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Jeff Zhang f00df40cfe [SPARK-11775][PYSPARK][SQL] Allow PySpark to register Java UDF 
Currently pyspark can only call the builtin java UDF, but can not call custom java UDF. It would be better to allow that. 2 benefits:
* Leverage the power of rich third party java library
* Improve the performance. Because if we use python UDF, python daemons will be started on worker which will affect the performance.

Author: Jeff Zhang <zjffdu@apache.org>

Closes #9766 from zjffdu/SPARK-11775.
2016-10-14 15:50:35 -07:00
..
catalyst [SPARK-11775][PYSPARK][SQL] Allow PySpark to register Java UDF 2016-10-14 15:50:35 -07:00
core [SPARK-11775][PYSPARK][SQL] Allow PySpark to register Java UDF 2016-10-14 15:50:35 -07:00
hive Revert "[SPARK-17620][SQL] Determine Serde by hive.default.fileformat when Creating Hive Serde Tables" 2016-10-14 14:09:35 -07:00
hive-thriftserver [SPARK-17707][WEBUI] Web UI prevents spark-submit application to be finished 2016-10-07 10:31:41 -07:00
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.