Spark Support For Orc

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Joint Blog Post: Bringing ORC Support into Apache Spark ...

    https://databricks.com/blog/2015/07/16/joint-blog-post-bringing-orc-support-into-apache-spark.html
    We are proud to announce that support for the Apache Optimized Row Columnar (ORC) file format is included in Apache Spark 1.4 as a new data source. This support was added through a collaboration between Hortonworks and Databricks, tracked by SPARK-2883.

ORC Files - Spark 2.4.4 Documentation

    https://spark.apache.org/docs/latest/sql-data-sources-orc.html
    The name of ORC implementation. It can be one of native and hive. native means the native ORC support that is built on Apache ORC 1.4. `hive` means the ORC library in Hive 1.2.1. spark.sql.orc.enableVectorizedReader: true: Enables vectorized orc decoding in native implementation. If false, a new non-vectorized ORC reader is used in native ...

Writing a Spark DataFrame to ORC files Kit Menke's Blog

    https://kitmenke.com/blog/2016/12/12/writing-a-spark-dataframe-to-orc-files/
    Dec 12, 2016 · Spark includes the ability to write multiple different file formats to HDFS. One of those is ORC which is columnar file format featuring great compression and improved query performance through Hive.. You’ll need to create a HiveContext in order to write using the ORC data source in Spark.

Hive Tables - Spark 2.4.4 Documentation

    https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html
    One of the most important pieces of Spark SQL’s Hive support is interaction with Hive metastore, which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive …

[SPARK-16060][SQL] Support Vectorized ORC Reader by ...

    https://github.com/apache/spark/pull/19943
    What changes were proposed in this pull request? This PR adds an ORC columnar-batch reader to native OrcFileFormat. Since both Spark ColumnarBatch and ORC RowBatch are used together, it is faster than the current Spark implementation. This replaces the prior PR, #17924. Also, this PR adds OrcReadBenchmark to show the performance improvement.

ORC improvement in Apache Spark 2.3 - DataWorks Summit

    https://dataworkssummit.com/berlin-2018/session/orc-improvement-in-apache-spark-2-3/
    Especially, ORC filter pushdown can be faster than Parquet due to in-file indexes. Second, as a part of native ORC support, Spark 2.3 can convert the Hive ORC tables into Spark ORC data sources automatically. This solves several existing ORC issues and Spark 2.4 will enable it by default.

[SPARK-2883] Spark Support for ORCFile format - ASF JIRA

    http://issues.apache.org/jira/browse/SPARK-2883
    SPARK-2883; Spark Support for ORCFile format. Log In. Export. XML Word Printable JSON. Details. ... Verify the support of OrcInputFormat in spark, fix issues if exists and add documentation of its usage. Attachments. Options. ... SPARK-3720 support ORC in spark sql. …

ORC improvement in Apache Spark 2.3 - SlideShare

    https://www.slideshare.net/Hadoop_Summit/orc-improvement-in-apache-spark-23-95295487
    Apr 28, 2018 · Apache Spark 2.3, released on February 2018, is the fourth release in 2.x line and has a lot of new improvements. One of the notable improvements is ORC support. Apache Spark 2.3 adds a native ORC file format implementation by using the latest Apache ORC 1.4.1. Users can switch between “native” and “hive” ORC file formats.

scala - Spark: Save Dataframe in ORC format - Stack Overflow

    https://stackoverflow.com/questions/32616841/spark-save-dataframe-in-orc-format
    Spark: Save Dataframe in ORC format. Ask Question Asked 4 years, 2 months ago. Active 4 years, 2 months ago. Viewed 9k times 7. In the previous version, we used to have a 'saveAsOrcFile()' method on RDD. ... Since Spark 1.4 you can simply use DataFrameWriter and set format to orc: peopleSchemaRDD.write.format("orc").save("people") or.

[SPARK-3720] support ORC in spark sql - ASF JIRA

    http://issues.apache.org/jira/browse/SPARK-3720?actionOrder=desc
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