[jira] [Commented] (SPARK-3720) support ORC in spark sql

["Zhan Zhang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-3720?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14393146#comment-14393146 ] 

Zhan Zhang commented on SPARK-3720:
-----------------------------------

[~iward] I have update the patch with new api support.  you can refer to https://issues.apache.org/jira/browse/SPARK-2883

> support ORC in spark sql
> ------------------------
>
>                 Key: SPARK-3720
>                 URL: https://issues.apache.org/jira/browse/SPARK-3720
>             Project: Spark
>          Issue Type: New Feature
>          Components: SQL
>    Affects Versions: 1.1.0
>            Reporter: Fei Wang
>         Attachments: orc.diff
>
>
> The Optimized Row Columnar (ORC) file format provides a highly efficient way to store data on hdfs.ORC file format has many advantages such as:
> 1 a single file as the output of each task, which reduces the NameNode's load
> 2 Hive type support including datetime, decimal, and the complex types (struct, list, map, and union)
> 3 light-weight indexes stored within the file
> skip row groups that don't pass predicate filtering
> seek to a given row
> 4 block-mode compression based on data type
> run-length encoding for integer columns
> dictionary encoding for string columns
> 5 concurrent reads of the same file using separate RecordReaders
> 6 ability to split files without scanning for markers
> 7 bound the amount of memory needed for reading or writing
> 8 metadata stored using Protocol Buffers, which allows addition and removal of fields
> Now spark sql support Parquet, support ORC provide people more opts.



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