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### What changes were proposed in this pull request? To improve the quality of our Spark SQL in different Hive metastore versions, this PR is to enable end-to-end testing using different versions. This PR allows the test cases in sql/hive to pass the existing Hive client to create a SparkSession. - Since Derby does not allow concurrent connections, the pre-built Hive clients use different database from the TestHive's built-in 1.2.1 client. - Since our test cases in sql/hive only can create a single Spark context in the same JVM, the newly created SparkSession share the same spark context with the existing TestHive's corresponding SparkSession. ### How was this patch tested? Fixed the existing test cases. Author: Xiao Li <gatorsmile@gmail.com> Closes #17260 from gatorsmile/versionSuite. |
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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.