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SQLConf.scala
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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 java.util.zip.Deflater
import scala.collection.JavaConverters._
import scala.collection.immutable
import scala.util.Try
import scala.util.matching.Regex
import org.apache.hadoop.fs.Path
import org.apache.spark.{SparkContext, TaskContext}
import org.apache.spark.internal.Logging
import org.apache.spark.internal.config._
import org.apache.spark.internal.config.{IGNORE_MISSING_FILES => SPARK_IGNORE_MISSING_FILES}
import org.apache.spark.network.util.ByteUnit
import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.catalyst.analysis.{HintErrorLogger, Resolver}
import org.apache.spark.sql.catalyst.expressions.CodegenObjectFactoryMode
import org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator
import org.apache.spark.sql.catalyst.plans.logical.HintErrorHandler
import org.apache.spark.sql.catalyst.util.DateTimeUtils
import org.apache.spark.sql.connector.catalog.CatalogManager.SESSION_CATALOG_NAME
import org.apache.spark.unsafe.array.ByteArrayMethods
import org.apache.spark.util.Utils
////////////////////////////////////////////////////////////////////////////////////////////////////
// This file defines the configuration options for Spark SQL.
////////////////////////////////////////////////////////////////////////////////////////////////////
object SQLConf {
private[sql] 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()
private lazy val existingConf = new ThreadLocal[SQLConf] {
override def initialValue: SQLConf = null
}
def withExistingConf[T](conf: SQLConf)(f: => T): T = {
val old = existingConf.get()
existingConf.set(conf)
try {
f
} finally {
if (old != null) {
existingConf.set(old)
} else {
existingConf.remove()
}
}
}
/**
* 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 active session is used. If it's called from
* tasks in the executor side, a SQLConf will be created from job local properties, which are set
* and propagated from the driver side, unless a `SQLConf` has been set in the scope by
* `withExistingConf` as done for propagating SQLConf for operations performed on RDDs created
* from DataFrames.
*
* 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 = {
if (TaskContext.get != null) {
val conf = existingConf.get()
if (conf != null) {
conf
} else {
new ReadOnlySQLConf(TaskContext.get())
}
} else {
val isSchedulerEventLoopThread = SparkContext.getActive
.flatMap { sc => Option(sc.dagScheduler) }
.map(_.eventProcessLoop.eventThread)
.exists(_.getId == Thread.currentThread().getId)
if (isSchedulerEventLoopThread) {
// DAGScheduler event loop thread does not have an active SparkSession, the `confGetter`
// will return `fallbackConf` which is unexpected. Here we require the caller to get the
// conf within `withExistingConf`, otherwise fail the query.
val conf = existingConf.get()
if (conf != null) {
conf
} else if (Utils.isTesting) {
throw new RuntimeException("Cannot get SQLConf inside scheduler event loop thread.")
} else {
confGetter.get()()
}
} else {
val conf = existingConf.get()
if (conf != null) {
conf
} else {
confGetter.get()()
}
}
}
}
val ANALYZER_MAX_ITERATIONS = buildConf("spark.sql.analyzer.maxIterations")
.internal()
.doc("The max number of iterations the analyzer runs.")
.version("3.0.0")
.intConf
.createWithDefault(100)
val OPTIMIZER_EXCLUDED_RULES = buildConf("spark.sql.optimizer.excludedRules")
.doc("Configures a list of rules to be disabled in the optimizer, in which the rules are " +
"specified by their rule names and separated by comma. It is not guaranteed that all the " +
"rules in this configuration will eventually be excluded, as some rules are necessary " +
"for correctness. The optimizer will log the rules that have indeed been excluded.")
.version("2.4.0")
.stringConf
.createOptional
val OPTIMIZER_MAX_ITERATIONS = buildConf("spark.sql.optimizer.maxIterations")
.internal()
.doc("The max number of iterations the optimizer runs.")
.version("2.0.0")
.intConf
.createWithDefault(100)
val OPTIMIZER_INSET_CONVERSION_THRESHOLD =
buildConf("spark.sql.optimizer.inSetConversionThreshold")
.internal()
.doc("The threshold of set size for InSet conversion.")
.version("2.0.0")
.intConf
.createWithDefault(10)
val OPTIMIZER_INSET_SWITCH_THRESHOLD =
buildConf("spark.sql.optimizer.inSetSwitchThreshold")
.internal()
.doc("Configures the max set size in InSet for which Spark will generate code with " +
"switch statements. This is applicable only to bytes, shorts, ints, dates.")
.version("3.0.0")
.intConf
.checkValue(threshold => threshold >= 0 && threshold <= 600, "The max set size " +
"for using switch statements in InSet must be non-negative and less than or equal to 600")
.createWithDefault(400)
val OPTIMIZER_PLAN_CHANGE_LOG_LEVEL = buildConf("spark.sql.optimizer.planChangeLog.level")
.internal()
.doc("Configures the log level for logging the change from the original plan to the new " +
"plan after a rule or batch is applied. The value can be 'trace', 'debug', 'info', " +
"'warn', or 'error'. The default log level is 'trace'.")
.version("3.0.0")
.stringConf
.transform(_.toUpperCase(Locale.ROOT))
.checkValue(logLevel => Set("TRACE", "DEBUG", "INFO", "WARN", "ERROR").contains(logLevel),
"Invalid value for 'spark.sql.optimizer.planChangeLog.level'. Valid values are " +
"'trace', 'debug', 'info', 'warn' and 'error'.")
.createWithDefault("trace")
val OPTIMIZER_PLAN_CHANGE_LOG_RULES = buildConf("spark.sql.optimizer.planChangeLog.rules")
.internal()
.doc("Configures a list of rules to be logged in the optimizer, in which the rules are " +
"specified by their rule names and separated by comma.")
.version("3.0.0")
.stringConf
.createOptional
val OPTIMIZER_PLAN_CHANGE_LOG_BATCHES = buildConf("spark.sql.optimizer.planChangeLog.batches")
.internal()
.doc("Configures a list of batches to be logged in the optimizer, in which the batches " +
"are specified by their batch names and separated by comma.")
.version("3.0.0")
.stringConf
.createOptional
val DYNAMIC_PARTITION_PRUNING_ENABLED =
buildConf("spark.sql.optimizer.dynamicPartitionPruning.enabled")
.doc("When true, we will generate predicate for partition column when it's used as join key")
.version("3.0.0")
.booleanConf
.createWithDefault(true)
val DYNAMIC_PARTITION_PRUNING_USE_STATS =
buildConf("spark.sql.optimizer.dynamicPartitionPruning.useStats")
.internal()
.doc("When true, distinct count statistics will be used for computing the data size of the " +
"partitioned table after dynamic partition pruning, in order to evaluate if it is worth " +
"adding an extra subquery as the pruning filter if broadcast reuse is not applicable.")
.version("3.0.0")
.booleanConf
.createWithDefault(true)
val DYNAMIC_PARTITION_PRUNING_FALLBACK_FILTER_RATIO = buildConf(
"spark.sql.optimizer.dynamicPartitionPruning.fallbackFilterRatio")
.internal()
.doc("When statistics are not available or configured not to be used, this config will be " +
"used as the fallback filter ratio for computing the data size of the partitioned table " +
"after dynamic partition pruning, in order to evaluate if it is worth adding an extra " +
"subquery as the pruning filter if broadcast reuse is not applicable.")
.version("3.0.0")
.doubleConf
.createWithDefault(0.5)
val DYNAMIC_PARTITION_PRUNING_REUSE_BROADCAST_ONLY =
buildConf("spark.sql.optimizer.dynamicPartitionPruning.reuseBroadcastOnly")
.internal()
.doc("When true, dynamic partition pruning will only apply when the broadcast exchange of " +
"a broadcast hash join operation can be reused as the dynamic pruning filter.")
.version("3.0.0")
.booleanConf
.createWithDefault(true)
val COMPRESS_CACHED = buildConf("spark.sql.inMemoryColumnarStorage.compressed")
.doc("When set to true Spark SQL will automatically select a compression codec for each " +
"column based on statistics of the data.")
.version("1.0.1")
.booleanConf
.createWithDefault(true)
val COLUMN_BATCH_SIZE = buildConf("spark.sql.inMemoryColumnarStorage.batchSize")
.doc("Controls the size of batches for columnar caching. Larger batch sizes can improve " +
"memory utilization and compression, but risk OOMs when caching data.")
.version("1.1.1")
.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.")
.version("1.2.0")
.booleanConf
.createWithDefault(true)
val IN_MEMORY_TABLE_SCAN_STATISTICS_ENABLED =
buildConf("spark.sql.inMemoryTableScanStatistics.enable")
.internal()
.doc("When true, enable in-memory table scan accumulators.")
.version("3.0.0")
.booleanConf
.createWithDefault(false)
val CACHE_VECTORIZED_READER_ENABLED =
buildConf("spark.sql.inMemoryColumnarStorage.enableVectorizedReader")
.doc("Enables vectorized reader for columnar caching.")
.version("2.3.1")
.booleanConf
.createWithDefault(true)
val COLUMN_VECTOR_OFFHEAP_ENABLED =
buildConf("spark.sql.columnVector.offheap.enabled")
.internal()
.doc("When true, use OffHeapColumnVector in ColumnarBatch.")
.version("2.3.0")
.booleanConf
.createWithDefault(false)
val PREFER_SORTMERGEJOIN = buildConf("spark.sql.join.preferSortMergeJoin")
.internal()
.doc("When true, prefer sort merge join over shuffle hash join.")
.version("2.0.0")
.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).")
.version("2.0.0")
.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 `ANALYZE TABLE <tableName> COMPUTE STATISTICS noscan` has been " +
"run, and file-based data source tables where the statistics are computed directly on " +
"the files of data.")
.version("1.1.0")
.bytesConf(ByteUnit.BYTE)
.createWithDefaultString("10MB")
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")
.version("2.1.1")
.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.")
.version("2.3.0")
.booleanConf
.createWithDefault(true)
val SHUFFLE_PARTITIONS = buildConf("spark.sql.shuffle.partitions")
.doc("The default number of partitions to use when shuffling data for joins or aggregations. " +
"Note: For structured streaming, this configuration cannot be changed between query " +
"restarts from the same checkpoint location.")
.version("1.1.0")
.intConf
.checkValue(_ > 0, "The value of spark.sql.shuffle.partitions must be positive")
.createWithDefault(200)
val SHUFFLE_TARGET_POSTSHUFFLE_INPUT_SIZE =
buildConf("spark.sql.adaptive.shuffle.targetPostShuffleInputSize")
.internal()
.doc("(Deprecated since Spark 3.0)")
.version("1.6.0")
.bytesConf(ByteUnit.BYTE)
.createWithDefaultString("64MB")
val ADAPTIVE_EXECUTION_ENABLED = buildConf("spark.sql.adaptive.enabled")
.doc("When true, enable adaptive query execution, which re-optimizes the query plan in the " +
"middle of query execution, based on accurate runtime statistics.")
.version("1.6.0")
.booleanConf
.createWithDefault(false)
val ADAPTIVE_EXECUTION_FORCE_APPLY = buildConf("spark.sql.adaptive.forceApply")
.internal()
.doc("Adaptive query execution is skipped when the query does not have exchanges or " +
"sub-queries. By setting this config to true (together with " +
s"'${ADAPTIVE_EXECUTION_ENABLED.key}' set to true), Spark will force apply adaptive query " +
"execution for all supported queries.")
.version("3.0.0")
.booleanConf
.createWithDefault(false)
val ADAPTIVE_EXECUTION_LOG_LEVEL = buildConf("spark.sql.adaptive.logLevel")
.internal()
.doc("Configures the log level for adaptive execution logging of plan changes. The value " +
"can be 'trace', 'debug', 'info', 'warn', or 'error'. The default log level is 'debug'.")
.version("3.0.0")
.stringConf
.transform(_.toUpperCase(Locale.ROOT))
.checkValues(Set("TRACE", "DEBUG", "INFO", "WARN", "ERROR"))
.createWithDefault("debug")
val ADVISORY_PARTITION_SIZE_IN_BYTES =
buildConf("spark.sql.adaptive.advisoryPartitionSizeInBytes")
.doc("The advisory size in bytes of the shuffle partition during adaptive optimization " +
s"(when ${ADAPTIVE_EXECUTION_ENABLED.key} is true). It takes effect when Spark " +
"coalesces small shuffle partitions or splits skewed shuffle partition.")
.version("3.0.0")
.fallbackConf(SHUFFLE_TARGET_POSTSHUFFLE_INPUT_SIZE)
val COALESCE_PARTITIONS_ENABLED =
buildConf("spark.sql.adaptive.coalescePartitions.enabled")
.doc(s"When true and '${ADAPTIVE_EXECUTION_ENABLED.key}' is true, Spark will coalesce " +
"contiguous shuffle partitions according to the target size (specified by " +
s"'${ADVISORY_PARTITION_SIZE_IN_BYTES.key}'), to avoid too many small tasks.")
.version("3.0.0")
.booleanConf
.createWithDefault(true)
val COALESCE_PARTITIONS_MIN_PARTITION_NUM =
buildConf("spark.sql.adaptive.coalescePartitions.minPartitionNum")
.doc("The suggested (not guaranteed) minimum number of shuffle partitions after " +
"coalescing. If not set, the default value is the default parallelism of the " +
"Spark cluster. This configuration only has an effect when " +
s"'${ADAPTIVE_EXECUTION_ENABLED.key}' and " +
s"'${COALESCE_PARTITIONS_ENABLED.key}' are both true.")
.version("3.0.0")
.intConf
.checkValue(_ > 0, "The minimum number of partitions must be positive.")
.createOptional
val COALESCE_PARTITIONS_INITIAL_PARTITION_NUM =
buildConf("spark.sql.adaptive.coalescePartitions.initialPartitionNum")
.doc("The initial number of shuffle partitions before coalescing. By default it equals to " +
s"${SHUFFLE_PARTITIONS.key}. This configuration only has an effect when " +
s"'${ADAPTIVE_EXECUTION_ENABLED.key}' and '${COALESCE_PARTITIONS_ENABLED.key}' " +
"are both true.")
.version("3.0.0")
.intConf
.checkValue(_ > 0, "The initial number of partitions must be positive.")
.createOptional
val FETCH_SHUFFLE_BLOCKS_IN_BATCH =
buildConf("spark.sql.adaptive.fetchShuffleBlocksInBatch")
.internal()
.doc("Whether to fetch the contiguous shuffle blocks in batch. Instead of fetching blocks " +
"one by one, fetching contiguous shuffle blocks for the same map task in batch can " +
"reduce IO and improve performance. Note, multiple contiguous blocks exist in single " +
s"fetch request only happen when '${ADAPTIVE_EXECUTION_ENABLED.key}' and " +
s"'${COALESCE_PARTITIONS_ENABLED.key}' are both true. This feature also depends " +
"on a relocatable serializer, the concatenation support codec in use and the new version " +
"shuffle fetch protocol.")
.version("3.0.0")
.booleanConf
.createWithDefault(true)
val LOCAL_SHUFFLE_READER_ENABLED =
buildConf("spark.sql.adaptive.localShuffleReader.enabled")
.doc(s"When true and '${ADAPTIVE_EXECUTION_ENABLED.key}' is true, Spark tries to use local " +
"shuffle reader to read the shuffle data when the shuffle partitioning is not needed, " +
"for example, after converting sort-merge join to broadcast-hash join.")
.version("3.0.0")
.booleanConf
.createWithDefault(true)
val SKEW_JOIN_ENABLED =
buildConf("spark.sql.adaptive.skewJoin.enabled")
.doc(s"When true and '${ADAPTIVE_EXECUTION_ENABLED.key}' is true, Spark dynamically " +
"handles skew in sort-merge join by splitting (and replicating if needed) skewed " +
"partitions.")
.version("3.0.0")
.booleanConf
.createWithDefault(true)
val SKEW_JOIN_SKEWED_PARTITION_FACTOR =
buildConf("spark.sql.adaptive.skewJoin.skewedPartitionFactor")
.doc("A partition is considered as skewed if its size is larger than this factor " +
"multiplying the median partition size and also larger than " +
"'spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes'")
.version("3.0.0")
.intConf
.checkValue(_ > 0, "The skew factor must be positive.")
.createWithDefault(5)
val SKEW_JOIN_SKEWED_PARTITION_THRESHOLD =
buildConf("spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes")
.doc("A partition is considered as skewed if its size in bytes is larger than this " +
s"threshold and also larger than '${SKEW_JOIN_SKEWED_PARTITION_FACTOR.key}' " +
"multiplying the median partition size. Ideally this config should be set larger " +
s"than '${ADVISORY_PARTITION_SIZE_IN_BYTES.key}'.")
.version("3.0.0")
.bytesConf(ByteUnit.BYTE)
.createWithDefaultString("256MB")
val NON_EMPTY_PARTITION_RATIO_FOR_BROADCAST_JOIN =
buildConf("spark.sql.adaptive.nonEmptyPartitionRatioForBroadcastJoin")
.internal()
.doc("The relation with a non-empty partition ratio lower than this config will not be " +
"considered as the build side of a broadcast-hash join in adaptive execution regardless " +
"of its size.This configuration only has an effect when " +
s"'${ADAPTIVE_EXECUTION_ENABLED.key}' is true.")
.version("3.0.0")
.doubleConf
.checkValue(_ >= 0, "The non-empty partition ratio must be positive number.")
.createWithDefault(0.2)
val SUBEXPRESSION_ELIMINATION_ENABLED =
buildConf("spark.sql.subexpressionElimination.enabled")
.internal()
.doc("When true, common subexpressions will be eliminated.")
.version("1.6.0")
.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.")
.version("1.4.0")
.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.")
.version("2.2.0")
.booleanConf
.createWithDefault(true)
val MAX_CNF_NODE_COUNT =
buildConf("spark.sql.optimizer.maxCNFNodeCount")
.internal()
.doc("Specifies the maximum allowable number of conjuncts in the result of CNF " +
"conversion. If the conversion exceeds the threshold, an empty sequence is returned. " +
"For example, CNF conversion of (a && b) || (c && d) generates " +
"four conjuncts (a || c) && (a || d) && (b || c) && (b || d).")
.version("3.1.0")
.intConf
.checkValue(_ >= 0,
"The depth of the maximum rewriting conjunction normal form must be positive.")
.createWithDefault(128)
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.")
.version("2.2.1")
.booleanConf
.createWithDefault(false)
val FILE_COMPRESSION_FACTOR = buildConf("spark.sql.sources.fileCompressionFactor")
.internal()
.doc("When estimating the output data size of a table scan, multiply the file size with this " +
"factor as the estimated data size, in case the data is compressed in the file and lead to" +
" a heavily underestimated result.")
.version("2.3.1")
.doubleConf
.checkValue(_ > 0, "the value of fileDataSizeFactor must be greater than 0")
.createWithDefault(1.0)
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.")
.version("1.5.0")
.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.")
.version("1.5.0")
.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.")
.version("1.1.1")
.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.")
.version("1.3.0")
.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.")
.version("2.3.0")
.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.")
.version("2.3.0")
.stringConf
.transform(_.toUpperCase(Locale.ROOT))
.checkValues(ParquetOutputTimestampType.values.map(_.toString))
.createWithDefault(ParquetOutputTimestampType.INT96.toString)
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, brotli, lz4, zstd.")
.version("1.1.1")
.stringConf
.transform(_.toLowerCase(Locale.ROOT))
.checkValues(Set("none", "uncompressed", "snappy", "gzip", "lzo", "lz4", "brotli", "zstd"))
.createWithDefault("snappy")
val PARQUET_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.parquet.filterPushdown")
.doc("Enables Parquet filter push-down optimization when set to true.")
.version("1.2.0")
.booleanConf
.createWithDefault(true)
val PARQUET_FILTER_PUSHDOWN_DATE_ENABLED = buildConf("spark.sql.parquet.filterPushdown.date")
.doc("If true, enables Parquet filter push-down optimization for Date. " +
s"This configuration only has an effect when '${PARQUET_FILTER_PUSHDOWN_ENABLED.key}' is " +
"enabled.")
.version("2.4.0")
.internal()
.booleanConf
.createWithDefault(true)
val PARQUET_FILTER_PUSHDOWN_TIMESTAMP_ENABLED =
buildConf("spark.sql.parquet.filterPushdown.timestamp")
.doc("If true, enables Parquet filter push-down optimization for Timestamp. " +
s"This configuration only has an effect when '${PARQUET_FILTER_PUSHDOWN_ENABLED.key}' is " +
"enabled and Timestamp stored as TIMESTAMP_MICROS or TIMESTAMP_MILLIS type.")
.version("2.4.0")
.internal()
.booleanConf
.createWithDefault(true)
val PARQUET_FILTER_PUSHDOWN_DECIMAL_ENABLED =
buildConf("spark.sql.parquet.filterPushdown.decimal")
.doc("If true, enables Parquet filter push-down optimization for Decimal. " +
s"This configuration only has an effect when '${PARQUET_FILTER_PUSHDOWN_ENABLED.key}' is " +
"enabled.")
.version("2.4.0")
.internal()
.booleanConf
.createWithDefault(true)
val PARQUET_FILTER_PUSHDOWN_STRING_STARTSWITH_ENABLED =
buildConf("spark.sql.parquet.filterPushdown.string.startsWith")
.doc("If true, enables Parquet filter push-down optimization for string startsWith function. " +
s"This configuration only has an effect when '${PARQUET_FILTER_PUSHDOWN_ENABLED.key}' is " +
"enabled.")
.version("2.4.0")
.internal()
.booleanConf
.createWithDefault(true)
val PARQUET_FILTER_PUSHDOWN_INFILTERTHRESHOLD =
buildConf("spark.sql.parquet.pushdown.inFilterThreshold")
.doc("The maximum number of values to filter push-down optimization for IN predicate. " +
"Large threshold won't necessarily provide much better performance. " +
"The experiment argued that 300 is the limit threshold. " +
"By setting this value to 0 this feature can be disabled. " +
s"This configuration only has an effect when '${PARQUET_FILTER_PUSHDOWN_ENABLED.key}' is " +
"enabled.")
.version("2.4.0")
.internal()
.intConf
.checkValue(threshold => threshold >= 0, "The threshold must not be negative.")
.createWithDefault(10)
val PARQUET_WRITE_LEGACY_FORMAT = buildConf("spark.sql.parquet.writeLegacyFormat")
.doc("If true, data will be written in a way of Spark 1.4 and earlier. For example, decimal " +
"values will be written in Apache Parquet's fixed-length byte array format, which other " +
"systems such as Apache Hive and Apache Impala use. If false, the newer format in Parquet " +
"will be used. For example, decimals will be written in int-based format. If Parquet " +
"output is intended for use with systems that do not support this newer format, set to true.")
.version("1.6.0")
.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.summary.metadata.level")
.version("1.5.0")
.internal()
.stringConf
.createWithDefault("org.apache.parquet.hadoop.ParquetOutputCommitter")
val PARQUET_VECTORIZED_READER_ENABLED =
buildConf("spark.sql.parquet.enableVectorizedReader")
.doc("Enables vectorized parquet decoding.")
.version("2.0.0")
.booleanConf
.createWithDefault(true)
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. " +
s"This configuration only has an effect when '${PARQUET_FILTER_PUSHDOWN_ENABLED.key}' " +
"is enabled and the vectorized reader is not used. You can ensure the vectorized reader " +
s"is not used by setting '${PARQUET_VECTORIZED_READER_ENABLED.key}' to false.")
.version("2.3.0")
.booleanConf
.createWithDefault(false)
val PARQUET_VECTORIZED_READER_BATCH_SIZE = buildConf("spark.sql.parquet.columnarReaderBatchSize")
.doc("The number of rows to include in a parquet vectorized reader batch. The number should " +
"be carefully chosen to minimize overhead and avoid OOMs in reading data.")
.version("2.4.0")
.intConf
.createWithDefault(4096)
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.")
.version("2.3.0")
.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. " +
"It is 'hive' by default prior to Spark 2.4.")
.version("2.3.0")
.internal()
.stringConf
.checkValues(Set("hive", "native"))
.createWithDefault("native")
val ORC_VECTORIZED_READER_ENABLED = buildConf("spark.sql.orc.enableVectorizedReader")
.doc("Enables vectorized orc decoding.")
.version("2.3.0")
.booleanConf
.createWithDefault(true)
val ORC_VECTORIZED_READER_BATCH_SIZE = buildConf("spark.sql.orc.columnarReaderBatchSize")
.doc("The number of rows to include in a orc vectorized reader batch. The number should " +
"be carefully chosen to minimize overhead and avoid OOMs in reading data.")
.version("2.4.0")
.intConf
.createWithDefault(4096)
val ORC_FILTER_PUSHDOWN_ENABLED = buildConf("spark.sql.orc.filterPushdown")
.doc("When true, enable filter pushdown for ORC files.")
.version("1.4.0")
.booleanConf
.createWithDefault(true)
val ORC_SCHEMA_MERGING_ENABLED = buildConf("spark.sql.orc.mergeSchema")
.doc("When true, the Orc data source merges schemas collected from all data files, " +
"otherwise the schema is picked from a random data file.")
.version("3.0.0")
.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. This configuration will be deprecated in the future " +
s"releases and replaced by ${SPARK_IGNORE_MISSING_FILES.key}.")
.version("1.4.0")
.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).")
.version("1.5.0")
.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.")
.version("2.1.1")
.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.")
.version("2.1.1")
.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")
.internal()
.doc("Sets the action to take when a case-sensitive schema cannot be read from a Hive Serde " +
"table's properties when reading the table with Spark native data sources. Valid options " +
"include INFER_AND_SAVE (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 (the default mode-- fallback " +
"to using the case-insensitive metastore schema instead of inferring).")
.version("2.1.1")
.stringConf
.transform(_.toUpperCase(Locale.ROOT))
.checkValues(HiveCaseSensitiveInferenceMode.values.map(_.toString))
.createWithDefault(HiveCaseSensitiveInferenceMode.NEVER_INFER.toString)
val OPTIMIZER_METADATA_ONLY = buildConf("spark.sql.optimizer.metadataOnly")
.internal()
.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. By default the optimization is disabled, and deprecated as of Spark " +
"3.0 since it may return incorrect results when the files are empty, see also SPARK-26709." +
"It will be removed in the future releases. If you must use, use 'SparkSessionExtensions' " +
"instead to inject it as a custom rule.")
.version("2.1.1")
.booleanConf
.createWithDefault(false)
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.")
.version("1.2.0")
.stringConf
.createWithDefault("_corrupt_record")
val BROADCAST_TIMEOUT = buildConf("spark.sql.broadcastTimeout")
.doc("Timeout in seconds for the broadcast wait time in broadcast joins.")
.version("1.3.0")
.timeConf(TimeUnit.SECONDS)
.createWithDefaultString(s"${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.")
.version("1.1.1")
.stringConf
.createOptional
val THRIFTSERVER_INCREMENTAL_COLLECT =
buildConf("spark.sql.thriftServer.incrementalCollect")
.internal()
.doc("When true, enable incremental collection for execution in Thrift Server.")
.version("2.0.3")
.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.")
.version("1.4.0")
.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.")
.version("1.4.0")
.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.")
.version("1.3.0")
.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, " +
s"using the data source set by ${DEFAULT_DATA_SOURCE_NAME.key}.")
.version("2.0.0")
.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.")
.version("2.0.1")
.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.")
.version("1.5.0")
.booleanConf
.createWithDefault(true)
val BUCKETING_ENABLED = buildConf("spark.sql.sources.bucketing.enabled")
.doc("When false, we will treat bucketed table as normal table")
.version("2.0.0")
.booleanConf
.createWithDefault(true)
val BUCKETING_MAX_BUCKETS = buildConf("spark.sql.sources.bucketing.maxBuckets")
.doc("The maximum number of buckets allowed.")
.version("2.4.0")
.intConf
.checkValue(_ > 0, "the value of spark.sql.sources.bucketing.maxBuckets must be greater than 0")
.createWithDefault(100000)
val CROSS_JOINS_ENABLED = buildConf("spark.sql.crossJoin.enabled")
.internal()
.doc("When false, we will throw an error if a query contains a cartesian product without " +
"explicit CROSS JOIN syntax.")
.version("2.0.0")
.booleanConf
.createWithDefault(true)
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.")
.version("2.0.0")
.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.")
.version("2.0.0")
.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.")
.version("2.2.0")
.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")
.version("1.4.0")
.internal()
.stringConf
.createOptional
val FILE_COMMIT_PROTOCOL_CLASS =
buildConf("spark.sql.sources.commitProtocolClass")
.version("2.1.1")
.internal()
.stringConf
.createWithDefault(
"org.apache.spark.sql.execution.datasources.SQLHadoopMapReduceCommitProtocol")
val PARALLEL_PARTITION_DISCOVERY_THRESHOLD =