Spark SQL configuration
# export by:
spark.sql("SET -v").show(n=200, truncate=False)
| key | value | meaning |
|---|---|---|
| spark.sql.adaptive.enabled | false | When true, enable adaptive query execution. |
| spark.sql.adaptive.shuffle.targetPostShuffleInputSize | 67108864b | The target post-shuffle input size in bytes of a task. |
| spark.sql.autoBroadcastJoinThreshold | 10485760 | 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. |
| spark.sql.broadcastTimeout | 300 | Timeout in seconds for the broadcast wait time in broadcast joins. |
| spark.sql.cbo.enabled | false | Enables CBO for estimation of plan statistics when set true. |
| spark.sql.cbo.joinReorder.dp.star.filter | false | Applies star-join filter heuristics to cost based join enumeration. |
| spark.sql.cbo.joinReorder.dp.threshold | 12 | The maximum number of joined nodes allowed in the dynamic programming algorithm. |
| spark.sql.cbo.joinReorder.enabled | false | Enables join reorder in CBO. |
| spark.sql.cbo.starSchemaDetection | false | When true, it enables join reordering based on star schema detection. |
| spark.sql.columnNameOfCorruptRecord | _corrupt_record | The name of internal column for storing raw/un-parsed JSON and CSV records that fail to parse. |
| spark.sql.crossJoin.enabled | false | When false, we will throw an error if a query contains a cartesian product without explicit CROSS JOIN syntax. |
| spark.sql.extensions | Name of the class used to configure Spark Session extensions. The class should implement Function1[SparkSessionExtension, Unit], and must have a no-args constructor. | |
| spark.sql.files.ignoreCorruptFiles | false | 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. |
| spark.sql.files.maxPartitionBytes | 134217728 | The maximum number of bytes to pack into a single partition when reading files. |
| spark.sql.files.maxRecordsPerFile | 0 | Maximum number of records to write out to a single file. If this value is zero or negative, there is no limit. |
| spark.sql.groupByAliases | true | When true, aliases in a select list can be used in group by clauses. When false, an analysis exception is thrown in the case. |
| spark.sql.groupByOrdinal | true | 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. |
| spark.sql.hive.caseSensitiveInferenceMode | INFER_AND_SAVE | 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). |
| spark.sql.hive.filesourcePartitionFileCacheSize | 262144000 | 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. |
| spark.sql.hive.manageFilesourcePartitions | true | 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. |
| spark.sql.hive.metastorePartitionPruning | true | 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). |
| spark.sql.hive.thriftServer.singleSession | false | When set to true, Hive Thrift server is running in a single session mode. All the JDBC/ODBC connections share the temporary views, function registries, SQL configuration and the current database. |
| spark.sql.hive.verifyPartitionPath | false | When true, check all the partition paths under the table's root directory when reading data stored in HDFS. |
| spark.sql.optimizer.metadataOnly | true | 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. |
| spark.sql.orc.filterPushdown | false | When true, enable filter pushdown for ORC files. |
| spark.sql.orderByOrdinal | true | 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. |
| spark.sql.parquet.binaryAsString | false | 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. |
| spark.sql.parquet.cacheMetadata | true | Turns on caching of Parquet schema metadata. Can speed up querying of static data. |
| spark.sql.parquet.compression.codec | snappy | Sets the compression codec use when writing Parquet files. Acceptable values include: uncompressed, snappy, gzip, lzo. |
| spark.sql.parquet.enableVectorizedReader | true | Enables vectorized parquet decoding. |
| spark.sql.parquet.filterPushdown | true | Enables Parquet filter push-down optimization when set to true. |
| spark.sql.parquet.int64AsTimestampMillis | false | 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 betruncated. |
| spark.sql.parquet.int96AsTimestamp | true | 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. |
| spark.sql.parquet.mergeSchema | false | 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. |
| spark.sql.parquet.respectSummaryFiles | false | 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. |
| spark.sql.parquet.writeLegacyFormat | false | Whether to follow Parquet's format specification when converting Parquet schema to Spark SQL schema and vice versa. |
| spark.sql.pivotMaxValues | 10000 | 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. |
| spark.sql.session.timeZone | Etc/UTC | The ID of session local timezone, e.g. "GMT", "America/Los_Angeles", etc. |
| spark.sql.shuffle.partitions | 80 | The default number of partitions to use when shuffling data for joins or aggregations. |
| spark.sql.sources.bucketing.enabled | true | When false, we will treat bucketed table as normal table |
| spark.sql.sources.default | parquet | The default data source to use in input/output. |
| spark.sql.sources.parallelPartitionDiscovery.threshold | 32 | 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. |
| spark.sql.sources.partitionColumnTypeInference.enabled | true | When true, automatically infer the data types for partitioned columns. |
| spark.sql.statistics.fallBackToHdfs | false | 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. |
| spark.sql.streaming.checkpointLocation | The default location for storing checkpoint data for streaming queries. | |
| spark.sql.streaming.metricsEnabled | false | Whether Dropwizard/Codahale metrics will be reported for active streaming queries. |
| spark.sql.streaming.numRecentProgressUpdates | 100 | The number of progress updates to retain for a streaming query |
| spark.sql.thriftserver.scheduler.pool | Set a Fair Scheduler pool for a JDBC client session. | |
| spark.sql.thriftserver.ui.retainedSessions | 200 | The number of SQL client sessions kept in the JDBC/ODBC web UI history. |
| spark.sql.thriftserver.ui.retainedStatements | 200 | The number of SQL statements kept in the JDBC/ODBC web UI history. |
| spark.sql.variable.substitute | true | This enables substitution using syntax like ${var} ${system:var} and ${env:var}. |
| spark.sql.warehouse.dir | file:/home/buildbot/datacalc/spark-warehouse/ | The default location for managed databases and tables. |
other Spark SQL config:
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
https://github.com/unnunique/Conclusions/blob/master/AADocs/bigdata-docs/compute-components-docs/sparkbasic-docs/standalone.md
