From 0f92be5b5f017b593bd29d4da7e89aad2b3adac2 Mon Sep 17 00:00:00 2001 From: Holden Karau Date: Tue, 23 Jun 2015 09:08:11 -0700 Subject: [PATCH 01/17] [SPARK-8498] [TUNGSTEN] fix npe in errorhandling path in unsafeshuffle writer Author: Holden Karau Closes #6918 from holdenk/SPARK-8498-fix-npe-in-errorhandling-path-in-unsafeshuffle-writer and squashes the following commits: f807832 [Holden Karau] Log error if we can't throw it 855f9aa [Holden Karau] Spelling - not my strongest suite. Fix Propegates to Propagates. 039d620 [Holden Karau] Add missing closeandwriteoutput 30e558d [Holden Karau] go back to try/finally e503b8c [Holden Karau] Improve the test to ensure we aren't masking the underlying exception ae0b7a7 [Holden Karau] Fix the test 2e6abf7 [Holden Karau] Be more cautious when cleaning up during failed write and re-throw user exceptions --- .../shuffle/unsafe/UnsafeShuffleWriter.java | 18 ++++++++++++++++-- .../unsafe/UnsafeShuffleWriterSuite.java | 17 +++++++++++++++++ 2 files changed, 33 insertions(+), 2 deletions(-) diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java index ad7eb04afcd8c..764578b181422 100644 --- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java +++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java @@ -139,6 +139,9 @@ public void write(Iterator> records) throws IOException { @Override public void write(scala.collection.Iterator> records) throws IOException { + // Keep track of success so we know if we ecountered an exception + // We do this rather than a standard try/catch/re-throw to handle + // generic throwables. boolean success = false; try { while (records.hasNext()) { @@ -147,8 +150,19 @@ public void write(scala.collection.Iterator> records) throws IOEx closeAndWriteOutput(); success = true; } finally { - if (!success) { - sorter.cleanupAfterError(); + if (sorter != null) { + try { + sorter.cleanupAfterError(); + } catch (Exception e) { + // Only throw this error if we won't be masking another + // error. + if (success) { + throw e; + } else { + logger.error("In addition to a failure during writing, we failed during " + + "cleanup.", e); + } + } } } } diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java index 83d109115aa5c..10c3eedbf4b46 100644 --- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java +++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java @@ -253,6 +253,23 @@ public void doNotNeedToCallWriteBeforeUnsuccessfulStop() throws IOException { createWriter(false).stop(false); } + class PandaException extends RuntimeException { + } + + @Test(expected=PandaException.class) + public void writeFailurePropagates() throws Exception { + class BadRecords extends scala.collection.AbstractIterator> { + @Override public boolean hasNext() { + throw new PandaException(); + } + @Override public Product2 next() { + return null; + } + } + final UnsafeShuffleWriter writer = createWriter(true); + writer.write(new BadRecords()); + } + @Test public void writeEmptyIterator() throws Exception { final UnsafeShuffleWriter writer = createWriter(true); From 4f7fbefb8db56ecaab66bb0ac2ab124416fefe58 Mon Sep 17 00:00:00 2001 From: lockwobr Date: Wed, 24 Jun 2015 02:48:56 +0900 Subject: [PATCH 02/17] [SQL] [DOCS] updated the documentation for explode the syntax was incorrect in the example in explode Author: lockwobr Closes #6943 from lockwobr/master and squashes the following commits: 3d864d1 [lockwobr] updated the documentation for explode --- sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 492a3321bc0bc..f3f0f5305318e 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 @@ -1049,7 +1049,7 @@ class DataFrame private[sql]( * columns of the input row are implicitly joined with each value that is output by the function. * * {{{ - * df.explode("words", "word")(words: String => words.split(" ")) + * df.explode("words", "word"){words: String => words.split(" ")} * }}} * @group dfops * @since 1.3.0 From 7b1450b666f88452e7fe969a6d59e8b24842ea39 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Tue, 23 Jun 2015 10:52:17 -0700 Subject: [PATCH 03/17] [SPARK-7235] [SQL] Refactor the grouping sets The logical plan `Expand` takes the `output` as constructor argument, which break the references chain. We need to refactor the code, as well as the column pruning. Author: Cheng Hao Closes #5780 from chenghao-intel/expand and squashes the following commits: 76e4aa4 [Cheng Hao] revert the change for case insenstive 7c10a83 [Cheng Hao] refactor the grouping sets --- .../sql/catalyst/analysis/Analyzer.scala | 55 ++---------- .../expressions/namedExpressions.scala | 2 +- .../sql/catalyst/optimizer/Optimizer.scala | 4 + .../plans/logical/basicOperators.scala | 84 ++++++++++++++----- .../spark/sql/execution/SparkStrategies.scala | 4 +- 5 files changed, 78 insertions(+), 71 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 6311784422a91..0a3f5a7b5cade 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -192,49 +192,17 @@ class Analyzer( Seq.tabulate(1 << c.groupByExprs.length)(i => i) } - /** - * Create an array of Projections for the child projection, and replace the projections' - * expressions which equal GroupBy expressions with Literal(null), if those expressions - * are not set for this grouping set (according to the bit mask). - */ - private[this] def expand(g: GroupingSets): Seq[Seq[Expression]] = { - val result = new scala.collection.mutable.ArrayBuffer[Seq[Expression]] - - g.bitmasks.foreach { bitmask => - // get the non selected grouping attributes according to the bit mask - val nonSelectedGroupExprs = ArrayBuffer.empty[Expression] - var bit = g.groupByExprs.length - 1 - while (bit >= 0) { - if (((bitmask >> bit) & 1) == 0) nonSelectedGroupExprs += g.groupByExprs(bit) - bit -= 1 - } - - val substitution = (g.child.output :+ g.gid).map(expr => expr transformDown { - case x: Expression if nonSelectedGroupExprs.find(_ semanticEquals x).isDefined => - // if the input attribute in the Invalid Grouping Expression set of for this group - // replace it with constant null - Literal.create(null, expr.dataType) - case x if x == g.gid => - // replace the groupingId with concrete value (the bit mask) - Literal.create(bitmask, IntegerType) - }) - - result += substitution - } - - result.toSeq - } - def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case a: Cube if a.resolved => - GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations, a.gid) - case a: Rollup if a.resolved => - GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations, a.gid) - case x: GroupingSets if x.resolved => + case a: Cube => + GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations) + case a: Rollup => + GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations) + case x: GroupingSets => + val gid = AttributeReference(VirtualColumn.groupingIdName, IntegerType, false)() Aggregate( - x.groupByExprs :+ x.gid, + x.groupByExprs :+ VirtualColumn.groupingIdAttribute, x.aggregations, - Expand(expand(x), x.child.output :+ x.gid, x.child)) + Expand(x.bitmasks, x.groupByExprs, gid, x.child)) } } @@ -368,12 +336,7 @@ class Analyzer( case q: LogicalPlan => logTrace(s"Attempting to resolve ${q.simpleString}") - q transformExpressionsUp { - case u @ UnresolvedAttribute(nameParts) if nameParts.length == 1 && - resolver(nameParts(0), VirtualColumn.groupingIdName) && - q.isInstanceOf[GroupingAnalytics] => - // Resolve the virtual column GROUPING__ID for the operator GroupingAnalytics - q.asInstanceOf[GroupingAnalytics].gid + q transformExpressionsUp { case u @ UnresolvedAttribute(nameParts) => // Leave unchanged if resolution fails. Hopefully will be resolved next round. val result = diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index 58dbeaf89cad5..9cacdceb13837 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -262,5 +262,5 @@ case class PrettyAttribute(name: String) extends Attribute with trees.LeafNode[E object VirtualColumn { val groupingIdName: String = "grouping__id" - def newGroupingId: AttributeReference = AttributeReference(groupingIdName, IntegerType, false)() + val groupingIdAttribute: UnresolvedAttribute = UnresolvedAttribute(groupingIdName) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 9132a786f77a7..98b4476076854 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -121,6 +121,10 @@ object UnionPushdown extends Rule[LogicalPlan] { */ object ColumnPruning extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { + case a @ Aggregate(_, _, e @ Expand(_, groupByExprs, _, child)) + if (child.outputSet -- AttributeSet(groupByExprs) -- a.references).nonEmpty => + a.copy(child = e.copy(child = prunedChild(child, AttributeSet(groupByExprs) ++ a.references))) + // Eliminate attributes that are not needed to calculate the specified aggregates. case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty => a.copy(child = Project(a.references.toSeq, child)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 7814e51628db6..fae339808c233 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.types._ +import org.apache.spark.util.collection.OpenHashSet case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = projectList.map(_.toAttribute) @@ -228,24 +229,76 @@ case class Window( /** * Apply the all of the GroupExpressions to every input row, hence we will get * multiple output rows for a input row. - * @param projections The group of expressions, all of the group expressions should - * output the same schema specified by the parameter `output` - * @param output The output Schema + * @param bitmasks The bitmask set represents the grouping sets + * @param groupByExprs The grouping by expressions * @param child Child operator */ case class Expand( - projections: Seq[Seq[Expression]], - output: Seq[Attribute], + bitmasks: Seq[Int], + groupByExprs: Seq[Expression], + gid: Attribute, child: LogicalPlan) extends UnaryNode { override def statistics: Statistics = { val sizeInBytes = child.statistics.sizeInBytes * projections.length Statistics(sizeInBytes = sizeInBytes) } + + val projections: Seq[Seq[Expression]] = expand() + + /** + * Extract attribute set according to the grouping id + * @param bitmask bitmask to represent the selected of the attribute sequence + * @param exprs the attributes in sequence + * @return the attributes of non selected specified via bitmask (with the bit set to 1) + */ + private def buildNonSelectExprSet(bitmask: Int, exprs: Seq[Expression]) + : OpenHashSet[Expression] = { + val set = new OpenHashSet[Expression](2) + + var bit = exprs.length - 1 + while (bit >= 0) { + if (((bitmask >> bit) & 1) == 0) set.add(exprs(bit)) + bit -= 1 + } + + set + } + + /** + * Create an array of Projections for the child projection, and replace the projections' + * expressions which equal GroupBy expressions with Literal(null), if those expressions + * are not set for this grouping set (according to the bit mask). + */ + private[this] def expand(): Seq[Seq[Expression]] = { + val result = new scala.collection.mutable.ArrayBuffer[Seq[Expression]] + + bitmasks.foreach { bitmask => + // get the non selected grouping attributes according to the bit mask + val nonSelectedGroupExprSet = buildNonSelectExprSet(bitmask, groupByExprs) + + val substitution = (child.output :+ gid).map(expr => expr transformDown { + case x: Expression if nonSelectedGroupExprSet.contains(x) => + // if the input attribute in the Invalid Grouping Expression set of for this group + // replace it with constant null + Literal.create(null, expr.dataType) + case x if x == gid => + // replace the groupingId with concrete value (the bit mask) + Literal.create(bitmask, IntegerType) + }) + + result += substitution + } + + result.toSeq + } + + override def output: Seq[Attribute] = { + child.output :+ gid + } } trait GroupingAnalytics extends UnaryNode { self: Product => - def gid: AttributeReference def groupByExprs: Seq[Expression] def aggregations: Seq[NamedExpression] @@ -266,17 +319,12 @@ trait GroupingAnalytics extends UnaryNode { * @param child Child operator * @param aggregations The Aggregation expressions, those non selected group by expressions * will be considered as constant null if it appears in the expressions - * @param gid The attribute represents the virtual column GROUPING__ID, and it's also - * the bitmask indicates the selected GroupBy Expressions for each - * aggregating output row. - * The associated output will be one of the value in `bitmasks` */ case class GroupingSets( bitmasks: Seq[Int], groupByExprs: Seq[Expression], child: LogicalPlan, - aggregations: Seq[NamedExpression], - gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics { + aggregations: Seq[NamedExpression]) extends GroupingAnalytics { def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics = this.copy(aggregations = aggs) @@ -290,15 +338,11 @@ case class GroupingSets( * @param child Child operator * @param aggregations The Aggregation expressions, those non selected group by expressions * will be considered as constant null if it appears in the expressions - * @param gid The attribute represents the virtual column GROUPING__ID, and it's also - * the bitmask indicates the selected GroupBy Expressions for each - * aggregating output row. */ case class Cube( groupByExprs: Seq[Expression], child: LogicalPlan, - aggregations: Seq[NamedExpression], - gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics { + aggregations: Seq[NamedExpression]) extends GroupingAnalytics { def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics = this.copy(aggregations = aggs) @@ -313,15 +357,11 @@ case class Cube( * @param child Child operator * @param aggregations The Aggregation expressions, those non selected group by expressions * will be considered as constant null if it appears in the expressions - * @param gid The attribute represents the virtual column GROUPING__ID, and it's also - * the bitmask indicates the selected GroupBy Expressions for each - * aggregating output row. */ case class Rollup( groupByExprs: Seq[Expression], child: LogicalPlan, - aggregations: Seq[NamedExpression], - gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics { + aggregations: Seq[NamedExpression]) extends GroupingAnalytics { def withNewAggs(aggs: Seq[NamedExpression]): GroupingAnalytics = this.copy(aggregations = aggs) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 5c420eb9d761f..1ff1cc224de8c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -308,8 +308,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { execution.Project(projectList, planLater(child)) :: Nil case logical.Filter(condition, child) => execution.Filter(condition, planLater(child)) :: Nil - case logical.Expand(projections, output, child) => - execution.Expand(projections, output, planLater(child)) :: Nil + case e @ logical.Expand(_, _, _, child) => + execution.Expand(e.projections, e.output, planLater(child)) :: Nil case logical.Aggregate(group, agg, child) => execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil case logical.Window(projectList, windowExpressions, spec, child) => From 6f4cadf5ee81467d077febc53d36571dd232295d Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Tue, 23 Jun 2015 11:55:47 -0700 Subject: [PATCH 04/17] [SPARK-8432] [SQL] fix hashCode() and equals() of BinaryType in Row Also added more tests in LiteralExpressionSuite Author: Davies Liu Closes #6876 from davies/fix_hashcode and squashes the following commits: 429c2c0 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_hashcode 32d9811 [Davies Liu] fix test a0626ed [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_hashcode 89c2432 [Davies Liu] fix style bd20780 [Davies Liu] check with catalyst types 41caec6 [Davies Liu] change for to while d96929b [Davies Liu] address comment 6ad2a90 [Davies Liu] fix style 5819d33 [Davies Liu] unify equals() and hashCode() 0fff25d [Davies Liu] fix style 53c38b1 [Davies Liu] fix hashCode() and equals() of BinaryType in Row --- .../java/org/apache/spark/sql/BaseRow.java | 21 ------ .../main/scala/org/apache/spark/sql/Row.scala | 32 --------- .../spark/sql/catalyst/InternalRow.scala | 67 ++++++++++++++++++- .../codegen/GenerateProjection.scala | 1 + .../spark/sql/catalyst/expressions/rows.scala | 52 -------------- .../expressions/ExpressionEvalHelper.scala | 27 ++++++-- .../expressions/LiteralExpressionSuite.scala | 61 ++++++++++++++--- .../expressions/StringFunctionsSuite.scala | 5 +- .../apache/spark/unsafe/types/UTF8String.java | 6 +- .../spark/unsafe/types/UTF8StringSuite.java | 2 - 10 files changed, 139 insertions(+), 135 deletions(-) diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java index 611e02d8fb666..6a2356f1f9c6f 100644 --- a/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/BaseRow.java @@ -155,27 +155,6 @@ public int fieldIndex(String name) { throw new UnsupportedOperationException(); } - /** - * A generic version of Row.equals(Row), which is used for tests. - */ - @Override - public boolean equals(Object other) { - if (other instanceof Row) { - Row row = (Row) other; - int n = size(); - if (n != row.size()) { - return false; - } - for (int i = 0; i < n; i ++) { - if (isNullAt(i) != row.isNullAt(i) || (!isNullAt(i) && !get(i).equals(row.get(i)))) { - return false; - } - } - return true; - } - return false; - } - @Override public InternalRow copy() { final int n = size(); diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala index 8aaf5d7d89154..e99d5c87a44fe 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala @@ -17,8 +17,6 @@ package org.apache.spark.sql -import scala.util.hashing.MurmurHash3 - import org.apache.spark.sql.catalyst.expressions.GenericRow import org.apache.spark.sql.types.StructType @@ -365,36 +363,6 @@ trait Row extends Serializable { false } - override def equals(that: Any): Boolean = that match { - case null => false - case that: Row => - if (this.length != that.length) { - return false - } - var i = 0 - val len = this.length - while (i < len) { - if (apply(i) != that.apply(i)) { - return false - } - i += 1 - } - true - case _ => false - } - - override def hashCode: Int = { - // Using Scala's Seq hash code implementation. - var n = 0 - var h = MurmurHash3.seqSeed - val len = length - while (n < len) { - h = MurmurHash3.mix(h, apply(n).##) - n += 1 - } - MurmurHash3.finalizeHash(h, n) - } - /* ---------------------- utility methods for Scala ---------------------- */ /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala index e3c2cc243310b..d7b537a9fe3bc 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.catalyst import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.expressions.GenericRow +import org.apache.spark.sql.catalyst.expressions._ /** * An abstract class for row used internal in Spark SQL, which only contain the columns as @@ -26,7 +26,70 @@ import org.apache.spark.sql.catalyst.expressions.GenericRow */ abstract class InternalRow extends Row { // A default implementation to change the return type - override def copy(): InternalRow = {this} + override def copy(): InternalRow = this + + override def equals(o: Any): Boolean = { + if (!o.isInstanceOf[Row]) { + return false + } + + val other = o.asInstanceOf[Row] + if (length != other.length) { + return false + } + + var i = 0 + while (i < length) { + if (isNullAt(i) != other.isNullAt(i)) { + return false + } + if (!isNullAt(i)) { + val o1 = apply(i) + val o2 = other.apply(i) + if (o1.isInstanceOf[Array[Byte]]) { + // handle equality of Array[Byte] + val b1 = o1.asInstanceOf[Array[Byte]] + if (!o2.isInstanceOf[Array[Byte]] || + !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) { + return false + } + } else if (o1 != o2) { + return false + } + } + i += 1 + } + true + } + + // Custom hashCode function that matches the efficient code generated version. + override def hashCode: Int = { + var result: Int = 37 + var i = 0 + while (i < length) { + val update: Int = + if (isNullAt(i)) { + 0 + } else { + apply(i) match { + case b: Boolean => if (b) 0 else 1 + case b: Byte => b.toInt + case s: Short => s.toInt + case i: Int => i + case l: Long => (l ^ (l >>> 32)).toInt + case f: Float => java.lang.Float.floatToIntBits(f) + case d: Double => + val b = java.lang.Double.doubleToLongBits(d) + (b ^ (b >>> 32)).toInt + case a: Array[Byte] => java.util.Arrays.hashCode(a) + case other => other.hashCode() + } + } + result = 37 * result + update + i += 1 + } + result + } } object InternalRow { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala index 2e20eda1a3002..e362625469e29 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala @@ -127,6 +127,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { case FloatType => s"Float.floatToIntBits($col)" case DoubleType => s"(int)(Double.doubleToLongBits($col) ^ (Double.doubleToLongBits($col) >>> 32))" + case BinaryType => s"java.util.Arrays.hashCode($col)" case _ => s"$col.hashCode()" } s"isNullAt($i) ? 0 : ($nonNull)" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index 1098962ddc018..0d4c9ace5e124 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -121,58 +121,6 @@ class GenericRow(protected[sql] val values: Array[Any]) extends InternalRow { } } - // TODO(davies): add getDate and getDecimal - - // Custom hashCode function that matches the efficient code generated version. - override def hashCode: Int = { - var result: Int = 37 - - var i = 0 - while (i < values.length) { - val update: Int = - if (isNullAt(i)) { - 0 - } else { - apply(i) match { - case b: Boolean => if (b) 0 else 1 - case b: Byte => b.toInt - case s: Short => s.toInt - case i: Int => i - case l: Long => (l ^ (l >>> 32)).toInt - case f: Float => java.lang.Float.floatToIntBits(f) - case d: Double => - val b = java.lang.Double.doubleToLongBits(d) - (b ^ (b >>> 32)).toInt - case other => other.hashCode() - } - } - result = 37 * result + update - i += 1 - } - result - } - - override def equals(o: Any): Boolean = o match { - case other: InternalRow => - if (values.length != other.length) { - return false - } - - var i = 0 - while (i < values.length) { - if (isNullAt(i) != other.isNullAt(i)) { - return false - } - if (apply(i) != other.apply(i)) { - return false - } - i += 1 - } - true - - case _ => false - } - override def copy(): InternalRow = this } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala index 12d2da8b33986..158f54af13802 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala @@ -38,10 +38,23 @@ trait ExpressionEvalHelper { protected def checkEvaluation( expression: Expression, expected: Any, inputRow: InternalRow = EmptyRow): Unit = { - checkEvaluationWithoutCodegen(expression, expected, inputRow) - checkEvaluationWithGeneratedMutableProjection(expression, expected, inputRow) - checkEvaluationWithGeneratedProjection(expression, expected, inputRow) - checkEvaluationWithOptimization(expression, expected, inputRow) + val catalystValue = CatalystTypeConverters.convertToCatalyst(expected) + checkEvaluationWithoutCodegen(expression, catalystValue, inputRow) + checkEvaluationWithGeneratedMutableProjection(expression, catalystValue, inputRow) + checkEvaluationWithGeneratedProjection(expression, catalystValue, inputRow) + checkEvaluationWithOptimization(expression, catalystValue, inputRow) + } + + /** + * Check the equality between result of expression and expected value, it will handle + * Array[Byte]. + */ + protected def checkResult(result: Any, expected: Any): Boolean = { + (result, expected) match { + case (result: Array[Byte], expected: Array[Byte]) => + java.util.Arrays.equals(result, expected) + case _ => result == expected + } } protected def evaluate(expression: Expression, inputRow: InternalRow = EmptyRow): Any = { @@ -55,7 +68,7 @@ trait ExpressionEvalHelper { val actual = try evaluate(expression, inputRow) catch { case e: Exception => fail(s"Exception evaluating $expression", e) } - if (actual != expected) { + if (!checkResult(actual, expected)) { val input = if (inputRow == EmptyRow) "" else s", input: $inputRow" fail(s"Incorrect evaluation (codegen off): $expression, " + s"actual: $actual, " + @@ -83,7 +96,7 @@ trait ExpressionEvalHelper { } val actual = plan(inputRow).apply(0) - if (actual != expected) { + if (!checkResult(actual, expected)) { val input = if (inputRow == EmptyRow) "" else s", input: $inputRow" fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") } @@ -109,7 +122,7 @@ trait ExpressionEvalHelper { } val actual = plan(inputRow) - val expectedRow = new GenericRow(Array[Any](CatalystTypeConverters.convertToCatalyst(expected))) + val expectedRow = new GenericRow(Array[Any](expected)) if (actual.hashCode() != expectedRow.hashCode()) { fail( s""" diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala index f44f55dfb92d1..d924ff7a102f6 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala @@ -18,12 +18,26 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.SparkFunSuite -import org.apache.spark.sql.types.StringType +import org.apache.spark.sql.types._ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper { - // TODO: Add tests for all data types. + test("null") { + checkEvaluation(Literal.create(null, BooleanType), null) + checkEvaluation(Literal.create(null, ByteType), null) + checkEvaluation(Literal.create(null, ShortType), null) + checkEvaluation(Literal.create(null, IntegerType), null) + checkEvaluation(Literal.create(null, LongType), null) + checkEvaluation(Literal.create(null, FloatType), null) + checkEvaluation(Literal.create(null, LongType), null) + checkEvaluation(Literal.create(null, StringType), null) + checkEvaluation(Literal.create(null, BinaryType), null) + checkEvaluation(Literal.create(null, DecimalType()), null) + checkEvaluation(Literal.create(null, ArrayType(ByteType, true)), null) + checkEvaluation(Literal.create(null, MapType(StringType, IntegerType)), null) + checkEvaluation(Literal.create(null, StructType(Seq.empty)), null) + } test("boolean literals") { checkEvaluation(Literal(true), true) @@ -31,25 +45,52 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper { } test("int literals") { - checkEvaluation(Literal(1), 1) - checkEvaluation(Literal(0L), 0L) + List(0, 1, Int.MinValue, Int.MaxValue).foreach { d => + checkEvaluation(Literal(d), d) + checkEvaluation(Literal(d.toLong), d.toLong) + checkEvaluation(Literal(d.toShort), d.toShort) + checkEvaluation(Literal(d.toByte), d.toByte) + } + checkEvaluation(Literal(Long.MinValue), Long.MinValue) + checkEvaluation(Literal(Long.MaxValue), Long.MaxValue) } test("double literals") { - List(0.0, -0.0, Double.NegativeInfinity, Double.PositiveInfinity).foreach { - d => { - checkEvaluation(Literal(d), d) - checkEvaluation(Literal(d.toFloat), d.toFloat) - } + List(0.0, -0.0, Double.NegativeInfinity, Double.PositiveInfinity).foreach { d => + checkEvaluation(Literal(d), d) + checkEvaluation(Literal(d.toFloat), d.toFloat) } + checkEvaluation(Literal(Double.MinValue), Double.MinValue) + checkEvaluation(Literal(Double.MaxValue), Double.MaxValue) + checkEvaluation(Literal(Float.MinValue), Float.MinValue) + checkEvaluation(Literal(Float.MaxValue), Float.MaxValue) + } test("string literals") { + checkEvaluation(Literal(""), "") checkEvaluation(Literal("test"), "test") - checkEvaluation(Literal.create(null, StringType), null) + checkEvaluation(Literal("\0"), "\0") } test("sum two literals") { checkEvaluation(Add(Literal(1), Literal(1)), 2) } + + test("binary literals") { + checkEvaluation(Literal.create(new Array[Byte](0), BinaryType), new Array[Byte](0)) + checkEvaluation(Literal.create(new Array[Byte](2), BinaryType), new Array[Byte](2)) + } + + test("decimal") { + List(0.0, 1.2, 1.1111, 5).foreach { d => + checkEvaluation(Literal(Decimal(d)), Decimal(d)) + checkEvaluation(Literal(Decimal(d.toInt)), Decimal(d.toInt)) + checkEvaluation(Literal(Decimal(d.toLong)), Decimal(d.toLong)) + checkEvaluation(Literal(Decimal((d * 1000L).toLong, 10, 1)), + Decimal((d * 1000L).toLong, 10, 1)) + } + } + + // TODO(davies): add tests for ArrayType, MapType and StructType } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala index d363e631540d8..5dbb1d562c1d9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringFunctionsSuite.scala @@ -222,9 +222,6 @@ class StringFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper { checkEvaluation(StringLength(regEx), 5, create_row("abdef")) checkEvaluation(StringLength(regEx), 0, create_row("")) checkEvaluation(StringLength(regEx), null, create_row(null)) - // TODO currently bug in codegen, let's temporally disable this - // checkEvaluation(StringLength(Literal.create(null, StringType)), null, create_row("abdef")) + checkEvaluation(StringLength(Literal.create(null, StringType)), null, create_row("abdef")) } - - } diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java index 9871a70a40e69..9302b472925ed 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java @@ -17,10 +17,10 @@ package org.apache.spark.unsafe.types; +import javax.annotation.Nonnull; import java.io.Serializable; import java.io.UnsupportedEncodingException; import java.util.Arrays; -import javax.annotation.Nonnull; import org.apache.spark.unsafe.PlatformDependent; @@ -202,10 +202,6 @@ public int compare(final UTF8String other) { public boolean equals(final Object other) { if (other instanceof UTF8String) { return Arrays.equals(bytes, ((UTF8String) other).getBytes()); - } else if (other instanceof String) { - // Used only in unit tests. - String s = (String) other; - return bytes.length >= s.length() && length() == s.length() && toString().equals(s); } else { return false; } diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java index 80c179a1b5e75..796cdc9dbebdb 100644 --- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java +++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java @@ -28,8 +28,6 @@ private void checkBasic(String str, int len) throws UnsupportedEncodingException Assert.assertEquals(UTF8String.fromString(str).length(), len); Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).length(), len); - Assert.assertEquals(UTF8String.fromString(str), str); - Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")), str); Assert.assertEquals(UTF8String.fromString(str).toString(), str); Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")).toString(), str); Assert.assertEquals(UTF8String.fromBytes(str.getBytes("utf8")), UTF8String.fromString(str)); From 2b1111dd0b8deb9ad8d43fec792e60e3d0c4de75 Mon Sep 17 00:00:00 2001 From: Holden Karau Date: Tue, 23 Jun 2015 12:42:17 -0700 Subject: [PATCH 05/17] [SPARK-7888] Be able to disable intercept in linear regression in ml package Author: Holden Karau Closes #6927 from holdenk/SPARK-7888-Be-able-to-disable-intercept-in-Linear-Regression-in-ML-package and squashes the following commits: 0ad384c [Holden Karau] Add MiMa excludes 4016fac [Holden Karau] Switch to wild card import, remove extra blank lines ae5baa8 [Holden Karau] CR feedback, move the fitIntercept down rather than changing ymean and etc above f34971c [Holden Karau] Fix some more long lines 319bd3f [Holden Karau] Fix long lines 3bb9ee1 [Holden Karau] Update the regression suite tests 7015b9f [Holden Karau] Our code performs the same with R, except we need more than one data point but that seems reasonable 0b0c8c0 [Holden Karau] fix the issue with the sample R code e2140ba [Holden Karau] Add a test, it fails! 5e84a0b [Holden Karau] Write out thoughts and use the correct trait 91ffc0a [Holden Karau] more murh 006246c [Holden Karau] murp? --- .../ml/regression/LinearRegression.scala | 30 +++- .../ml/regression/LinearRegressionSuite.scala | 149 +++++++++++++++++- project/MimaExcludes.scala | 5 + 3 files changed, 172 insertions(+), 12 deletions(-) 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 01306545fc7cd..1b1d7299fb496 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 @@ -26,7 +26,7 @@ import org.apache.spark.Logging import org.apache.spark.annotation.Experimental import org.apache.spark.ml.PredictorParams import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol} +import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util.Identifiable import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.linalg.BLAS._ @@ -41,7 +41,8 @@ import org.apache.spark.util.StatCounter * Params for linear regression. */ private[regression] trait LinearRegressionParams extends PredictorParams - with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol + with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol + with HasFitIntercept /** * :: Experimental :: @@ -72,6 +73,14 @@ class LinearRegression(override val uid: String) def setRegParam(value: Double): this.type = set(regParam, value) setDefault(regParam -> 0.0) + /** + * Set if we should fit the intercept + * Default is true. + * @group setParam + */ + def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) + setDefault(fitIntercept -> true) + /** * Set the ElasticNet mixing parameter. * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. @@ -123,6 +132,7 @@ class LinearRegression(override val uid: String) val numFeatures = summarizer.mean.size val yMean = statCounter.mean val yStd = math.sqrt(statCounter.variance) + // look at glmnet5.m L761 maaaybe that has info // If the yStd is zero, then the intercept is yMean with zero weights; // as a result, training is not needed. @@ -142,7 +152,7 @@ class LinearRegression(override val uid: String) val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam - val costFun = new LeastSquaresCostFun(instances, yStd, yMean, + val costFun = new LeastSquaresCostFun(instances, yStd, yMean, $(fitIntercept), featuresStd, featuresMean, effectiveL2RegParam) val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { @@ -180,7 +190,7 @@ class LinearRegression(override val uid: String) // The intercept in R's GLMNET is computed using closed form after the coefficients are // converged. See the following discussion for detail. // http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet - val intercept = yMean - dot(weights, Vectors.dense(featuresMean)) + val intercept = if ($(fitIntercept)) yMean - dot(weights, Vectors.dense(featuresMean)) else 0.0 if (handlePersistence) instances.unpersist() // TODO: Converts to sparse format based on the storage, but may base on the scoring speed. @@ -234,6 +244,7 @@ class LinearRegressionModel private[ml] ( * See this discussion for detail. * http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet * + * When training with intercept enabled, * The objective function in the scaled space is given by * {{{ * L = 1/2n ||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2, @@ -241,6 +252,10 @@ class LinearRegressionModel private[ml] ( * where \bar{x_i} is the mean of x_i, \hat{x_i} is the standard deviation of x_i, * \bar{y} is the mean of label, and \hat{y} is the standard deviation of label. * + * If we fitting the intercept disabled (that is forced through 0.0), + * we can use the same equation except we set \bar{y} and \bar{x_i} to 0 instead + * of the respective means. + * * This can be rewritten as * {{{ * L = 1/2n ||\sum_i (w_i/\hat{x_i})x_i - \sum_i (w_i/\hat{x_i})\bar{x_i} - y / \hat{y} @@ -255,6 +270,7 @@ class LinearRegressionModel private[ml] ( * \sum_i w_i^\prime x_i - y / \hat{y} + offset * }}} * + * * Note that the effective weights and offset don't depend on training dataset, * so they can be precomputed. * @@ -301,6 +317,7 @@ private class LeastSquaresAggregator( weights: Vector, labelStd: Double, labelMean: Double, + fitIntercept: Boolean, featuresStd: Array[Double], featuresMean: Array[Double]) extends Serializable { @@ -321,7 +338,7 @@ private class LeastSquaresAggregator( } i += 1 } - (weightsArray, -sum + labelMean / labelStd, weightsArray.length) + (weightsArray, if (fitIntercept) labelMean / labelStd - sum else 0.0, weightsArray.length) } private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray) @@ -404,6 +421,7 @@ private class LeastSquaresCostFun( data: RDD[(Double, Vector)], labelStd: Double, labelMean: Double, + fitIntercept: Boolean, featuresStd: Array[Double], featuresMean: Array[Double], effectiveL2regParam: Double) extends DiffFunction[BDV[Double]] { @@ -412,7 +430,7 @@ private class LeastSquaresCostFun( val w = Vectors.fromBreeze(weights) val leastSquaresAggregator = data.treeAggregate(new LeastSquaresAggregator(w, labelStd, - labelMean, featuresStd, featuresMean))( + labelMean, fitIntercept, featuresStd, featuresMean))( seqOp = (c, v) => (c, v) match { case (aggregator, (label, features)) => aggregator.add(label, features) }, diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala index 732e2c42be144..ad1e9da692ee2 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala @@ -26,6 +26,7 @@ import org.apache.spark.sql.{DataFrame, Row} class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { @transient var dataset: DataFrame = _ + @transient var datasetWithoutIntercept: DataFrame = _ /** * In `LinearRegressionSuite`, we will make sure that the model trained by SparkML @@ -34,14 +35,24 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { * * import org.apache.spark.mllib.util.LinearDataGenerator * val data = - * sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2), 10000, 42), 2) - * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).saveAsTextFile("path") + * sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2), + * Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2) + * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).coalesce(1) + * .saveAsTextFile("path") */ override def beforeAll(): Unit = { super.beforeAll() dataset = sqlContext.createDataFrame( sc.parallelize(LinearDataGenerator.generateLinearInput( 6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)) + /** + * datasetWithoutIntercept is not needed for correctness testing but is useful for illustrating + * training model without intercept + */ + datasetWithoutIntercept = sqlContext.createDataFrame( + sc.parallelize(LinearDataGenerator.generateLinearInput( + 0.0, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)) + } test("linear regression with intercept without regularization") { @@ -78,6 +89,42 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { } } + test("linear regression without intercept without regularization") { + val trainer = (new LinearRegression).setFitIntercept(false) + val model = trainer.fit(dataset) + val modelWithoutIntercept = trainer.fit(datasetWithoutIntercept) + + /** + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0, + * intercept = FALSE)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * as.numeric.data.V2. 6.995908 + * as.numeric.data.V3. 5.275131 + */ + val weightsR = Array(6.995908, 5.275131) + + assert(model.intercept ~== 0 relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + /** + * Then again with the data with no intercept: + * > weightsWithoutIntercept + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * as.numeric.data3.V2. 4.70011 + * as.numeric.data3.V3. 7.19943 + */ + val weightsWithoutInterceptR = Array(4.70011, 7.19943) + + assert(modelWithoutIntercept.intercept ~== 0 relTol 1E-3) + assert(modelWithoutIntercept.weights(0) ~== weightsWithoutInterceptR(0) relTol 1E-3) + assert(modelWithoutIntercept.weights(1) ~== weightsWithoutInterceptR(1) relTol 1E-3) + } + test("linear regression with intercept with L1 regularization") { val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57) val model = trainer.fit(dataset) @@ -87,11 +134,11 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { * > weights * 3 x 1 sparse Matrix of class "dgCMatrix" * s0 - * (Intercept) 6.311546 - * as.numeric.data.V2. 2.123522 - * as.numeric.data.V3. 4.605651 + * (Intercept) 6.24300 + * as.numeric.data.V2. 4.024821 + * as.numeric.data.V3. 6.679841 */ - val interceptR = 6.243000 + val interceptR = 6.24300 val weightsR = Array(4.024821, 6.679841) assert(model.intercept ~== interceptR relTol 1E-3) @@ -106,6 +153,36 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { } } + test("linear regression without intercept with L1 regularization") { + val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57) + .setFitIntercept(false) + val model = trainer.fit(dataset) + + /** + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57, + * intercept=FALSE)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * as.numeric.data.V2. 6.299752 + * as.numeric.data.V3. 4.772913 + */ + val interceptR = 0.0 + val weightsR = Array(6.299752, 4.772913) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + + model.transform(dataset).select("features", "prediction").collect().foreach { + case Row(features: DenseVector, prediction1: Double) => + val prediction2 = + features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept + assert(prediction1 ~== prediction2 relTol 1E-5) + } + } + test("linear regression with intercept with L2 regularization") { val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3) val model = trainer.fit(dataset) @@ -134,6 +211,36 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { } } + test("linear regression without intercept with L2 regularization") { + val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3) + .setFitIntercept(false) + val model = trainer.fit(dataset) + + /** + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3, + * intercept = FALSE)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * as.numeric.data.V2. 5.522875 + * as.numeric.data.V3. 4.214502 + */ + val interceptR = 0.0 + val weightsR = Array(5.522875, 4.214502) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + + model.transform(dataset).select("features", "prediction").collect().foreach { + case Row(features: DenseVector, prediction1: Double) => + val prediction2 = + features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept + assert(prediction1 ~== prediction2 relTol 1E-5) + } + } + test("linear regression with intercept with ElasticNet regularization") { val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6) val model = trainer.fit(dataset) @@ -161,4 +268,34 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { assert(prediction1 ~== prediction2 relTol 1E-5) } } + + test("linear regression without intercept with ElasticNet regularization") { + val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6) + .setFitIntercept(false) + val model = trainer.fit(dataset) + + /** + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6, + * intercept=FALSE)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) . + * as.numeric.dataM.V2. 5.673348 + * as.numeric.dataM.V3. 4.322251 + */ + val interceptR = 0.0 + val weightsR = Array(5.673348, 4.322251) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + + model.transform(dataset).select("features", "prediction").collect().foreach { + case Row(features: DenseVector, prediction1: Double) => + val prediction2 = + features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept + assert(prediction1 ~== prediction2 relTol 1E-5) + } + } } diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 7a748fb5e38bd..f678c69a6dfa9 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -53,6 +53,11 @@ object MimaExcludes { // Removing a testing method from a private class ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"), + // While private MiMa is still not happy about the changes, + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.ml.regression.LeastSquaresAggregator.this"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.ml.regression.LeastSquaresCostFun.this"), // SQL execution is considered private. excludePackage("org.apache.spark.sql.execution"), // NanoTime and CatalystTimestampConverter is only used inside catalyst, From f2022fa0d375c804eca7803e172543b23ecbb9b7 Mon Sep 17 00:00:00 2001 From: MechCoder Date: Tue, 23 Jun 2015 12:43:32 -0700 Subject: [PATCH 06/17] [SPARK-8265] [MLLIB] [PYSPARK] Add LinearDataGenerator to pyspark.mllib.utils It is useful to generate linear data for easy testing of linear models and in general. Scala already has it. This is just a wrapper around the Scala code. Author: MechCoder Closes #6715 from MechCoder/generate_linear_input and squashes the following commits: 6182884 [MechCoder] Minor changes 8bda047 [MechCoder] Minor style fixes 0f1053c [MechCoder] [SPARK-8265] Add LinearDataGenerator to pyspark.mllib.utils --- .../mllib/api/python/PythonMLLibAPI.scala | 32 ++++++++++++++++- python/pyspark/mllib/tests.py | 22 ++++++++++-- python/pyspark/mllib/util.py | 35 +++++++++++++++++++ 3 files changed, 86 insertions(+), 3 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index f9a271f47ee2c..c4bea7c2cad4f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -51,6 +51,7 @@ import org.apache.spark.mllib.tree.loss.Losses import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel, RandomForestModel} import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest} import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.util.LinearDataGenerator import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel @@ -972,7 +973,7 @@ private[python] class PythonMLLibAPI extends Serializable { def estimateKernelDensity( sample: JavaRDD[Double], bandwidth: Double, points: java.util.ArrayList[Double]): Array[Double] = { - return new KernelDensity().setSample(sample).setBandwidth(bandwidth).estimate( + new KernelDensity().setSample(sample).setBandwidth(bandwidth).estimate( points.asScala.toArray) } @@ -991,6 +992,35 @@ private[python] class PythonMLLibAPI extends Serializable { List[AnyRef](model.clusterCenters, Vectors.dense(model.clusterWeights)).asJava } + /** + * Wrapper around the generateLinearInput method of LinearDataGenerator. + */ + def generateLinearInputWrapper( + intercept: Double, + weights: JList[Double], + xMean: JList[Double], + xVariance: JList[Double], + nPoints: Int, + seed: Int, + eps: Double): Array[LabeledPoint] = { + LinearDataGenerator.generateLinearInput( + intercept, weights.asScala.toArray, xMean.asScala.toArray, + xVariance.asScala.toArray, nPoints, seed, eps).toArray + } + + /** + * Wrapper around the generateLinearRDD method of LinearDataGenerator. + */ + def generateLinearRDDWrapper( + sc: JavaSparkContext, + nexamples: Int, + nfeatures: Int, + eps: Double, + nparts: Int, + intercept: Double): JavaRDD[LabeledPoint] = { + LinearDataGenerator.generateLinearRDD( + sc, nexamples, nfeatures, eps, nparts, intercept) + } } /** diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index c8d61b9855a69..509faa11df170 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -49,8 +49,8 @@ from pyspark.mllib.stat import Statistics from pyspark.mllib.feature import Word2Vec from pyspark.mllib.feature import IDF -from pyspark.mllib.feature import StandardScaler -from pyspark.mllib.feature import ElementwiseProduct +from pyspark.mllib.feature import StandardScaler, ElementwiseProduct +from pyspark.mllib.util import LinearDataGenerator from pyspark.serializers import PickleSerializer from pyspark.streaming import StreamingContext from pyspark.sql import SQLContext @@ -1019,6 +1019,24 @@ def collect(rdd): self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]]) +class LinearDataGeneratorTests(MLlibTestCase): + def test_dim(self): + linear_data = LinearDataGenerator.generateLinearInput( + intercept=0.0, weights=[0.0, 0.0, 0.0], + xMean=[0.0, 0.0, 0.0], xVariance=[0.33, 0.33, 0.33], + nPoints=4, seed=0, eps=0.1) + self.assertEqual(len(linear_data), 4) + for point in linear_data: + self.assertEqual(len(point.features), 3) + + linear_data = LinearDataGenerator.generateLinearRDD( + sc=sc, nexamples=6, nfeatures=2, eps=0.1, + nParts=2, intercept=0.0).collect() + self.assertEqual(len(linear_data), 6) + for point in linear_data: + self.assertEqual(len(point.features), 2) + + if __name__ == "__main__": if not _have_scipy: print("NOTE: Skipping SciPy tests as it does not seem to be installed") diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py index 16a90db146ef0..348238319e407 100644 --- a/python/pyspark/mllib/util.py +++ b/python/pyspark/mllib/util.py @@ -257,6 +257,41 @@ def load(cls, sc, path): return cls(java_model) +class LinearDataGenerator(object): + """Utils for generating linear data""" + + @staticmethod + def generateLinearInput(intercept, weights, xMean, xVariance, + nPoints, seed, eps): + """ + :param: intercept bias factor, the term c in X'w + c + :param: weights feature vector, the term w in X'w + c + :param: xMean Point around which the data X is centered. + :param: xVariance Variance of the given data + :param: nPoints Number of points to be generated + :param: seed Random Seed + :param: eps Used to scale the noise. If eps is set high, + the amount of gaussian noise added is more. + Returns a list of LabeledPoints of length nPoints + """ + weights = [float(weight) for weight in weights] + xMean = [float(mean) for mean in xMean] + xVariance = [float(var) for var in xVariance] + return list(callMLlibFunc( + "generateLinearInputWrapper", float(intercept), weights, xMean, + xVariance, int(nPoints), int(seed), float(eps))) + + @staticmethod + def generateLinearRDD(sc, nexamples, nfeatures, eps, + nParts=2, intercept=0.0): + """ + Generate a RDD of LabeledPoints. + """ + return callMLlibFunc( + "generateLinearRDDWrapper", sc, int(nexamples), int(nfeatures), + float(eps), int(nParts), float(intercept)) + + def _test(): import doctest from pyspark.context import SparkContext From f2fb0285ab6d4225c5350f109dea6c1c017bb491 Mon Sep 17 00:00:00 2001 From: Alok Singh Date: Tue, 23 Jun 2015 12:47:55 -0700 Subject: [PATCH 07/17] [SPARK-8111] [SPARKR] SparkR shell should display Spark logo and version banner on startup. spark version is taken from the environment variable SPARK_VERSION Author: Alok Singh Author: Alok Singh Closes #6944 from aloknsingh/aloknsingh_spark_jiras and squashes the following commits: ed607bd [Alok Singh] [SPARK-8111][SparkR] As per suggestion, 1) using the version from sparkContext rather than the Sys.env. 2) change "Welcome to SparkR!" to "Welcome to" followed by Spark logo and version acd5b85 [Alok Singh] fix the jira SPARK-8111 to add the spark version and logo. Currently spark version is taken from the environment variable SPARK_VERSION --- R/pkg/inst/profile/shell.R | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/R/pkg/inst/profile/shell.R b/R/pkg/inst/profile/shell.R index 773b6ecf582d9..7189f1a260934 100644 --- a/R/pkg/inst/profile/shell.R +++ b/R/pkg/inst/profile/shell.R @@ -27,7 +27,21 @@ sc <- SparkR::sparkR.init() assign("sc", sc, envir=.GlobalEnv) sqlContext <- SparkR::sparkRSQL.init(sc) + sparkVer <- SparkR:::callJMethod(sc, "version") assign("sqlContext", sqlContext, envir=.GlobalEnv) - cat("\n Welcome to SparkR!") + cat("\n Welcome to") + cat("\n") + cat(" ____ __", "\n") + cat(" / __/__ ___ _____/ /__", "\n") + cat(" _\\ \\/ _ \\/ _ `/ __/ '_/", "\n") + cat(" /___/ .__/\\_,_/_/ /_/\\_\\") + if (nchar(sparkVer) == 0) { + cat("\n") + } else { + cat(" version ", sparkVer, "\n") + } + cat(" /_/", "\n") + cat("\n") + cat("\n Spark context is available as sc, SQL context is available as sqlContext\n") } From a8031183aff2e23de9204ddfc7e7f5edbf052a7e Mon Sep 17 00:00:00 2001 From: Oleksiy Dyagilev Date: Tue, 23 Jun 2015 13:12:19 -0700 Subject: [PATCH 08/17] [SPARK-8525] [MLLIB] fix LabeledPoint parser when there is a whitespace between label and features vector fix LabeledPoint parser when there is a whitespace between label and features vector, e.g. (y, [x1, x2, x3]) Author: Oleksiy Dyagilev Closes #6954 from fe2s/SPARK-8525 and squashes the following commits: 0755b9d [Oleksiy Dyagilev] [SPARK-8525][MLLIB] addressing comment, removing dep on commons-lang c1abc2b [Oleksiy Dyagilev] [SPARK-8525][MLLIB] fix LabeledPoint parser when there is a whitespace on specific position --- .../scala/org/apache/spark/mllib/util/NumericParser.scala | 2 ++ .../apache/spark/mllib/regression/LabeledPointSuite.scala | 5 +++++ .../org/apache/spark/mllib/util/NumericParserSuite.scala | 7 +++++++ 3 files changed, 14 insertions(+) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala index 308f7f3578e21..a841c5caf0142 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala @@ -98,6 +98,8 @@ private[mllib] object NumericParser { } } else if (token == ")") { parsing = false + } else if (token.trim.isEmpty){ + // ignore whitespaces between delim chars, e.g. ", [" } else { // expecting a number items.append(parseDouble(token)) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala index d8364a06de4da..f8d0af8820e64 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/LabeledPointSuite.scala @@ -31,6 +31,11 @@ class LabeledPointSuite extends SparkFunSuite { } } + test("parse labeled points with whitespaces") { + val point = LabeledPoint.parse("(0.0, [1.0, 2.0])") + assert(point === LabeledPoint(0.0, Vectors.dense(1.0, 2.0))) + } + test("parse labeled points with v0.9 format") { val point = LabeledPoint.parse("1.0,1.0 0.0 -2.0") assert(point === LabeledPoint(1.0, Vectors.dense(1.0, 0.0, -2.0))) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala index 8dcb9ba9be108..fa4f74d71b7e7 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/NumericParserSuite.scala @@ -37,4 +37,11 @@ class NumericParserSuite extends SparkFunSuite { } } } + + test("parser with whitespaces") { + val s = "(0.0, [1.0, 2.0])" + val parsed = NumericParser.parse(s).asInstanceOf[Seq[_]] + assert(parsed(0).asInstanceOf[Double] === 0.0) + assert(parsed(1).asInstanceOf[Array[Double]] === Array(1.0, 2.0)) + } } From d96d7b55746cf034e3935ec4b22614a99e48c498 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Tue, 23 Jun 2015 14:19:21 -0700 Subject: [PATCH 09/17] [DOC] [SQL] Addes Hive metastore Parquet table conversion section This PR adds a section about Hive metastore Parquet table conversion. It documents: 1. Schema reconciliation rules introduced in #5214 (see [this comment] [1] in #5188) 2. Metadata refreshing requirement introduced in #5339 [1]: https://github.com/apache/spark/pull/5188#issuecomment-86531248 Author: Cheng Lian Closes #5348 from liancheng/sql-doc-parquet-conversion and squashes the following commits: 42ae0d0 [Cheng Lian] Adds Python `refreshTable` snippet 4c9847d [Cheng Lian] Resorts to SQL for Python metadata refreshing snippet 756e660 [Cheng Lian] Adds Python snippet for metadata refreshing 50675db [Cheng Lian] Addes Hive metastore Parquet table conversion section --- docs/sql-programming-guide.md | 94 ++++++++++++++++++++++++++++++++--- 1 file changed, 88 insertions(+), 6 deletions(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 26c036f6648da..9107c9b67681f 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -22,7 +22,7 @@ The DataFrame API is available in [Scala](api/scala/index.html#org.apache.spark. All of the examples on this page use sample data included in the Spark distribution and can be run in the `spark-shell`, `pyspark` shell, or `sparkR` shell. -## Starting Point: `SQLContext` +## Starting Point: SQLContext
@@ -1036,6 +1036,15 @@ for (teenName in collect(teenNames)) {
+
+ +{% highlight python %} +# sqlContext is an existing HiveContext +sqlContext.sql("REFRESH TABLE my_table") +{% endhighlight %} + +
+
{% highlight sql %} @@ -1054,7 +1063,7 @@ SELECT * FROM parquetTable
-### Partition discovery +### Partition Discovery Table partitioning is a common optimization approach used in systems like Hive. In a partitioned table, data are usually stored in different directories, with partitioning column values encoded in @@ -1108,7 +1117,7 @@ can be configured by `spark.sql.sources.partitionColumnTypeInference.enabled`, w `true`. When type inference is disabled, string type will be used for the partitioning columns. -### Schema merging +### Schema Merging Like ProtocolBuffer, Avro, and Thrift, Parquet also supports schema evolution. Users can start with a simple schema, and gradually add more columns to the schema as needed. In this way, users may end @@ -1208,6 +1217,79 @@ printSchema(df3)
+### Hive metastore Parquet table conversion + +When reading from and writing to Hive metastore Parquet tables, Spark SQL will try to use its own +Parquet support instead of Hive SerDe for better performance. This behavior is controlled by the +`spark.sql.hive.convertMetastoreParquet` configuration, and is turned on by default. + +#### Hive/Parquet Schema Reconciliation + +There are two key differences between Hive and Parquet from the perspective of table schema +processing. + +1. Hive is case insensitive, while Parquet is not +1. Hive considers all columns nullable, while nullability in Parquet is significant + +Due to this reason, we must reconcile Hive metastore schema with Parquet schema when converting a +Hive metastore Parquet table to a Spark SQL Parquet table. The reconciliation rules are: + +1. Fields that have the same name in both schema must have the same data type regardless of + nullability. The reconciled field should have the data type of the Parquet side, so that + nullability is respected. + +1. The reconciled schema contains exactly those fields defined in Hive metastore schema. + + - Any fields that only appear in the Parquet schema are dropped in the reconciled schema. + - Any fileds that only appear in the Hive metastore schema are added as nullable field in the + reconciled schema. + +#### Metadata Refreshing + +Spark SQL caches Parquet metadata for better performance. When Hive metastore Parquet table +conversion is enabled, metadata of those converted tables are also cached. If these tables are +updated by Hive or other external tools, you need to refresh them manually to ensure consistent +metadata. + +
+ +
+ +{% highlight scala %} +// sqlContext is an existing HiveContext +sqlContext.refreshTable("my_table") +{% endhighlight %} + +
+ +
+ +{% highlight java %} +// sqlContext is an existing HiveContext +sqlContext.refreshTable("my_table") +{% endhighlight %} + +
+ +
+ +{% highlight python %} +# sqlContext is an existing HiveContext +sqlContext.refreshTable("my_table") +{% endhighlight %} + +
+ +
+ +{% highlight sql %} +REFRESH TABLE my_table; +{% endhighlight %} + +
+ +
+ ### Configuration Configuration of Parquet can be done using the `setConf` method on `SQLContext` or by running @@ -1445,8 +1527,8 @@ This command builds a new assembly jar that includes Hive. Note that this Hive a on all of the worker nodes, as they will need access to the Hive serialization and deserialization libraries (SerDes) in order to access data stored in Hive. -Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running -the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory +Configuration of Hive is done by placing your `hive-site.xml` file in `conf/`. Please note when running +the query on a YARN cluster (`yarn-cluster` mode), the `datanucleus` jars under the `lib_managed/jars` directory and `hive-site.xml` under `conf/` directory need to be available on the driver and all executors launched by the YARN cluster. The convenient way to do this is adding them through the `--jars` option and `--file` option of the `spark-submit` command. @@ -1889,7 +1971,7 @@ options. #### DataFrame data reader/writer interface Based on user feedback, we created a new, more fluid API for reading data in (`SQLContext.read`) -and writing data out (`DataFrame.write`), +and writing data out (`DataFrame.write`), and deprecated the old APIs (e.g. `SQLContext.parquetFile`, `SQLContext.jsonFile`). See the API docs for `SQLContext.read` ( From 7fb5ae5024284593204779ff463bfbdb4d1c6da5 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Tue, 23 Jun 2015 15:51:16 -0700 Subject: [PATCH 10/17] [SPARK-8573] [SPARK-8568] [SQL] [PYSPARK] raise Exception if column is used in booelan expression It's a common mistake that user will put Column in a boolean expression (together with `and` , `or`), which does not work as expected, we should raise a exception in that case, and suggest user to use `&`, `|` instead. Author: Davies Liu Closes #6961 from davies/column_bool and squashes the following commits: 9f19beb [Davies Liu] update message af74bd6 [Davies Liu] fix tests 07dff84 [Davies Liu] address comments, fix tests f70c08e [Davies Liu] raise Exception if column is used in booelan expression --- python/pyspark/sql/column.py | 5 +++++ python/pyspark/sql/tests.py | 10 +++++++++- 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py index 1ecec5b126505..0a85da7443d3d 100644 --- a/python/pyspark/sql/column.py +++ b/python/pyspark/sql/column.py @@ -396,6 +396,11 @@ def over(self, window): jc = self._jc.over(window._jspec) return Column(jc) + def __nonzero__(self): + raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', " + "'~' for 'not' when building DataFrame boolean expressions.") + __bool__ = __nonzero__ + def __repr__(self): return 'Column<%s>' % self._jc.toString().encode('utf8') diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 13f4556943ac8..e6a434e4b2dff 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -164,6 +164,14 @@ def test_explode(self): self.assertEqual(result[0][0], "a") self.assertEqual(result[0][1], "b") + def test_and_in_expression(self): + self.assertEqual(4, self.df.filter((self.df.key <= 10) & (self.df.value <= "2")).count()) + self.assertRaises(ValueError, lambda: (self.df.key <= 10) and (self.df.value <= "2")) + self.assertEqual(14, self.df.filter((self.df.key <= 3) | (self.df.value < "2")).count()) + self.assertRaises(ValueError, lambda: self.df.key <= 3 or self.df.value < "2") + self.assertEqual(99, self.df.filter(~(self.df.key == 1)).count()) + self.assertRaises(ValueError, lambda: not self.df.key == 1) + def test_udf_with_callable(self): d = [Row(number=i, squared=i**2) for i in range(10)] rdd = self.sc.parallelize(d) @@ -408,7 +416,7 @@ def test_column_operators(self): self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column)) rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci) self.assertTrue(all(isinstance(c, Column) for c in rcc)) - cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7, ci and cs, ci or cs] + cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7] self.assertTrue(all(isinstance(c, Column) for c in cb)) cbool = (ci & ci), (ci | ci), (~ci) self.assertTrue(all(isinstance(c, Column) for c in cbool)) From 111d6b9b8a584b962b6ae80c7aa8c45845ce0099 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Tue, 23 Jun 2015 17:24:26 -0700 Subject: [PATCH 11/17] [SPARK-8139] [SQL] Updates docs and comments of data sources and Parquet output committer options This PR only applies to master branch (1.5.0-SNAPSHOT) since it references `org.apache.parquet` classes which only appear in Parquet 1.7.0. Author: Cheng Lian Closes #6683 from liancheng/output-committer-docs and squashes the following commits: b4648b8 [Cheng Lian] Removes spark.sql.sources.outputCommitterClass as it's not a public option ee63923 [Cheng Lian] Updates docs and comments of data sources and Parquet output committer options --- docs/sql-programming-guide.md | 30 +++++++++++++++- .../scala/org/apache/spark/sql/SQLConf.scala | 30 ++++++++++++---- .../DirectParquetOutputCommitter.scala | 34 +++++++++++++------ .../apache/spark/sql/parquet/newParquet.scala | 4 +-- 4 files changed, 78 insertions(+), 20 deletions(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 9107c9b67681f..2786e3d2cd6bf 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1348,6 +1348,34 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext` support. + + spark.sql.parquet.output.committer.class + org.apache.parquet.hadoop.
ParquetOutputCommitter + +

+ The output committer class used by Parquet. The specified class needs to be a subclass of + org.apache.hadoop.
mapreduce.OutputCommitter. Typically, it's also a + subclass of org.apache.parquet.hadoop.ParquetOutputCommitter. +

+

+ Note: +

    +
  • + This option must be set via Hadoop Configuration rather than Spark + SQLConf. +
    • +
  • + This option overrides spark.sql.sources.
    outputCommitterClass    . +
    • +
+

+

+ Spark SQL comes with a builtin + org.apache.spark.sql.
parquet.DirectParquetOutputCommitter, which can be more + efficient then the default Parquet output committer when writing data to S3. +

+ + ## JSON Datasets @@ -1876,7 +1904,7 @@ that these options will be deprecated in future release as more optimizations ar Configures the number of partitions to use when shuffling data for joins or aggregations. - + spark.sql.planner.externalSort false 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 16493c3d7c19c..265352647fa9f 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 @@ -22,6 +22,8 @@ import java.util.Properties import scala.collection.immutable import scala.collection.JavaConversions._ +import org.apache.parquet.hadoop.ParquetOutputCommitter + import org.apache.spark.sql.catalyst.CatalystConf private[spark] object SQLConf { @@ -252,9 +254,9 @@ private[spark] object SQLConf { val PARQUET_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.parquet.filterPushdown", defaultValue = Some(false), - doc = "Turn on Parquet filter pushdown optimization. This feature is turned off by default" + - " because of a known bug in Paruet 1.6.0rc3 " + - "(PARQUET-136). However, " + + doc = "Turn on Parquet filter pushdown optimization. This feature is turned off by default " + + "because of a known bug in Parquet 1.6.0rc3 " + + "(PARQUET-136, https://issues.apache.org/jira/browse/PARQUET-136). However, " + "if your table doesn't contain any nullable string or binary columns, it's still safe to " + "turn this feature on.") @@ -262,11 +264,21 @@ private[spark] object SQLConf { defaultValue = Some(true), doc = "") + val PARQUET_OUTPUT_COMMITTER_CLASS = stringConf( + key = "spark.sql.parquet.output.committer.class", + defaultValue = Some(classOf[ParquetOutputCommitter].getName), + doc = "The output committer class used by Parquet. The specified class needs to be a " + + "subclass of org.apache.hadoop.mapreduce.OutputCommitter. Typically, it's also a subclass " + + "of org.apache.parquet.hadoop.ParquetOutputCommitter. NOTE: 1. Instead of SQLConf, this " + + "option must be set in Hadoop Configuration. 2. This option overrides " + + "\"spark.sql.sources.outputCommitterClass\"." + ) + val ORC_FILTER_PUSHDOWN_ENABLED = booleanConf("spark.sql.orc.filterPushdown", defaultValue = Some(false), doc = "") - val HIVE_VERIFY_PARTITIONPATH = booleanConf("spark.sql.hive.verifyPartitionPath", + val HIVE_VERIFY_PARTITION_PATH = booleanConf("spark.sql.hive.verifyPartitionPath", defaultValue = Some(true), doc = "") @@ -325,9 +337,13 @@ private[spark] object SQLConf { defaultValue = Some(true), doc = "") - // The output committer class used by FSBasedRelation. The specified class needs to be a + // The output committer class used by HadoopFsRelation. The specified class needs to be a // subclass of org.apache.hadoop.mapreduce.OutputCommitter. - // NOTE: This property should be set in Hadoop `Configuration` rather than Spark `SQLConf` + // + // NOTE: + // + // 1. Instead of SQLConf, this option *must be set in Hadoop Configuration*. + // 2. This option can be overriden by "spark.sql.parquet.output.committer.class". val OUTPUT_COMMITTER_CLASS = stringConf("spark.sql.sources.outputCommitterClass", isPublic = false) @@ -415,7 +431,7 @@ private[sql] class SQLConf extends Serializable with CatalystConf { private[spark] def orcFilterPushDown: Boolean = getConf(ORC_FILTER_PUSHDOWN_ENABLED) /** When true uses verifyPartitionPath to prune the path which is not exists. */ - private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITIONPATH) + private[spark] def verifyPartitionPath: Boolean = getConf(HIVE_VERIFY_PARTITION_PATH) /** When true the planner will use the external sort, which may spill to disk. */ private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala index 62c4e92ebec68..1551afd7b7bf2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala @@ -17,19 +17,35 @@ package org.apache.spark.sql.parquet +import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter - +import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} import org.apache.parquet.Log import org.apache.parquet.hadoop.util.ContextUtil import org.apache.parquet.hadoop.{ParquetFileReader, ParquetFileWriter, ParquetOutputCommitter, ParquetOutputFormat} +/** + * An output committer for writing Parquet files. In stead of writing to the `_temporary` folder + * like what [[ParquetOutputCommitter]] does, this output committer writes data directly to the + * destination folder. This can be useful for data stored in S3, where directory operations are + * relatively expensive. + * + * To enable this output committer, users may set the "spark.sql.parquet.output.committer.class" + * property via Hadoop [[Configuration]]. Not that this property overrides + * "spark.sql.sources.outputCommitterClass". + * + * *NOTE* + * + * NEVER use [[DirectParquetOutputCommitter]] when appending data, because currently there's + * no safe way undo a failed appending job (that's why both `abortTask()` and `abortJob()` are + * left * empty). + */ private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext) extends ParquetOutputCommitter(outputPath, context) { val LOG = Log.getLog(classOf[ParquetOutputCommitter]) - override def getWorkPath(): Path = outputPath + override def getWorkPath: Path = outputPath override def abortTask(taskContext: TaskAttemptContext): Unit = {} override def commitTask(taskContext: TaskAttemptContext): Unit = {} override def needsTaskCommit(taskContext: TaskAttemptContext): Boolean = true @@ -46,13 +62,11 @@ private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: T val footers = ParquetFileReader.readAllFootersInParallel(configuration, outputStatus) try { ParquetFileWriter.writeMetadataFile(configuration, outputPath, footers) - } catch { - case e: Exception => { - LOG.warn("could not write summary file for " + outputPath, e) - val metadataPath = new Path(outputPath, ParquetFileWriter.PARQUET_METADATA_FILE) - if (fileSystem.exists(metadataPath)) { - fileSystem.delete(metadataPath, true) - } + } catch { case e: Exception => + LOG.warn("could not write summary file for " + outputPath, e) + val metadataPath = new Path(outputPath, ParquetFileWriter.PARQUET_METADATA_FILE) + if (fileSystem.exists(metadataPath)) { + fileSystem.delete(metadataPath, true) } } } catch { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index e049d54bf55dc..1d353bd8e1114 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -178,11 +178,11 @@ private[sql] class ParquetRelation2( val committerClass = conf.getClass( - "spark.sql.parquet.output.committer.class", + SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key, classOf[ParquetOutputCommitter], classOf[ParquetOutputCommitter]) - if (conf.get("spark.sql.parquet.output.committer.class") == null) { + if (conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key) == null) { logInfo("Using default output committer for Parquet: " + classOf[ParquetOutputCommitter].getCanonicalName) } else { From 0401cbaa8ee51c71f43604f338b65022a479da0a Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 23 Jun 2015 17:46:29 -0700 Subject: [PATCH 12/17] [SPARK-7157][SQL] add sampleBy to DataFrame Add `sampleBy` to DataFrame. rxin Author: Xiangrui Meng Closes #6769 from mengxr/SPARK-7157 and squashes the following commits: 991f26f [Xiangrui Meng] fix seed 4a14834 [Xiangrui Meng] move sampleBy to stat 832f7cc [Xiangrui Meng] add sampleBy to DataFrame --- python/pyspark/sql/dataframe.py | 40 +++++++++++++++++++ .../spark/sql/DataFrameStatFunctions.scala | 24 +++++++++++ .../apache/spark/sql/DataFrameStatSuite.scala | 12 +++++- 3 files changed, 74 insertions(+), 2 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 152b87351db31..213338dfe58a4 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -448,6 +448,41 @@ def sample(self, withReplacement, fraction, seed=None): rdd = self._jdf.sample(withReplacement, fraction, long(seed)) return DataFrame(rdd, self.sql_ctx) + @since(1.5) + def sampleBy(self, col, fractions, seed=None): + """ + Returns a stratified sample without replacement based on the + fraction given on each stratum. + + :param col: column that defines strata + :param fractions: + sampling fraction for each stratum. If a stratum is not + specified, we treat its fraction as zero. + :param seed: random seed + :return: a new DataFrame that represents the stratified sample + + >>> from pyspark.sql.functions import col + >>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key")) + >>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0) + >>> sampled.groupBy("key").count().orderBy("key").show() + +---+-----+ + |key|count| + +---+-----+ + | 0| 5| + | 1| 8| + +---+-----+ + """ + if not isinstance(col, str): + raise ValueError("col must be a string, but got %r" % type(col)) + if not isinstance(fractions, dict): + raise ValueError("fractions must be a dict but got %r" % type(fractions)) + for k, v in fractions.items(): + if not isinstance(k, (float, int, long, basestring)): + raise ValueError("key must be float, int, long, or string, but got %r" % type(k)) + fractions[k] = float(v) + seed = seed if seed is not None else random.randint(0, sys.maxsize) + return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx) + @since(1.4) def randomSplit(self, weights, seed=None): """Randomly splits this :class:`DataFrame` with the provided weights. @@ -1322,6 +1357,11 @@ def freqItems(self, cols, support=None): freqItems.__doc__ = DataFrame.freqItems.__doc__ + def sampleBy(self, col, fractions, seed=None): + return self.df.sampleBy(col, fractions, seed) + + sampleBy.__doc__ = DataFrame.sampleBy.__doc__ + def _test(): import doctest 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 edb9ed7bba56a..955d28771b4df 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 @@ -17,6 +17,8 @@ package org.apache.spark.sql +import java.util.UUID + import org.apache.spark.annotation.Experimental import org.apache.spark.sql.execution.stat._ @@ -163,4 +165,26 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { def freqItems(cols: Seq[String]): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, 0.01) } + + /** + * Returns a stratified sample without replacement based on the fraction given on each stratum. + * @param col column that defines strata + * @param fractions sampling fraction for each stratum. If a stratum is not specified, we treat + * its fraction as zero. + * @param seed random seed + * @return a new [[DataFrame]] that represents the stratified sample + * + * @since 1.5.0 + */ + def sampleBy(col: String, fractions: Map[Any, Double], seed: Long): DataFrame = { + require(fractions.values.forall(p => p >= 0.0 && p <= 1.0), + s"Fractions must be in [0, 1], but got $fractions.") + import org.apache.spark.sql.functions.rand + val c = Column(col) + val r = rand(seed).as("rand_" + UUID.randomUUID().toString.take(8)) + val expr = fractions.toSeq.map { case (k, v) => + (c === k) && (r < v) + }.reduce(_ || _) || false + df.filter(expr) + } } 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 0d3ff899dad72..3dd46889127ff 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 @@ -19,9 +19,9 @@ package org.apache.spark.sql import org.scalatest.Matchers._ -import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.functions.col -class DataFrameStatSuite extends SparkFunSuite { +class DataFrameStatSuite extends QueryTest { private val sqlCtx = org.apache.spark.sql.test.TestSQLContext import sqlCtx.implicits._ @@ -98,4 +98,12 @@ class DataFrameStatSuite extends SparkFunSuite { val items2 = singleColResults.collect().head items2.getSeq[Double](0) should contain (-1.0) } + + test("sampleBy") { + val df = sqlCtx.range(0, 100).select((col("id") % 3).as("key")) + val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L) + checkAnswer( + sampled.groupBy("key").count().orderBy("key"), + Seq(Row(0, 4), Row(1, 9))) + } } From a458efc66c31dc281af379b914bfa2b077ca6635 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 23 Jun 2015 19:30:25 -0700 Subject: [PATCH 13/17] Revert "[SPARK-7157][SQL] add sampleBy to DataFrame" This reverts commit 0401cbaa8ee51c71f43604f338b65022a479da0a. The new test case on Jenkins is failing. --- python/pyspark/sql/dataframe.py | 40 ------------------- .../spark/sql/DataFrameStatFunctions.scala | 24 ----------- .../apache/spark/sql/DataFrameStatSuite.scala | 12 +----- 3 files changed, 2 insertions(+), 74 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 213338dfe58a4..152b87351db31 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -448,41 +448,6 @@ def sample(self, withReplacement, fraction, seed=None): rdd = self._jdf.sample(withReplacement, fraction, long(seed)) return DataFrame(rdd, self.sql_ctx) - @since(1.5) - def sampleBy(self, col, fractions, seed=None): - """ - Returns a stratified sample without replacement based on the - fraction given on each stratum. - - :param col: column that defines strata - :param fractions: - sampling fraction for each stratum. If a stratum is not - specified, we treat its fraction as zero. - :param seed: random seed - :return: a new DataFrame that represents the stratified sample - - >>> from pyspark.sql.functions import col - >>> dataset = sqlContext.range(0, 100).select((col("id") % 3).alias("key")) - >>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0) - >>> sampled.groupBy("key").count().orderBy("key").show() - +---+-----+ - |key|count| - +---+-----+ - | 0| 5| - | 1| 8| - +---+-----+ - """ - if not isinstance(col, str): - raise ValueError("col must be a string, but got %r" % type(col)) - if not isinstance(fractions, dict): - raise ValueError("fractions must be a dict but got %r" % type(fractions)) - for k, v in fractions.items(): - if not isinstance(k, (float, int, long, basestring)): - raise ValueError("key must be float, int, long, or string, but got %r" % type(k)) - fractions[k] = float(v) - seed = seed if seed is not None else random.randint(0, sys.maxsize) - return DataFrame(self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sql_ctx) - @since(1.4) def randomSplit(self, weights, seed=None): """Randomly splits this :class:`DataFrame` with the provided weights. @@ -1357,11 +1322,6 @@ def freqItems(self, cols, support=None): freqItems.__doc__ = DataFrame.freqItems.__doc__ - def sampleBy(self, col, fractions, seed=None): - return self.df.sampleBy(col, fractions, seed) - - sampleBy.__doc__ = DataFrame.sampleBy.__doc__ - def _test(): import doctest 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 955d28771b4df..edb9ed7bba56a 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 @@ -17,8 +17,6 @@ package org.apache.spark.sql -import java.util.UUID - import org.apache.spark.annotation.Experimental import org.apache.spark.sql.execution.stat._ @@ -165,26 +163,4 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { def freqItems(cols: Seq[String]): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, 0.01) } - - /** - * Returns a stratified sample without replacement based on the fraction given on each stratum. - * @param col column that defines strata - * @param fractions sampling fraction for each stratum. If a stratum is not specified, we treat - * its fraction as zero. - * @param seed random seed - * @return a new [[DataFrame]] that represents the stratified sample - * - * @since 1.5.0 - */ - def sampleBy(col: String, fractions: Map[Any, Double], seed: Long): DataFrame = { - require(fractions.values.forall(p => p >= 0.0 && p <= 1.0), - s"Fractions must be in [0, 1], but got $fractions.") - import org.apache.spark.sql.functions.rand - val c = Column(col) - val r = rand(seed).as("rand_" + UUID.randomUUID().toString.take(8)) - val expr = fractions.toSeq.map { case (k, v) => - (c === k) && (r < v) - }.reduce(_ || _) || false - df.filter(expr) - } } 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 3dd46889127ff..0d3ff899dad72 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 @@ -19,9 +19,9 @@ package org.apache.spark.sql import org.scalatest.Matchers._ -import org.apache.spark.sql.functions.col +import org.apache.spark.SparkFunSuite -class DataFrameStatSuite extends QueryTest { +class DataFrameStatSuite extends SparkFunSuite { private val sqlCtx = org.apache.spark.sql.test.TestSQLContext import sqlCtx.implicits._ @@ -98,12 +98,4 @@ class DataFrameStatSuite extends QueryTest { val items2 = singleColResults.collect().head items2.getSeq[Double](0) should contain (-1.0) } - - test("sampleBy") { - val df = sqlCtx.range(0, 100).select((col("id") % 3).as("key")) - val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L) - checkAnswer( - sampled.groupBy("key").count().orderBy("key"), - Seq(Row(0, 4), Row(1, 9))) - } } From 50c3a86f42d7dfd1acbda65c1e5afbd3db1406df Mon Sep 17 00:00:00 2001 From: Eric Liang Date: Tue, 23 Jun 2015 22:27:17 -0700 Subject: [PATCH 14/17] [SPARK-6749] [SQL] Make metastore client robust to underlying socket connection loss This works around a bug in the underlying RetryingMetaStoreClient (HIVE-10384) by refreshing the metastore client on thrift exceptions. We attempt to emulate the proper hive behavior by retrying only as configured by hiveconf. Author: Eric Liang Closes #6912 from ericl/spark-6749 and squashes the following commits: 2d54b55 [Eric Liang] use conf from state 0e3a74e [Eric Liang] use shim properly 980b3e5 [Eric Liang] Fix conf parsing hive 0.14 conf. 92459b6 [Eric Liang] Work around RetryingMetaStoreClient bug --- .../spark/sql/hive/client/ClientWrapper.scala | 55 ++++++++++++++++++- .../spark/sql/hive/client/HiveShim.scala | 19 +++++++ 2 files changed, 72 insertions(+), 2 deletions(-) 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 index 42c2d4c98ffb2..2f771d76793e5 100644 --- 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 @@ -20,6 +20,7 @@ package org.apache.spark.sql.hive.client import java.io.{BufferedReader, InputStreamReader, File, PrintStream} import java.net.URI import java.util.{ArrayList => JArrayList, Map => JMap, List => JList, Set => JSet} +import javax.annotation.concurrent.GuardedBy import scala.collection.JavaConversions._ import scala.language.reflectiveCalls @@ -136,12 +137,62 @@ private[hive] class ClientWrapper( // TODO: should be a def?s // When we create this val client, the HiveConf of it (conf) is the one associated with state. - private val client = Hive.get(conf) + @GuardedBy("this") + private var client = Hive.get(conf) + + // We use hive's conf for compatibility. + private val retryLimit = conf.getIntVar(HiveConf.ConfVars.METASTORETHRIFTFAILURERETRIES) + private val retryDelayMillis = shim.getMetastoreClientConnectRetryDelayMillis(conf) + + /** + * Runs `f` with multiple retries in case the hive metastore is temporarily unreachable. + */ + private def retryLocked[A](f: => A): A = synchronized { + // Hive sometimes retries internally, so set a deadline to avoid compounding delays. + val deadline = System.nanoTime + (retryLimit * retryDelayMillis * 1e6).toLong + var numTries = 0 + var caughtException: Exception = null + do { + numTries += 1 + try { + return f + } catch { + case e: Exception if causedByThrift(e) => + caughtException = e + logWarning( + "HiveClientWrapper got thrift exception, destroying client and retrying " + + s"(${retryLimit - numTries} tries remaining)", e) + Thread.sleep(retryDelayMillis) + try { + client = Hive.get(state.getConf, true) + } catch { + case e: Exception if causedByThrift(e) => + logWarning("Failed to refresh hive client, will retry.", e) + } + } + } while (numTries <= retryLimit && System.nanoTime < deadline) + if (System.nanoTime > deadline) { + logWarning("Deadline exceeded") + } + throw caughtException + } + + private def causedByThrift(e: Throwable): Boolean = { + var target = e + while (target != null) { + val msg = target.getMessage() + if (msg != null && msg.matches("(?s).*(TApplication|TProtocol|TTransport)Exception.*")) { + return true + } + target = target.getCause() + } + false + } /** * Runs `f` with ThreadLocal session state and classloaders configured for this version of hive. */ - private def withHiveState[A](f: => A): A = synchronized { + private def withHiveState[A](f: => A): A = retryLocked { val original = Thread.currentThread().getContextClassLoader // Set the thread local metastore client to the client associated with this ClientWrapper. Hive.set(client) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala index 5ae2dbb50d86b..e7c1779f80ce6 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala @@ -21,6 +21,7 @@ import java.lang.{Boolean => JBoolean, Integer => JInteger} import java.lang.reflect.{Method, Modifier} import java.net.URI import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, Set => JSet} +import java.util.concurrent.TimeUnit import scala.collection.JavaConversions._ @@ -64,6 +65,8 @@ private[client] sealed abstract class Shim { def getDriverResults(driver: Driver): Seq[String] + def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long + def loadPartition( hive: Hive, loadPath: Path, @@ -192,6 +195,10 @@ private[client] class Shim_v0_12 extends Shim { res.toSeq } + override def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long = { + conf.getIntVar(HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY) * 1000 + } + override def loadPartition( hive: Hive, loadPath: Path, @@ -321,6 +328,12 @@ private[client] class Shim_v0_14 extends Shim_v0_13 { JBoolean.TYPE, JBoolean.TYPE, JBoolean.TYPE) + private lazy val getTimeVarMethod = + findMethod( + classOf[HiveConf], + "getTimeVar", + classOf[HiveConf.ConfVars], + classOf[TimeUnit]) override def loadPartition( hive: Hive, @@ -359,4 +372,10 @@ private[client] class Shim_v0_14 extends Shim_v0_13 { numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean, JBoolean.FALSE) } + override def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long = { + getTimeVarMethod.invoke( + conf, + HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY, + TimeUnit.MILLISECONDS).asInstanceOf[Long] + } } From 13ae806b255cfb2bd5470b599a95c87a2cd5e978 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Tue, 23 Jun 2015 23:03:59 -0700 Subject: [PATCH 15/17] [HOTFIX] [BUILD] Fix MiMa checks in master branch; enable MiMa for launcher project This commit changes the MiMa tests to test against the released 1.4.0 artifacts rather than 1.4.0-rc4; this change is necessary to fix a Jenkins build break since it seems that the RC4 snapshot is no longer available via Maven. I also enabled MiMa checks for the `launcher` subproject, which we should have done right after 1.4.0 was released. Author: Josh Rosen Closes #6974 from JoshRosen/mima-hotfix and squashes the following commits: 4b4175a [Josh Rosen] [HOTFIX] [BUILD] Fix MiMa checks in master branch; enable MiMa for launcher project --- project/MimaBuild.scala | 3 +-- project/SparkBuild.scala | 3 +-- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala index 5812b72f0aa78..f16bf989f200b 100644 --- a/project/MimaBuild.scala +++ b/project/MimaBuild.scala @@ -91,8 +91,7 @@ object MimaBuild { def mimaSettings(sparkHome: File, projectRef: ProjectRef) = { val organization = "org.apache.spark" - // TODO: Change this once Spark 1.4.0 is released - val previousSparkVersion = "1.4.0-rc4" + val previousSparkVersion = "1.4.0" val fullId = "spark-" + projectRef.project + "_2.10" mimaDefaultSettings ++ Seq(previousArtifact := Some(organization % fullId % previousSparkVersion), diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index e01720296fed0..f5f1c9a1a247a 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -166,9 +166,8 @@ object SparkBuild extends PomBuild { /* Enable tests settings for all projects except examples, assembly and tools */ (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings)) - // TODO: remove launcher from this list after 1.4.0 allProjects.filterNot(x => Seq(spark, hive, hiveThriftServer, catalyst, repl, - networkCommon, networkShuffle, networkYarn, launcher, unsafe).contains(x)).foreach { + networkCommon, networkShuffle, networkYarn, unsafe).contains(x)).foreach { x => enable(MimaBuild.mimaSettings(sparkHome, x))(x) } From 09fcf96b8f881988a4bc7fe26a3f6ed12dfb6adb Mon Sep 17 00:00:00 2001 From: Wenchen Fan Date: Tue, 23 Jun 2015 23:11:42 -0700 Subject: [PATCH 16/17] [SPARK-8371] [SQL] improve unit test for MaxOf and MinOf and fix bugs a follow up of https://github.com/apache/spark/pull/6813 Author: Wenchen Fan Closes #6825 from cloud-fan/cg and squashes the following commits: 43170cc [Wenchen Fan] fix bugs in code gen --- .../expressions/codegen/CodeGenerator.scala | 4 +- .../ArithmeticExpressionSuite.scala | 46 +++++++++++++------ 2 files changed, 34 insertions(+), 16 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index bd5475d2066fc..47c5455435ec6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -175,8 +175,10 @@ class CodeGenContext { * Generate code for compare expression in Java */ def genComp(dataType: DataType, c1: String, c2: String): String = dataType match { + // java boolean doesn't support > or < operator + case BooleanType => s"($c1 == $c2 ? 0 : ($c1 ? 1 : -1))" // use c1 - c2 may overflow - case dt: DataType if isPrimitiveType(dt) => s"(int)($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)" + case dt: DataType if isPrimitiveType(dt) => s"($c1 > $c2 ? 1 : $c1 < $c2 ? -1 : 0)" case BinaryType => s"org.apache.spark.sql.catalyst.util.TypeUtils.compareBinary($c1, $c2)" case other => s"$c1.compare($c2)" } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala index 4bbbbe6c7f091..6c93698f8017b 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.SparkFunSuite import org.apache.spark.sql.catalyst.dsl.expressions._ -import org.apache.spark.sql.types.{Decimal, DoubleType, IntegerType} +import org.apache.spark.sql.types.Decimal class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper { @@ -123,23 +123,39 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper } } - test("MaxOf") { - checkEvaluation(MaxOf(1, 2), 2) - checkEvaluation(MaxOf(2, 1), 2) - checkEvaluation(MaxOf(1L, 2L), 2L) - checkEvaluation(MaxOf(2L, 1L), 2L) + test("MaxOf basic") { + testNumericDataTypes { convert => + val small = Literal(convert(1)) + val large = Literal(convert(2)) + checkEvaluation(MaxOf(small, large), convert(2)) + checkEvaluation(MaxOf(large, small), convert(2)) + checkEvaluation(MaxOf(Literal.create(null, small.dataType), large), convert(2)) + checkEvaluation(MaxOf(large, Literal.create(null, small.dataType)), convert(2)) + } + } - checkEvaluation(MaxOf(Literal.create(null, IntegerType), 2), 2) - checkEvaluation(MaxOf(2, Literal.create(null, IntegerType)), 2) + test("MaxOf for atomic type") { + checkEvaluation(MaxOf(true, false), true) + checkEvaluation(MaxOf("abc", "bcd"), "bcd") + checkEvaluation(MaxOf(Array(1.toByte, 2.toByte), Array(1.toByte, 3.toByte)), + Array(1.toByte, 3.toByte)) } - test("MinOf") { - checkEvaluation(MinOf(1, 2), 1) - checkEvaluation(MinOf(2, 1), 1) - checkEvaluation(MinOf(1L, 2L), 1L) - checkEvaluation(MinOf(2L, 1L), 1L) + test("MinOf basic") { + testNumericDataTypes { convert => + val small = Literal(convert(1)) + val large = Literal(convert(2)) + checkEvaluation(MinOf(small, large), convert(1)) + checkEvaluation(MinOf(large, small), convert(1)) + checkEvaluation(MinOf(Literal.create(null, small.dataType), large), convert(2)) + checkEvaluation(MinOf(small, Literal.create(null, small.dataType)), convert(1)) + } + } - checkEvaluation(MinOf(Literal.create(null, IntegerType), 1), 1) - checkEvaluation(MinOf(1, Literal.create(null, IntegerType)), 1) + test("MinOf for atomic type") { + checkEvaluation(MinOf(true, false), false) + checkEvaluation(MinOf("abc", "bcd"), "abc") + checkEvaluation(MinOf(Array(1.toByte, 2.toByte), Array(1.toByte, 3.toByte)), + Array(1.toByte, 2.toByte)) } } From cc465fd92482737c21971d82e30d4cf247acf932 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Wed, 24 Jun 2015 02:17:12 -0700 Subject: [PATCH 17/17] [SPARK-8138] [SQL] Improves error message when conflicting partition columns are found This PR improves the error message shown when conflicting partition column names are detected. This can be particularly annoying and confusing when there are a large number of partitions while a handful of them happened to contain unexpected temporary file(s). Now all suspicious directories are listed as below: ``` java.lang.AssertionError: assertion failed: Conflicting partition column names detected: Partition column name list #0: b, c, d Partition column name list #1: b, c Partition column name list #2: b For partitioned table directories, data files should only live in leaf directories. Please check the following directories for unexpected files: file:/tmp/foo/b=0 file:/tmp/foo/b=1 file:/tmp/foo/b=1/c=1 file:/tmp/foo/b=0/c=0 ``` Author: Cheng Lian Closes #6610 from liancheng/part-errmsg and squashes the following commits: 7d05f2c [Cheng Lian] Fixes Scala style issue a149250 [Cheng Lian] Adds test case for the error message 6b74dd8 [Cheng Lian] Also lists suspicious non-leaf partition directories a935eb8 [Cheng Lian] Improves error message when conflicting partition columns are found --- .../spark/sql/sources/PartitioningUtils.scala | 47 +++++++++++++++---- .../ParquetPartitionDiscoverySuite.scala | 45 ++++++++++++++++++ 2 files changed, 82 insertions(+), 10 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala index c6f535dde7676..8b2a45d8e970a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/PartitioningUtils.scala @@ -84,7 +84,7 @@ private[sql] object PartitioningUtils { } else { // This dataset is partitioned. We need to check whether all partitions have the same // partition columns and resolve potential type conflicts. - val resolvedPartitionValues = resolvePartitions(pathsWithPartitionValues.map(_._2)) + val resolvedPartitionValues = resolvePartitions(pathsWithPartitionValues) // Creates the StructType which represents the partition columns. val fields = { @@ -181,19 +181,18 @@ private[sql] object PartitioningUtils { * StringType * }}} */ - private[sql] def resolvePartitions(values: Seq[PartitionValues]): Seq[PartitionValues] = { - // Column names of all partitions must match - val distinctPartitionsColNames = values.map(_.columnNames).distinct - - if (distinctPartitionsColNames.isEmpty) { + private[sql] def resolvePartitions( + pathsWithPartitionValues: Seq[(Path, PartitionValues)]): Seq[PartitionValues] = { + if (pathsWithPartitionValues.isEmpty) { Seq.empty } else { - assert(distinctPartitionsColNames.size == 1, { - val list = distinctPartitionsColNames.mkString("\t", "\n\t", "") - s"Conflicting partition column names detected:\n$list" - }) + val distinctPartColNames = pathsWithPartitionValues.map(_._2.columnNames).distinct + assert( + distinctPartColNames.size == 1, + listConflictingPartitionColumns(pathsWithPartitionValues)) // Resolves possible type conflicts for each column + val values = pathsWithPartitionValues.map(_._2) val columnCount = values.head.columnNames.size val resolvedValues = (0 until columnCount).map { i => resolveTypeConflicts(values.map(_.literals(i))) @@ -206,6 +205,34 @@ private[sql] object PartitioningUtils { } } + private[sql] def listConflictingPartitionColumns( + pathWithPartitionValues: Seq[(Path, PartitionValues)]): String = { + val distinctPartColNames = pathWithPartitionValues.map(_._2.columnNames).distinct + + def groupByKey[K, V](seq: Seq[(K, V)]): Map[K, Iterable[V]] = + seq.groupBy { case (key, _) => key }.mapValues(_.map { case (_, value) => value }) + + val partColNamesToPaths = groupByKey(pathWithPartitionValues.map { + case (path, partValues) => partValues.columnNames -> path + }) + + val distinctPartColLists = distinctPartColNames.map(_.mkString(", ")).zipWithIndex.map { + case (names, index) => + s"Partition column name list #$index: $names" + } + + // Lists out those non-leaf partition directories that also contain files + val suspiciousPaths = distinctPartColNames.sortBy(_.length).flatMap(partColNamesToPaths) + + s"Conflicting partition column names detected:\n" + + distinctPartColLists.mkString("\n\t", "\n\t", "\n\n") + + "For partitioned table directories, data files should only live in leaf directories.\n" + + "And directories at the same level should have the same partition column name.\n" + + "Please check the following directories for unexpected files or " + + "inconsistent partition column names:\n" + + suspiciousPaths.map("\t" + _).mkString("\n", "\n", "") + } + /** * Converts a string to a [[Literal]] with automatic type inference. Currently only supports * [[IntegerType]], [[LongType]], [[DoubleType]], [[DecimalType.Unlimited]], and diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala index 01df189d1f3be..d0ebb11b063f0 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala @@ -538,4 +538,49 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { checkAnswer(sqlContext.read.format("parquet").load(dir.getCanonicalPath), df) } } + + test("listConflictingPartitionColumns") { + def makeExpectedMessage(colNameLists: Seq[String], paths: Seq[String]): String = { + val conflictingColNameLists = colNameLists.zipWithIndex.map { case (list, index) => + s"\tPartition column name list #$index: $list" + }.mkString("\n", "\n", "\n") + + // scalastyle:off + s"""Conflicting partition column names detected: + |$conflictingColNameLists + |For partitioned table directories, data files should only live in leaf directories. + |And directories at the same level should have the same partition column name. + |Please check the following directories for unexpected files or inconsistent partition column names: + |${paths.map("\t" + _).mkString("\n", "\n", "")} + """.stripMargin.trim + // scalastyle:on + } + + assert( + listConflictingPartitionColumns( + Seq( + (new Path("file:/tmp/foo/a=1"), PartitionValues(Seq("a"), Seq(Literal(1)))), + (new Path("file:/tmp/foo/b=1"), PartitionValues(Seq("b"), Seq(Literal(1)))))).trim === + makeExpectedMessage(Seq("a", "b"), Seq("file:/tmp/foo/a=1", "file:/tmp/foo/b=1"))) + + assert( + listConflictingPartitionColumns( + Seq( + (new Path("file:/tmp/foo/a=1/_temporary"), PartitionValues(Seq("a"), Seq(Literal(1)))), + (new Path("file:/tmp/foo/a=1"), PartitionValues(Seq("a"), Seq(Literal(1)))))).trim === + makeExpectedMessage( + Seq("a"), + Seq("file:/tmp/foo/a=1/_temporary", "file:/tmp/foo/a=1"))) + + assert( + listConflictingPartitionColumns( + Seq( + (new Path("file:/tmp/foo/a=1"), + PartitionValues(Seq("a"), Seq(Literal(1)))), + (new Path("file:/tmp/foo/a=1/b=foo"), + PartitionValues(Seq("a", "b"), Seq(Literal(1), Literal("foo")))))).trim === + makeExpectedMessage( + Seq("a", "a, b"), + Seq("file:/tmp/foo/a=1", "file:/tmp/foo/a=1/b=foo"))) + } }