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https://issues.apache.org/jira/browse/FLINK-9506?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16499972#comment-16499972
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swy commented on FLINK-9506:
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[~srichter] thanks for good explanation. We have no choice but need to store
that much of record(10 mil to 1 bil, record size of 1kb) for
aggregation(multiple fields aggr) because of business requirement. Due to the
large state, RocksDB will be setup for production. We know passing/accessing
object to/from RocksDB is not easy to performance but hopefully at least the
result is scale-able. In our case now, not only the result is fluctuated, the
scaling performance also capped, this is the real pain. Do you mind to share
your thought on how to handle such case? Or any similar use case to share?
Next Step, we are going to tune memory related configuration, and also setup
RocksDB hopefully the performance is scale-able. Thanks again.
> Flink ReducingState.add causing more than 100% performance drop
> ---------------------------------------------------------------
>
> Key: FLINK-9506
> URL: https://issues.apache.org/jira/browse/FLINK-9506
> Project: Flink
> Issue Type: Improvement
> Affects Versions: 1.4.2
> Reporter: swy
> Priority: Major
> Attachments: KeyNoHash_VS_KeyHash.png, flink.png
>
>
> Hi, we found out application performance drop more than 100% when
> ReducingState.add is used in the source code. In the test checkpoint is
> disable. And filesystem(hdfs) as statebackend.
> It could be easyly reproduce with a simple app, without checkpoint, just
> simply keep storing record, also with simple reduction function(in fact with
> empty function would see the same result). Any idea would be appreciated.
> What an unbelievable obvious issue.
> Basically the app just keep storing record into the state, and we measure how
> many record per second in "JsonTranslator", which is shown in the graph. The
> difference between is just 1 line, comment/un-comment "recStore.add(r)".
> {code}
> DataStream<String> stream = env.addSource(new GeneratorSource(loop);
> DataStream<JSONObject> convert = stream.map(new JsonTranslator())
> .keyBy()
> .process(new ProcessAggregation())
> .map(new PassthruFunction());
> public class ProcessAggregation extends ProcessFunction {
> private ReducingState<Record> recStore;
> public void processElement(Recordr, Context ctx, Collector<Record> out) {
> recStore.add(r); //this line make the difference
> }
> {code}
> Record is POJO class contain 50 String private member.
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