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Josh Rosen bf8ff833e3 [SPARK-17194] Use single quotes when generating SQL for string literals 
When Spark emits SQL for a string literal, it should wrap the string in single quotes, not double quotes. Databases which adhere more strictly to the ANSI SQL standards, such as Postgres, allow only single-quotes to be used for denoting string literals (see http://stackoverflow.com/a/1992331/590203).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #14763 from JoshRosen/SPARK-17194.
2016-08-23 22:31:58 +02:00
..
catalyst [SPARK-17194] Use single quotes when generating SQL for string literals 2016-08-23 22:31:58 +02:00
core [SPARK-13286] [SQL] add the next expression of SQLException as cause 2016-08-23 09:45:13 -07:00
hive [SPARK-17194] Use single quotes when generating SQL for string literals 2016-08-23 22:31:58 +02:00
hive-thriftserver [SPARK-16941] Use concurrentHashMap instead of scala Map in SparkSQLOperationManager. 2016-08-11 11:28:28 +01:00
README.md [SPARK-16557][SQL] Remove stale doc in sql/README.md 2016-07-14 19:24:42 -07:00

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.