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## What changes were proposed in this pull request? This PR aims to improve DataSource option keys to be more case-insensitive DataSource partially use CaseInsensitiveMap in code-path. For example, the following fails to find url. ```scala val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2) df.write.format("jdbc") .option("UrL", url1) .option("dbtable", "TEST.SAVETEST") .options(properties.asScala) .save() ``` This PR makes DataSource options to use CaseInsensitiveMap internally and also makes DataSource to use CaseInsensitiveMap generally except `InMemoryFileIndex` and `InsertIntoHadoopFsRelationCommand`. We can not pass them CaseInsensitiveMap because they creates new case-sensitive HadoopConfs by calling newHadoopConfWithOptions(options) inside. ## How was this patch tested? Pass the Jenkins test with newly added test cases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15884 from dongjoon-hyun/SPARK-18433. |
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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.