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Date: Tue, 6 Jun 2017 21:15:18 +0000 (UTC)
From: "Wes McKinney (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.13076732.1496378240000.10855.1496783718747@Atlassian.JIRA>
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Subject: [jira] [Commented] (SPARK-20960) make ColumnVector public
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archived-at: Tue, 06 Jun 2017 21:15:23 -0000


    [ https://issues.apache.org/jira/browse/SPARK-20960?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16039648#comment-16039648 ] 

Wes McKinney commented on SPARK-20960:
--------------------------------------

[~cloud_fan] this will be very exciting to have as a supported public API for more efficient UDF execution. We're ready to help with improvements to Arrow (like in-memory encodings / compression a la ARROW-300) to help with these use cases.

cc [~jnadeau] [~julienledem]

> make ColumnVector public
> ------------------------
>
>                 Key: SPARK-20960
>                 URL: https://issues.apache.org/jira/browse/SPARK-20960
>             Project: Spark
>          Issue Type: New Feature
>          Components: SQL
>    Affects Versions: 2.3.0
>            Reporter: Wenchen Fan
>
> ColumnVector is an internal interface in Spark SQL, which is only used for vectorized parquet reader to represent the in-memory columnar format.
> In Spark 2.3 we want to make ColumnVector public, so that we can provide a more efficient way for data exchanges between Spark and external systems. For example, we can use ColumnVector to build the columnar read API in data source framework, we can use ColumnVector to build a more efficient UDF API, etc.
> We also want to introduce a new ColumnVector implementation based on Apache Arrow(basically just a wrapper over Arrow), so that external systems(like Python Pandas DataFrame) can build ColumnVector very easily.


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