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https://issues.apache.org/jira/browse/SPARK-40659?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17612849#comment-17612849
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Raghu Angadi commented on SPARK-40659:
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For 1): No schema evolution is necessary. We keep reading old and latest
messages.
For 2): Schema evolution is for this case so that we don't drop fields.
Say a streaming application reads from Kafka and writes all the fields to a
delta table. This pipeline keeps running for a long time. Meanwhile customer
adds a new field 'zip_code' to the schema. What should happen?
* (a) Without schema evolution: 'zip_code' field would be dropped and would
not appear in the destination table.
* (b) With schema evolution: we create new column 'zip_code' and populate the
column.
We want to have (b). In terms of implementation, if throw a specific error,
structured streaming stops the pipeline and restarts, which will fetch the new
schema and handle 'zip_code' correctly.
> Schema evolution for protobuf (and Avro too?)
> ---------------------------------------------
>
> Key: SPARK-40659
> URL: https://issues.apache.org/jira/browse/SPARK-40659
> Project: Spark
> Issue Type: Improvement
> Components: Structured Streaming
> Affects Versions: 3.3.0
> Reporter: Raghu Angadi
> Priority: Major
>
> Protobuf & Avro should support schema evolution in streaming. We need to
> throw a specific error message when we detect newer version of the the schema
> in schema registry.
> A couple of options for detecting version change at runtime:
> * How do we detect newer version from schema registry? It is contacted only
> during planning currently.
> * We could detect version id in coming messages.
> ** What if the id in the incoming message is newer than what our
> schema-registry reports after the restart?
> *** This indicates delayed syncs between customers schema-registry servers
> (should be rare). We can keep erroring out until it is fixed.
> *** Make sure we log the schema id used during planning.
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