39181ff209
### What changes were proposed in this pull request? Based on a follow up comment in https://github.com/apache/spark/pull/28123, where we can coalesce buckets for shuffled hash join as well. The note here is we only coalesce the buckets from shuffled hash join stream side (i.e. the side not building hash map), so we don't need to worry about OOM when coalescing multiple buckets in one task for building hash map. > If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. Refactor existing physical plan rule `CoalesceBucketsInSortMergeJoin` to `CoalesceBucketsInJoin`, for covering shuffled hash join as well. Refactor existing unit test `CoalesceBucketsInSortMergeJoinSuite` to `CoalesceBucketsInJoinSuite`, for covering shuffled hash join as well. ### Why are the changes needed? Avoid shuffle for joining different bucketed tables, is also useful for shuffled hash join. In production, we are seeing users to use shuffled hash join to join bucketed tables (set `spark.sql.join.preferSortMergeJoin`=false, to avoid sort), and this can help avoid shuffle if number of buckets are not same. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Added unit tests in `CoalesceBucketsInJoinSuite` for verifying shuffled hash join physical plan. ### Performance number per request from maropu I was looking at TPCDS per suggestion from maropu. But I found most of queries from TPCDS are doing aggregate, and only several ones are doing join. None of input tables are bucketed. So I took the approach to test a modified version of `TPCDS q93` as ``` SELECT ss_ticket_number, sr_ticket_number FROM store_sales JOIN store_returns ON ss_ticket_number = sr_ticket_number ``` And make `store_sales` and `store_returns` to be bucketed tables. Physical query plan without coalesce: ``` ShuffledHashJoin [ss_ticket_number#109L], [sr_ticket_number#120L], Inner, BuildLeft :- Exchange hashpartitioning(ss_ticket_number#109L, 4), true, [id=#67] : +- *(1) Project [ss_ticket_number#109L] : +- *(1) Filter isnotnull(ss_ticket_number#109L) : +- *(1) ColumnarToRow : +- FileScan parquet default.store_sales[ss_ticket_number#109L] Batched: true, DataFilters: [isnotnull(ss_ticket_number#109L)], Format: Parquet, Location: InMemoryFileIndex[file:/Users/chengsu/spark/spark-warehouse/store_sales], PartitionFilters: [], PushedFilters: [IsNotNull(ss_ticket_number)], ReadSchema: struct<ss_ticket_number:bigint>, SelectedBucketsCount: 2 out of 2 +- *(2) Project [sr_returned_date_sk#111L, sr_return_time_sk#112L, sr_item_sk#113L, sr_customer_sk#114L, sr_cdemo_sk#115L, sr_hdemo_sk#116L, sr_addr_sk#117L, sr_store_sk#118L, sr_reason_sk#119L, sr_ticket_number#120L, sr_return_quantity#121L, sr_return_amt#122, sr_return_tax#123, sr_return_amt_inc_tax#124, sr_fee#125, sr_return_ship_cost#126, sr_refunded_cash#127, sr_reversed_charge#128, sr_store_credit#129, sr_net_loss#130] +- *(2) Filter isnotnull(sr_ticket_number#120L) +- *(2) ColumnarToRow +- FileScan parquet default.store_returns[sr_returned_date_sk#111L,sr_return_time_sk#112L,sr_item_sk#113L,sr_customer_sk#114L,sr_cdemo_sk#115L,sr_hdemo_sk#116L,sr_addr_sk#117L,sr_store_sk#118L,sr_reason_sk#119L,sr_ticket_number#120L,sr_return_quantity#121L,sr_return_amt#122,sr_return_tax#123,sr_return_amt_inc_tax#124,sr_fee#125,sr_return_ship_cost#126,sr_refunded_cash#127,sr_reversed_charge#128,sr_store_credit#129,sr_net_loss#130] Batched: true, DataFilters: [isnotnull(sr_ticket_number#120L)], Format: Parquet, Location: InMemoryFileIndex[file:/Users/chengsu/spark/spark-warehouse/store_returns], PartitionFilters: [], PushedFilters: [IsNotNull(sr_ticket_number)], ReadSchema: struct<sr_returned_date_sk:bigint,sr_return_time_sk:bigint,sr_item_sk:bigint,sr_customer_sk:bigin..., SelectedBucketsCount: 4 out of 4 ``` Physical query plan with coalesce: ``` ShuffledHashJoin [ss_ticket_number#109L], [sr_ticket_number#120L], Inner, BuildLeft :- *(1) Project [ss_ticket_number#109L] : +- *(1) Filter isnotnull(ss_ticket_number#109L) : +- *(1) ColumnarToRow : +- FileScan parquet default.store_sales[ss_ticket_number#109L] Batched: true, DataFilters: [isnotnull(ss_ticket_number#109L)], Format: Parquet, Location: InMemoryFileIndex[file:/Users/chengsu/spark/spark-warehouse/store_sales], PartitionFilters: [], PushedFilters: [IsNotNull(ss_ticket_number)], ReadSchema: struct<ss_ticket_number:bigint>, SelectedBucketsCount: 2 out of 2 +- *(2) Project [sr_returned_date_sk#111L, sr_return_time_sk#112L, sr_item_sk#113L, sr_customer_sk#114L, sr_cdemo_sk#115L, sr_hdemo_sk#116L, sr_addr_sk#117L, sr_store_sk#118L, sr_reason_sk#119L, sr_ticket_number#120L, sr_return_quantity#121L, sr_return_amt#122, sr_return_tax#123, sr_return_amt_inc_tax#124, sr_fee#125, sr_return_ship_cost#126, sr_refunded_cash#127, sr_reversed_charge#128, sr_store_credit#129, sr_net_loss#130] +- *(2) Filter isnotnull(sr_ticket_number#120L) +- *(2) ColumnarToRow +- FileScan parquet default.store_returns[sr_returned_date_sk#111L,sr_return_time_sk#112L,sr_item_sk#113L,sr_customer_sk#114L,sr_cdemo_sk#115L,sr_hdemo_sk#116L,sr_addr_sk#117L,sr_store_sk#118L,sr_reason_sk#119L,sr_ticket_number#120L,sr_return_quantity#121L,sr_return_amt#122,sr_return_tax#123,sr_return_amt_inc_tax#124,sr_fee#125,sr_return_ship_cost#126,sr_refunded_cash#127,sr_reversed_charge#128,sr_store_credit#129,sr_net_loss#130] Batched: true, DataFilters: [isnotnull(sr_ticket_number#120L)], Format: Parquet, Location: InMemoryFileIndex[file:/Users/chengsu/spark/spark-warehouse/store_returns], PartitionFilters: [], PushedFilters: [IsNotNull(sr_ticket_number)], ReadSchema: struct<sr_returned_date_sk:bigint,sr_return_time_sk:bigint,sr_item_sk:bigint,sr_customer_sk:bigin..., SelectedBucketsCount: 4 out of 4 (Coalesced to 2) ``` Run time improvement as 50% of wall clock time: ``` Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Mac OS X 10.15.4 Intel(R) Core(TM) i9-9980HK CPU 2.40GHz shuffle hash join: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ shuffle hash join coalesce bucket off 1541 1664 106 1.9 535.1 1.0X shuffle hash join coalesce bucket on 1060 1169 81 2.7 368.1 1.5X ``` Closes #29079 from c21/split-bucket. Authored-by: Cheng Su <chengsu@fb.com> Signed-off-by: Takeshi Yamamuro <yamamuro@apache.org> |
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|---|---|---|
| .. | ||
| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| create-docs.sh | ||
| gen-sql-api-docs.py | ||
| gen-sql-config-docs.py | ||
| gen-sql-functions-docs.py | ||
| mkdocs.yml | ||
| README.md | ||
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 extensions that allow users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow 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.
Running ./sql/create-docs.sh generates SQL documentation for built-in functions under sql/site, and SQL configuration documentation that gets included as part of configuration.md in the main docs directory.