/
SQLConf.scala
1583 lines (1274 loc) · 66.7 KB
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SQLConf.scala
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.internal
import java.util.{Locale, NoSuchElementException, Properties, TimeZone}
import java.util.concurrent.TimeUnit
import java.util.concurrent.atomic.AtomicReference
import scala.collection.JavaConverters._
import scala.collection.immutable
import scala.util.matching.Regex
import org.apache.hadoop.fs.Path
import org.apache.spark.{SparkContext, SparkEnv}
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.network.util.ByteUnit
import org.apache.spark.sql.catalyst.analysis.Resolver
import org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator
import org.apache.spark.util.Utils
////////////////////////////////////////////////////////////////////////////////////////////////////
// This file defines the configuration options for Spark SQL.
////////////////////////////////////////////////////////////////////////////////////////////////////
object SQLConf {
private val sqlConfEntries = java.util.Collections.synchronizedMap(
new java.util.HashMap[String, ConfigEntry[_]]())
val staticConfKeys: java.util.Set[String] =
java.util.Collections.synchronizedSet(new java.util.HashSet[String]())
private def register(entry: ConfigEntry[_]): Unit = sqlConfEntries.synchronized {
require(!sqlConfEntries.containsKey(entry.key),
s"Duplicate SQLConfigEntry. ${entry.key} has been registered")
sqlConfEntries.put(entry.key, entry)
}
// For testing only
private[sql] def unregister(entry: ConfigEntry[_]): Unit = sqlConfEntries.synchronized {
sqlConfEntries.remove(entry.key)
}
def buildConf(key: String): ConfigBuilder = ConfigBuilder(key).onCreate(register)
def buildStaticConf(key: String): ConfigBuilder = {
ConfigBuilder(key).onCreate { entry =>
staticConfKeys.add(entry.key)
SQLConf.register(entry)
}
}
/**
* Default config. Only used when there is no active SparkSession for the thread.
* See [[get]] for more information.
*/
private lazy val fallbackConf = new ThreadLocal[SQLConf] {
override def initialValue: SQLConf = new SQLConf
}
/** See [[get]] for more information. */
def getFallbackConf: SQLConf = fallbackConf.get()
/**
* Defines a getter that returns the SQLConf within scope.
* See [[get]] for more information.
*/
private val confGetter = new AtomicReference[() => SQLConf](() => fallbackConf.get())
/**
* Sets the active config object within the current scope.
* See [[get]] for more information.
*/
def setSQLConfGetter(getter: () => SQLConf): Unit = {
confGetter.set(getter)
}
/**
* Returns the active config object within the current scope. If there is an active SparkSession,
* the proper SQLConf associated with the thread's session is used.
*
* The way this works is a little bit convoluted, due to the fact that config was added initially
* only for physical plans (and as a result not in sql/catalyst module).
*
* The first time a SparkSession is instantiated, we set the [[confGetter]] to return the
* active SparkSession's config. If there is no active SparkSession, it returns using the thread
* local [[fallbackConf]]. The reason [[fallbackConf]] is a thread local (rather than just a conf)
* is to support setting different config options for different threads so we can potentially
* run tests in parallel. At the time this feature was implemented, this was a no-op since we
* run unit tests (that does not involve SparkSession) in serial order.
*/
def get: SQLConf = confGetter.get()()
val OPTIMIZER_MAX_ITERATIONS = buildConf("spark.sql.optimizer.maxIterations")
.internal()
.doc("The max number of iterations the optimizer and analyzer runs.")
.intConf
.createWithDefault(100)
val OPTIMIZER_INSET_CONVERSION_THRESHOLD =
buildConf("spark.sql.optimizer.inSetConversionThreshold")
.internal()
.doc("The threshold of set size for InSet conversion.")
.intConf
.createWithDefault(10)
val COMPRESS_CACHED = buildConf("spark.sql.inMemoryColumnarStorage.compressed")
.internal()
.doc("When set to true Spark SQL will automatically select a compression codec for each " +
"column based on statistics of the data.")
.booleanConf
.createWithDefault(true)
val COLUMN_BATCH_SIZE = buildConf("spark.sql.inMemoryColumnarStorage.batchSize")
.internal()
.doc("Controls the size of batches for columnar caching. Larger batch sizes can improve " +
"memory utilization and compression, but risk OOMs when caching data.")
.intConf
.createWithDefault(10000)
val IN_MEMORY_PARTITION_PRUNING =
buildConf("spark.sql.inMemoryColumnarStorage.partitionPruning")
.internal()
.doc("When true, enable partition pruning for in-memory columnar tables.")
.booleanConf
.createWithDefault(true)
val COLUMN_VECTOR_OFFHEAP_ENABLED =
buildConf("spark.sql.columnVector.offheap.enabled")
.internal()
.doc("When true, use OffHeapColumnVector in ColumnarBatch.")
.booleanConf
.createWithDefault(false)
val PREFER_SORTMERGEJOIN = buildConf("spark.sql.join.preferSortMergeJoin")
.internal()
.doc("When true, prefer sort merge join over shuffle hash join.")
.booleanConf
.createWithDefault(true)
val RADIX_SORT_ENABLED = buildConf("spark.sql.sort.enableRadixSort")
.internal()
.doc("When true, enable use of radix sort when possible. Radix sort is much faster but " +
"requires additional memory to be reserved up-front. The memory overhead may be " +
"significant when sorting very small rows (up to 50% more in this case).")
.booleanConf
.createWithDefault(true)
val AUTO_BROADCASTJOIN_THRESHOLD = buildConf("spark.sql.autoBroadcastJoinThreshold")
.doc("Configures the maximum size in bytes for a table that will be broadcast to all worker " +
"nodes when performing a join. By setting this value to -1 broadcasting can be disabled. " +
"Note that currently statistics are only supported for Hive Metastore tables where the " +
"command <code>ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan</code> has been " +
"run, and file-based data source tables where the statistics are computed directly on " +
"the files of data.")
.longConf
.createWithDefault(10L * 1024 * 1024)
val LIMIT_SCALE_UP_FACTOR = buildConf("spark.sql.limit.scaleUpFactor")
.internal()
.doc("Minimal increase rate in number of partitions between attempts when executing a take " +
"on a query. Higher values lead to more partitions read. Lower values might lead to " +
"longer execution times as more jobs will be run")
.intConf
.createWithDefault(4)
val ADVANCED_PARTITION_PREDICATE_PUSHDOWN =
buildConf("spark.sql.hive.advancedPartitionPredicatePushdown.enabled")
.internal()
.doc("When true, advanced partition predicate pushdown into Hive metastore is enabled.")
.booleanConf
.createWithDefault(true)
val ENABLE_FALL_BACK_TO_HDFS_FOR_STATS =
buildConf("spark.sql.statistics.fallBackToHdfs")
.doc("If the table statistics are not available from table metadata enable fall back to hdfs." +
" This is useful in determining if a table is small enough to use auto broadcast joins.")
.booleanConf
.createWithDefault(false)
val DEFAULT_SIZE_IN_BYTES = buildConf("spark.sql.defaultSizeInBytes")
.internal()
.doc("The default table size used in query planning. By default, it is set to Long.MaxValue " +
"which is larger than `spark.sql.autoBroadcastJoinThreshold` to be more conservative. " +
"That is to say by default the optimizer will not choose to broadcast a table unless it " +
"knows for sure its size is small enough.")
.longConf
.createWithDefault(Long.MaxValue)
val SHUFFLE_PARTITIONS = buildConf("spark.sql.shuffle.partitions")
.doc("The default number of partitions to use when shuffling data for joins or aggregations.")
.intConf
.createWithDefault(200)
val SHUFFLE_TARGET_POSTSHUFFLE_INPUT_SIZE =
buildConf("spark.sql.adaptive.shuffle.targetPostShuffleInputSize")
.doc("The target post-shuffle input size in bytes of a task.")
.bytesConf(ByteUnit.BYTE)
.createWithDefault(64 * 1024 * 1024)
val ADAPTIVE_EXECUTION_ENABLED = buildConf("spark.sql.adaptive.enabled")
.doc("When true, enable adaptive query execution.")
.booleanConf
.createWithDefault(false)
val SHUFFLE_MIN_NUM_POSTSHUFFLE_PARTITIONS =
buildConf("spark.sql.adaptive.minNumPostShufflePartitions")
.internal()
.doc("The advisory minimal number of post-shuffle partitions provided to " +
"ExchangeCoordinator. This setting is used in our test to make sure we " +
"have enough parallelism to expose issues that will not be exposed with a " +
"single partition. When the value is a non-positive value, this setting will " +
"not be provided to ExchangeCoordinator.")
.intConf
.createWithDefault(-1)
val SUBEXPRESSION_ELIMINATION_ENABLED =
buildConf("spark.sql.subexpressionElimination.enabled")
.internal()
.doc("When true, common subexpressions will be eliminated.")
.booleanConf
.createWithDefault(true)
val CASE_SENSITIVE = buildConf("spark.sql.caseSensitive")
.internal()
.doc("Whether the query analyzer should be case sensitive or not. " +
"Default to case insensitive. It is highly discouraged to turn on case sensitive mode.")
.booleanConf
.createWithDefault(false)
val CONSTRAINT_PROPAGATION_ENABLED = buildConf("spark.sql.constraintPropagation.enabled")
.internal()
.doc("When true, the query optimizer will infer and propagate data constraints in the query " +
"plan to optimize them. Constraint propagation can sometimes be computationally expensive" +
"for certain kinds of query plans (such as those with a large number of predicates and " +
"aliases) which might negatively impact overall runtime.")
.booleanConf
.createWithDefault(true)
val ESCAPED_STRING_LITERALS = buildConf("spark.sql.parser.escapedStringLiterals")
.internal()
.doc("When true, string literals (including regex patterns) remain escaped in our SQL " +
"parser. The default is false since Spark 2.0. Setting it to true can restore the behavior " +
"prior to Spark 2.0.")
.booleanConf
.createWithDefault(false)
val PARQUET_SCHEMA_MERGING_ENABLED = buildConf("spark.sql.parquet.mergeSchema")
.doc("When true, the Parquet data source merges schemas collected from all data files, " +
"otherwise the schema is picked from the summary file or a random data file " +
"if no summary file is available.")
.booleanConf
.createWithDefault(false)
val PARQUET_SCHEMA_RESPECT_SUMMARIES = buildConf("spark.sql.parquet.respectSummaryFiles")
.doc("When true, we make assumption that all part-files of Parquet are consistent with " +
"summary files and we will ignore them when merging schema. Otherwise, if this is " +
"false, which is the default, we will merge all part-files. This should be considered " +
"as expert-only option, and shouldn't be enabled before knowing what it means exactly.")
.booleanConf
.createWithDefault(false)
val PARQUET_BINARY_AS_STRING = buildConf("spark.sql.parquet.binaryAsString")
.doc("Some other Parquet-producing systems, in particular Impala and older versions of " +
"Spark SQL, do not differentiate between binary data and strings when writing out the " +
"Parquet schema. This flag tells Spark SQL to interpret binary data as a string to provide " +
"compatibility with these systems.")
.booleanConf
.createWithDefault(false)
val PARQUET_INT96_AS_TIMESTAMP = buildConf("spark.sql.parquet.int96AsTimestamp")
.doc("Some Parquet-producing systems, in particular Impala, store Timestamp into INT96. " +
"Spark would also store Timestamp as INT96 because we need to avoid precision lost of the " +
"nanoseconds field. This flag tells Spark SQL to interpret INT96 data as a timestamp to " +
"provide compatibility with these systems.")
.booleanConf
.createWithDefault(true)
val PARQUET_INT96_TIMESTAMP_CONVERSION = buildConf("spark.sql.parquet.int96TimestampConversion")
.doc("This controls whether timestamp adjustments should be applied to INT96 data when " +
"converting to timestamps, for data written by Impala. This is necessary because Impala " +
"stores INT96 data with a different timezone offset than Hive & Spark.")
.booleanConf
.createWithDefault(false)
object ParquetOutputTimestampType extends Enumeration {
val INT96, TIMESTAMP_MICROS, TIMESTAMP_MILLIS = Value
}
val PARQUET_OUTPUT_TIMESTAMP_TYPE = buildConf("spark.sql.parquet.outputTimestampType")
.doc("Sets which Parquet timestamp type to use when Spark writes data to Parquet files. " +
"INT96 is a non-standard but commonly used timestamp type in Parquet. TIMESTAMP_MICROS " +
"is a standard timestamp type in Parquet, which stores number of microseconds from the " +
"Unix epoch. TIMESTAMP_MILLIS is also standard, but with millisecond precision, which " +
"means Spark has to truncate the microsecond portion of its timestamp value.")
.stringConf
.transform(_.toUpperCase(Locale.ROOT))
.checkValues(ParquetOutputTimestampType.values.map(_.toString))
.createWithDefault(ParquetOutputTimestampType.INT96.toString)
val PARQUET_INT64_AS_TIMESTAMP_MILLIS = buildConf("spark.sql.parquet.int64AsTimestampMillis")
.doc(s"(Deprecated since Spark 2.3, please set ${PARQUET_OUTPUT_TIMESTAMP_TYPE.key}.) " +
"When true, timestamp values will be stored as INT64 with TIMESTAMP_MILLIS as the " +
"extended type. In this mode, the microsecond portion of the timestamp value will be" +
"truncated.")
.booleanConf
.createWithDefault(false)
val PARQUET_COMPRESSION = buildConf("spark.sql.parquet.compression.codec")
.doc("Sets the compression codec used when writing Parquet files. If either `compression` or " +
"`parquet.compression` is specified in the table-specific options/properties, the " +
"precedence would be `compression`, `parquet.compression`, " +
"`spark.sql.parquet.compression.codec`. Acceptable values include: none, uncompressed, " +
"snappy, gzip, lzo.")
.stringConf
.transform(_.toLowerCase(Locale.ROOT))
.checkValues(Set("none", "uncompressed", "snappy", "gzip", "lzo"))
.createWithDefault("snappy")
val PARQUET_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.parquet.filterPushdown")
.doc("Enables Parquet filter push-down optimization when set to true.")
.booleanConf
.createWithDefault(true)
val PARQUET_WRITE_LEGACY_FORMAT = buildConf("spark.sql.parquet.writeLegacyFormat")
.doc("Whether to be compatible with the legacy Parquet format adopted by Spark 1.4 and prior " +
"versions, when converting Parquet schema to Spark SQL schema and vice versa.")
.booleanConf
.createWithDefault(false)
val PARQUET_RECORD_FILTER_ENABLED = buildConf("spark.sql.parquet.recordLevelFilter.enabled")
.doc("If true, enables Parquet's native record-level filtering using the pushed down " +
"filters. This configuration only has an effect when 'spark.sql.parquet.filterPushdown' " +
"is enabled.")
.booleanConf
.createWithDefault(false)
val PARQUET_OUTPUT_COMMITTER_CLASS = buildConf("spark.sql.parquet.output.committer.class")
.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. If it is not, then metadata summaries" +
"will never be created, irrespective of the value of parquet.enable.summary-metadata")
.internal()
.stringConf
.createWithDefault("org.apache.parquet.hadoop.ParquetOutputCommitter")
val PARQUET_VECTORIZED_READER_ENABLED =
buildConf("spark.sql.parquet.enableVectorizedReader")
.doc("Enables vectorized parquet decoding.")
.booleanConf
.createWithDefault(true)
val ORC_COMPRESSION = buildConf("spark.sql.orc.compression.codec")
.doc("Sets the compression codec used when writing ORC files. If either `compression` or " +
"`orc.compress` is specified in the table-specific options/properties, the precedence " +
"would be `compression`, `orc.compress`, `spark.sql.orc.compression.codec`." +
"Acceptable values include: none, uncompressed, snappy, zlib, lzo.")
.stringConf
.transform(_.toLowerCase(Locale.ROOT))
.checkValues(Set("none", "uncompressed", "snappy", "zlib", "lzo"))
.createWithDefault("snappy")
val ORC_IMPLEMENTATION = buildConf("spark.sql.orc.impl")
.doc("When native, use the native version of ORC support instead of the ORC library in Hive " +
"1.2.1. It is 'hive' by default prior to Spark 2.3.")
.internal()
.stringConf
.checkValues(Set("hive", "native"))
.createWithDefault("native")
val ORC_VECTORIZED_READER_ENABLED = buildConf("spark.sql.orc.enableVectorizedReader")
.doc("Enables vectorized orc decoding.")
.booleanConf
.createWithDefault(true)
val ORC_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.orc.filterPushdown")
.doc("When true, enable filter pushdown for ORC files.")
.booleanConf
.createWithDefault(false)
val HIVE_VERIFY_PARTITION_PATH = buildConf("spark.sql.hive.verifyPartitionPath")
.doc("When true, check all the partition paths under the table\'s root directory " +
"when reading data stored in HDFS.")
.booleanConf
.createWithDefault(false)
val HIVE_METASTORE_PARTITION_PRUNING =
buildConf("spark.sql.hive.metastorePartitionPruning")
.doc("When true, some predicates will be pushed down into the Hive metastore so that " +
"unmatching partitions can be eliminated earlier. This only affects Hive tables " +
"not converted to filesource relations (see HiveUtils.CONVERT_METASTORE_PARQUET and " +
"HiveUtils.CONVERT_METASTORE_ORC for more information).")
.booleanConf
.createWithDefault(true)
val HIVE_MANAGE_FILESOURCE_PARTITIONS =
buildConf("spark.sql.hive.manageFilesourcePartitions")
.doc("When true, enable metastore partition management for file source tables as well. " +
"This includes both datasource and converted Hive tables. When partition management " +
"is enabled, datasource tables store partition in the Hive metastore, and use the " +
"metastore to prune partitions during query planning.")
.booleanConf
.createWithDefault(true)
val HIVE_FILESOURCE_PARTITION_FILE_CACHE_SIZE =
buildConf("spark.sql.hive.filesourcePartitionFileCacheSize")
.doc("When nonzero, enable caching of partition file metadata in memory. All tables share " +
"a cache that can use up to specified num bytes for file metadata. This conf only " +
"has an effect when hive filesource partition management is enabled.")
.longConf
.createWithDefault(250 * 1024 * 1024)
object HiveCaseSensitiveInferenceMode extends Enumeration {
val INFER_AND_SAVE, INFER_ONLY, NEVER_INFER = Value
}
val HIVE_CASE_SENSITIVE_INFERENCE = buildConf("spark.sql.hive.caseSensitiveInferenceMode")
.doc("Sets the action to take when a case-sensitive schema cannot be read from a Hive " +
"table's properties. Although Spark SQL itself is not case-sensitive, Hive compatible file " +
"formats such as Parquet are. Spark SQL must use a case-preserving schema when querying " +
"any table backed by files containing case-sensitive field names or queries may not return " +
"accurate results. Valid options include INFER_AND_SAVE (the default mode-- infer the " +
"case-sensitive schema from the underlying data files and write it back to the table " +
"properties), INFER_ONLY (infer the schema but don't attempt to write it to the table " +
"properties) and NEVER_INFER (fallback to using the case-insensitive metastore schema " +
"instead of inferring).")
.stringConf
.transform(_.toUpperCase(Locale.ROOT))
.checkValues(HiveCaseSensitiveInferenceMode.values.map(_.toString))
.createWithDefault(HiveCaseSensitiveInferenceMode.INFER_AND_SAVE.toString)
val OPTIMIZER_METADATA_ONLY = buildConf("spark.sql.optimizer.metadataOnly")
.doc("When true, enable the metadata-only query optimization that use the table's metadata " +
"to produce the partition columns instead of table scans. It applies when all the columns " +
"scanned are partition columns and the query has an aggregate operator that satisfies " +
"distinct semantics.")
.booleanConf
.createWithDefault(true)
val COLUMN_NAME_OF_CORRUPT_RECORD = buildConf("spark.sql.columnNameOfCorruptRecord")
.doc("The name of internal column for storing raw/un-parsed JSON and CSV records that fail " +
"to parse.")
.stringConf
.createWithDefault("_corrupt_record")
val BROADCAST_TIMEOUT = buildConf("spark.sql.broadcastTimeout")
.doc("Timeout in seconds for the broadcast wait time in broadcast joins.")
.timeConf(TimeUnit.SECONDS)
.createWithDefault(5 * 60)
// This is only used for the thriftserver
val THRIFTSERVER_POOL = buildConf("spark.sql.thriftserver.scheduler.pool")
.doc("Set a Fair Scheduler pool for a JDBC client session.")
.stringConf
.createOptional
val THRIFTSERVER_INCREMENTAL_COLLECT =
buildConf("spark.sql.thriftServer.incrementalCollect")
.internal()
.doc("When true, enable incremental collection for execution in Thrift Server.")
.booleanConf
.createWithDefault(false)
val THRIFTSERVER_UI_STATEMENT_LIMIT =
buildConf("spark.sql.thriftserver.ui.retainedStatements")
.doc("The number of SQL statements kept in the JDBC/ODBC web UI history.")
.intConf
.createWithDefault(200)
val THRIFTSERVER_UI_SESSION_LIMIT = buildConf("spark.sql.thriftserver.ui.retainedSessions")
.doc("The number of SQL client sessions kept in the JDBC/ODBC web UI history.")
.intConf
.createWithDefault(200)
// This is used to set the default data source
val DEFAULT_DATA_SOURCE_NAME = buildConf("spark.sql.sources.default")
.doc("The default data source to use in input/output.")
.stringConf
.createWithDefault("parquet")
val CONVERT_CTAS = buildConf("spark.sql.hive.convertCTAS")
.internal()
.doc("When true, a table created by a Hive CTAS statement (no USING clause) " +
"without specifying any storage property will be converted to a data source table, " +
"using the data source set by spark.sql.sources.default.")
.booleanConf
.createWithDefault(false)
val GATHER_FASTSTAT = buildConf("spark.sql.hive.gatherFastStats")
.internal()
.doc("When true, fast stats (number of files and total size of all files) will be gathered" +
" in parallel while repairing table partitions to avoid the sequential listing in Hive" +
" metastore.")
.booleanConf
.createWithDefault(true)
val PARTITION_COLUMN_TYPE_INFERENCE =
buildConf("spark.sql.sources.partitionColumnTypeInference.enabled")
.doc("When true, automatically infer the data types for partitioned columns.")
.booleanConf
.createWithDefault(true)
val BUCKETING_ENABLED = buildConf("spark.sql.sources.bucketing.enabled")
.doc("When false, we will treat bucketed table as normal table")
.booleanConf
.createWithDefault(true)
val CROSS_JOINS_ENABLED = buildConf("spark.sql.crossJoin.enabled")
.doc("When false, we will throw an error if a query contains a cartesian product without " +
"explicit CROSS JOIN syntax.")
.booleanConf
.createWithDefault(false)
val ORDER_BY_ORDINAL = buildConf("spark.sql.orderByOrdinal")
.doc("When true, the ordinal numbers are treated as the position in the select list. " +
"When false, the ordinal numbers in order/sort by clause are ignored.")
.booleanConf
.createWithDefault(true)
val GROUP_BY_ORDINAL = buildConf("spark.sql.groupByOrdinal")
.doc("When true, the ordinal numbers in group by clauses are treated as the position " +
"in the select list. When false, the ordinal numbers are ignored.")
.booleanConf
.createWithDefault(true)
val GROUP_BY_ALIASES = buildConf("spark.sql.groupByAliases")
.doc("When true, aliases in a select list can be used in group by clauses. When false, " +
"an analysis exception is thrown in the case.")
.booleanConf
.createWithDefault(true)
// The output committer class used by data sources. The specified class needs to be a
// subclass of org.apache.hadoop.mapreduce.OutputCommitter.
val OUTPUT_COMMITTER_CLASS = buildConf("spark.sql.sources.outputCommitterClass")
.internal()
.stringConf
.createOptional
val FILE_COMMIT_PROTOCOL_CLASS =
buildConf("spark.sql.sources.commitProtocolClass")
.internal()
.stringConf
.createWithDefault(
"org.apache.spark.sql.execution.datasources.SQLHadoopMapReduceCommitProtocol")
val PARALLEL_PARTITION_DISCOVERY_THRESHOLD =
buildConf("spark.sql.sources.parallelPartitionDiscovery.threshold")
.doc("The maximum number of paths allowed for listing files at driver side. If the number " +
"of detected paths exceeds this value during partition discovery, it tries to list the " +
"files with another Spark distributed job. This applies to Parquet, ORC, CSV, JSON and " +
"LibSVM data sources.")
.intConf
.checkValue(parallel => parallel >= 0, "The maximum number of paths allowed for listing " +
"files at driver side must not be negative")
.createWithDefault(32)
val PARALLEL_PARTITION_DISCOVERY_PARALLELISM =
buildConf("spark.sql.sources.parallelPartitionDiscovery.parallelism")
.doc("The number of parallelism to list a collection of path recursively, Set the " +
"number to prevent file listing from generating too many tasks.")
.internal()
.intConf
.createWithDefault(10000)
// Whether to automatically resolve ambiguity in join conditions for self-joins.
// See SPARK-6231.
val DATAFRAME_SELF_JOIN_AUTO_RESOLVE_AMBIGUITY =
buildConf("spark.sql.selfJoinAutoResolveAmbiguity")
.internal()
.booleanConf
.createWithDefault(true)
// Whether to retain group by columns or not in GroupedData.agg.
val DATAFRAME_RETAIN_GROUP_COLUMNS = buildConf("spark.sql.retainGroupColumns")
.internal()
.booleanConf
.createWithDefault(true)
val DATAFRAME_PIVOT_MAX_VALUES = buildConf("spark.sql.pivotMaxValues")
.doc("When doing a pivot without specifying values for the pivot column this is the maximum " +
"number of (distinct) values that will be collected without error.")
.intConf
.createWithDefault(10000)
val RUN_SQL_ON_FILES = buildConf("spark.sql.runSQLOnFiles")
.internal()
.doc("When true, we could use `datasource`.`path` as table in SQL query.")
.booleanConf
.createWithDefault(true)
val WHOLESTAGE_CODEGEN_ENABLED = buildConf("spark.sql.codegen.wholeStage")
.internal()
.doc("When true, the whole stage (of multiple operators) will be compiled into single java" +
" method.")
.booleanConf
.createWithDefault(true)
val WHOLESTAGE_MAX_NUM_FIELDS = buildConf("spark.sql.codegen.maxFields")
.internal()
.doc("The maximum number of fields (including nested fields) that will be supported before" +
" deactivating whole-stage codegen.")
.intConf
.createWithDefault(100)
val CODEGEN_FALLBACK = buildConf("spark.sql.codegen.fallback")
.internal()
.doc("When true, (whole stage) codegen could be temporary disabled for the part of query that" +
" fail to compile generated code")
.booleanConf
.createWithDefault(true)
val CODEGEN_LOGGING_MAX_LINES = buildConf("spark.sql.codegen.logging.maxLines")
.internal()
.doc("The maximum number of codegen lines to log when errors occur. Use -1 for unlimited.")
.intConf
.checkValue(maxLines => maxLines >= -1, "The maximum must be a positive integer, 0 to " +
"disable logging or -1 to apply no limit.")
.createWithDefault(1000)
val WHOLESTAGE_HUGE_METHOD_LIMIT = buildConf("spark.sql.codegen.hugeMethodLimit")
.internal()
.doc("The maximum bytecode size of a single compiled Java function generated by whole-stage " +
"codegen. When the compiled function exceeds this threshold, " +
"the whole-stage codegen is deactivated for this subtree of the current query plan. " +
s"The default value is ${CodeGenerator.DEFAULT_JVM_HUGE_METHOD_LIMIT} and " +
"this is a limit in the OpenJDK JVM implementation.")
.intConf
.createWithDefault(CodeGenerator.DEFAULT_JVM_HUGE_METHOD_LIMIT)
val FILES_MAX_PARTITION_BYTES = buildConf("spark.sql.files.maxPartitionBytes")
.doc("The maximum number of bytes to pack into a single partition when reading files.")
.longConf
.createWithDefault(128 * 1024 * 1024) // parquet.block.size
val FILES_OPEN_COST_IN_BYTES = buildConf("spark.sql.files.openCostInBytes")
.internal()
.doc("The estimated cost to open a file, measured by the number of bytes could be scanned in" +
" the same time. This is used when putting multiple files into a partition. It's better to" +
" over estimated, then the partitions with small files will be faster than partitions with" +
" bigger files (which is scheduled first).")
.longConf
.createWithDefault(4 * 1024 * 1024)
val IGNORE_CORRUPT_FILES = buildConf("spark.sql.files.ignoreCorruptFiles")
.doc("Whether to ignore corrupt files. If true, the Spark jobs will continue to run when " +
"encountering corrupted files and the contents that have been read will still be returned.")
.booleanConf
.createWithDefault(false)
val IGNORE_MISSING_FILES = buildConf("spark.sql.files.ignoreMissingFiles")
.doc("Whether to ignore missing files. If true, the Spark jobs will continue to run when " +
"encountering missing files and the contents that have been read will still be returned.")
.booleanConf
.createWithDefault(false)
val MAX_RECORDS_PER_FILE = buildConf("spark.sql.files.maxRecordsPerFile")
.doc("Maximum number of records to write out to a single file. " +
"If this value is zero or negative, there is no limit.")
.longConf
.createWithDefault(0)
val EXCHANGE_REUSE_ENABLED = buildConf("spark.sql.exchange.reuse")
.internal()
.doc("When true, the planner will try to find out duplicated exchanges and re-use them.")
.booleanConf
.createWithDefault(true)
val STATE_STORE_PROVIDER_CLASS =
buildConf("spark.sql.streaming.stateStore.providerClass")
.internal()
.doc(
"The class used to manage state data in stateful streaming queries. This class must " +
"be a subclass of StateStoreProvider, and must have a zero-arg constructor.")
.stringConf
.createWithDefault(
"org.apache.spark.sql.execution.streaming.state.HDFSBackedStateStoreProvider")
val STATE_STORE_MIN_DELTAS_FOR_SNAPSHOT =
buildConf("spark.sql.streaming.stateStore.minDeltasForSnapshot")
.internal()
.doc("Minimum number of state store delta files that needs to be generated before they " +
"consolidated into snapshots.")
.intConf
.createWithDefault(10)
val CHECKPOINT_LOCATION = buildConf("spark.sql.streaming.checkpointLocation")
.doc("The default location for storing checkpoint data for streaming queries.")
.stringConf
.createOptional
val MIN_BATCHES_TO_RETAIN = buildConf("spark.sql.streaming.minBatchesToRetain")
.internal()
.doc("The minimum number of batches that must be retained and made recoverable.")
.intConf
.createWithDefault(100)
val UNSUPPORTED_OPERATION_CHECK_ENABLED =
buildConf("spark.sql.streaming.unsupportedOperationCheck")
.internal()
.doc("When true, the logical plan for streaming query will be checked for unsupported" +
" operations.")
.booleanConf
.createWithDefault(true)
val VARIABLE_SUBSTITUTE_ENABLED =
buildConf("spark.sql.variable.substitute")
.doc("This enables substitution using syntax like ${var} ${system:var} and ${env:var}.")
.booleanConf
.createWithDefault(true)
val VARIABLE_SUBSTITUTE_DEPTH =
buildConf("spark.sql.variable.substitute.depth")
.internal()
.doc("Deprecated: The maximum replacements the substitution engine will do.")
.intConf
.createWithDefault(40)
val ENABLE_TWOLEVEL_AGG_MAP =
buildConf("spark.sql.codegen.aggregate.map.twolevel.enabled")
.internal()
.doc("Enable two-level aggregate hash map. When enabled, records will first be " +
"inserted/looked-up at a 1st-level, small, fast map, and then fallback to a " +
"2nd-level, larger, slower map when 1st level is full or keys cannot be found. " +
"When disabled, records go directly to the 2nd level. Defaults to true.")
.booleanConf
.createWithDefault(true)
val MAX_NESTED_VIEW_DEPTH =
buildConf("spark.sql.view.maxNestedViewDepth")
.internal()
.doc("The maximum depth of a view reference in a nested view. A nested view may reference " +
"other nested views, the dependencies are organized in a directed acyclic graph (DAG). " +
"However the DAG depth may become too large and cause unexpected behavior. This " +
"configuration puts a limit on this: when the depth of a view exceeds this value during " +
"analysis, we terminate the resolution to avoid potential errors.")
.intConf
.checkValue(depth => depth > 0, "The maximum depth of a view reference in a nested view " +
"must be positive.")
.createWithDefault(100)
val STREAMING_FILE_COMMIT_PROTOCOL_CLASS =
buildConf("spark.sql.streaming.commitProtocolClass")
.internal()
.stringConf
.createWithDefault("org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol")
val OBJECT_AGG_SORT_BASED_FALLBACK_THRESHOLD =
buildConf("spark.sql.objectHashAggregate.sortBased.fallbackThreshold")
.internal()
.doc("In the case of ObjectHashAggregateExec, when the size of the in-memory hash map " +
"grows too large, we will fall back to sort-based aggregation. This option sets a row " +
"count threshold for the size of the hash map.")
.intConf
// We are trying to be conservative and use a relatively small default count threshold here
// since the state object of some TypedImperativeAggregate function can be quite large (e.g.
// percentile_approx).
.createWithDefault(128)
val USE_OBJECT_HASH_AGG = buildConf("spark.sql.execution.useObjectHashAggregateExec")
.internal()
.doc("Decides if we use ObjectHashAggregateExec")
.booleanConf
.createWithDefault(true)
val FILE_SINK_LOG_DELETION = buildConf("spark.sql.streaming.fileSink.log.deletion")
.internal()
.doc("Whether to delete the expired log files in file stream sink.")
.booleanConf
.createWithDefault(true)
val FILE_SINK_LOG_COMPACT_INTERVAL =
buildConf("spark.sql.streaming.fileSink.log.compactInterval")
.internal()
.doc("Number of log files after which all the previous files " +
"are compacted into the next log file.")
.intConf
.createWithDefault(10)
val FILE_SINK_LOG_CLEANUP_DELAY =
buildConf("spark.sql.streaming.fileSink.log.cleanupDelay")
.internal()
.doc("How long that a file is guaranteed to be visible for all readers.")
.timeConf(TimeUnit.MILLISECONDS)
.createWithDefault(TimeUnit.MINUTES.toMillis(10)) // 10 minutes
val FILE_SOURCE_LOG_DELETION = buildConf("spark.sql.streaming.fileSource.log.deletion")
.internal()
.doc("Whether to delete the expired log files in file stream source.")
.booleanConf
.createWithDefault(true)
val FILE_SOURCE_LOG_COMPACT_INTERVAL =
buildConf("spark.sql.streaming.fileSource.log.compactInterval")
.internal()
.doc("Number of log files after which all the previous files " +
"are compacted into the next log file.")
.intConf
.createWithDefault(10)
val FILE_SOURCE_LOG_CLEANUP_DELAY =
buildConf("spark.sql.streaming.fileSource.log.cleanupDelay")
.internal()
.doc("How long in milliseconds a file is guaranteed to be visible for all readers.")
.timeConf(TimeUnit.MILLISECONDS)
.createWithDefault(TimeUnit.MINUTES.toMillis(10)) // 10 minutes
val STREAMING_SCHEMA_INFERENCE =
buildConf("spark.sql.streaming.schemaInference")
.internal()
.doc("Whether file-based streaming sources will infer its own schema")
.booleanConf
.createWithDefault(false)
val STREAMING_POLLING_DELAY =
buildConf("spark.sql.streaming.pollingDelay")
.internal()
.doc("How long to delay polling new data when no data is available")
.timeConf(TimeUnit.MILLISECONDS)
.createWithDefault(10L)
val STREAMING_NO_DATA_PROGRESS_EVENT_INTERVAL =
buildConf("spark.sql.streaming.noDataProgressEventInterval")
.internal()
.doc("How long to wait between two progress events when there is no data")
.timeConf(TimeUnit.MILLISECONDS)
.createWithDefault(10000L)
val STREAMING_METRICS_ENABLED =
buildConf("spark.sql.streaming.metricsEnabled")
.doc("Whether Dropwizard/Codahale metrics will be reported for active streaming queries.")
.booleanConf
.createWithDefault(false)
val STREAMING_PROGRESS_RETENTION =
buildConf("spark.sql.streaming.numRecentProgressUpdates")
.doc("The number of progress updates to retain for a streaming query")
.intConf
.createWithDefault(100)
val NDV_MAX_ERROR =
buildConf("spark.sql.statistics.ndv.maxError")
.internal()
.doc("The maximum estimation error allowed in HyperLogLog++ algorithm when generating " +
"column level statistics.")
.doubleConf
.createWithDefault(0.05)
val HISTOGRAM_ENABLED =
buildConf("spark.sql.statistics.histogram.enabled")
.doc("Generates histograms when computing column statistics if enabled. Histograms can " +
"provide better estimation accuracy. Currently, Spark only supports equi-height " +
"histogram. Note that collecting histograms takes extra cost. For example, collecting " +
"column statistics usually takes only one table scan, but generating equi-height " +
"histogram will cause an extra table scan.")
.booleanConf
.createWithDefault(false)
val HISTOGRAM_NUM_BINS =
buildConf("spark.sql.statistics.histogram.numBins")
.internal()
.doc("The number of bins when generating histograms.")
.intConf
.checkValue(num => num > 1, "The number of bins must be large than 1.")
.createWithDefault(254)
val PERCENTILE_ACCURACY =
buildConf("spark.sql.statistics.percentile.accuracy")
.internal()
.doc("Accuracy of percentile approximation when generating equi-height histograms. " +
"Larger value means better accuracy. The relative error can be deduced by " +
"1.0 / PERCENTILE_ACCURACY.")
.intConf
.createWithDefault(10000)
val AUTO_SIZE_UPDATE_ENABLED =
buildConf("spark.sql.statistics.size.autoUpdate.enabled")
.doc("Enables automatic update for table size once table's data is changed. Note that if " +
"the total number of files of the table is very large, this can be expensive and slow " +
"down data change commands.")
.booleanConf
.createWithDefault(false)
val CBO_ENABLED =
buildConf("spark.sql.cbo.enabled")
.doc("Enables CBO for estimation of plan statistics when set true.")
.booleanConf
.createWithDefault(false)
val JOIN_REORDER_ENABLED =
buildConf("spark.sql.cbo.joinReorder.enabled")
.doc("Enables join reorder in CBO.")
.booleanConf
.createWithDefault(false)
val JOIN_REORDER_DP_THRESHOLD =
buildConf("spark.sql.cbo.joinReorder.dp.threshold")
.doc("The maximum number of joined nodes allowed in the dynamic programming algorithm.")
.intConf
.checkValue(number => number > 0, "The maximum number must be a positive integer.")
.createWithDefault(12)
val JOIN_REORDER_CARD_WEIGHT =
buildConf("spark.sql.cbo.joinReorder.card.weight")
.internal()
.doc("The weight of cardinality (number of rows) for plan cost comparison in join reorder: " +
"rows * weight + size * (1 - weight).")
.doubleConf
.checkValue(weight => weight >= 0 && weight <= 1, "The weight value must be in [0, 1].")
.createWithDefault(0.7)
val JOIN_REORDER_DP_STAR_FILTER =
buildConf("spark.sql.cbo.joinReorder.dp.star.filter")
.doc("Applies star-join filter heuristics to cost based join enumeration.")
.booleanConf
.createWithDefault(false)
val STARSCHEMA_DETECTION = buildConf("spark.sql.cbo.starSchemaDetection")
.doc("When true, it enables join reordering based on star schema detection. ")
.booleanConf
.createWithDefault(false)
val STARSCHEMA_FACT_TABLE_RATIO = buildConf("spark.sql.cbo.starJoinFTRatio")
.internal()
.doc("Specifies the upper limit of the ratio between the largest fact tables" +
" for a star join to be considered. ")
.doubleConf
.createWithDefault(0.9)
val SESSION_LOCAL_TIMEZONE =
buildConf("spark.sql.session.timeZone")
.doc("""The ID of session local timezone, e.g. "GMT", "America/Los_Angeles", etc.""")
.stringConf
.createWithDefaultFunction(() => TimeZone.getDefault.getID)
val WINDOW_EXEC_BUFFER_IN_MEMORY_THRESHOLD =
buildConf("spark.sql.windowExec.buffer.in.memory.threshold")
.internal()
.doc("Threshold for number of rows guaranteed to be held in memory by the window operator")
.intConf
.createWithDefault(4096)
val WINDOW_EXEC_BUFFER_SPILL_THRESHOLD =
buildConf("spark.sql.windowExec.buffer.spill.threshold")
.internal()
.doc("Threshold for number of rows to be spilled by window operator")
.intConf
.createWithDefault(SHUFFLE_SPILL_NUM_ELEMENTS_FORCE_SPILL_THRESHOLD.defaultValue.get)
val SORT_MERGE_JOIN_EXEC_BUFFER_IN_MEMORY_THRESHOLD =
buildConf("spark.sql.sortMergeJoinExec.buffer.in.memory.threshold")
.internal()
.doc("Threshold for number of rows guaranteed to be held in memory by the sort merge " +
"join operator")
.intConf
.createWithDefault(Int.MaxValue)
val SORT_MERGE_JOIN_EXEC_BUFFER_SPILL_THRESHOLD =
buildConf("spark.sql.sortMergeJoinExec.buffer.spill.threshold")
.internal()
.doc("Threshold for number of rows to be spilled by sort merge join operator")
.intConf
.createWithDefault(SHUFFLE_SPILL_NUM_ELEMENTS_FORCE_SPILL_THRESHOLD.defaultValue.get)
val CARTESIAN_PRODUCT_EXEC_BUFFER_IN_MEMORY_THRESHOLD =
buildConf("spark.sql.cartesianProductExec.buffer.in.memory.threshold")
.internal()
.doc("Threshold for number of rows guaranteed to be held in memory by the cartesian " +
"product operator")
.intConf
.createWithDefault(4096)
val CARTESIAN_PRODUCT_EXEC_BUFFER_SPILL_THRESHOLD =
buildConf("spark.sql.cartesianProductExec.buffer.spill.threshold")
.internal()
.doc("Threshold for number of rows to be spilled by cartesian product operator")
.intConf
.createWithDefault(SHUFFLE_SPILL_NUM_ELEMENTS_FORCE_SPILL_THRESHOLD.defaultValue.get)
val SUPPORT_QUOTED_REGEX_COLUMN_NAME = buildConf("spark.sql.parser.quotedRegexColumnNames")
.doc("When true, quoted Identifiers (using backticks) in SELECT statement are interpreted" +
" as regular expressions.")
.booleanConf