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### What changes were proposed in this pull request? Upgrade Apache Arrow to version 0.15.1. This includes Java artifacts and increases the minimum required version of PyArrow also. Version 0.12.0 to 0.15.1 includes the following selected fixes/improvements relevant to Spark users: * ARROW-6898 - [Java] Fix potential memory leak in ArrowWriter and several test classes * ARROW-6874 - [Python] Memory leak in Table.to_pandas() when conversion to object dtype * ARROW-5579 - [Java] shade flatbuffer dependency * ARROW-5843 - [Java] Improve the readability and performance of BitVectorHelper#getNullCount * ARROW-5881 - [Java] Provide functionalities to efficiently determine if a validity buffer has completely 1 bits/0 bits * ARROW-5893 - [C++] Remove arrow::Column class from C++ library * ARROW-5970 - [Java] Provide pointer to Arrow buffer * ARROW-6070 - [Java] Avoid creating new schema before IPC sending * ARROW-6279 - [Python] Add Table.slice method or allow slices in \_\_getitem\_\_ * ARROW-6313 - [Format] Tracking for ensuring flatbuffer serialized values are aligned in stream/files. * ARROW-6557 - [Python] Always return pandas.Series from Array/ChunkedArray.to_pandas, propagate field names to Series from RecordBatch, Table * ARROW-2015 - [Java] Use Java Time and Date APIs instead of JodaTime * ARROW-1261 - [Java] Add container type for Map logical type * ARROW-1207 - [C++] Implement Map logical type Changelog can be seen at https://arrow.apache.org/release/0.15.0.html ### Why are the changes needed? Upgrade to get bug fixes, improvements, and maintain compatibility with future versions of PyArrow. ### Does this PR introduce any user-facing change? No ### How was this patch tested? Existing tests, manually tested with Python 3.7, 3.8 Closes #26133 from BryanCutler/arrow-upgrade-015-SPARK-29376. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
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| pyspark | ||
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| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
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| README.md | ||
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| run-tests.py | ||
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| setup.py | ||
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.
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).