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https://issues.apache.org/jira/browse/HIVE-21240?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16779427#comment-16779427
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slim bouguerra commented on HIVE-21240:
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[~belugabehr] for question
https://issues.apache.org/jira/browse/HIVE-21240?focusedCommentId=16778489&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-16778489
yes , thanks for doing this.
Now i did not review this work end to end, am wondering why you are proposing
to modify the Serde return type? form native arrays to Java collections.
I think this can have a major performance draw back since Java collection tend
to use more memory space and can cause more GCs.
Can you please explain why are you making this change ? like pros, cons ?
Thanks looking forward for this work to be included.
> JSON SerDe Re-Write
> -------------------
>
> Key: HIVE-21240
> URL: https://issues.apache.org/jira/browse/HIVE-21240
> Project: Hive
> Issue Type: Improvement
> Components: Serializers/Deserializers
> Affects Versions: 4.0.0, 3.1.1
> Reporter: BELUGA BEHR
> Assignee: BELUGA BEHR
> Priority: Major
> Labels: pull-request-available
> Fix For: 4.0.0
>
> Attachments: HIVE-21240.1.patch, HIVE-21240.1.patch,
> HIVE-21240.10.patch, HIVE-21240.11.patch, HIVE-21240.11.patch,
> HIVE-21240.11.patch, HIVE-21240.11.patch, HIVE-21240.2.patch,
> HIVE-21240.3.patch, HIVE-21240.4.patch, HIVE-21240.5.patch,
> HIVE-21240.6.patch, HIVE-21240.7.patch, HIVE-21240.9.patch,
> HIVE-24240.8.patch, kafka_storage_handler.diff
>
> Time Spent: 10m
> Remaining Estimate: 0h
>
> The JSON SerDe has a few issues, I will link them to this JIRA.
> * Use Jackson Tree parser instead of manually parsing
> * Added support for base-64 encoded data (the expected format when using JSON)
> * Added support to skip blank lines (returns all columns as null values)
> * Current JSON parser accepts, but does not apply, custom timestamp formats
> in most cases
> * Added some unit tests
> * Added cache for column-name to column-index searches, currently O\(n\) for
> each row processed, for each column in the row
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