bb49c80c89
### What changes were proposed in this pull request? We shall have a new mechanism that the downstream operators may notify its parents that they may release the output data stream. In this PR, we implement the mechanism as below: - Add function named `cleanupResources` in SparkPlan, which default call children's `cleanupResources` function, the operator which need a resource cleanup should rewrite this with the self cleanup and also call `super.cleanupResources`, like SortExec in this PR. - Add logic support on the trigger side, in this PR is SortMergeJoinExec, which make sure and call the `cleanupResources` to do the cleanup job for all its upstream(children) operator. ### Why are the changes needed? Bugfix for SortMergeJoin memory leak, and implement a general framework for SparkPlan resource cleanup. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? UT: Add new test suite JoinWithResourceCleanSuite to check both standard and code generation scenario. Integrate Test: Test with driver/executor default memory set 1g, local mode 10 thread. The below test(thanks taosaildrone for providing this test [here](https://github.com/apache/spark/pull/23762#issuecomment-463303175)) will pass with this PR. ``` from pyspark.sql.functions import rand, col spark.conf.set("spark.sql.join.preferSortMergeJoin", "true") spark.conf.set("spark.sql.autoBroadcastJoinThreshold", -1) # spark.conf.set("spark.sql.sortMergeJoinExec.eagerCleanupResources", "true") r1 = spark.range(1, 1001).select(col("id").alias("timestamp1")) r1 = r1.withColumn('value', rand()) r2 = spark.range(1000, 1001).select(col("id").alias("timestamp2")) r2 = r2.withColumn('value2', rand()) joined = r1.join(r2, r1.timestamp1 == r2.timestamp2, "inner") joined = joined.coalesce(1) joined.explain() joined.show() ``` Closes #26164 from xuanyuanking/SPARK-21492. Authored-by: Yuanjian Li <xyliyuanjian@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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| .. | ||
| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| create-docs.sh | ||
| gen-sql-markdown.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.