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https://issues.apache.org/jira/browse/SPARK-25937?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-25937:
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Affects Version/s: (was: 3.0.0)
3.1.0
> Support user-defined schema in Kafka Source & Sink
> --------------------------------------------------
>
> Key: SPARK-25937
> URL: https://issues.apache.org/jira/browse/SPARK-25937
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 3.1.0
> Reporter: Jackey Lee
> Priority: Major
>
> Kafka Source & Sink is widely used in Spark and has the highest frequency
> in streaming production environment. But at present, both Kafka Source and
> Link use the fixed schema, which force user to do data conversion when
> reading and writing Kafka. So why not we use fileformat to do this just like
> hive?
> Flink has implemented Kafka's Json/Csv/Avro extended Source & Sink, we
> can also support it in Spark.
> *Main Goals:*
> 1. Provide a Source and Sink that support user defined Schema. Users can read
> and write Kafka directly in the program without additional data conversion.
> 2. Provides read-write mechanism based on FileFormat. User's data conversion
> is similar to FileFormat's read and write process, we can provide a mechanism
> similar to FileFormat, which provide common read-write format conversion. It
> also allow users to customize format conversion.
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