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https://issues.apache.org/jira/browse/SPARK-3146?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14258593#comment-14258593
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Tathagata Das commented on SPARK-3146:
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I am a little wary of adding any functionality to a single input stream that we
would like to be added to all input streams in a general way in the future. It
leads to a lot of API mess. However this is also true that I dont see (yet) an
obvious way to add the general interceptor (M => Iterable[T]) pattern to all
receiver input streams. So I am conflicted on this. I am going to create a JIRA
for this feature, on which we as a community can brainstorm different ways to
create the feature.
> Improve the flexibility of Spark Streaming Kafka API to offer user the
> ability to process message before storing into BM
> ------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-3146
> URL: https://issues.apache.org/jira/browse/SPARK-3146
> Project: Spark
> Issue Type: Improvement
> Components: Streaming
> Affects Versions: 1.0.2, 1.1.0
> Reporter: Saisai Shao
>
> Currently Spark Streaming Kafka API stores the key and value of each message
> into BM for processing, potentially this may lose the flexibility for
> different requirements:
> 1. currently topic/partition/offset information for each message is discarded
> by KafkaInputDStream. In some scenarios people may need this information to
> better filter the message, like SPARK-2388 described.
> 2. People may need to add timestamp for each message when feeding into Spark
> Streaming, which can better measure the system latency.
> 3. Checkpointing the partition/offsets or others...
> So here we add a messageHandler in interface to give people the flexibility
> to preprocess the message before storing into BM. In the meantime time this
> improvement keep compatible with current API.
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