 --- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java @@ -23,14 +23,15 @@ import org.junit.After; import org.junit.Before; import org.junit.Test; +import static org.junit.Assert.assertEquals; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; -import static org.apache.spark.mllib.classification.LogisticRegressionSuite - .generateLogisticInputAsList; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.SQLContext; +import static org.apache.spark.mllib.classification.LogisticRegressionSuite + .generateLogisticInputAsList; public class JavaLinearRegressionSuite implements Serializable { @@ -65,8 +66,8 @@ public void linearRegressionDefaultParams() { DataFrame predictions = jsql.sql("SELECT label, prediction FROM prediction"); predictions.collect(); // Check defaults - assert(model.getFeaturesCol().equals("features")); - assert(model.getPredictionCol().equals("prediction")); + assertEquals("features", model.getFeaturesCol()); + assertEquals("prediction", model.getPredictionCol()); } @Test @@ -76,14 +77,16 @@ public void linearRegressionWithSetters() { .setMaxIter(10) .setRegParam(1.0); LinearRegressionModel model = lr.fit(dataset); - assert(model.fittingParamMap().apply(lr.maxIter()).equals(10)); - assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); + LinearRegression parent = model.parent(); + assertEquals(10, parent.getMaxIter()); + assertEquals(1.0, parent.getRegParam(), 0.0); // Call fit() with new params, and check as many params as we can. LinearRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred")); - assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5)); - assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); - assert(model2.getPredictionCol().equals("thePred")); + LinearRegression parent2 = model2.parent(); + assertEquals(5, parent2.getMaxIter()); + assertEquals(0.1, parent2.getRegParam(), 0.0); + assertEquals("thePred", model2.getPredictionCol()); } } diff --git a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java index 0bb6b489f2757..08eeca53f0721 100644 --- a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java @@ -68,8 +68,8 @@ public void crossValidationWithLogisticRegression() { .setEvaluator(eval) .setNumFolds(3); CrossValidatorModel cvModel = cv.fit(dataset); - ParamMap bestParamMap = cvModel.bestModel().fittingParamMap(); - Assert.assertEquals(0.001, bestParamMap.apply(lr.regParam())); - Assert.assertEquals(10, bestParamMap.apply(lr.maxIter())); + LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent(); + Assert.assertEquals(0.001, parent.getRegParam(), 0.0); + Assert.assertEquals(10, parent.getMaxIter()); } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala index 2f175fb117941..2b04a3034782e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala @@ -42,30 +42,32 @@ class PipelineSuite extends FunSuite { val dataset3 = mock[DataFrame] val dataset4 = mock[DataFrame] - when(estimator0.fit(meq(dataset0), any[ParamMap]())).thenReturn(model0) - when(model0.transform(meq(dataset0), any[ParamMap]())).thenReturn(dataset1) + when(estimator0.copy(any[ParamMap])).thenReturn(estimator0) + when(model0.copy(any[ParamMap])).thenReturn(model0) + when(transformer1.copy(any[ParamMap])).thenReturn(transformer1) + when(estimator2.copy(any[ParamMap])).thenReturn(estimator2) + when(model2.copy(any[ParamMap])).thenReturn(model2) + when(transformer3.copy(any[ParamMap])).thenReturn(transformer3) + + when(estimator0.fit(meq(dataset0))).thenReturn(model0) + when(model0.transform(meq(dataset0))).thenReturn(dataset1) when(model0.parent).thenReturn(estimator0) - when(transformer1.transform(meq(dataset1), any[ParamMap])).thenReturn(dataset2) - when(estimator2.fit(meq(dataset2), any[ParamMap]())).thenReturn(model2) - when(model2.transform(meq(dataset2), any[ParamMap]())).thenReturn(dataset3) + when(transformer1.transform(meq(dataset1))).thenReturn(dataset2) + when(estimator2.fit(meq(dataset2))).thenReturn(model2) + when(model2.transform(meq(dataset2))).thenReturn(dataset3) when(model2.parent).thenReturn(estimator2) - when(transformer3.transform(meq(dataset3), any[ParamMap]())).thenReturn(dataset4) + when(transformer3.transform(meq(dataset3))).thenReturn(dataset4) val pipeline = new Pipeline() .setStages(Array(estimator0, transformer1, estimator2, transformer3)) val pipelineModel = pipeline.fit(dataset0) - assert(pipelineModel.stages.size === 4) + assert(pipelineModel.stages.length === 4) assert(pipelineModel.stages(0).eq(model0)) assert(pipelineModel.stages(1).eq(transformer1)) assert(pipelineModel.stages(2).eq(model2)) assert(pipelineModel.stages(3).eq(transformer3)) - assert(pipelineModel.getModel(estimator0).eq(model0)) - assert(pipelineModel.getModel(estimator2).eq(model2)) - intercept[NoSuchElementException] { - pipelineModel.getModel(mock[Estimator[MyModel]]) - } val output = pipelineModel.transform(dataset0) assert(output.eq(dataset4)) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala index 9b31adecdcb1c..03af4ecd7a7e0 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -267,8 +267,8 @@ private[ml] object DecisionTreeClassifierSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newTree = dt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(oldTree, newTree.parent, - newTree.fittingParamMap, categoricalFeatures) + val oldTreeAsNew = DecisionTreeClassificationModel.fromOld( + oldTree, newTree.parent, categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala index e6ccc2c93cba8..16c758b82c7cd 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala @@ -129,8 +129,8 @@ private object GBTClassifierSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2) val newModel = gbt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = GBTClassificationModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = GBTClassificationModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index 35d8c2e16c6cd..6dd1fdf05514e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -74,9 +74,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { .setThreshold(0.6) .setProbabilityCol("myProbability") val model = lr.fit(dataset) - assert(model.fittingParamMap.get(lr.maxIter) === Some(10)) - assert(model.fittingParamMap.get(lr.regParam) === Some(1.0)) - assert(model.fittingParamMap.get(lr.threshold) === Some(0.6)) + val parent = model.parent + assert(parent.getMaxIter === 10) + assert(parent.getRegParam === 1.0) + assert(parent.getThreshold === 0.6) assert(model.getThreshold === 0.6) // Modify model params, and check that the params worked. @@ -99,9 +100,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { // Call fit() with new params, and check as many params as we can. val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4, lr.probabilityCol -> "theProb") - assert(model2.fittingParamMap.get(lr.maxIter).get === 5) - assert(model2.fittingParamMap.get(lr.regParam).get === 0.1) - assert(model2.fittingParamMap.get(lr.threshold).get === 0.4) + val parent2 = model2.parent + assert(parent2.getMaxIter === 5) + assert(parent2.getRegParam === 0.1) + assert(parent2.getThreshold === 0.4) assert(model2.getThreshold === 0.4) assert(model2.getProbabilityCol == "theProb") } @@ -117,7 +119,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { val results = model.transform(dataset) // Compare rawPrediction with probability - results.select("rawPrediction", "probability").collect().map { + results.select("rawPrediction", "probability").collect().foreach { case Row(raw: Vector, prob: Vector) => assert(raw.size === 2) assert(prob.size === 2) @@ -127,7 +129,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { } // Compare prediction with probability - results.select("prediction", "probability").collect().map { + results.select("prediction", "probability").collect().foreach { case Row(pred: Double, prob: Vector) => val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2 assert(pred == predFromProb) diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala index ed41a9664f94f..c41def9330504 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala @@ -159,8 +159,8 @@ private object RandomForestClassifierSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newModel = rf.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = RandomForestClassificationModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = RandomForestClassificationModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index f8852606abbf2..6056e7d3f6ff8 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -122,19 +122,21 @@ class ParamsSuite extends FunSuite { assert(solver.getParam("inputCol").eq(inputCol)) assert(solver.getParam("maxIter").eq(maxIter)) + assert(solver.hasParam("inputCol")) + assert(!solver.hasParam("abc")) intercept[NoSuchElementException] { solver.getParam("abc") } intercept[IllegalArgumentException] { - solver.validate() + solver.validateParams() } - solver.validate(ParamMap(inputCol -> "input")) + solver.validateParams(ParamMap(inputCol -> "input")) solver.setInputCol("input") assert(solver.isSet(inputCol)) assert(solver.isDefined(inputCol)) assert(solver.getInputCol === "input") - solver.validate() + solver.validateParams() intercept[IllegalArgumentException] { ParamMap(maxIter -> -10) } @@ -144,6 +146,11 @@ class ParamsSuite extends FunSuite { solver.clearMaxIter() assert(!solver.isSet(maxIter)) + + val copied = solver.copy(ParamMap(solver.maxIter -> 50)) + assert(copied.uid !== solver.uid) + assert(copied.getInputCol === solver.getInputCol) + assert(copied.getMaxIter === 50) } test("ParamValidate") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index 6f9c9cb5166ae..dc16073640407 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -23,15 +23,19 @@ import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter} class TestParams extends Params with HasMaxIter with HasInputCol { def setMaxIter(value: Int): this.type = { set(maxIter, value); this } + def setInputCol(value: String): this.type = { set(inputCol, value); this } setDefault(maxIter -> 10) - override def validate(paramMap: ParamMap): Unit = { - val m = extractParamMap(paramMap) - // Note: maxIter is validated when it is set. - require(m.contains(inputCol)) + def clearMaxIter(): this.type = clear(maxIter) + + override def validateParams(): Unit = { + super.validateParams() + require(isDefined(inputCol)) } - def clearMaxIter(): this.type = clear(maxIter) + override def copy(extra: ParamMap): TestParams = { + super.copy(extra).asInstanceOf[TestParams] + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala index c87a171b4b229..5aa81b44ddaf9 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala @@ -84,8 +84,8 @@ private[ml] object DecisionTreeRegressorSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newTree = dt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(oldTree, newTree.parent, - newTree.fittingParamMap, categoricalFeatures) + val oldTreeAsNew = DecisionTreeRegressionModel.fromOld( + oldTree, newTree.parent, categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala index 4aec36948ac92..25b36ab08b67c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala @@ -130,8 +130,7 @@ private object GBTRegressorSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = gbt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala index c6dc1cc29b6ff..45f09f4fdab81 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala @@ -115,8 +115,8 @@ private object RandomForestRegressorSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = rf.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = RandomForestRegressionModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = RandomForestRegressionModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala index 761ea821ef7c6..05313d440fbf6 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala @@ -49,8 +49,8 @@ class CrossValidatorSuite extends FunSuite with MLlibTestSparkContext { .setEvaluator(eval) .setNumFolds(3) val cvModel = cv.fit(dataset) - val bestParamMap = cvModel.bestModel.fittingParamMap - assert(bestParamMap(lr.regParam) === 0.001) - assert(bestParamMap(lr.maxIter) === 10) + val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression] + assert(parent.getRegParam === 0.001) + assert(parent.getMaxIter === 10) } } From f32e69ecc333867fc966f65cd0aeaeddd43e0945 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=91=E5=B3=A4?= Date: Mon, 4 May 2015 12:08:38 -0700 Subject: [PATCH 10/41] [SPARK-7319][SQL] Improve the output from DataFrame.show() MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Author: 云峤 Closes #5865 from kaka1992/df.show and squashes the following commits: c79204b [云峤] Update a1338f6 [云峤] Update python dataFrame show test and add empty df unit test. 734369c [云峤] Update python dataFrame show test and add empty df unit test. 84aec3e [云峤] Update python dataFrame show test and add empty df unit test. 159b3d5 [云峤] update 03ef434 [云峤] update 7394fd5 [云峤] update test show ced487a [云峤] update pep8 b6e690b [云峤] Merge remote-tracking branch 'upstream/master' into df.show 30ac311 [云峤] [SPARK-7294] ADD BETWEEN 7d62368 [云峤] [SPARK-7294] ADD BETWEEN baf839b [云峤] [SPARK-7294] ADD BETWEEN d11d5b9 [云峤] [SPARK-7294] ADD BETWEEN --- R/pkg/R/DataFrame.R | 2 +- R/pkg/inst/tests/test_sparkSQL.R | 2 +- python/pyspark/sql/dataframe.py | 105 ++++++++++++------ .../org/apache/spark/sql/DataFrame.scala | 28 ++++- .../org/apache/spark/sql/DataFrameSuite.scala | 19 ++++ 5 files changed, 112 insertions(+), 44 deletions(-) diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index b59b700af5dc9..841e77e55e0d8 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -167,7 +167,7 @@ setMethod("isLocal", setMethod("showDF", signature(x = "DataFrame"), function(x, numRows = 20) { - cat(callJMethod(x@sdf, "showString", numToInt(numRows)), "\n") + callJMethod(x@sdf, "showString", numToInt(numRows)) }) #' show diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R index af7a6c582047a..f82e56fdd8278 100644 --- a/R/pkg/inst/tests/test_sparkSQL.R +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -641,7 +641,7 @@ test_that("toJSON() returns an RDD of the correct values", { test_that("showDF()", { df <- jsonFile(sqlCtx, jsonPath) - expect_output(showDF(df), "age name \nnull Michael\n30 Andy \n19 Justin ") + expect_output(showDF(df), "+----+-------+\n| age| name|\n+----+-------+\n|null|Michael|\n| 30| Andy|\n| 19| Justin|\n+----+-------+\n") }) test_that("isLocal()", { diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index aac5b8c4c5770..22762c5bbbcd0 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -275,9 +275,12 @@ def show(self, n=20): >>> df DataFrame[age: int, name: string] >>> df.show() - age name - 2 Alice - 5 Bob + +---+-----+ + |age| name| + +---+-----+ + | 2|Alice| + | 5| Bob| + +---+-----+ """ print(self._jdf.showString(n)) @@ -591,12 +594,15 @@ def describe(self, *cols): given, this function computes statistics for all numerical columns. >>> df.describe().show() - summary age - count 2 - mean 3.5 - stddev 1.5 - min 2 - max 5 + +-------+---+ + |summary|age| + +-------+---+ + | count| 2| + | mean|3.5| + | stddev|1.5| + | min| 2| + | max| 5| + +-------+---+ """ jdf = self._jdf.describe(self._jseq(cols)) return DataFrame(jdf, self.sql_ctx) @@ -801,12 +807,18 @@ def dropna(self, how='any', thresh=None, subset=None): :param subset: optional list of column names to consider. >>> df4.dropna().show() - age height name - 10 80 Alice + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + +---+------+-----+ >>> df4.na.drop().show() - age height name - 10 80 Alice + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + +---+------+-----+ """ if how is not None and how not in ['any', 'all']: raise ValueError("how ('" + how + "') should be 'any' or 'all'") @@ -837,25 +849,34 @@ def fillna(self, value, subset=None): then the non-string column is simply ignored. >>> df4.fillna(50).show() - age height name - 10 80 Alice - 5 50 Bob - 50 50 Tom - 50 50 null + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + | 5| 50| Bob| + | 50| 50| Tom| + | 50| 50| null| + +---+------+-----+ >>> df4.fillna({'age': 50, 'name': 'unknown'}).show() - age height name - 10 80 Alice - 5 null Bob - 50 null Tom - 50 null unknown + +---+------+-------+ + |age|height| name| + +---+------+-------+ + | 10| 80| Alice| + | 5| null| Bob| + | 50| null| Tom| + | 50| null|unknown| + +---+------+-------+ >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show() - age height name - 10 80 Alice - 5 null Bob - 50 null Tom - 50 null unknown + +---+------+-------+ + |age|height| name| + +---+------+-------+ + | 10| 80| Alice| + | 5| null| Bob| + | 50| null| Tom| + | 50| null|unknown| + +---+------+-------+ """ if not isinstance(value, (float, int, long, basestring, dict)): raise ValueError("value should be a float, int, long, string, or dict") @@ -1241,11 +1262,17 @@ def getItem(self, key): >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"]) >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() - l[0] d[key] - 1 value + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ >>> df.select(df.l[0], df.d["key"]).show() - l[0] d[key] - 1 value + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ """ return self[key] @@ -1255,11 +1282,17 @@ def getField(self, name): >>> from pyspark.sql import Row >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF() >>> df.select(df.r.getField("b")).show() - r.b - b + +---+ + |r.b| + +---+ + | b| + +---+ >>> df.select(df.r.a).show() - r.a - 1 + +---+ + |r.a| + +---+ + | 1| + +---+ """ return Column(self._jc.getField(name)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index c421006c8fd2d..cf344710ff8b4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql import java.io.CharArrayWriter import java.sql.DriverManager + import scala.collection.JavaConversions._ import scala.language.implicitConversions import scala.reflect.ClassTag @@ -28,6 +29,7 @@ import scala.util.control.NonFatal import com.fasterxml.jackson.core.JsonFactory +import org.apache.commons.lang3.StringUtils import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.python.SerDeUtil @@ -175,6 +177,7 @@ class DataFrame private[sql]( * @param numRows Number of rows to show */ private[sql] def showString(numRows: Int): String = { + val sb = new StringBuilder val data = take(numRows) val numCols = schema.fieldNames.length @@ -194,12 +197,25 @@ class DataFrame private[sql]( } } - // Pad the cells - rows.map { row => - row.zipWithIndex.map { case (cell, i) => - String.format(s"%-${colWidths(i)}s", cell) - }.mkString(" ") - }.mkString("\n") + // Create SeparateLine + val sep: String = colWidths.map("-" * _).addString(sb, "+", "+", "+\n").toString() + + // column names + rows.head.zipWithIndex.map { case (cell, i) => + StringUtils.leftPad(cell.toString, colWidths(i)) + }.addString(sb, "|", "|", "|\n") + + sb.append(sep) + + // data + rows.tail.map { + _.zipWithIndex.map { case (cell, i) => + StringUtils.leftPad(cell.toString, colWidths(i)) + }.addString(sb, "|", "|", "|\n") + } + + sb.append(sep) + sb.toString() } override def toString: String = { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index e286fef23caa4..ff31e15e2d472 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -598,6 +598,25 @@ class DataFrameSuite extends QueryTest { testData.select($"*").show(1000) } + test("SPARK-7319 showString") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + || 1| 1| + |+---+-----+ + |""".stripMargin + assert(testData.select($"*").showString(1) === expectedAnswer) + } + + test("SPARK-7327 show with empty dataFrame") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + |+---+-----+ + |""".stripMargin + assert(testData.select($"*").filter($"key" < 0).showString(1) === expectedAnswer) + } + test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") { val rowRDD = TestSQLContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0)))) val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false))) From fc8b58195afa67fbb75b4c8303e022f703cbf007 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Mon, 4 May 2015 16:21:36 -0700 Subject: [PATCH 11/41] [SPARK-6943] [SPARK-6944] DAG visualization on SparkUI This patch adds the functionality to display the RDD DAG on the SparkUI. This DAG describes the relationships between - an RDD and its dependencies, - an RDD and its operation scopes, and - an RDD's operation scopes and the stage / job hierarchy An operation scope here refers to the existing public APIs that created the RDDs (e.g. `textFile`, `treeAggregate`). In the future, we can expand this to include higher level operations like SQL queries. *Note: This blatantly stole a few lines of HTML and JavaScript from #5547 (thanks shroffpradyumn!)* Here's what the job page looks like: and the stage page: Author: Andrew Or Closes #5729 from andrewor14/viz2 and squashes the following commits: 666c03b [Andrew Or] Round corners of RDD boxes on stage page (minor) 01ba336 [Andrew Or] Change RDD cache color to red (minor) 6f9574a [Andrew Or] Add tests for RDDOperationScope 1c310e4 [Andrew Or] Wrap a few more RDD functions in an operation scope 3ffe566 [Andrew Or] Restore "null" as default for RDD name 5fdd89d [Andrew Or] children -> child (minor) 0d07a84 [Andrew Or] Fix python style afb98e2 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 0d7aa32 [Andrew Or] Fix python tests 3459ab2 [Andrew Or] Fix tests 832443c [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 429e9e1 [Andrew Or] Display cached RDDs on the viz b1f0fd1 [Andrew Or] Rename OperatorScope -> RDDOperationScope 31aae06 [Andrew Or] Extract visualization logic from listener 83f9c58 [Andrew Or] Implement a programmatic representation of operator scopes 5a7faf4 [Andrew Or] Rename references to viz scopes to viz clusters ee33d52 [Andrew Or] Separate HTML generating code from listener f9830a2 [Andrew Or] Refactor + clean up + document JS visualization code b80cc52 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 0706992 [Andrew Or] Add link from jobs to stages deb48a0 [Andrew Or] Translate stage boxes taking into account the width 5c7ce16 [Andrew Or] Connect RDDs across stages + update style ab91416 [Andrew Or] Introduce visualization to the Job Page 5f07e9c [Andrew Or] Remove more return statements from scopes 5e388ea [Andrew Or] Fix line too long 43de96e [Andrew Or] Add parent IDs to StageInfo 6e2cfea [Andrew Or] Remove all return statements in `withScope` d19c4da [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 7ef957c [Andrew Or] Fix scala style 4310271 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 aa868a9 [Andrew Or] Ensure that HadoopRDD is actually serializable c3bfcae [Andrew Or] Re-implement scopes using closures instead of annotations 52187fc [Andrew Or] Rat excludes 09d361e [Andrew Or] Add ID to node label (minor) 71281fa [Andrew Or] Embed the viz in the UI in a toggleable manner 8dd5af2 [Andrew Or] Fill in documentation + miscellaneous minor changes fe7816f [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz 205f838 [Andrew Or] Reimplement rendering with dagre-d3 instead of viz.js 5e22946 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz 6a7cdca [Andrew Or] Move RDD scope util methods and logic to its own file 494d5c2 [Andrew Or] Revert a few unintended style changes 9fac6f3 [Andrew Or] Re-implement scopes through annotations instead f22f337 [Andrew Or] First working implementation of visualization with vis.js 2184348 [Andrew Or] Translate RDD information to dot file 5143523 [Andrew Or] Expose the necessary information in RDDInfo a9ed4f9 [Andrew Or] Add a few missing scopes to certain RDD methods 6b3403b [Andrew Or] Scope all RDD methods --- .rat-excludes | 3 + .../org/apache/spark/ui/static/d3.min.js | 5 + .../apache/spark/ui/static/dagre-d3.min.js | 29 ++ .../spark/ui/static/graphlib-dot.min.js | 4 + .../apache/spark/ui/static/spark-dag-viz.js | 392 ++++++++++++++++++ .../org/apache/spark/ui/static/webui.css | 2 +- .../scala/org/apache/spark/SparkContext.scala | 97 +++-- .../apache/spark/rdd/AsyncRDDActions.scala | 10 +- .../apache/spark/rdd/DoubleRDDFunctions.scala | 38 +- .../org/apache/spark/rdd/HadoopRDD.scala | 6 +- .../spark/rdd/OrderedRDDFunctions.scala | 6 +- .../apache/spark/rdd/PairRDDFunctions.scala | 167 +++++--- .../main/scala/org/apache/spark/rdd/RDD.scala | 341 +++++++++------ .../apache/spark/rdd/RDDOperationScope.scala | 137 ++++++ .../spark/rdd/SequenceFileRDDFunctions.scala | 4 +- .../apache/spark/scheduler/StageInfo.scala | 2 + .../org/apache/spark/storage/RDDInfo.scala | 11 +- .../scala/org/apache/spark/ui/SparkUI.scala | 10 +- .../scala/org/apache/spark/ui/UIUtils.scala | 55 ++- .../apache/spark/ui/jobs/AllJobsPage.scala | 2 +- .../apache/spark/ui/jobs/AllStagesPage.scala | 10 +- .../apache/spark/ui/jobs/ExecutorTable.scala | 2 +- .../org/apache/spark/ui/jobs/JobPage.scala | 14 +- .../spark/ui/jobs/JobProgressListener.scala | 17 +- .../org/apache/spark/ui/jobs/JobsTab.scala | 6 +- .../org/apache/spark/ui/jobs/PoolPage.scala | 4 +- .../org/apache/spark/ui/jobs/PoolTable.scala | 2 +- .../org/apache/spark/ui/jobs/StagePage.scala | 17 +- .../org/apache/spark/ui/jobs/StagesTab.scala | 7 +- .../spark/ui/scope/RDDOperationGraph.scala | 205 +++++++++ .../ui/scope/RDDOperationGraphListener.scala | 68 +++ .../org/apache/spark/util/JsonProtocol.scala | 28 +- .../ExecutorAllocationManagerSuite.scala | 2 +- .../spark/rdd/RDDOperationScopeSuite.scala | 133 ++++++ .../apache/spark/storage/StorageSuite.scala | 4 +- .../ui/jobs/JobProgressListenerSuite.scala | 6 +- .../spark/ui/storage/StorageTabSuite.scala | 30 +- .../apache/spark/util/JsonProtocolSuite.scala | 45 +- 38 files changed, 1584 insertions(+), 337 deletions(-) create mode 100644 core/src/main/resources/org/apache/spark/ui/static/d3.min.js create mode 100644 core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js create mode 100644 core/src/main/resources/org/apache/spark/ui/static/graphlib-dot.min.js create mode 100644 core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js create mode 100644 core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala create mode 100644 core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala create mode 100644 core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala create mode 100644 core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala diff --git a/.rat-excludes b/.rat-excludes index 2238a5b68e359..dccf2db8055ce 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -30,6 +30,9 @@ spark-env.sh.template log4j-defaults.properties bootstrap-tooltip.js jquery-1.11.1.min.js +d3.min.js +dagre-d3.min.js +graphlib-dot.min.js sorttable.js vis.min.js vis.min.css diff --git a/core/src/main/resources/org/apache/spark/ui/static/d3.min.js b/core/src/main/resources/org/apache/spark/ui/static/d3.min.js new file mode 100644 index 0000000000000..30cd292198b91 --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/d3.min.js @@ -0,0 +1,5 @@ +/*v3.5.5*/!function(){function n(n){return n&&(n.ownerDocument||n.document||n).documentElement}function t(n){return n&&(n.ownerDocument&&n.ownerDocument.defaultView||n.document&&n||n.defaultView)}function e(n,t){return t>n?-1:n>t?1:n>=t?0:0/0}function r(n){return null===n?0/0:+n}function u(n){return!isNaN(n)}function 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o=0;o0}},{}],14:[function(require,module,exports){module.exports=intersectNode;function intersectNode(node,point){return node.intersect(point)}},{}],15:[function(require,module,exports){var intersectLine=require("./intersect-line");module.exports=intersectPolygon;function intersectPolygon(node,polyPoints,point){var x1=node.x;var y1=node.y;var intersections=[];var minX=Number.POSITIVE_INFINITY,minY=Number.POSITIVE_INFINITY;polyPoints.forEach(function(entry){minX=Math.min(minX,entry.x);minY=Math.min(minY,entry.y)});var left=x1-node.width/2-minX;var top=y1-node.height/2-minY;for(var i=0;i1){intersections.sort(function(p,q){var pdx=p.x-point.x,pdy=p.y-point.y,distp=Math.sqrt(pdx*pdx+pdy*pdy),qdx=q.x-point.x,qdy=q.y-point.y,distq=Math.sqrt(qdx*qdx+qdy*qdy);return distpMath.abs(dx)*h){if(dy<0){h=-h}sx=dy===0?0:h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=dx===0?0:w*dy/dx}return{x:x+sx,y:y+sy}}},{}],17:[function(require,module,exports){var util=require("../util");module.exports=addHtmlLabel;function addHtmlLabel(root,node){var fo=root.append("foreignObject").attr("width","100000");var div=fo.append("xhtml:div");var label=node.label;switch(typeof label){case"function":div.insert(label);break;case"object":div.insert(function(){return label});break;default:div.html(label)}util.applyStyle(div,node.labelStyle);div.style("display","inline-block");div.style("white-space","nowrap");var w,h;div.each(function(){w=this.clientWidth;h=this.clientHeight});fo.attr("width",w).attr("height",h);return fo}},{"../util":25}],18:[function(require,module,exports){var addTextLabel=require("./add-text-label"),addHtmlLabel=require("./add-html-label");module.exports=addLabel;function addLabel(root,node){var label=node.label;var labelSvg=root.append("g");if(typeof label!=="string"||node.labelType==="html"){addHtmlLabel(labelSvg,node)}else{addTextLabel(labelSvg,node)}var labelBBox=labelSvg.node().getBBox();labelSvg.attr("transform","translate("+-labelBBox.width/2+","+-labelBBox.height/2+")");return labelSvg}},{"./add-html-label":17,"./add-text-label":19}],19:[function(require,module,exports){var util=require("../util");module.exports=addTextLabel;function addTextLabel(root,node){var domNode=root.append("text");var lines=processEscapeSequences(node.label).split("\n");for(var i=0;i0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,entry,true));break}}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.each(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors){results.push({v:edge.v,w:edge.w})}uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.each(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.each(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.each(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});var zeroIdx=maxIn+1;_.each(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out){buckets[0].enqueue(entry)}else if(!entry["in"]){buckets[buckets.length-1].enqueue(entry)}else{buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}}},{"./data/list":31,"./graphlib":33,"./lodash":36}],35:[function(require,module,exports){"use strict";var _=require("./lodash"),acyclic=require("./acyclic"),normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time(" buildLayoutGraph",function(){return buildLayoutGraph(g)});time(" runLayout",function(){runLayout(layoutGraph,time)});time(" updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time(" makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time(" removeSelfEdges",function(){removeSelfEdges(g)});time(" acyclic",function(){acyclic.run(g)});time(" nestingGraph.run",function(){nestingGraph.run(g)});time(" rank",function(){rank(util.asNonCompoundGraph(g))});time(" injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time(" removeEmptyRanks",function(){removeEmptyRanks(g)});time(" nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time(" normalizeRanks",function(){normalizeRanks(g)});time(" assignRankMinMax",function(){assignRankMinMax(g)});time(" removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time(" normalize.run",function(){normalize.run(g)});time(" parentDummyChains",function(){parentDummyChains(g)});time(" addBorderSegments",function(){addBorderSegments(g)});time(" 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inputLabel=inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.each(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));_.each(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.each(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph(); + +graph.ranksep/=2;_.each(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c"){if(graph.rankdir==="TB"||graph.rankdir==="BT"){edge.width+=edge.labeloffset}else{edge.height+=edge.labeloffset}}})}function injectEdgeLabelProxies(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.each(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.each(g.nodes(),function(v){getExtremes(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){getExtremes(edge)}});minX-=marginX;minY-=marginY;_.each(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x")){edge.x-=minX}if(_.has(edge,"y")){edge.y-=minY}});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.each(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r"){edge.width-=edge.labeloffset}switch(edge.labelpos){case"l":edge.x-=edge.width/2+edge.labeloffset;break;case"r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.reversed){edge.points.reverse()}})}function removeBorderNodes(g){_.each(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.each(g.nodes(),function(v){if(g.node(v).dummy==="border"){g.removeNode(v)}})}function removeSelfEdges(g){_.each(g.edges(),function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges){node.selfEdges=[]}node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.each(layers,function(layer){var orderShift=0;_.each(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.each(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.each(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":28,"./add-border-segments":29,"./coordinate-system":30,"./graphlib":33,"./lodash":36,"./nesting-graph":37,"./normalize":38,"./order":43,"./parent-dummy-chains":48,"./position":50,"./rank":52,"./util":55}],36:[function(require,module,exports){arguments[4][20][0].apply(exports,arguments)},{dup:20,lodash:77}],37:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root"),depths=treeDepths(g),height=_.max(depths)-1,nodeSep=2*height+1;g.graph().nestingRoot=root;_.each(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.each(g.children(),function(child){dfs(g,root,nodeSep,weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root){g.setEdge(root,v,{weight:0,minlen:nodeSep})}return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.each(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v)){g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}}function treeDepths(g){var depths={};function dfs(v,depth){var children=g.children(v);if(children&&children.length){_.each(children,function(child){dfs(child,depth+1)})}depths[v]=depth}_.each(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge){g.removeEdge(e)}})}},{"./lodash":36,"./util":55}],38:[function(require,module,exports){"use strict";var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.each(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenterentryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRanklow||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.each(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.each(g.children(),dfs);return result}},{"./lodash":36}],49:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.each(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.each(layer.slice(scanPos,i+1),function(scanNode){_.each(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPosnextNorthBorder)){addConflict(conflicts,u,v)}})}})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.each(south,function(v,southLookahead){if(g.node(v).dummy==="border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy){return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}}function addConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV){conflicts[v]=conflictsV={}}conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.each(layering,function(layer){_.each(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.each(layering,function(layer){var prevIdx=-1;_.each(layer,function(v){var ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdxwLabel.lim){tailLabel=wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.min(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);_.each(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":33,"../lodash":36,"../util":55,"./feasible-tree":51,"./util":54}],54:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v)){return label.rank}visited[v]=true;var rank=_.min(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY){rank=0}return label.rank=rank}_.each(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":36}],55:[function(require,module,exports){"use strict";var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v;do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){ +var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":75,"./dijkstra":61}],61:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0){throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight)}if(distance0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY){break}edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":71,"../lodash":75}],62:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),function(cmpt){return cmpt.length>1})}},{"../lodash":75,"./tarjan":69}],63:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]={};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w){results[v][w]={distance:Number.POSITIVE_INFINITY}}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance0){v=pq.removeMin();if(_.has(parents,v)){result.setEdge(v,parents[v])}else if(init){throw new Error("Input graph is not connected: "+g)}else{init=true}g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":71,"../graph":72,"../lodash":75}],69:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack){entry.lowlink=Math.min(entry.lowlink,visited[w].index)}});if(entry.lowlink===entry.index){var cmpt=[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v)){dfs(v)}});return results}},{"../lodash":75}],70:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node)){throw new CycleException}if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount()){throw new CycleException}return results}function CycleException(){}},{"../lodash":75}],71:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined){return this._arr[index].priority}};PriorityQueue.prototype.min=function(){if(this.size()===0){throw new Error("Queue underflow")}return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority){throw new Error("New priority is greater than current priority. "+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l>1;if(arr[parent].priority1){this.setNode(v,value)}else{this.setNode(v)}},this);return this};Graph.prototype.setNode=function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1){this._nodes[v]=value}return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete 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"+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority)}this._arr[index].priority=priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l>1;if(arr[parent].priority1){this.setNode(v,value)}else{this.setNode(v)}},this);return this};Graph.prototype.setNode=function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1){this._nodes[v]=value}return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete this._children[this._parent[v]][v]};Graph.prototype.parent=function(v){if(this._isCompound){var parent=this._parent[v];if(parent!==GRAPH_NODE){return parent}}};Graph.prototype.children=function(v){if(_.isUndefined(v)){v=GRAPH_NODE}if(this._isCompound){var children=this._children[v];if(children){return _.keys(children)}}else if(v===GRAPH_NODE){return this.nodes()}else if(this.hasNode(v)){return[]}};Graph.prototype.predecessors=function(v){var predsV=this._preds[v];if(predsV){return _.keys(predsV)}};Graph.prototype.successors=function(v){var sucsV=this._sucs[v];if(sucsV){return _.keys(sucsV)}};Graph.prototype.neighbors=function(v){var preds=this.predecessors(v);if(preds){return _.union(preds,this.successors(v))}};Graph.prototype.setDefaultEdgeLabel=function(newDefault){if(!_.isFunction(newDefault)){newDefault=_.constant(newDefault)}this._defaultEdgeLabelFn=newDefault;return this};Graph.prototype.edgeCount=function(){return this._edgeCount};Graph.prototype.edges=function(){return _.values(this._edgeObjs)};Graph.prototype.setPath=function(vs,value){var self=this,args=arguments;_.reduce(vs,function(v,w){if(args.length>1){self.setEdge(v,w,value)}else{self.setEdge(v,w)}return w});return this};Graph.prototype.setEdge=function(v,w,value,name){var valueSpecified=arguments.length>2;if(_.isPlainObject(arguments[0])){v=arguments[0].v;w=arguments[0].w;name=arguments[0].name;if(arguments.length===2){value=arguments[1];valueSpecified=true}}var e=edgeArgsToId(this._isDirected,v,w,name);if(_.has(this._edgeLabels,e)){if(valueSpecified){this._edgeLabels[e]=value}return this}if(!_.isUndefined(name)&&!this._isMultigraph){throw new Error("Cannot set a named edge when isMultigraph = false")}this.setNode(v);this.setNode(w);this._edgeLabels[e]=valueSpecified?value:this._defaultEdgeLabelFn(v,w,name);var edgeObj=edgeArgsToObj(this._isDirected,v,w,name);v=edgeObj.v;w=edgeObj.w;Object.freeze(edgeObj);this._edgeObjs[e]=edgeObj;incrementOrInitEntry(this._preds[w],v);incrementOrInitEntry(this._sucs[v],w);this._in[w][e]=edgeObj;this._out[v][e]=edgeObj;this._edgeCount++;return this};Graph.prototype.edge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return this._edgeLabels[e]};Graph.prototype.hasEdge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return _.has(this._edgeLabels,e)};Graph.prototype.removeEdge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name),edge=this._edgeObjs[e];if(edge){v=edge.v;w=edge.w;delete this._edgeLabels[e];delete this._edgeObjs[e];decrementOrRemoveEntry(this._preds[w],v);decrementOrRemoveEntry(this._sucs[v],w);delete this._in[w][e];delete this._out[v][e];this._edgeCount--}return this};Graph.prototype.inEdges=function(v,u){var inV=this._in[v];if(inV){var edges=_.values(inV);if(!u){return edges}return _.filter(edges,function(edge){return edge.v===u})}};Graph.prototype.outEdges=function(v,w){var outV=this._out[v];if(outV){var edges=_.values(outV);if(!w){return edges}return _.filter(edges,function(edge){return edge.w===w})}};Graph.prototype.nodeEdges=function(v,w){var inEdges=this.inEdges(v,w);if(inEdges){return inEdges.concat(this.outEdges(v,w))}};function incrementOrInitEntry(map,k){if(_.has(map,k)){map[k]++}else{map[k]=1}}function decrementOrRemoveEntry(map,k){if(!--map[k]){delete map[k]}}function edgeArgsToId(isDirected,v,w,name){if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}return v+EDGE_KEY_DELIM+w+EDGE_KEY_DELIM+(_.isUndefined(name)?DEFAULT_EDGE_NAME:name)}function edgeArgsToObj(isDirected,v,w,name){if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}var edgeObj={v:v,w:w};if(name){edgeObj.name=name}return 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Unfortunately, this means we + * cannot rely on dagre-d3 to render edges that cross stages and must render + * these manually on our own. + */ +function renderDagVizForJob(svgContainer) { + var crossStageEdges = []; + + metadataContainer().selectAll(".stage-metadata").each(function(d, i) { + var metadata = d3.select(this); + var dot = metadata.select(".dot-file").text(); + var stageId = metadata.attr("stageId"); + var containerId = VizConstants.graphPrefix + stageId; + // TODO: handle stage attempts + var stageLink = + "/stages/stage/?id=" + stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0"; + var container = svgContainer + .append("a").attr("xlink:href", stageLink) + .append("g").attr("id", containerId); + // Now we need to shift the container for this stage so it doesn't overlap + // with existing ones. We do not need to do this for the first stage. + if (i > 0) { + // Take into account the position and width of the last stage's container + var existingStages = stageClusters(); + if (!existingStages.empty()) { + var lastStage = existingStages[0].pop(); + var lastStageId = d3.select(lastStage).attr("id"); + var lastStageWidth = toFloat(d3.select("#" + lastStageId + " rect").attr("width")); + var lastStagePosition = getAbsolutePosition(lastStageId); + var offset = lastStagePosition.x + lastStageWidth + VizConstants.stageSep; + container.attr("transform", "translate(" + offset + ", 0)"); + } + } + renderDot(dot, container); + // If there are any incoming edges into this graph, keep track of them to render + // them separately later. Note that we cannot draw them now because we need to + // put these edges in a separate container that is on top of all stage graphs. + metadata.selectAll(".incoming-edge").each(function(v) { + var edge = d3.select(this).text().split(","); // e.g. 3,4 => [3, 4] + crossStageEdges.push(edge); + }); + }); + + // Draw edges that cross stages + if (crossStageEdges.length > 0) { + var container = svgContainer.append("g").attr("id", "cross-stage-edges"); + for (var i = 0; i < crossStageEdges.length; i++) { + var fromRDDId = crossStageEdges[i][0]; + var toRDDId = crossStageEdges[i][1]; + connectRDDs(fromRDDId, toRDDId, container); + } + } +} + +/* Render the dot file as an SVG in the given container. */ +function renderDot(dot, container) { + var escaped_dot = dot + .replace(/</g, "<") + .replace(/>/g, ">") + .replace(/"/g, "\""); + var g = graphlibDot.read(escaped_dot); + var renderer = new dagreD3.render(); + renderer(container, g); +} + +/* Style the visualization we just rendered. */ +function styleDagViz(forJob) { + graphContainer().selectAll("svg g.cluster rect") + .style("fill", "white") + .style("stroke", VizConstants.rddOperationColor) + .style("stroke-width", "4px") + .style("stroke-opacity", "0.5"); + graphContainer().selectAll("svg g.cluster text") + .attr("fill", VizConstants.clusterLabelColor) + .attr("font-size", "11px"); + graphContainer().selectAll("svg path") + .style("stroke", VizConstants.edgeColor) + .style("stroke-width", VizConstants.edgeWidth); + stageClusters() + .select("rect") + .style("stroke", VizConstants.stageColor) + .style("strokeWidth", "6px"); + + // Put an arrow at the end of every edge + // We need to do this because we manually render some edges ourselves + // For these edges, we borrow the arrow marker generated by dagre-d3 + var dagreD3Marker = graphContainer().select("svg g.edgePaths marker").node(); + graphContainer().select("svg") + .append(function() { return dagreD3Marker.cloneNode(true); }) + .attr("id", "marker-arrow") + .select("path") + .attr("fill", VizConstants.edgeColor) + .attr("strokeWidth", "0px"); + graphContainer().selectAll("svg g > path").attr("marker-end", "url(#marker-arrow)"); + graphContainer().selectAll("svg g.edgePaths def").remove(); // We no longer need these + + // Apply any job or stage specific styles + if (forJob) { + styleDagVizForJob(); + } else { + styleDagVizForStage(); + } +} + +/* Apply job-page-specific style to the visualization. */ +function styleDagVizForJob() { + graphContainer().selectAll("svg g.node circle") + .style("fill", VizConstants.rddColor); + // TODO: add a legend to explain what a highlighted dot means + graphContainer().selectAll("svg g.cached circle") + .style("fill", VizConstants.rddCachedColor); + graphContainer().selectAll("svg g#cross-stage-edges path") + .style("fill", "none"); +} + +/* Apply stage-page-specific style to the visualization. */ +function styleDagVizForStage() { + graphContainer().selectAll("svg g.node rect") + .style("fill", "none") + .style("stroke", VizConstants.rddColor) + .style("stroke-width", "2px") + .attr("rx", "5") // round corners + .attr("ry", "5"); + // TODO: add a legend to explain what a highlighted RDD means + graphContainer().selectAll("svg g.cached rect") + .style("stroke", VizConstants.rddCachedColor); + graphContainer().selectAll("svg g.node g.label text tspan") + .style("fill", VizConstants.rddColor); +} + +/* + * (Job page only) Helper method to compute the absolute + * position of the group element identified by the given ID. + */ +function getAbsolutePosition(groupId) { + var obj = d3.select("#" + groupId).filter("g"); + var _x = 0, _y = 0; + while (!obj.empty()) { + var transformText = obj.attr("transform"); + var translate = d3.transform(transformText).translate + _x += translate[0]; + _y += translate[1]; + obj = d3.select(obj.node().parentNode).filter("g") + } + return { x: _x, y: _y }; +} + +/* (Job page only) Connect two RDD nodes with a curved edge. */ +function connectRDDs(fromRDDId, toRDDId, container) { + var fromNodeId = VizConstants.nodePrefix + fromRDDId; + var toNodeId = VizConstants.nodePrefix + toRDDId + var fromPos = getAbsolutePosition(fromNodeId); + var toPos = getAbsolutePosition(toNodeId); + + // On the job page, RDDs are rendered as dots (circles). When rendering the path, + // we need to account for the radii of these circles. Otherwise the arrow heads + // will bleed into the circle itself. + var delta = toFloat(graphContainer() + .select("g.node#" + toNodeId) + .select("circle") + .attr("r")); + if (fromPos.x < toPos.x) { + fromPos.x += delta; + toPos.x -= delta; + } else if (fromPos.x > toPos.x) { + fromPos.x -= delta; + toPos.x += delta; + } + + if (fromPos.y == toPos.y) { + // If they are on the same rank, curve the middle part of the edge + // upward a little to avoid interference with things in between + // e.g. _______ + // _____/ \_____ + var points = [ + [fromPos.x, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.2, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.3, fromPos.y - 20], + [fromPos.x + (toPos.x - fromPos.x) * 0.7, fromPos.y - 20], + [fromPos.x + (toPos.x - fromPos.x) * 0.8, toPos.y], + [toPos.x, toPos.y] + ]; + } else { + // Otherwise, draw a curved edge that flattens out on both ends + // e.g. _____ + // / + // | + // _____/ + var points = [ + [fromPos.x, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.4, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.6, toPos.y], + [toPos.x, toPos.y] + ]; + } + + var line = d3.svg.line().interpolate("basis"); + container.append("path").datum(points).attr("d", line); +} + +/* Helper d3 accessor to clusters that represent stages. */ +function stageClusters() { + return graphContainer().selectAll("g.cluster").filter(function() { + return d3.select(this).attr("id").indexOf(VizConstants.stageClusterPrefix) > -1; + }); +} + +/* Helper method to convert attributes to numeric values. */ +function toFloat(f) { + return parseFloat(f.replace(/px$/, "")); +} + diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css index 4910744d1d790..669ad48937c05 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/webui.css +++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css @@ -145,7 +145,7 @@ pre { border: none; } -span.expand-additional-metrics { +span.expand-additional-metrics, span.expand-dag-viz { cursor: pointer; } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 4ef90546a2452..b98a54b418cd0 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -659,6 +659,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null) } + /** + * Execute a block of code in a scope such that all new RDDs created in this body will + * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}. + * + * Note: Return statements are NOT allowed in the given body. + */ + private def withScope[U](body: => U): U = RDDOperationScope.withScope[U](this)(body) + // Methods for creating RDDs /** Distribute a local Scala collection to form an RDD. @@ -669,7 +677,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @note avoid using `parallelize(Seq())` to create an empty `RDD`. Consider `emptyRDD` for an * RDD with no partitions, or `parallelize(Seq[T]())` for an RDD of `T` with empty partitions. */ - def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def parallelize[T: ClassTag]( + seq: Seq[T], + numSlices: Int = defaultParallelism): RDD[T] = withScope { assertNotStopped() new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]()) } @@ -678,14 +688,16 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * This method is identical to `parallelize`. */ - def makeRDD[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def makeRDD[T: ClassTag]( + seq: Seq[T], + numSlices: Int = defaultParallelism): RDD[T] = withScope { parallelize(seq, numSlices) } /** Distribute a local Scala collection to form an RDD, with one or more * location preferences (hostnames of Spark nodes) for each object. * Create a new partition for each collection item. */ - def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = { + def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = withScope { assertNotStopped() val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1._2)).toMap new ParallelCollectionRDD[T](this, seq.map(_._1), seq.size, indexToPrefs) @@ -695,10 +707,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Read a text file from HDFS, a local file system (available on all nodes), or any * Hadoop-supported file system URI, and return it as an RDD of Strings. */ - def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = { + def textFile( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[String] = withScope { assertNotStopped() hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text], - minPartitions).map(pair => pair._2.toString).setName(path) + minPartitions).map(pair => pair._2.toString) } /** @@ -728,8 +742,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * @param minPartitions A suggestion value of the minimal splitting number for input data. */ - def wholeTextFiles(path: String, minPartitions: Int = defaultMinPartitions): - RDD[(String, String)] = { + def wholeTextFiles( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[(String, String)] = withScope { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking @@ -776,8 +791,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @note Small files are preferred; very large files may cause bad performance. */ @Experimental - def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions): - RDD[(String, PortableDataStream)] = { + def binaryFiles( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[(String, PortableDataStream)] = withScope { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking @@ -806,8 +822,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @return An RDD of data with values, represented as byte arrays */ @Experimental - def binaryRecords(path: String, recordLength: Int, conf: Configuration = hadoopConfiguration) - : RDD[Array[Byte]] = { + def binaryRecords( + path: String, + recordLength: Int, + conf: Configuration = hadoopConfiguration): RDD[Array[Byte]] = withScope { assertNotStopped() conf.setInt(FixedLengthBinaryInputFormat.RECORD_LENGTH_PROPERTY, recordLength) val br = newAPIHadoopFile[LongWritable, BytesWritable, FixedLengthBinaryInputFormat](path, @@ -848,8 +866,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], - minPartitions: Int = defaultMinPartitions - ): RDD[(K, V)] = { + minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope { assertNotStopped() // Add necessary security credentials to the JobConf before broadcasting it. SparkHadoopUtil.get.addCredentials(conf) @@ -869,8 +886,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], - minPartitions: Int = defaultMinPartitions - ): RDD[(K, V)] = { + minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope { assertNotStopped() // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it. val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration)) @@ -901,7 +917,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def hadoopFile[K, V, F <: InputFormat[K, V]] (path: String, minPartitions: Int) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = { + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { hadoopFile(path, fm.runtimeClass.asInstanceOf[Class[F]], km.runtimeClass.asInstanceOf[Class[K]], @@ -924,13 +940,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * copy them using a `map` function. */ def hadoopFile[K, V, F <: InputFormat[K, V]](path: String) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { hadoopFile[K, V, F](path, defaultMinPartitions) + } /** Get an RDD for a Hadoop file with an arbitrary new API InputFormat. */ def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]] (path: String) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = { + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { newAPIHadoopFile( path, fm.runtimeClass.asInstanceOf[Class[F]], @@ -953,7 +970,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli fClass: Class[F], kClass: Class[K], vClass: Class[V], - conf: Configuration = hadoopConfiguration): RDD[(K, V)] = { + conf: Configuration = hadoopConfiguration): RDD[(K, V)] = withScope { assertNotStopped() // The call to new NewHadoopJob automatically adds security credentials to conf, // so we don't need to explicitly add them ourselves @@ -987,7 +1004,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli conf: Configuration = hadoopConfiguration, fClass: Class[F], kClass: Class[K], - vClass: Class[V]): RDD[(K, V)] = { + vClass: Class[V]): RDD[(K, V)] = withScope { assertNotStopped() // Add necessary security credentials to the JobConf. Required to access secure HDFS. val jconf = new JobConf(conf) @@ -1007,7 +1024,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli keyClass: Class[K], valueClass: Class[V], minPartitions: Int - ): RDD[(K, V)] = { + ): RDD[(K, V)] = withScope { assertNotStopped() val inputFormatClass = classOf[SequenceFileInputFormat[K, V]] hadoopFile(path, inputFormatClass, keyClass, valueClass, minPartitions) @@ -1021,7 +1038,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first * copy them using a `map` function. * */ - def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]): RDD[(K, V)] = { + def sequenceFile[K, V]( + path: String, + keyClass: Class[K], + valueClass: Class[V]): RDD[(K, V)] = withScope { assertNotStopped() sequenceFile(path, keyClass, valueClass, defaultMinPartitions) } @@ -1051,16 +1071,17 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli def sequenceFile[K, V] (path: String, minPartitions: Int = defaultMinPartitions) (implicit km: ClassTag[K], vm: ClassTag[V], - kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]) - : RDD[(K, V)] = { - assertNotStopped() - val kc = kcf() - val vc = vcf() - val format = classOf[SequenceFileInputFormat[Writable, Writable]] - val writables = hadoopFile(path, format, + kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]): RDD[(K, V)] = { + withScope { + assertNotStopped() + val kc = kcf() + val vc = vcf() + val format = classOf[SequenceFileInputFormat[Writable, Writable]] + val writables = hadoopFile(path, format, kc.writableClass(km).asInstanceOf[Class[Writable]], vc.writableClass(vm).asInstanceOf[Class[Writable]], minPartitions) - writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) } + writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) } + } } /** @@ -1073,21 +1094,18 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def objectFile[T: ClassTag]( path: String, - minPartitions: Int = defaultMinPartitions - ): RDD[T] = { + minPartitions: Int = defaultMinPartitions): RDD[T] = withScope { assertNotStopped() sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions) .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader)) } - protected[spark] def checkpointFile[T: ClassTag]( - path: String - ): RDD[T] = { + protected[spark] def checkpointFile[T: ClassTag](path: String): RDD[T] = withScope { new CheckpointRDD[T](this, path) } /** Build the union of a list of RDDs. */ - def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = { + def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = withScope { val partitioners = rdds.flatMap(_.partitioner).toSet if (rdds.forall(_.partitioner.isDefined) && partitioners.size == 1) { new PartitionerAwareUnionRDD(this, rdds) @@ -1097,8 +1115,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } /** Build the union of a list of RDDs passed as variable-length arguments. */ - def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = + def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = withScope { union(Seq(first) ++ rest) + } /** Get an RDD that has no partitions or elements. */ def emptyRDD[T: ClassTag]: EmptyRDD[T] = new EmptyRDD[T](this) @@ -2060,10 +2079,10 @@ object SparkContext extends Logging { } private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description" - private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id" - private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel" + private[spark] val RDD_SCOPE_KEY = "spark.rdd.scope" + private[spark] val RDD_SCOPE_NO_OVERRIDE_KEY = "spark.rdd.scope.noOverride" /** * Executor id for the driver. In earlier versions of Spark, this was ``, but this was diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala index 3406a7e97e368..ec185340c3a2d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -33,7 +33,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for counting the number of elements in the RDD. */ - def countAsync(): FutureAction[Long] = { + def countAsync(): FutureAction[Long] = self.withScope { val totalCount = new AtomicLong self.context.submitJob( self, @@ -53,7 +53,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for retrieving all elements of this RDD. */ - def collectAsync(): FutureAction[Seq[T]] = { + def collectAsync(): FutureAction[Seq[T]] = self.withScope { val results = new Array[Array[T]](self.partitions.length) self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.length), (index, data) => results(index) = data, results.flatten.toSeq) @@ -62,7 +62,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for retrieving the first num elements of the RDD. */ - def takeAsync(num: Int): FutureAction[Seq[T]] = { + def takeAsync(num: Int): FutureAction[Seq[T]] = self.withScope { val f = new ComplexFutureAction[Seq[T]] f.run { @@ -109,7 +109,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Applies a function f to all elements of this RDD. */ - def foreachAsync(f: T => Unit): FutureAction[Unit] = { + def foreachAsync(f: T => Unit): FutureAction[Unit] = self.withScope { val cleanF = self.context.clean(f) self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.length), (index, data) => Unit, Unit) @@ -118,7 +118,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Applies a function f to each partition of this RDD. */ - def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = { + def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = self.withScope { self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.length), (index, data) => Unit, Unit) } diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index 843a893235e56..926bce6f15a2a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -30,7 +30,7 @@ import org.apache.spark.util.StatCounter */ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { /** Add up the elements in this RDD. */ - def sum(): Double = { + def sum(): Double = self.withScope { self.fold(0.0)(_ + _) } @@ -38,37 +38,49 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * Return a [[org.apache.spark.util.StatCounter]] object that captures the mean, variance and * count of the RDD's elements in one operation. */ - def stats(): StatCounter = { + def stats(): StatCounter = self.withScope { self.mapPartitions(nums => Iterator(StatCounter(nums))).reduce((a, b) => a.merge(b)) } /** Compute the mean of this RDD's elements. */ - def mean(): Double = stats().mean + def mean(): Double = self.withScope { + stats().mean + } /** Compute the variance of this RDD's elements. */ - def variance(): Double = stats().variance + def variance(): Double = self.withScope { + stats().variance + } /** Compute the standard deviation of this RDD's elements. */ - def stdev(): Double = stats().stdev + def stdev(): Double = self.withScope { + stats().stdev + } /** * Compute the sample standard deviation of this RDD's elements (which corrects for bias in * estimating the standard deviation by dividing by N-1 instead of N). */ - def sampleStdev(): Double = stats().sampleStdev + def sampleStdev(): Double = self.withScope { + stats().sampleStdev + } /** * Compute the sample variance of this RDD's elements (which corrects for bias in * estimating the variance by dividing by N-1 instead of N). */ - def sampleVariance(): Double = stats().sampleVariance + def sampleVariance(): Double = self.withScope { + stats().sampleVariance + } /** * :: Experimental :: * Approximate operation to return the mean within a timeout. */ @Experimental - def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def meanApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) val evaluator = new MeanEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) @@ -79,7 +91,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * Approximate operation to return the sum within a timeout. */ @Experimental - def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def sumApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) val evaluator = new SumEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) @@ -93,7 +107,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * If the RDD contains infinity, NaN throws an exception * If the elements in RDD do not vary (max == min) always returns a single bucket. */ - def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = { + def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = self.withScope { // Scala's built-in range has issues. See #SI-8782 def customRange(min: Double, max: Double, steps: Int): IndexedSeq[Double] = { val span = max - min @@ -140,7 +154,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * the maximum value of the last position and all NaN entries will be counted * in that bucket. */ - def histogram(buckets: Array[Double], evenBuckets: Boolean = false): Array[Long] = { + def histogram( + buckets: Array[Double], + evenBuckets: Boolean = false): Array[Long] = self.withScope { if (buckets.length < 2) { throw new IllegalArgumentException("buckets array must have at least two elements") } diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala index f77abac42b623..2cefe63d44b20 100644 --- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala @@ -99,7 +99,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp */ @DeveloperApi class HadoopRDD[K, V]( - sc: SparkContext, + @transient sc: SparkContext, broadcastedConf: Broadcast[SerializableWritable[Configuration]], initLocalJobConfFuncOpt: Option[JobConf => Unit], inputFormatClass: Class[_ <: InputFormat[K, V]], @@ -108,6 +108,10 @@ class HadoopRDD[K, V]( minPartitions: Int) extends RDD[(K, V)](sc, Nil) with Logging { + if (initLocalJobConfFuncOpt.isDefined) { + sc.clean(initLocalJobConfFuncOpt.get) + } + def this( sc: SparkContext, conf: JobConf, diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala index 6afe50161dacd..d71bb63000904 100644 --- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala @@ -57,7 +57,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, */ // TODO: this currently doesn't work on P other than Tuple2! def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length) - : RDD[(K, V)] = + : RDD[(K, V)] = self.withScope { val part = new RangePartitioner(numPartitions, self, ascending) new ShuffledRDD[K, V, V](self, part) @@ -71,7 +71,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * This is more efficient than calling `repartition` and then sorting within each partition * because it can push the sorting down into the shuffle machinery. */ - def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = { + def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = self.withScope { new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering) } @@ -81,7 +81,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * performed efficiently by only scanning the partitions that might contain matching elements. * Otherwise, a standard `filter` is applied to all partitions. */ - def filterByRange(lower: K, upper: K): RDD[P] = { + def filterByRange(lower: K, upper: K): RDD[P] = self.withScope { def inRange(k: K): Boolean = ordering.gteq(k, lower) && ordering.lteq(k, upper) diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 05351ba4ff76b..93d338fe0530c 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -29,7 +29,7 @@ import scala.util.DynamicVariable import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus import org.apache.hadoop.conf.{Configurable, Configuration} -import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.fs.FileSystem import org.apache.hadoop.io.SequenceFile.CompressionType import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat} @@ -75,7 +75,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) mergeCombiners: (C, C) => C, partitioner: Partitioner, mapSideCombine: Boolean = true, - serializer: Serializer = null): RDD[(K, C)] = { + serializer: Serializer = null): RDD[(K, C)] = self.withScope { require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0 if (keyClass.isArray) { if (mapSideCombine) { @@ -108,7 +108,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, - numPartitions: Int): RDD[(K, C)] = { + numPartitions: Int): RDD[(K, C)] = self.withScope { combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions)) } @@ -122,7 +122,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { // Serialize the zero value to a byte array so that we can get a new clone of it on each key val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue) val zeroArray = new Array[Byte](zeroBuffer.limit) @@ -144,7 +144,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U, numPartitions: Int)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { aggregateByKey(zeroValue, new HashPartitioner(numPartitions))(seqOp, combOp) } @@ -158,7 +158,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { aggregateByKey(zeroValue, defaultPartitioner(self))(seqOp, combOp) } @@ -167,7 +167,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey( + zeroValue: V, + partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = self.withScope { // Serialize the zero value to a byte array so that we can get a new clone of it on each key val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue) val zeroArray = new Array[Byte](zeroBuffer.limit) @@ -185,7 +187,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = self.withScope { foldByKey(zeroValue, new HashPartitioner(numPartitions))(func) } @@ -194,7 +196,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = self.withScope { foldByKey(zeroValue, defaultPartitioner(self))(func) } @@ -213,7 +215,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) */ def sampleByKey(withReplacement: Boolean, fractions: Map[K, Double], - seed: Long = Utils.random.nextLong): RDD[(K, V)] = { + seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope { require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.") @@ -242,9 +244,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @return RDD containing the sampled subset */ @Experimental - def sampleByKeyExact(withReplacement: Boolean, + def sampleByKeyExact( + withReplacement: Boolean, fractions: Map[K, Double], - seed: Long = Utils.random.nextLong): RDD[(K, V)] = { + seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope { require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.") @@ -261,7 +264,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * the merging locally on each mapper before sending results to a reducer, similarly to a * "combiner" in MapReduce. */ - def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = { + def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope { combineByKey[V]((v: V) => v, func, func, partitioner) } @@ -270,7 +273,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * the merging locally on each mapper before sending results to a reducer, similarly to a * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions. */ - def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = { + def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = self.withScope { reduceByKey(new HashPartitioner(numPartitions), func) } @@ -280,7 +283,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ * parallelism level. */ - def reduceByKey(func: (V, V) => V): RDD[(K, V)] = { + def reduceByKey(func: (V, V) => V): RDD[(K, V)] = self.withScope { reduceByKey(defaultPartitioner(self), func) } @@ -289,7 +292,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * immediately to the master as a Map. This will also perform the merging locally on each mapper * before sending results to a reducer, similarly to a "combiner" in MapReduce. */ - def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = { + def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = self.withScope { if (keyClass.isArray) { throw new SparkException("reduceByKeyLocally() does not support array keys") @@ -317,7 +320,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) /** Alias for reduceByKeyLocally */ @deprecated("Use reduceByKeyLocally", "1.0.0") - def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = reduceByKeyLocally(func) + def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = self.withScope { + reduceByKeyLocally(func) + } /** * Count the number of elements for each key, collecting the results to a local Map. @@ -327,7 +332,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which * returns an RDD[T, Long] instead of a map. */ - def countByKey(): Map[K, Long] = self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap + def countByKey(): Map[K, Long] = self.withScope { + self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap + } /** * :: Experimental :: @@ -336,7 +343,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) */ @Experimental def countByKeyApprox(timeout: Long, confidence: Double = 0.95) - : PartialResult[Map[K, BoundedDouble]] = { + : PartialResult[Map[K, BoundedDouble]] = self.withScope { self.map(_._1).countByValueApprox(timeout, confidence) } @@ -360,7 +367,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @param partitioner Partitioner to use for the resulting RDD. */ @Experimental - def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner): RDD[(K, Long)] = { + def countApproxDistinctByKey( + p: Int, + sp: Int, + partitioner: Partitioner): RDD[(K, Long)] = self.withScope { require(p >= 4, s"p ($p) must be >= 4") require(sp <= 32, s"sp ($sp) must be <= 32") require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)") @@ -392,7 +402,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * It must be greater than 0.000017. * @param partitioner partitioner of the resulting RDD */ - def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = { + def countApproxDistinctByKey( + relativeSD: Double, + partitioner: Partitioner): RDD[(K, Long)] = self.withScope { require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017") val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt assert(p <= 32) @@ -410,7 +422,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * It must be greater than 0.000017. * @param numPartitions number of partitions of the resulting RDD */ - def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): RDD[(K, Long)] = { + def countApproxDistinctByKey( + relativeSD: Double, + numPartitions: Int): RDD[(K, Long)] = self.withScope { countApproxDistinctByKey(relativeSD, new HashPartitioner(numPartitions)) } @@ -424,7 +438,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @param relativeSD Relative accuracy. Smaller values create counters that require more space. * It must be greater than 0.000017. */ - def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = { + def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = self.withScope { countApproxDistinctByKey(relativeSD, defaultPartitioner(self)) } @@ -441,7 +455,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]]. */ - def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = { + def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope { // groupByKey shouldn't use map side combine because map side combine does not // reduce the amount of data shuffled and requires all map side data be inserted // into a hash table, leading to more objects in the old gen. @@ -465,14 +479,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]]. */ - def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = { + def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = self.withScope { groupByKey(new HashPartitioner(numPartitions)) } /** * Return a copy of the RDD partitioned using the specified partitioner. */ - def partitionBy(partitioner: Partitioner): RDD[(K, V)] = { + def partitionBy(partitioner: Partitioner): RDD[(K, V)] = self.withScope { if (keyClass.isArray && partitioner.isInstanceOf[HashPartitioner]) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -488,7 +502,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD. */ - def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = self.withScope { this.cogroup(other, partitioner).flatMapValues( pair => for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w) ) @@ -500,7 +514,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to * partition the output RDD. */ - def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W]( + other: RDD[(K, W)], + partitioner: Partitioner): RDD[(K, (V, Option[W]))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { pair => if (pair._2.isEmpty) { pair._1.iterator.map(v => (v, None)) @@ -517,7 +533,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * partition the output RDD. */ def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Option[V], W))] = { + : RDD[(K, (Option[V], W))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { pair => if (pair._1.isEmpty) { pair._2.iterator.map(w => (None, w)) @@ -536,7 +552,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * in `this` have key k. Uses the given Partitioner to partition the output RDD. */ def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Option[V], Option[W]))] = { + : RDD[(K, (Option[V], Option[W]))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { case (vs, Seq()) => vs.iterator.map(v => (Some(v), None)) case (Seq(), ws) => ws.iterator.map(w => (None, Some(w))) @@ -549,7 +565,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * existing partitioner/parallelism level. */ def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C) - : RDD[(K, C)] = { + : RDD[(K, C)] = self.withScope { combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self)) } @@ -563,7 +579,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupByKey(): RDD[(K, Iterable[V])] = { + def groupByKey(): RDD[(K, Iterable[V])] = self.withScope { groupByKey(defaultPartitioner(self)) } @@ -572,7 +588,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Performs a hash join across the cluster. */ - def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = self.withScope { join(other, defaultPartitioner(self, other)) } @@ -581,7 +597,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Performs a hash join across the cluster. */ - def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = self.withScope { join(other, new HashPartitioner(numPartitions)) } @@ -591,7 +607,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * using the existing partitioner/parallelism level. */ - def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = self.withScope { leftOuterJoin(other, defaultPartitioner(self, other)) } @@ -601,7 +617,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * into `numPartitions` partitions. */ - def leftOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (V, Option[W]))] = self.withScope { leftOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -611,7 +629,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD using the existing partitioner/parallelism level. */ - def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = { + def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = self.withScope { rightOuterJoin(other, defaultPartitioner(self, other)) } @@ -621,7 +639,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD into the given number of partitions. */ - def rightOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], W))] = { + def rightOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Option[V], W))] = self.withScope { rightOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -634,7 +654,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/ * parallelism level. */ - def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = { + def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = self.withScope { fullOuterJoin(other, defaultPartitioner(self, other)) } @@ -646,7 +666,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements * in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions. */ - def fullOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = { + def fullOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = self.withScope { fullOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -656,7 +678,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Warning: this doesn't return a multimap (so if you have multiple values to the same key, only * one value per key is preserved in the map returned) */ - def collectAsMap(): Map[K, V] = { + def collectAsMap(): Map[K, V] = self.withScope { val data = self.collect() val map = new mutable.HashMap[K, V] map.sizeHint(data.length) @@ -668,7 +690,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Pass each value in the key-value pair RDD through a map function without changing the keys; * this also retains the original RDD's partitioning. */ - def mapValues[U](f: V => U): RDD[(K, U)] = { + def mapValues[U](f: V => U): RDD[(K, U)] = self.withScope { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.map { case (k, v) => (k, cleanF(v)) }, @@ -679,7 +701,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Pass each value in the key-value pair RDD through a flatMap function without changing the * keys; this also retains the original RDD's partitioning. */ - def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = { + def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = self.withScope { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.flatMap { case (k, v) => @@ -697,7 +719,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) other2: RDD[(K, W2)], other3: RDD[(K, W3)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -715,7 +737,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * list of values for that key in `this` as well as `other`. */ def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W]))] = { + : RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -730,7 +752,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -748,7 +770,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * for that key in `this`, `other1`, `other2` and `other3`. */ def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3)) } @@ -756,7 +778,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the * list of values for that key in `this` as well as `other`. */ - def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, defaultPartitioner(self, other)) } @@ -765,7 +787,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } @@ -773,7 +795,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the * list of values for that key in `this` as well as `other`. */ - def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, new HashPartitioner(numPartitions)) } @@ -782,7 +806,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, new HashPartitioner(numPartitions)) } @@ -795,24 +819,24 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) other2: RDD[(K, W2)], other3: RDD[(K, W3)], numPartitions: Int) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, new HashPartitioner(numPartitions)) } /** Alias for cogroup. */ - def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { + def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, defaultPartitioner(self, other)) } /** Alias for cogroup. */ def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } /** Alias for cogroup. */ def groupWith[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3)) } @@ -822,22 +846,27 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = self.withScope { subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length))) + } /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] = + def subtractByKey[W: ClassTag]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, V)] = self.withScope { subtractByKey(other, new HashPartitioner(numPartitions)) + } /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = self.withScope { new SubtractedRDD[K, V, W](self, other, p) + } /** * Return the list of values in the RDD for key `key`. This operation is done efficiently if the * RDD has a known partitioner by only searching the partition that the key maps to. */ - def lookup(key: K): Seq[V] = { + def lookup(key: K): Seq[V] = self.withScope { self.partitioner match { case Some(p) => val index = p.getPartition(key) @@ -859,7 +888,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class * supporting the key and value types K and V in this RDD. */ - def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + def saveAsHadoopFile[F <: OutputFormat[K, V]]( + path: String)(implicit fm: ClassTag[F]): Unit = self.withScope { saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -869,7 +899,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * supplied codec. */ def saveAsHadoopFile[F <: OutputFormat[K, V]]( - path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassTag[F]) { + path: String, + codec: Class[_ <: CompressionCodec])(implicit fm: ClassTag[F]): Unit = self.withScope { val runtimeClass = fm.runtimeClass saveAsHadoopFile(path, keyClass, valueClass, runtimeClass.asInstanceOf[Class[F]], codec) } @@ -878,7 +909,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` * (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. */ - def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]]( + path: String)(implicit fm: ClassTag[F]): Unit = self.withScope { saveAsNewAPIHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -891,8 +923,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: NewOutputFormat[_, _]], - conf: Configuration = self.context.hadoopConfiguration) - { + conf: Configuration = self.context.hadoopConfiguration): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val job = new NewAPIHadoopJob(hadoopConf) @@ -912,7 +943,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], - codec: Class[_ <: CompressionCodec]) { + codec: Class[_ <: CompressionCodec]): Unit = self.withScope { saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, new JobConf(self.context.hadoopConfiguration), Some(codec)) } @@ -927,7 +958,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf = new JobConf(self.context.hadoopConfiguration), - codec: Option[Class[_ <: CompressionCodec]] = None) { + codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf hadoopConf.setOutputKeyClass(keyClass) @@ -960,7 +991,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * output paths required (e.g. a table name to write to) in the same way as it would be * configured for a Hadoop MapReduce job. */ - def saveAsNewAPIHadoopDataset(conf: Configuration) { + def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val job = new NewAPIHadoopJob(hadoopConf) @@ -1027,7 +1058,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * (e.g. a table name to write to) in the same way as it would be configured for a Hadoop * MapReduce job. */ - def saveAsHadoopDataset(conf: JobConf) { + def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val wrappedConf = new SerializableWritable(hadoopConf) diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 31c07c73fe07b..7f7c7ed144eb3 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -25,7 +25,7 @@ import scala.language.implicitConversions import scala.reflect.{classTag, ClassTag} import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus -import org.apache.hadoop.io.{Writable, BytesWritable, NullWritable, Text} +import org.apache.hadoop.io.{BytesWritable, NullWritable, Text} import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.TextOutputFormat @@ -277,12 +277,20 @@ abstract class RDD[T: ClassTag]( if (isCheckpointed) firstParent[T].iterator(split, context) else compute(split, context) } + /** + * Execute a block of code in a scope such that all new RDDs created in this body will + * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}. + * + * Note: Return statements are NOT allowed in the given body. + */ + private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](sc)(body) + // Transformations (return a new RDD) /** * Return a new RDD by applying a function to all elements of this RDD. */ - def map[U: ClassTag](f: T => U): RDD[U] = { + def map[U: ClassTag](f: T => U): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF)) } @@ -291,7 +299,7 @@ abstract class RDD[T: ClassTag]( * Return a new RDD by first applying a function to all elements of this * RDD, and then flattening the results. */ - def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = { + def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.flatMap(cleanF)) } @@ -299,7 +307,7 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing only the elements that satisfy a predicate. */ - def filter(f: T => Boolean): RDD[T] = { + def filter(f: T => Boolean): RDD[T] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[T, T]( this, @@ -310,13 +318,16 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = + def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope { map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1) + } /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(): RDD[T] = distinct(partitions.length) + def distinct(): RDD[T] = withScope { + distinct(partitions.length) + } /** * Return a new RDD that has exactly numPartitions partitions. @@ -327,7 +338,7 @@ abstract class RDD[T: ClassTag]( * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, * which can avoid performing a shuffle. */ - def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = { + def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope { coalesce(numPartitions, shuffle = true) } @@ -352,7 +363,7 @@ abstract class RDD[T: ClassTag]( * data distributed using a hash partitioner. */ def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null) - : RDD[T] = { + : RDD[T] = withScope { if (shuffle) { /** Distributes elements evenly across output partitions, starting from a random partition. */ val distributePartition = (index: Int, items: Iterator[T]) => { @@ -377,16 +388,17 @@ abstract class RDD[T: ClassTag]( /** * Return a sampled subset of this RDD. - * + * * @param withReplacement can elements be sampled multiple times (replaced when sampled out) * @param fraction expected size of the sample as a fraction of this RDD's size * without replacement: probability that each element is chosen; fraction must be [0, 1] * with replacement: expected number of times each element is chosen; fraction must be >= 0 * @param seed seed for the random number generator */ - def sample(withReplacement: Boolean, + def sample( + withReplacement: Boolean, fraction: Double, - seed: Long = Utils.random.nextLong): RDD[T] = { + seed: Long = Utils.random.nextLong): RDD[T] = withScope { require(fraction >= 0.0, "Negative fraction value: " + fraction) if (withReplacement) { new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed) @@ -403,7 +415,9 @@ abstract class RDD[T: ClassTag]( * * @return split RDDs in an array */ - def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[T]] = { + def randomSplit( + weights: Array[Double], + seed: Long = Utils.random.nextLong): Array[RDD[T]] = withScope { val sum = weights.sum val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _) normalizedCumWeights.sliding(2).map { x => @@ -435,7 +449,9 @@ abstract class RDD[T: ClassTag]( * @param seed seed for the random number generator * @return sample of specified size in an array */ - def takeSample(withReplacement: Boolean, + // TODO: rewrite this without return statements so we can wrap it in a scope + def takeSample( + withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] = { val numStDev = 10.0 @@ -483,7 +499,7 @@ abstract class RDD[T: ClassTag]( * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ - def union(other: RDD[T]): RDD[T] = { + def union(other: RDD[T]): RDD[T] = withScope { if (partitioner.isDefined && other.partitioner == partitioner) { new PartitionerAwareUnionRDD(sc, Array(this, other)) } else { @@ -495,7 +511,9 @@ abstract class RDD[T: ClassTag]( * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ - def ++(other: RDD[T]): RDD[T] = this.union(other) + def ++(other: RDD[T]): RDD[T] = withScope { + this.union(other) + } /** * Return this RDD sorted by the given key function. @@ -504,10 +522,11 @@ abstract class RDD[T: ClassTag]( f: (T) => K, ascending: Boolean = true, numPartitions: Int = this.partitions.length) - (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = + (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = withScope { this.keyBy[K](f) .sortByKey(ascending, numPartitions) .values + } /** * Return the intersection of this RDD and another one. The output will not contain any duplicate @@ -515,7 +534,7 @@ abstract class RDD[T: ClassTag]( * * Note that this method performs a shuffle internally. */ - def intersection(other: RDD[T]): RDD[T] = { + def intersection(other: RDD[T]): RDD[T] = withScope { this.map(v => (v, null)).cogroup(other.map(v => (v, null))) .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } .keys @@ -529,8 +548,9 @@ abstract class RDD[T: ClassTag]( * * @param partitioner Partitioner to use for the resulting RDD */ - def intersection(other: RDD[T], partitioner: Partitioner)(implicit ord: Ordering[T] = null) - : RDD[T] = { + def intersection( + other: RDD[T], + partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope { this.map(v => (v, null)).cogroup(other.map(v => (v, null)), partitioner) .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } .keys @@ -544,16 +564,14 @@ abstract class RDD[T: ClassTag]( * * @param numPartitions How many partitions to use in the resulting RDD */ - def intersection(other: RDD[T], numPartitions: Int): RDD[T] = { - this.map(v => (v, null)).cogroup(other.map(v => (v, null)), new HashPartitioner(numPartitions)) - .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } - .keys + def intersection(other: RDD[T], numPartitions: Int): RDD[T] = withScope { + intersection(other, new HashPartitioner(numPartitions)) } /** * Return an RDD created by coalescing all elements within each partition into an array. */ - def glom(): RDD[Array[T]] = { + def glom(): RDD[Array[T]] = withScope { new MapPartitionsRDD[Array[T], T](this, (context, pid, iter) => Iterator(iter.toArray)) } @@ -561,7 +579,9 @@ abstract class RDD[T: ClassTag]( * Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of * elements (a, b) where a is in `this` and b is in `other`. */ - def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = new CartesianRDD(sc, this, other) + def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope { + new CartesianRDD(sc, this, other) + } /** * Return an RDD of grouped items. Each group consists of a key and a sequence of elements @@ -572,8 +592,9 @@ abstract class RDD[T: ClassTag]( * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = + def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope { groupBy[K](f, defaultPartitioner(this)) + } /** * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements @@ -584,8 +605,11 @@ abstract class RDD[T: ClassTag]( * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupBy[K](f: T => K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = + def groupBy[K]( + f: T => K, + numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope { groupBy(f, new HashPartitioner(numPartitions)) + } /** * Return an RDD of grouped items. Each group consists of a key and a sequence of elements @@ -597,7 +621,7 @@ abstract class RDD[T: ClassTag]( * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ def groupBy[K](f: T => K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null) - : RDD[(K, Iterable[T])] = { + : RDD[(K, Iterable[T])] = withScope { val cleanF = sc.clean(f) this.map(t => (cleanF(t), t)).groupByKey(p) } @@ -605,13 +629,16 @@ abstract class RDD[T: ClassTag]( /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: String): RDD[String] = new PipedRDD(this, command) + def pipe(command: String): RDD[String] = withScope { + new PipedRDD(this, command) + } /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: String, env: Map[String, String]): RDD[String] = + def pipe(command: String, env: Map[String, String]): RDD[String] = withScope { new PipedRDD(this, command, env) + } /** * Return an RDD created by piping elements to a forked external process. @@ -619,7 +646,7 @@ abstract class RDD[T: ClassTag]( * * @param command command to run in forked process. * @param env environment variables to set. - * @param printPipeContext Before piping elements, this function is called as an oppotunity + * @param printPipeContext Before piping elements, this function is called as an opportunity * to pipe context data. Print line function (like out.println) will be * passed as printPipeContext's parameter. * @param printRDDElement Use this function to customize how to pipe elements. This function @@ -637,7 +664,7 @@ abstract class RDD[T: ClassTag]( env: Map[String, String] = Map(), printPipeContext: (String => Unit) => Unit = null, printRDDElement: (T, String => Unit) => Unit = null, - separateWorkingDir: Boolean = false): RDD[String] = { + separateWorkingDir: Boolean = false): RDD[String] = withScope { new PipedRDD(this, command, env, if (printPipeContext ne null) sc.clean(printPipeContext) else null, if (printRDDElement ne null) sc.clean(printRDDElement) else null, @@ -651,7 +678,7 @@ abstract class RDD[T: ClassTag]( * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitions[U: ClassTag]( - f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = withScope { val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -664,7 +691,8 @@ abstract class RDD[T: ClassTag]( * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitionsWithIndex[U: ClassTag]( - f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + f: (Int, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = withScope { val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(index, iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -681,7 +709,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use TaskContext.get", "1.2.0") def mapPartitionsWithContext[U: ClassTag]( f: (TaskContext, Iterator[T]) => Iterator[U], - preservesPartitioning: Boolean = false): RDD[U] = { + preservesPartitioning: Boolean = false): RDD[U] = withScope { val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(context, iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -692,7 +720,8 @@ abstract class RDD[T: ClassTag]( */ @deprecated("use mapPartitionsWithIndex", "0.7.0") def mapPartitionsWithSplit[U: ClassTag]( - f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + f: (Int, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = withScope { mapPartitionsWithIndex(f, preservesPartitioning) } @@ -704,7 +733,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use mapPartitionsWithIndex", "1.0.0") def mapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) - (f: (T, A) => U): RDD[U] = { + (f: (T, A) => U): RDD[U] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.map(t => f(t, a)) @@ -719,7 +748,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use mapPartitionsWithIndex and flatMap", "1.0.0") def flatMapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) - (f: (T, A) => Seq[U]): RDD[U] = { + (f: (T, A) => Seq[U]): RDD[U] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.flatMap(t => f(t, a)) @@ -732,11 +761,11 @@ abstract class RDD[T: ClassTag]( * partition with the index of that partition. */ @deprecated("use mapPartitionsWithIndex and foreach", "1.0.0") - def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit) { + def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit): Unit = withScope { mapPartitionsWithIndex { (index, iter) => val a = constructA(index) iter.map(t => {f(t, a); t}) - }.foreach(_ => {}) + } } /** @@ -745,7 +774,7 @@ abstract class RDD[T: ClassTag]( * partition with the index of that partition. */ @deprecated("use mapPartitionsWithIndex and filter", "1.0.0") - def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = { + def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.filter(t => p(t, a)) @@ -758,7 +787,7 @@ abstract class RDD[T: ClassTag]( * partitions* and the *same number of elements in each partition* (e.g. one was made through * a map on the other). */ - def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = { + def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope { zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) => new Iterator[(T, U)] { def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match { @@ -780,33 +809,39 @@ abstract class RDD[T: ClassTag]( */ def zipPartitions[B: ClassTag, V: ClassTag] (rdd2: RDD[B], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, preservesPartitioning) + } def zipPartitions[B: ClassTag, V: ClassTag] (rdd2: RDD[B]) - (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, false) + (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, preservesPartitioning = false)(f) + } def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, preservesPartitioning) + } def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C]) - (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, false) + (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, rdd3, preservesPartitioning = false)(f) + } def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, preservesPartitioning) + } def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D]) - (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, false) + (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, rdd3, rdd4, preservesPartitioning = false)(f) + } // Actions (launch a job to return a value to the user program) @@ -814,7 +849,7 @@ abstract class RDD[T: ClassTag]( /** * Applies a function f to all elements of this RDD. */ - def foreach(f: T => Unit) { + def foreach(f: T => Unit): Unit = withScope { val cleanF = sc.clean(f) sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF)) } @@ -822,7 +857,7 @@ abstract class RDD[T: ClassTag]( /** * Applies a function f to each partition of this RDD. */ - def foreachPartition(f: Iterator[T] => Unit) { + def foreachPartition(f: Iterator[T] => Unit): Unit = withScope { val cleanF = sc.clean(f) sc.runJob(this, (iter: Iterator[T]) => cleanF(iter)) } @@ -830,7 +865,7 @@ abstract class RDD[T: ClassTag]( /** * Return an array that contains all of the elements in this RDD. */ - def collect(): Array[T] = { + def collect(): Array[T] = withScope { val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray) Array.concat(results: _*) } @@ -840,7 +875,7 @@ abstract class RDD[T: ClassTag]( * * The iterator will consume as much memory as the largest partition in this RDD. */ - def toLocalIterator: Iterator[T] = { + def toLocalIterator: Iterator[T] = withScope { def collectPartition(p: Int): Array[T] = { sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal = false).head } @@ -851,12 +886,14 @@ abstract class RDD[T: ClassTag]( * Return an array that contains all of the elements in this RDD. */ @deprecated("use collect", "1.0.0") - def toArray(): Array[T] = collect() + def toArray(): Array[T] = withScope { + collect() + } /** * Return an RDD that contains all matching values by applying `f`. */ - def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = { + def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = withScope { filter(f.isDefinedAt).map(f) } @@ -866,19 +903,23 @@ abstract class RDD[T: ClassTag]( * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ - def subtract(other: RDD[T]): RDD[T] = + def subtract(other: RDD[T]): RDD[T] = withScope { subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.length))) + } /** * Return an RDD with the elements from `this` that are not in `other`. */ - def subtract(other: RDD[T], numPartitions: Int): RDD[T] = + def subtract(other: RDD[T], numPartitions: Int): RDD[T] = withScope { subtract(other, new HashPartitioner(numPartitions)) + } /** * Return an RDD with the elements from `this` that are not in `other`. */ - def subtract(other: RDD[T], p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = { + def subtract( + other: RDD[T], + p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope { if (partitioner == Some(p)) { // Our partitioner knows how to handle T (which, since we have a partitioner, is // really (K, V)) so make a new Partitioner that will de-tuple our fake tuples @@ -900,7 +941,7 @@ abstract class RDD[T: ClassTag]( * Reduces the elements of this RDD using the specified commutative and * associative binary operator. */ - def reduce(f: (T, T) => T): T = { + def reduce(f: (T, T) => T): T = withScope { val cleanF = sc.clean(f) val reducePartition: Iterator[T] => Option[T] = iter => { if (iter.hasNext) { @@ -929,7 +970,7 @@ abstract class RDD[T: ClassTag]( * @param depth suggested depth of the tree (default: 2) * @see [[org.apache.spark.rdd.RDD#reduce]] */ - def treeReduce(f: (T, T) => T, depth: Int = 2): T = { + def treeReduce(f: (T, T) => T, depth: Int = 2): T = withScope { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") val cleanF = context.clean(f) val reducePartition: Iterator[T] => Option[T] = iter => { @@ -961,7 +1002,7 @@ abstract class RDD[T: ClassTag]( * modify t1 and return it as its result value to avoid object allocation; however, it should not * modify t2. */ - def fold(zeroValue: T)(op: (T, T) => T): T = { + def fold(zeroValue: T)(op: (T, T) => T): T = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance()) val cleanOp = sc.clean(op) @@ -979,7 +1020,7 @@ abstract class RDD[T: ClassTag]( * allowed to modify and return their first argument instead of creating a new U to avoid memory * allocation. */ - def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = { + def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance()) val cleanSeqOp = sc.clean(seqOp) @@ -999,26 +1040,29 @@ abstract class RDD[T: ClassTag]( def treeAggregate[U: ClassTag](zeroValue: U)( seqOp: (U, T) => U, combOp: (U, U) => U, - depth: Int = 2): U = { + depth: Int = 2): U = withScope { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") if (partitions.length == 0) { - return Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) - } - val cleanSeqOp = context.clean(seqOp) - val cleanCombOp = context.clean(combOp) - val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) - var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) - var numPartitions = partiallyAggregated.partitions.length - val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) - // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation. - while (numPartitions > scale + numPartitions / scale) { - numPartitions /= scale - val curNumPartitions = numPartitions - partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) => - iter.map((i % curNumPartitions, _)) - }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values + Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) + } else { + val cleanSeqOp = context.clean(seqOp) + val cleanCombOp = context.clean(combOp) + val aggregatePartition = + (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) + var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) + var numPartitions = partiallyAggregated.partitions.length + val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) + // If creating an extra level doesn't help reduce + // the wall-clock time, we stop tree aggregation. + while (numPartitions > scale + numPartitions / scale) { + numPartitions /= scale + val curNumPartitions = numPartitions + partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { + (i, iter) => iter.map((i % curNumPartitions, _)) + }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values + } + partiallyAggregated.reduce(cleanCombOp) } - partiallyAggregated.reduce(cleanCombOp) } /** @@ -1032,7 +1076,9 @@ abstract class RDD[T: ClassTag]( * within a timeout, even if not all tasks have finished. */ @Experimental - def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def countApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = withScope { val countElements: (TaskContext, Iterator[T]) => Long = { (ctx, iter) => var result = 0L while (iter.hasNext) { @@ -1053,7 +1099,7 @@ abstract class RDD[T: ClassTag]( * To handle very large results, consider using rdd.map(x => (x, 1L)).reduceByKey(_ + _), which * returns an RDD[T, Long] instead of a map. */ - def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = { + def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = withScope { map(value => (value, null)).countByKey() } @@ -1064,8 +1110,7 @@ abstract class RDD[T: ClassTag]( @Experimental def countByValueApprox(timeout: Long, confidence: Double = 0.95) (implicit ord: Ordering[T] = null) - : PartialResult[Map[T, BoundedDouble]] = - { + : PartialResult[Map[T, BoundedDouble]] = withScope { if (elementClassTag.runtimeClass.isArray) { throw new SparkException("countByValueApprox() does not support arrays") } @@ -1098,7 +1143,7 @@ abstract class RDD[T: ClassTag]( * If `sp` equals 0, the sparse representation is skipped. */ @Experimental - def countApproxDistinct(p: Int, sp: Int): Long = { + def countApproxDistinct(p: Int, sp: Int): Long = withScope { require(p >= 4, s"p ($p) must be at least 4") require(sp <= 32, s"sp ($sp) cannot be greater than 32") require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)") @@ -1124,7 +1169,7 @@ abstract class RDD[T: ClassTag]( * @param relativeSD Relative accuracy. Smaller values create counters that require more space. * It must be greater than 0.000017. */ - def countApproxDistinct(relativeSD: Double = 0.05): Long = { + def countApproxDistinct(relativeSD: Double = 0.05): Long = withScope { val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt countApproxDistinct(p, 0) } @@ -1142,7 +1187,9 @@ abstract class RDD[T: ClassTag]( * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ - def zipWithIndex(): RDD[(T, Long)] = new ZippedWithIndexRDD(this) + def zipWithIndex(): RDD[(T, Long)] = withScope { + new ZippedWithIndexRDD(this) + } /** * Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, @@ -1154,7 +1201,7 @@ abstract class RDD[T: ClassTag]( * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ - def zipWithUniqueId(): RDD[(T, Long)] = { + def zipWithUniqueId(): RDD[(T, Long)] = withScope { val n = this.partitions.length.toLong this.mapPartitionsWithIndex { case (k, iter) => iter.zipWithIndex.map { case (item, i) => @@ -1171,48 +1218,50 @@ abstract class RDD[T: ClassTag]( * @note due to complications in the internal implementation, this method will raise * an exception if called on an RDD of `Nothing` or `Null`. */ - def take(num: Int): Array[T] = { + def take(num: Int): Array[T] = withScope { if (num == 0) { - return new Array[T](0) - } - - val buf = new ArrayBuffer[T] - val totalParts = this.partitions.length - var partsScanned = 0 - while (buf.size < num && partsScanned < totalParts) { - // The number of partitions to try in this iteration. It is ok for this number to be - // greater than totalParts because we actually cap it at totalParts in runJob. - var numPartsToTry = 1 - if (partsScanned > 0) { - // If we didn't find any rows after the previous iteration, quadruple and retry. Otherwise, - // interpolate the number of partitions we need to try, but overestimate it by 50%. - // We also cap the estimation in the end. - if (buf.size == 0) { - numPartsToTry = partsScanned * 4 - } else { - // the left side of max is >=1 whenever partsScanned >= 2 - numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1) - numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + new Array[T](0) + } else { + val buf = new ArrayBuffer[T] + val totalParts = this.partitions.length + var partsScanned = 0 + while (buf.size < num && partsScanned < totalParts) { + // The number of partitions to try in this iteration. It is ok for this number to be + // greater than totalParts because we actually cap it at totalParts in runJob. + var numPartsToTry = 1 + if (partsScanned > 0) { + // If we didn't find any rows after the previous iteration, quadruple and retry. + // Otherwise, interpolate the number of partitions we need to try, but overestimate + // it by 50%. We also cap the estimation in the end. + if (buf.size == 0) { + numPartsToTry = partsScanned * 4 + } else { + // the left side of max is >=1 whenever partsScanned >= 2 + numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1) + numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + } } - } - val left = num - buf.size - val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) - val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true) + val left = num - buf.size + val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) + val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true) - res.foreach(buf ++= _.take(num - buf.size)) - partsScanned += numPartsToTry - } + res.foreach(buf ++= _.take(num - buf.size)) + partsScanned += numPartsToTry + } - buf.toArray + buf.toArray + } } /** * Return the first element in this RDD. */ - def first(): T = take(1) match { - case Array(t) => t - case _ => throw new UnsupportedOperationException("empty collection") + def first(): T = withScope { + take(1) match { + case Array(t) => t + case _ => throw new UnsupportedOperationException("empty collection") + } } /** @@ -1230,7 +1279,9 @@ abstract class RDD[T: ClassTag]( * @param ord the implicit ordering for T * @return an array of top elements */ - def top(num: Int)(implicit ord: Ordering[T]): Array[T] = takeOrdered(num)(ord.reverse) + def top(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { + takeOrdered(num)(ord.reverse) + } /** * Returns the first k (smallest) elements from this RDD as defined by the specified @@ -1248,7 +1299,7 @@ abstract class RDD[T: ClassTag]( * @param ord the implicit ordering for T * @return an array of top elements */ - def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = { + def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { if (num == 0) { Array.empty } else { @@ -1273,13 +1324,17 @@ abstract class RDD[T: ClassTag]( * Returns the max of this RDD as defined by the implicit Ordering[T]. * @return the maximum element of the RDD * */ - def max()(implicit ord: Ordering[T]): T = this.reduce(ord.max) + def max()(implicit ord: Ordering[T]): T = withScope { + this.reduce(ord.max) + } /** * Returns the min of this RDD as defined by the implicit Ordering[T]. * @return the minimum element of the RDD * */ - def min()(implicit ord: Ordering[T]): T = this.reduce(ord.min) + def min()(implicit ord: Ordering[T]): T = withScope { + this.reduce(ord.min) + } /** * @note due to complications in the internal implementation, this method will raise an @@ -1289,12 +1344,14 @@ abstract class RDD[T: ClassTag]( * @return true if and only if the RDD contains no elements at all. Note that an RDD * may be empty even when it has at least 1 partition. */ - def isEmpty(): Boolean = partitions.length == 0 || take(1).length == 0 + def isEmpty(): Boolean = withScope { + partitions.length == 0 || take(1).length == 0 + } /** * Save this RDD as a text file, using string representations of elements. */ - def saveAsTextFile(path: String) { + def saveAsTextFile(path: String): Unit = withScope { // https://issues.apache.org/jira/browse/SPARK-2075 // // NullWritable is a `Comparable` in Hadoop 1.+, so the compiler cannot find an implicit @@ -1321,7 +1378,7 @@ abstract class RDD[T: ClassTag]( /** * Save this RDD as a compressed text file, using string representations of elements. */ - def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) { + def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit = withScope { // https://issues.apache.org/jira/browse/SPARK-2075 val nullWritableClassTag = implicitly[ClassTag[NullWritable]] val textClassTag = implicitly[ClassTag[Text]] @@ -1339,7 +1396,7 @@ abstract class RDD[T: ClassTag]( /** * Save this RDD as a SequenceFile of serialized objects. */ - def saveAsObjectFile(path: String) { + def saveAsObjectFile(path: String): Unit = withScope { this.mapPartitions(iter => iter.grouped(10).map(_.toArray)) .map(x => (NullWritable.get(), new BytesWritable(Utils.serialize(x)))) .saveAsSequenceFile(path) @@ -1348,12 +1405,12 @@ abstract class RDD[T: ClassTag]( /** * Creates tuples of the elements in this RDD by applying `f`. */ - def keyBy[K](f: T => K): RDD[(K, T)] = { + def keyBy[K](f: T => K): RDD[(K, T)] = withScope { map(x => (f(x), x)) } /** A private method for tests, to look at the contents of each partition */ - private[spark] def collectPartitions(): Array[Array[T]] = { + private[spark] def collectPartitions(): Array[Array[T]] = withScope { sc.runJob(this, (iter: Iterator[T]) => iter.toArray) } @@ -1392,6 +1449,17 @@ abstract class RDD[T: ClassTag]( /** User code that created this RDD (e.g. `textFile`, `parallelize`). */ @transient private[spark] val creationSite = sc.getCallSite() + /** + * The scope associated with the operation that created this RDD. + * + * This is more flexible than the call site and can be defined hierarchically. For more + * detail, see the documentation of {{RDDOperationScope}}. This scope is not defined if the + * user instantiates this RDD himself without using any Spark operations. + */ + @transient private[spark] val scope: Option[RDDOperationScope] = { + Option(sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY)).map(RDDOperationScope.fromJson) + } + private[spark] def getCreationSite: String = Option(creationSite).map(_.shortForm).getOrElse("") private[spark] def elementClassTag: ClassTag[T] = classTag[T] @@ -1470,7 +1538,7 @@ abstract class RDD[T: ClassTag]( /** A description of this RDD and its recursive dependencies for debugging. */ def toDebugString: String = { // Get a debug description of an rdd without its children - def debugSelf (rdd: RDD[_]): Seq[String] = { + def debugSelf(rdd: RDD[_]): Seq[String] = { import Utils.bytesToString val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else "" @@ -1527,10 +1595,11 @@ abstract class RDD[T: ClassTag]( case (desc: String, _) => s"$nextPrefix$desc" } ++ debugChildren(rdd, nextPrefix) } - def debugString(rdd: RDD[_], - prefix: String = "", - isShuffle: Boolean = true, - isLastChild: Boolean = false): Seq[String] = { + def debugString( + rdd: RDD[_], + prefix: String = "", + isShuffle: Boolean = true, + isLastChild: Boolean = false): Seq[String] = { if (isShuffle) { shuffleDebugString(rdd, prefix, isLastChild) } else { diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala new file mode 100644 index 0000000000000..537b56b49f866 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala @@ -0,0 +1,137 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import java.util.concurrent.atomic.AtomicInteger + +import com.fasterxml.jackson.annotation.{JsonIgnore, JsonInclude, JsonPropertyOrder} +import com.fasterxml.jackson.annotation.JsonInclude.Include +import com.fasterxml.jackson.databind.ObjectMapper +import com.fasterxml.jackson.module.scala.DefaultScalaModule + +import org.apache.spark.SparkContext + +/** + * A general, named code block representing an operation that instantiates RDDs. + * + * All RDDs instantiated in the corresponding code block will store a pointer to this object. + * Examples include, but will not be limited to, existing RDD operations, such as textFile, + * reduceByKey, and treeAggregate. + * + * An operation scope may be nested in other scopes. For instance, a SQL query may enclose + * scopes associated with the public RDD APIs it uses under the hood. + * + * There is no particular relationship between an operation scope and a stage or a job. + * A scope may live inside one stage (e.g. map) or span across multiple jobs (e.g. take). + */ +@JsonInclude(Include.NON_NULL) +@JsonPropertyOrder(Array("id", "name", "parent")) +private[spark] class RDDOperationScope( + val name: String, + val parent: Option[RDDOperationScope] = None) { + + val id: Int = RDDOperationScope.nextScopeId() + + def toJson: String = { + RDDOperationScope.jsonMapper.writeValueAsString(this) + } + + /** + * Return a list of scopes that this scope is a part of, including this scope itself. + * The result is ordered from the outermost scope (eldest ancestor) to this scope. + */ + @JsonIgnore + def getAllScopes: Seq[RDDOperationScope] = { + parent.map(_.getAllScopes).getOrElse(Seq.empty) ++ Seq(this) + } + + override def equals(other: Any): Boolean = { + other match { + case s: RDDOperationScope => + id == s.id && name == s.name && parent == s.parent + case _ => false + } + } + + override def toString: String = toJson +} + +/** + * A collection of utility methods to construct a hierarchical representation of RDD scopes. + * An RDD scope tracks the series of operations that created a given RDD. + */ +private[spark] object RDDOperationScope { + private val jsonMapper = new ObjectMapper().registerModule(DefaultScalaModule) + private val scopeCounter = new AtomicInteger(0) + + def fromJson(s: String): RDDOperationScope = { + jsonMapper.readValue(s, classOf[RDDOperationScope]) + } + + /** Return a globally unique operation scope ID. */ + def nextScopeId(): Int = scopeCounter.getAndIncrement + + /** + * Execute the given body such that all RDDs created in this body will have the same scope. + * The name of the scope will be the name of the method that immediately encloses this one. + * + * Note: Return statements are NOT allowed in body. + */ + private[spark] def withScope[T]( + sc: SparkContext, + allowNesting: Boolean = false)(body: => T): T = { + val callerMethodName = Thread.currentThread.getStackTrace()(3).getMethodName + withScope[T](sc, callerMethodName, allowNesting)(body) + } + + /** + * Execute the given body such that all RDDs created in this body will have the same scope. + * + * If nesting is allowed, this concatenates the previous scope with the new one in a way that + * signifies the hierarchy. Otherwise, if nesting is not allowed, then any children calls to + * this method executed in the body will have no effect. + * + * Note: Return statements are NOT allowed in body. + */ + private[spark] def withScope[T]( + sc: SparkContext, + name: String, + allowNesting: Boolean = false)(body: => T): T = { + // Save the old scope to restore it later + val scopeKey = SparkContext.RDD_SCOPE_KEY + val noOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY + val oldScopeJson = sc.getLocalProperty(scopeKey) + val oldScope = Option(oldScopeJson).map(RDDOperationScope.fromJson) + val oldNoOverride = sc.getLocalProperty(noOverrideKey) + try { + // Set the scope only if the higher level caller allows us to do so + if (sc.getLocalProperty(noOverrideKey) == null) { + sc.setLocalProperty(scopeKey, new RDDOperationScope(name, oldScope).toJson) + } + // Optionally disallow the child body to override our scope + if (!allowNesting) { + sc.setLocalProperty(noOverrideKey, "true") + } + body + } finally { + // Remember to restore any state that was modified before exiting + sc.setLocalProperty(scopeKey, oldScopeJson) + sc.setLocalProperty(noOverrideKey, oldNoOverride) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala index 059f8963691f0..3dfcf67f0eb66 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala @@ -85,7 +85,9 @@ class SequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable : ClassTag * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported * file system. */ - def saveAsSequenceFile(path: String, codec: Option[Class[_ <: CompressionCodec]] = None) { + def saveAsSequenceFile( + path: String, + codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { def anyToWritable[U <% Writable](u: U): Writable = u // TODO We cannot force the return type of `anyToWritable` be same as keyWritableClass and diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala index cf3db0b94a0b3..e439d2a7e1229 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala @@ -33,6 +33,7 @@ class StageInfo( val name: String, val numTasks: Int, val rddInfos: Seq[RDDInfo], + val parentIds: Seq[Int], val details: String) { /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */ var submissionTime: Option[Long] = None @@ -78,6 +79,7 @@ private[spark] object StageInfo { stage.name, numTasks.getOrElse(stage.numTasks), rddInfos, + stage.parents.map(_.id), stage.details) } } diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala index ad53a3edc7cc1..96062626b5045 100644 --- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala +++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala @@ -18,7 +18,7 @@ package org.apache.spark.storage import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDDOperationScope, RDD} import org.apache.spark.util.Utils @DeveloperApi @@ -26,7 +26,9 @@ class RDDInfo( val id: Int, val name: String, val numPartitions: Int, - var storageLevel: StorageLevel) + var storageLevel: StorageLevel, + val parentIds: Seq[Int], + val scope: Option[RDDOperationScope] = None) extends Ordered[RDDInfo] { var numCachedPartitions = 0 @@ -52,7 +54,8 @@ class RDDInfo( private[spark] object RDDInfo { def fromRdd(rdd: RDD[_]): RDDInfo = { - val rddName = Option(rdd.name).getOrElse(rdd.id.toString) - new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel) + val rddName = Option(rdd.name).getOrElse(Utils.getFormattedClassName(rdd)) + val parentIds = rdd.dependencies.map(_.rdd.id) + new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel, parentIds, rdd.scope) } } diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index 06fce86bd38d2..a5271f0574e6c 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -25,6 +25,7 @@ import org.apache.spark.ui.env.{EnvironmentListener, EnvironmentTab} import org.apache.spark.ui.exec.{ExecutorsListener, ExecutorsTab} import org.apache.spark.ui.jobs.{JobsTab, JobProgressListener, StagesTab} import org.apache.spark.ui.storage.{StorageListener, StorageTab} +import org.apache.spark.ui.scope.RDDOperationGraphListener /** * Top level user interface for a Spark application. @@ -38,6 +39,7 @@ private[spark] class SparkUI private ( val executorsListener: ExecutorsListener, val jobProgressListener: JobProgressListener, val storageListener: StorageListener, + val operationGraphListener: RDDOperationGraphListener, var appName: String, val basePath: String) extends WebUI(securityManager, SparkUI.getUIPort(conf), conf, basePath, "SparkUI") @@ -93,6 +95,9 @@ private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String) private[spark] object SparkUI { val DEFAULT_PORT = 4040 val STATIC_RESOURCE_DIR = "org/apache/spark/ui/static" + val DEFAULT_POOL_NAME = "default" + val DEFAULT_RETAINED_STAGES = 1000 + val DEFAULT_RETAINED_JOBS = 1000 def getUIPort(conf: SparkConf): Int = { conf.getInt("spark.ui.port", SparkUI.DEFAULT_PORT) @@ -144,13 +149,16 @@ private[spark] object SparkUI { val storageStatusListener = new StorageStatusListener val executorsListener = new ExecutorsListener(storageStatusListener) val storageListener = new StorageListener(storageStatusListener) + val operationGraphListener = new RDDOperationGraphListener(conf) listenerBus.addListener(environmentListener) listenerBus.addListener(storageStatusListener) listenerBus.addListener(executorsListener) listenerBus.addListener(storageListener) + listenerBus.addListener(operationGraphListener) new SparkUI(sc, conf, securityManager, environmentListener, storageStatusListener, - executorsListener, _jobProgressListener, storageListener, appName, basePath) + executorsListener, _jobProgressListener, storageListener, operationGraphListener, + appName, basePath) } } diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala index 395af2ea30b9d..2f3fb181e4026 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -23,6 +23,7 @@ import java.util.{Locale, Date} import scala.xml.{Node, Text} import org.apache.spark.Logging +import org.apache.spark.ui.scope.RDDOperationGraph /** Utility functions for generating XML pages with spark content. */ private[spark] object UIUtils extends Logging { @@ -172,13 +173,21 @@ private[spark] object UIUtils extends Logging { } + def vizHeaderNodes: Seq[Node] = { + + + + + } + /** Returns a spark page with correctly formatted headers */ def headerSparkPage( title: String, content: => Seq[Node], activeTab: SparkUITab, refreshInterval: Option[Int] = None, - helpText: Option[String] = None): Seq[Node] = { + helpText: Option[String] = None, + showVisualization: Boolean = false): Seq[Node] = { val appName = activeTab.appName val shortAppName = if (appName.length < 36) appName else appName.take(32) + "..." @@ -196,6 +205,7 @@ private[spark] object UIUtils extends Logging { {commonHeaderNodes} + {if (showVisualization) vizHeaderNodes else Seq.empty} {appName} - {title} @@ -320,4 +330,47 @@ private[spark] object UIUtils extends Logging {
} + + /** Return a "DAG visualization" DOM element that expands into a visualization for a stage. */ + def showDagVizForStage(stageId: Int, graph: Option[RDDOperationGraph]): Seq[Node] = { + showDagViz(graph.toSeq, forJob = false) + } + + /** Return a "DAG visualization" DOM element that expands into a visualization for a job. */ + def showDagVizForJob(jobId: Int, graphs: Seq[RDDOperationGraph]): Seq[Node] = { + showDagViz(graphs, forJob = true) + } + + /** + * Return a "DAG visualization" DOM element that expands into a visualization on the UI. + * + * This populates metadata necessary for generating the visualization on the front-end in + * a format that is expected by spark-dag-viz.js. Any changes in the format here must be + * reflected there. + */ + private def showDagViz(graphs: Seq[RDDOperationGraph], forJob: Boolean): Seq[Node] = { +
+ + + DAG visualization + +
+
+ { + graphs.map { g => + + } + } +
+
+ } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala index a7ea12b1655fe..f6abf27db49dd 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala @@ -179,7 +179,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { - Event Timeline + Event timeline ++