7ca7a63524
## What changes were proposed in this pull request? This PR adds code generation to `Generate`. It supports two code paths: - General `TraversableOnce` based iteration. This used for regular `Generator` (code generation supporting) expressions. This code path expects the expression to return a `TraversableOnce[InternalRow]` and it will iterate over the returned collection. This PR adds code generation for the `stack` generator. - Specialized `ArrayData/MapData` based iteration. This is used for the `explode`, `posexplode` & `inline` functions and operates directly on the `ArrayData`/`MapData` result that the child of the generator returns. ### Benchmarks I have added some benchmarks and it seems we can create a nice speedup for explode: #### Environment ``` Java HotSpot(TM) 64-Bit Server VM 1.8.0_92-b14 on Mac OS X 10.11.6 Intel(R) Core(TM) i7-4980HQ CPU 2.80GHz ``` #### Explode Array ##### Before ``` generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode array wholestage off 7377 / 7607 2.3 439.7 1.0X generate explode array wholestage on 6055 / 6086 2.8 360.9 1.2X ``` ##### After ``` generate explode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode array wholestage off 7432 / 7696 2.3 443.0 1.0X generate explode array wholestage on 631 / 646 26.6 37.6 11.8X ``` #### Explode Map ##### Before ``` generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode map wholestage off 12792 / 12848 1.3 762.5 1.0X generate explode map wholestage on 11181 / 11237 1.5 666.5 1.1X ``` ##### After ``` generate explode map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate explode map wholestage off 10949 / 10972 1.5 652.6 1.0X generate explode map wholestage on 870 / 913 19.3 51.9 12.6X ``` #### Posexplode ##### Before ``` generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate posexplode array wholestage off 7547 / 7580 2.2 449.8 1.0X generate posexplode array wholestage on 5786 / 5838 2.9 344.9 1.3X ``` ##### After ``` generate posexplode array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate posexplode array wholestage off 7535 / 7548 2.2 449.1 1.0X generate posexplode array wholestage on 620 / 624 27.1 37.0 12.1X ``` #### Inline ##### Before ``` generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate inline array wholestage off 6935 / 6978 2.4 413.3 1.0X generate inline array wholestage on 6360 / 6400 2.6 379.1 1.1X ``` ##### After ``` generate inline array: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate inline array wholestage off 6940 / 6966 2.4 413.6 1.0X generate inline array wholestage on 1002 / 1012 16.7 59.7 6.9X ``` #### Stack ##### Before ``` generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate stack wholestage off 12980 / 13104 1.3 773.7 1.0X generate stack wholestage on 11566 / 11580 1.5 689.4 1.1X ``` ##### After ``` generate stack: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ generate stack wholestage off 12875 / 12949 1.3 767.4 1.0X generate stack wholestage on 840 / 845 20.0 50.0 15.3X ``` ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #13065 from hvanhovell/SPARK-15214. |
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Spark SQL
This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.
Spark SQL is broken up into four subprojects:
- Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
- Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
- Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.
- HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.