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https://issues.apache.org/jira/browse/SPARK-32342?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17178531#comment-17178531
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Sean R. Owen commented on SPARK-32342:
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Is the magic byte supposed to be part of Avro's spec or specific to Confluent
somehow?
> Kafka events are missing magic byte
> -----------------------------------
>
> Key: SPARK-32342
> URL: https://issues.apache.org/jira/browse/SPARK-32342
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 3.0.0
> Environment: Pyspark 3.0.0, Python 3.7 Confluent cloud Kafka with
> Schema registry 5.5
> Reporter: Sridhar Baddela
> Priority: Major
>
> Please refer to the documentation link for to_avro and from_avro.[
> http://spark.apache.org/docs/latest/sql-data-sources-avro.html|http://spark.apache.org/docs/latest/sql-data-sources-avro.html]
> Tested the to_avro function by making sure that data is sent to Kafka topic.
> But when a Confluent Avro consumer is used to read data from the same topic,
> the consumer fails with an error message that event is missing the magic
> byte.
> Used another topic to simulate reads from Kafka and further deserialization
> using from_avro. Use case is, use a Confluent Avro producer to produce a few
> events. And when I attempt to read this topic using structured streaming and
> applying the function from_avro, it fails with a message indicating that
> malformed records are present.
> Using from_avro (deserialization) and to_avro (serialization) from Spark,
> only works with Spark. And other consumers outside of Spark which do not use
> this approach are failing.
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