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## What changes were proposed in this pull request? To use treeAggregate instead of aggregate in DataFrame.stat.bloomFilter to parallelize the operation of merging the bloom filters (Please fill in changes proposed in this fix) ## How was this patch tested? unit tests passed (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Rishabh Bhardwaj <rbnext29@gmail.com> Author: Rishabh Bhardwaj <admin@rishabh.local> Author: Rishabh Bhardwaj <r0b00ko@rishabh.Dlink> Author: Rishabh Bhardwaj <admin@Admins-MacBook-Pro.local> Author: Rishabh Bhardwaj <r0b00ko@rishabh.local> Closes #18263 from rishabhbhardwaj/SPARK-21039. |
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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 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.