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https://issues.apache.org/jira/browse/FLINK-4854?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15588850#comment-15588850
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Greg Hogan commented on FLINK-4854:
-----------------------------------
Hi and thanks for submitting a ticket for this idea! Has this been discussed on
the dev mailing list (if yes, please add a link)? It looks like a major
addition to the DataStream API and should be fully vetted before starting.
> Efficient Batch Operator in Streaming
> -------------------------------------
>
> Key: FLINK-4854
> URL: https://issues.apache.org/jira/browse/FLINK-4854
> Project: Flink
> Issue Type: Improvement
> Components: DataStream API
> Reporter: Xiaowei Jiang
> Assignee: MaGuowei
> Labels: features
> Original Estimate: 168h
> Remaining Estimate: 168h
>
> Very often, it's more efficient to process a batch of records at once instead
> of processing them one by one. We can use window to achieve this
> functionality. However, window will store all records in states, which can be
> costly. It's desirable to have an efficient implementation of batch operator.
> The batch operator works per task and behave similarly to aligned windows.
> Here is an example of how the interface looks like to a user.
> {code}
> interface BatchFunction {
> // add the record to the buffer
> // returns if the batch is ready to be flushed
> boolean addRecord(T record);
> // process all pending records in the buffer
> void flush(Collector collector) ;
> }
> DataStream ds = ...
> BatchFunction func = ...
> ds.batch(func);
> {code}
> The operator calls addRecord for each record. The batch function saves the
> record in its own buffer. The addRecord returns if the pending buffer should
> be flushed. In that case, the operator invokes flush.
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