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https://issues.apache.org/jira/browse/FLINK-9506?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16500211#comment-16500211
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swy commented on FLINK-9506:
----------------------------
[~srichter] Really appreciated your help. To your points,
*[First, when using the FSStateBackend, try to set asynchronous checkpoints to
true]*
We are using 1.4.2 now, is "state.backend.async" available? Just found this
parameter available in 1.5 according to Flink website.
*[your comparison does not consider that in case that you are using the
reducing state, out.collect(output); in onTimer produces an output and not just
forwards null]*
This again is sample app issue, in real application we have a NULL check to
prevent NULL record to next operator.
*[you can think about the object reuse setting
env.getConfig().enableObjectReuse()]*
This is good idea indeed, will try it out.
*[And you can also make your AggregationKey much more efficient]*
Agree. Now only has one key from one field in Record structure,
.keyBy(new KeySelector<Record, String>() {
@Override
public String getKey(Record r) throws Exception
{ return r.getUNIQUE_KEY(); }
})
> 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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