spark-instrumented-optimizer/dev/deps/spark-deps-hadoop-2.7

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JavaEWAH-0.3.2.jar
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[SPARK-13534][PYSPARK] Using Apache Arrow to increase performance of DataFrame.toPandas ## What changes were proposed in this pull request? Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown. Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default). ## How was this patch tested? Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly. Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas. Author: Bryan Cutler <cutlerb@gmail.com> Author: Li Jin <ice.xelloss@gmail.com> Author: Li Jin <li.jin@twosigma.com> Author: Wes McKinney <wes.mckinney@twosigma.com> Closes #18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
2017-07-10 18:21:03 -04:00
arrow-format-0.4.0.jar
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[SPARK-13534][PYSPARK] Using Apache Arrow to increase performance of DataFrame.toPandas ## What changes were proposed in this pull request? Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown. Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default). ## How was this patch tested? Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly. Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas. Author: Bryan Cutler <cutlerb@gmail.com> Author: Li Jin <ice.xelloss@gmail.com> Author: Li Jin <li.jin@twosigma.com> Author: Wes McKinney <wes.mckinney@twosigma.com> Closes #18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
2017-07-10 18:21:03 -04:00
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[SPARK-13534][PYSPARK] Using Apache Arrow to increase performance of DataFrame.toPandas ## What changes were proposed in this pull request? Integrate Apache Arrow with Spark to increase performance of `DataFrame.toPandas`. This has been done by using Arrow to convert data partitions on the executor JVM to Arrow payload byte arrays where they are then served to the Python process. The Python DataFrame can then collect the Arrow payloads where they are combined and converted to a Pandas DataFrame. Data types except complex, date, timestamp, and decimal are currently supported, otherwise an `UnsupportedOperation` exception is thrown. Additions to Spark include a Scala package private method `Dataset.toArrowPayload` that will convert data partitions in the executor JVM to `ArrowPayload`s as byte arrays so they can be easily served. A package private class/object `ArrowConverters` that provide data type mappings and conversion routines. In Python, a private method `DataFrame._collectAsArrow` is added to collect Arrow payloads and a SQLConf "spark.sql.execution.arrow.enable" can be used in `toPandas()` to enable using Arrow (uses the old conversion by default). ## How was this patch tested? Added a new test suite `ArrowConvertersSuite` that will run tests on conversion of Datasets to Arrow payloads for supported types. The suite will generate a Dataset and matching Arrow JSON data, then the dataset is converted to an Arrow payload and finally validated against the JSON data. This will ensure that the schema and data has been converted correctly. Added PySpark tests to verify the `toPandas` method is producing equal DataFrames with and without pyarrow. A roundtrip test to ensure the pandas DataFrame produced by pyspark is equal to a one made directly with pandas. Author: Bryan Cutler <cutlerb@gmail.com> Author: Li Jin <ice.xelloss@gmail.com> Author: Li Jin <li.jin@twosigma.com> Author: Wes McKinney <wes.mckinney@twosigma.com> Closes #18459 from BryanCutler/toPandas_with_arrow-SPARK-13534.
2017-07-10 18:21:03 -04:00
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[SPARK-17583][SQL] Remove uesless rowSeparator variable and set auto-expanding buffer as default for maxCharsPerColumn option in CSV ## What changes were proposed in this pull request? This PR includes the changes below: 1. Upgrade Univocity library from 2.1.1 to 2.2.1 This includes some performance improvement and also enabling auto-extending buffer in `maxCharsPerColumn` option in CSV. Please refer the [release notes](https://github.com/uniVocity/univocity-parsers/releases). 2. Remove useless `rowSeparator` variable existing in `CSVOptions` We have this unused variable in [CSVOptions.scala#L127](https://github.com/apache/spark/blob/29952ed096fd2a0a19079933ff691671d6f00835/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala#L127) but it seems possibly causing confusion that it actually does not care of `\r\n`. For example, we have an issue open about this, [SPARK-17227](https://issues.apache.org/jira/browse/SPARK-17227), describing this variable. This variable is virtually not being used because we rely on `LineRecordReader` in Hadoop which deals with only both `\n` and `\r\n`. 3. Set the default value of `maxCharsPerColumn` to auto-expending. We are setting 1000000 for the length of each column. It'd be more sensible we allow auto-expending rather than fixed length by default. To make sure, using `-1` is being described in the release note, [2.2.0](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.2.0). ## How was this patch tested? N/A Author: hyukjinkwon <gurwls223@gmail.com> Closes #15138 from HyukjinKwon/SPARK-17583.
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univocity-parsers-2.2.1.jar
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