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HyukjinKwon f984f6acfe Revert "[SPARK-27870][SQL][PYSPARK] Flush batch timely for pandas UDF (for improving pandas UDFs pipeline)" 
## What changes were proposed in this pull request?

This PR reverts 9c4eb99c52 for the reasons below:

1. An alternative was not considered properly, https://github.com/apache/spark/pull/24734#issuecomment-500101639 https://github.com/apache/spark/pull/24734#issuecomment-500102340 https://github.com/apache/spark/pull/24734#issuecomment-499202982 - I opened a PR https://github.com/apache/spark/pull/24826

2. 9c4eb99c52 fixed timely flushing which behaviour is somewhat hacky and the timing isn't also guaranteed (in case each batch takes longer to process).

3. For pipelining for smaller batches, looks it's better to allow to configure buffer size rather than having another factor to flush

## How was this patch tested?

N/A

Closes #24827 from HyukjinKwon/revert-flush.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2019-06-09 08:28:31 -07:00
..
catalyst [SPARK-27798][SQL] from_avro shouldn't produces same value when converted to local relation 2019-06-07 13:47:36 -07:00
core Revert "[SPARK-27870][SQL][PYSPARK] Flush batch timely for pandas UDF (for improving pandas UDFs pipeline)" 2019-06-09 08:28:31 -07:00
hive [SPARK-27970][SQL] Support Hive 3.0 metastore 2019-06-07 15:24:07 -07:00
hive-thriftserver [SPARK-27749][SQL] hadoop-3.2 support hive-thriftserver 2019-06-05 08:40:05 -07:00
create-docs.sh [MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script 2017-08-26 13:56:24 +09:00
gen-sql-markdown.py [SPARK-27328][SQL] Add 'deprecated' in ExpressionDescription for extended usage and SQL doc 2019-04-09 13:49:42 +08:00
mkdocs.yml [SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions 2017-07-26 09:38:51 -07:00
README.md [MINOR][DOC] Fix some typos and grammar issues 2018-04-06 13:37:08 +08:00

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 allow 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.

Running sql/create-docs.sh generates SQL documentation for built-in functions under sql/site.