# 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

Spark SQL configuration的更多相关文章

  1. Spark SQL 之 Data Sources

    #Spark SQL 之 Data Sources 转载请注明出处:http://www.cnblogs.com/BYRans/ 数据源(Data Source) Spark SQL的DataFram ...

  2. Spark SQL 官方文档-中文翻译

    Spark SQL 官方文档-中文翻译 Spark版本:Spark 1.5.2 转载请注明出处:http://www.cnblogs.com/BYRans/ 1 概述(Overview) 2 Data ...

  3. Spark SQL 之 Performance Tuning & Distributed SQL Engine

    Spark SQL 之 Performance Tuning & Distributed SQL Engine 转载请注明出处:http://www.cnblogs.com/BYRans/ 缓 ...

  4. SparkSQL使用之Spark SQL CLI

    Spark SQL CLI描述 Spark SQL CLI的引入使得在SparkSQL中通过hive metastore就可以直接对hive进行查询更加方便:当前版本中还不能使用Spark SQL C ...

  5. Apache Spark 2.2.0 中文文档 - Spark SQL, DataFrames and Datasets Guide | ApacheCN

    Spark SQL, DataFrames and Datasets Guide Overview SQL Datasets and DataFrames 开始入门 起始点: SparkSession ...

  6. Spark官方1 ---------Spark SQL和DataFrame指南(1.5.0)

    概述 Spark SQL是用于结构化数据处理的Spark模块.它提供了一个称为DataFrames的编程抽象,也可以作为分布式SQL查询引擎. Spark SQL也可用于从现有的Hive安装中读取数据 ...

  7. Spark SQL官方文档阅读--待完善

    1,DataFrame是一个将数据格式化为列形式的分布式容器,类似于一个关系型数据库表. 编程入口:SQLContext 2,SQLContext由SparkContext对象创建 也可创建一个功能更 ...

  8. 【原创】大叔经验分享(23)spark sql插入表时的文件个数研究

    spark sql执行insert overwrite table时,写到新表或者新分区的文件个数,有可能是200个,也有可能是任意个,为什么会有这种差别? 首先看一下spark sql执行inser ...

  9. 【慕课网实战】八、以慕课网日志分析为例 进入大数据 Spark SQL 的世界

    用户行为日志:用户每次访问网站时所有的行为数据(访问.浏览.搜索.点击...)     用户行为轨迹.流量日志   日志数据内容: 1)访问的系统属性: 操作系统.浏览器等等 2)访问特征:点击的ur ...

随机推荐

  1. Java基础知识总结--final、finally、finalize的区别

    谈谈final.finally.finalize的区别 1.final修饰符:如果一个类被声明为final,意味着这个类不能再被派生出新的子类,不能作为父类被别的类继承.因此,一个类不能即被声明为ab ...

  2. list-列表练习

    #list列表取值更方便灵活 列表.数组说的都是1个东西#列表中每个字符都有一个编号,就是我们说的下标,从0开始#如果你输入的下标在列表中不存在,会报下标越界的错误 1.查询user表中下标为0的记录 ...

  3. python-循环小练习

    作业:登录程序,最多循环输入三次,账号密码校验输入为空的情况: 知识点总结 1.while和for循环的区别是,1个需要定义计数器,1个不需要定义计数器: 2.break正常循环结束后会用到,意思是不 ...

  4. js 创建Table,每行3列的方式

    table: <table style="width:100%" id="appDatas"></table> 1. var table ...

  5. Brendan Gregg ----Linux Performance Tools NEWS

  6. javagc日志详解

    https://blog.csdn.net/aliveTime https://plumbr.io/blog/garbage-collection/understanding-garbage-coll ...

  7. Unity 5.4版本 Application.systemLanguage 失效

    最近在上线双语版本(一个包支持中文.英文二种语言)时,遇到一个坑点 if (ToolUtils.isAndroid()) { if (Application.systemLanguage == Sys ...

  8. 用distinct or array_unique

    在 Mysql 获取数据时,如果想获取某一列去重数据,如果获取呢 举个例子: advert_pro_ad 表 CREATE TABLE `advert_pro_ad` ( `advert_id` ) ...

  9. mysql select into 不支持

    不支持的 select * into order_new from orders 改为 Create table order_new(select * from orders)  

  10. 您的第一个C++Builder程序(Hello, world!)

    最近有些老旧的项目是C++Builder开发的,虽然和Delphi的IDE的界面和操作十分相似,但是还是找本<C++ Builder 5 Developer's Guide>来看看熟悉下, ...