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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
..
docs [SPARK-27557][DOC] Add copy button to Python API docs for easier copying of code-blocks 2019-05-01 11:26:18 -05:00
lib [SPARK-25891][PYTHON] Upgrade to Py4J 0.10.8.1 2018-10-31 09:55:03 -07:00
pyspark Revert "[SPARK-27870][SQL][PYSPARK] Flush batch timely for pandas UDF (for improving pandas UDFs pipeline)" 2019-06-09 08:28:31 -07:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-26803][PYTHON] Add sbin subdirectory to pyspark 2019-02-27 08:39:55 -06:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [MINOR][DOCS] Tighten up some key links to the project and download pages to use HTTPS 2019-05-21 10:56:42 -07:00
run-tests [SPARK-8583] [SPARK-5482] [BUILD] Refactor python/run-tests to integrate with dev/run-tests module system 2015-06-27 20:24:34 -07:00
run-tests-with-coverage [SPARK-26252][PYTHON] Add support to run specific unittests and/or doctests in python/run-tests script 2018-12-05 15:22:08 +08:00
run-tests.py [SPARK-25079][PYTHON] update python3 executable to 3.6.x 2019-04-19 10:03:50 +09:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-27276][PYTHON][SQL] Increase minimum version of pyarrow to 0.12.1 and remove prior workarounds 2019-04-22 19:30:31 +09:00

README.md

Apache Spark

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

https://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the Apache Spark downloads page.

NOTE: If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.8.1), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).