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Posted to issues@spark.apache.org by "Li Yuanjian (JIRA)" <ji...@apache.org> on 2018/06/26 01:59:00 UTC
[jira] [Commented] (SPARK-24630) SPIP: Support SQLStreaming in
Spark
[ https://issues.apache.org/jira/browse/SPARK-24630?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16523064#comment-16523064 ]
Li Yuanjian commented on SPARK-24630:
-------------------------------------
cc [~zsxwing] and [~tdas]
We have some practice over SQLStreaming as [~Jackey Lee] described in SPIP doc, but maybe there's two points need more discussion:
1. The "STREAM" key words in SQL grammar
2. Combining with Hive table is enough in internal usage, but maybe we should integrate with Datasoure V2 for stronger generality
> SPIP: Support SQLStreaming in Spark
> -----------------------------------
>
> Key: SPARK-24630
> URL: https://issues.apache.org/jira/browse/SPARK-24630
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.0, 2.2.1
> Reporter: Jackey Lee
> Priority: Minor
> Labels: SQLStreaming
> Attachments: SQLStreaming SPIP.pdf
>
>
> At present, KafkaSQL, Flink SQL(which is actually based on Calcite), SQLStream, StormSQL all provide a stream type SQL interface, with which users with little knowledge about streaming, can easily develop a flow system processing model. In Spark, we can also support SQL API based on StructStreamig.
> To support for SQL Streaming, there are two key points:
> 1, Analysis should be able to parse streaming type SQL.
> 2, Analyzer should be able to map metadata information to the corresponding
> Relation.
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