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hyukjinkwon 2cac3b2d4a [SPARK-16516][SQL] Support for pushing down filters for decimal and timestamp types in ORC 
## What changes were proposed in this pull request?

It seems ORC supports all the types in  ([`PredicateLeaf.Type`](e085b7e9bd/storage-api/src/java/org/apache/hadoop/hive/ql/io/sarg/PredicateLeaf.java (L50-L56))) which includes timestamp type and decimal type.

In more details, the types listed in [`SearchArgumentImpl.boxLiteral()`](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) can be used as a filter value.

FYI, inital `case` caluse for supported types was introduced in 65d71bd9fb and this was not changed overtime. At that time, Hive version was, 0.13 which supports only some types for filter-push down (See [SearchArgumentImpl.java#L945-L965](https://github.com/apache/hive/blob/branch-0.13/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L945-L965) at 0.13).

However, the version was upgraded into 1.2.x and now it supports more types (See [SearchArgumentImpl.java#L1068-L1093](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) at 1.2.0)

## How was this patch tested?

Unit tests in `OrcFilterSuite` and `OrcQuerySuite`

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #14172 from HyukjinKwon/SPARK-16516.
2016-09-28 00:50:12 +08:00
..
catalyst [SPARK-15962][SQL] Introduce implementation with a dense format for UnsafeArrayData 2016-09-27 14:18:32 +08:00
core [SPARK-16777][SQL] Do not use deprecated listType API in ParquetSchemaConverter 2016-09-28 00:39:47 +08:00
hive [SPARK-16516][SQL] Support for pushing down filters for decimal and timestamp types in ORC 2016-09-28 00:50:12 +08:00
hive-thriftserver [SPARK-17190][SQL] Removal of HiveSharedState 2016-08-25 12:50:03 +08:00
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.