The following Java codes can work in my cluster environment:
```
.mapGroupsWithState((MapGroupsWithStateFunction<String, String, Long,
LeadingCharCount>) (key, values, state) -> {
myAcc.add(1);
<...>
state.update(newState);
return new LeadingCharCount(key, newState);
},
Encoders.LONG(),
Encoders.bean(LeadingCharCount.class),
GroupStateTimeout.ProcessingTimeTimeout())
```
Also works fine with my `StateUpdateTask`:
```
.mapGroupsWithState(
new StateUpdateTask(myAcc),
Encoders.LONG(),
Encoders.bean(LeadingCharCount.class),
GroupStateTimeout.ProcessingTimeTimeout());
public class StateUpdateTask
implements MapGroupsWithStateFunction<String, String, Long,
LeadingCharCount> {
private LongAccumulator myAccInTask;
public StateUpdateTask(LongAccumulator acc) {
this.myAccInTask = acc;
}
@Override
public LeadingCharCount call(String key, Iterator<String> values,
GroupState<Long> state) throws Exception {
myAccInTask.add(1);
<...>
state.update(newState);
return new LeadingCharCount(key, newState);
}
}
```
--
Cheers,
-z
On Tue, 2 Jun 2020 10:28:36 +0800
ZHANG Wei <wezh...@outlook.com> wrote:
> Yes, verified on the cluster with 5 executors.
>
> --
> Cheers,
> -z
>
> On Fri, 29 May 2020 11:16:12 -0700
> Something Something <mailinglist...@gmail.com> wrote:
>
> > Did you try this on the Cluster? Note: This works just fine under 'Local'
> > mode.
> >
> > On Thu, May 28, 2020 at 9:12 PM ZHANG Wei <wezh...@outlook.com> wrote:
> >
> > > I can't reproduce the issue with my simple code:
> > > ```scala
> > > spark.streams.addListener(new StreamingQueryListener {
> > > override def onQueryProgress(event:
> > > StreamingQueryListener.QueryProgressEvent): Unit = {
> > > println(event.progress.id + " is on progress")
> > > println(s"My accu is ${myAcc.value} on query progress")
> > > }
> > > ...
> > > })
> > >
> > > def mappingFunc(key: Long, values: Iterator[String], state:
> > > GroupState[Long]): ... = {
> > > myAcc.add(1)
> > > println(s">>> key: $key => state: ${state}")
> > > ...
> > > }
> > >
> > > val wordCounts = words
> > > .groupByKey(v => ...)
> > > .mapGroupsWithState(timeoutConf =
> > > GroupStateTimeout.ProcessingTimeTimeout)(func = mappingFunc)
> > >
> > > val query = wordCounts.writeStream
> > > .outputMode(OutputMode.Update)
> > > ...
> > > ```
> > >
> > > I'm wondering if there were any errors can be found from driver logs? The
> > > micro-batch
> > > exceptions won't terminate the streaming job running.
> > >
> > > For the following code, we have to make sure that `StateUpdateTask` is
> > > started:
> > > > .mapGroupsWithState(
> > > > new
> > > StateUpdateTask(Long.parseLong(appConfig.getSparkStructuredStreamingConfig().STATE_TIMEOUT),
> > > > appConfig, accumulators),
> > > > Encoders.bean(ModelStateInfo.class),
> > > > Encoders.bean(ModelUpdate.class),
> > > > GroupStateTimeout.ProcessingTimeTimeout());
> > >
> > > --
> > > Cheers,
> > > -z
> > >
> > > On Thu, 28 May 2020 19:59:31 +0530
> > > Srinivas V <srini....@gmail.com> wrote:
> > >
> > > > Giving the code below:
> > > > //accumulators is a class level variable in driver.
> > > >
> > > > sparkSession.streams().addListener(new StreamingQueryListener() {
> > > > @Override
> > > > public void onQueryStarted(QueryStartedEvent queryStarted) {
> > > > logger.info("Query started: " + queryStarted.id());
> > > > }
> > > > @Override
> > > > public void onQueryTerminated(QueryTerminatedEvent
> > > > queryTerminated) {
> > > > logger.info("Query terminated: " +
> > > queryTerminated.id());
> > > > }
> > > > @Override
> > > > public void onQueryProgress(QueryProgressEvent
> > > queryProgress) {
> > > >
> > > > accumulators.eventsReceived(queryProgress.progress().numInputRows());
> > > > long eventsReceived = 0;
> > > > long eventsExpired = 0;
> > > > long eventSentSuccess = 0;
> > > > try {
> > > > eventsReceived =
> > > > accumulators.getLong(InstrumentationCounters.EVENTS_RECEIVED);
> > > > eventsExpired =
> > > > accumulators.getLong(InstrumentationCounters.EVENTS_STATE_EXPIRED);
> > > > eventSentSuccess =
> > > > accumulators.getLong(InstrumentationCounters.EVENTS_SENT);
> > > > } catch (MissingKeyException e) {
> > > > logger.error("Accumulator key not found due to
> > > > Exception {}", e.getMessage());
> > > > }
> > > > logger.info("Events Received:{}", eventsReceived);
> > > > logger.info("Events State Expired:{}", eventsExpired);
> > > > logger.info("Events Sent Success:{}", eventSentSuccess);
> > > > logger.info("Query made progress - batchId: {}
> > > > numInputRows:{} inputRowsPerSecond:{} processedRowsPerSecond:{}
> > > > durationMs:{}" ,
> > > > queryProgress.progress().batchId(),
> > > > queryProgress.progress().numInputRows(),
> > > > queryProgress.progress().inputRowsPerSecond(),
> > > >
> > > queryProgress.progress().processedRowsPerSecond(),
> > > > queryProgress.progress().durationMs());
> > > >
> > > >
> > > > On Thu, May 28, 2020 at 7:04 PM ZHANG Wei <wezh...@outlook.com> wrote:
> > > >
> > > > > May I get how the accumulator is accessed in the method
> > > > > `onQueryProgress()`?
> > > > >
> > > > > AFAICT, the accumulator is incremented well. There is a way to verify
> > > that
> > > > > in cluster like this:
> > > > > ```
> > > > > // Add the following while loop before invoking awaitTermination
> > > > > while (true) {
> > > > > println("My acc: " + myAcc.value)
> > > > > Thread.sleep(5 * 1000)
> > > > > }
> > > > >
> > > > > //query.awaitTermination()
> > > > > ```
> > > > >
> > > > > And the accumulator value updated can be found from driver stdout.
> > > > >
> > > > > --
> > > > > Cheers,
> > > > > -z
> > > > >
> > > > > On Thu, 28 May 2020 17:12:48 +0530
> > > > > Srinivas V <srini....@gmail.com> wrote:
> > > > >
> > > > > > yes, I am using stateful structured streaming. Yes similar to what
> > > you
> > > > > do.
> > > > > > This is in Java
> > > > > > I do it this way:
> > > > > > Dataset<ModelUpdate> productUpdates = watermarkedDS
> > > > > > .groupByKey(
> > > > > > (MapFunction<InputEventModel, String>) event
> > > ->
> > > > > > event.getId(), Encoders.STRING())
> > > > > > .mapGroupsWithState(
> > > > > > new
> > > > > >
> > > > >
> > > StateUpdateTask(Long.parseLong(appConfig.getSparkStructuredStreamingConfig().STATE_TIMEOUT),
> > > > > > appConfig, accumulators),
> > > > > > Encoders.bean(ModelStateInfo.class),
> > > > > > Encoders.bean(ModelUpdate.class),
> > > > > > GroupStateTimeout.ProcessingTimeTimeout());
> > > > > >
> > > > > > StateUpdateTask contains the update method.
> > > > > >
> > > > > > On Thu, May 28, 2020 at 4:41 AM Something Something <
> > > > > > mailinglist...@gmail.com> wrote:
> > > > > >
> > > > > > > Yes, that's exactly how I am creating them.
> > > > > > >
> > > > > > > Question... Are you using 'Stateful Structured Streaming' in which
> > > > > you've
> > > > > > > something like this?
> > > > > > >
> > > > > > > .mapGroupsWithState(GroupStateTimeout.ProcessingTimeTimeout())(
> > > > > > > updateAcrossEvents
> > > > > > > )
> > > > > > >
> > > > > > > And updating the Accumulator inside 'updateAcrossEvents'? We're
> > > > > experiencing this only under 'Stateful Structured Streaming'. In other
> > > > > streaming applications it works as expected.
> > > > > > >
> > > > > > >
> > > > > > >
> > > > > > > On Wed, May 27, 2020 at 9:01 AM Srinivas V <srini....@gmail.com>
> > > > > wrote:
> > > > > > >
> > > > > > >> Yes, I am talking about Application specific Accumulators.
> > > Actually I
> > > > > am
> > > > > > >> getting the values printed in my driver log as well as sent to
> > > > > Grafana. Not
> > > > > > >> sure where and when I saw 0 before. My deploy mode is “client” on
> > > a
> > > > > yarn
> > > > > > >> cluster(not local Mac) where I submit from master node. It should
> > > > > work the
> > > > > > >> same for cluster mode as well.
> > > > > > >> Create accumulators like this:
> > > > > > >> AccumulatorV2 accumulator = sparkContext.longAccumulator(name);
> > > > > > >>
> > > > > > >>
> > > > > > >> On Tue, May 26, 2020 at 8:42 PM Something Something <
> > > > > > >> mailinglist...@gmail.com> wrote:
> > > > > > >>
> > > > > > >>> Hmm... how would they go to Graphana if they are not getting
> > > > > computed in
> > > > > > >>> your code? I am talking about the Application Specific
> > > Accumulators.
> > > > > The
> > > > > > >>> other standard counters such as
> > > 'event.progress.inputRowsPerSecond'
> > > > > are
> > > > > > >>> getting populated correctly!
> > > > > > >>>
> > > > > > >>> On Mon, May 25, 2020 at 8:39 PM Srinivas V <srini....@gmail.com>
> > > > > wrote:
> > > > > > >>>
> > > > > > >>>> Hello,
> > > > > > >>>> Even for me it comes as 0 when I print in OnQueryProgress. I
> > > > > > >>>> use
> > > > > > >>>> LongAccumulator as well. Yes, it prints on my local but not on
> > > > > cluster.
> > > > > > >>>> But one consolation is that when I send metrics to Graphana,
> > > > > > >>>> the
> > > > > values
> > > > > > >>>> are coming there.
> > > > > > >>>>
> > > > > > >>>> On Tue, May 26, 2020 at 3:10 AM Something Something <
> > > > > > >>>> mailinglist...@gmail.com> wrote:
> > > > > > >>>>
> > > > > > >>>>> No this is not working even if I use LongAccumulator.
> > > > > > >>>>>
> > > > > > >>>>> On Fri, May 15, 2020 at 9:54 PM ZHANG Wei <wezh...@outlook.com
> > > >
> > > > > wrote:
> > > > > > >>>>>
> > > > > > >>>>>> There is a restriction in AccumulatorV2 API [1], the OUT type
> > > > > should
> > > > > > >>>>>> be atomic or thread safe. I'm wondering if the implementation
> > > for
> > > > > > >>>>>> `java.util.Map[T, Long]` can meet it or not. Is there any
> > > chance
> > > > > to replace
> > > > > > >>>>>> CollectionLongAccumulator by CollectionAccumulator[2] or
> > > > > LongAccumulator[3]
> > > > > > >>>>>> and test if the StreamingListener and other codes are able to
> > > > > work?
> > > > > > >>>>>>
> > > > > > >>>>>> ---
> > > > > > >>>>>> Cheers,
> > > > > > >>>>>> -z
> > > > > > >>>>>> [1]
> > > > > > >>>>>>
> > > > >
> > > https://nam05.safelinks.protection.outlook.com/?url=http%3A%2F%2Fspark.apache.org%2Fdocs%2Flatest%2Fapi%2Fscala%2Findex.html%23org.apache.spark.util.AccumulatorV2&data=02%7C01%7C%7Cf802f480bbab46ae07b308d803fc661f%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637263729860033353&sdata=NPpiZC%2Bnx9rec6G35QvMDV1D3FgvD%2FnIct6OJ06I728%3D&reserved=0
> > > > > > >>>>>> [2]
> > > > > > >>>>>>
> > > > >
> > > https://nam05.safelinks.protection.outlook.com/?url=http%3A%2F%2Fspark.apache.org%2Fdocs%2Flatest%2Fapi%2Fscala%2Findex.html%23org.apache.spark.util.CollectionAccumulator&data=02%7C01%7C%7Cf802f480bbab46ae07b308d803fc661f%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637263729860038343&sdata=wMskE72per9Js0V7UHJ0qi4UzCEEYh%2Fk53fuP2e92mA%3D&reserved=0
> > > > > > >>>>>> [3]
> > > > > > >>>>>>
> > > > >
> > > https://nam05.safelinks.protection.outlook.com/?url=http%3A%2F%2Fspark.apache.org%2Fdocs%2Flatest%2Fapi%2Fscala%2Findex.html%23org.apache.spark.util.LongAccumulator&data=02%7C01%7C%7Cf802f480bbab46ae07b308d803fc661f%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637263729860038343&sdata=INgHzc0rc6jj7UapB%2FRLfCiGNWEBSKWfgmuJ2dUZ3eM%3D&reserved=0
> > > > > > >>>>>>
> > > > > > >>>>>> ________________________________________
> > > > > > >>>>>> From: Something Something <mailinglist...@gmail.com>
> > > > > > >>>>>> Sent: Saturday, May 16, 2020 0:38
> > > > > > >>>>>> To: spark-user
> > > > > > >>>>>> Subject: Re: Using Spark Accumulators with Structured
> > > Streaming
> > > > > > >>>>>>
> > > > > > >>>>>> Can someone from Spark Development team tell me if this
> > > > > functionality
> > > > > > >>>>>> is supported and tested? I've spent a lot of time on this but
> > > > > can't get it
> > > > > > >>>>>> to work. Just to add more context, we've our own Accumulator
> > > > > class that
> > > > > > >>>>>> extends from AccumulatorV2. In this class we keep track of
> > > one or
> > > > > more
> > > > > > >>>>>> accumulators. Here's the definition:
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>> class CollectionLongAccumulator[T]
> > > > > > >>>>>> extends AccumulatorV2[T, java.util.Map[T, Long]]
> > > > > > >>>>>>
> > > > > > >>>>>> When the job begins we register an instance of this class:
> > > > > > >>>>>>
> > > > > > >>>>>> spark.sparkContext.register(myAccumulator, "MyAccumulator")
> > > > > > >>>>>>
> > > > > > >>>>>> Is this working under Structured Streaming?
> > > > > > >>>>>>
> > > > > > >>>>>> I will keep looking for alternate approaches but any help
> > > would be
> > > > > > >>>>>> greatly appreciated. Thanks.
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>> On Thu, May 14, 2020 at 2:36 PM Something Something <
> > > > > > >>>>>> mailinglist...@gmail.com<mailto:mailinglist...@gmail.com>>
> > > wrote:
> > > > > > >>>>>>
> > > > > > >>>>>> In my structured streaming job I am updating Spark
> > > Accumulators in
> > > > > > >>>>>> the updateAcrossEvents method but they are always 0 when I
> > > try to
> > > > > print
> > > > > > >>>>>> them in my StreamingListener. Here's the code:
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > .mapGroupsWithState(GroupStateTimeout.ProcessingTimeTimeout())(
> > > > > > >>>>>> updateAcrossEvents
> > > > > > >>>>>> )
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>> The accumulators get incremented in 'updateAcrossEvents'.
> > > I've a
> > > > > > >>>>>> StreamingListener which writes values of the accumulators in
> > > > > > >>>>>> 'onQueryProgress' method but in this method the Accumulators
> > > are
> > > > > ALWAYS
> > > > > > >>>>>> ZERO!
> > > > > > >>>>>>
> > > > > > >>>>>> When I added log statements in the updateAcrossEvents, I
> > > could see
> > > > > > >>>>>> that these accumulators are getting incremented as expected.
> > > > > > >>>>>>
> > > > > > >>>>>> This only happens when I run in the 'Cluster' mode. In Local
> > > mode
> > > > > it
> > > > > > >>>>>> works fine which implies that the Accumulators are not
> > > > > > >>>>>> getting
> > > > > distributed
> > > > > > >>>>>> correctly - or something like that!
> > > > > > >>>>>>
> > > > > > >>>>>> Note: I've seen quite a few answers on the Web that tell me
> > > > > > >>>>>> to
> > > > > > >>>>>> perform an "Action". That's not a solution here. This is a
> > > > > 'Stateful
> > > > > > >>>>>> Structured Streaming' job. Yes, I am also 'registering' them
> > > in
> > > > > > >>>>>> SparkContext.
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > > > >>>>>>
> > > > >
> > >
>
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