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https://issues.apache.org/jira/browse/SPARK-25937?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16676374#comment-16676374
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Jackey Lee commented on SPARK-25937:
------------------------------------
Not exactly the same thing.
The current implementation is to receive the format and schema, and then parse
or merge user data. The following is a simple example.
{code}
val format = "json"
val schema: StructType = new StructType().add ("name", StringType).add ("age",
IntegerType)
df.withFormat(format, schema).parseValue(df.value).select("name", "age")
{code}
For other unsupported formats, like "protobuf", "avro", user could redefine
parseValue/combineValue and use the same code to parse data.
> 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: 2.4.0
> 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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