Return-Path: X-Original-To: archive-asf-public-internal@cust-asf2.ponee.io Delivered-To: archive-asf-public-internal@cust-asf2.ponee.io Received: from cust-asf.ponee.io (cust-asf.ponee.io [163.172.22.183]) by cust-asf2.ponee.io (Postfix) with ESMTP id 22307200B73 for ; Mon, 29 Aug 2016 15:15:14 +0200 (CEST) Received: by cust-asf.ponee.io (Postfix) id 1D56A160AB8; Mon, 29 Aug 2016 13:15:14 +0000 (UTC) Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by cust-asf.ponee.io (Postfix) with SMTP id 75B2B160AA7 for ; Mon, 29 Aug 2016 15:15:11 +0200 (CEST) Received: (qmail 26484 invoked by uid 500); 29 Aug 2016 13:15:10 -0000 Mailing-List: contact user-help@flink.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@flink.apache.org Delivered-To: mailing list user@flink.apache.org Received: (qmail 26474 invoked by uid 99); 29 Aug 2016 13:15:10 -0000 Received: from pnap-us-west-generic-nat.apache.org (HELO spamd4-us-west.apache.org) (209.188.14.142) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 29 Aug 2016 13:15:10 +0000 Received: from localhost (localhost [127.0.0.1]) by spamd4-us-west.apache.org (ASF Mail Server at spamd4-us-west.apache.org) with ESMTP id CCF54C031E for ; Mon, 29 Aug 2016 13:15:09 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd4-us-west.apache.org X-Spam-Flag: NO X-Spam-Score: 1.431 X-Spam-Level: * X-Spam-Status: No, score=1.431 tagged_above=-999 required=6.31 tests=[DC_PNG_UNO_LARGO=0.001, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_ENVFROM_END_DIGIT=0.25, HTML_MESSAGE=2, HTML_OBFUSCATE_05_10=0.001, RCVD_IN_DNSWL_LOW=-0.7, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=disabled Authentication-Results: spamd4-us-west.apache.org (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mx2-lw-eu.apache.org ([10.40.0.8]) by localhost (spamd4-us-west.apache.org [10.40.0.11]) (amavisd-new, port 10024) with ESMTP id XWqLlDKtm1J4 for ; Mon, 29 Aug 2016 13:15:03 +0000 (UTC) Received: from mail-wm0-f51.google.com (mail-wm0-f51.google.com [74.125.82.51]) by mx2-lw-eu.apache.org (ASF Mail Server at mx2-lw-eu.apache.org) with ESMTPS id A43785FBC2 for ; Mon, 29 Aug 2016 13:15:02 +0000 (UTC) Received: by mail-wm0-f51.google.com with SMTP id d196so16045568wmd.0 for ; Mon, 29 Aug 2016 06:15:02 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=RGm7v+7rYTXhIqJ64iTj6un3XjznrBtPV6BylYg1O+U=; b=pB9PJ4PGbeHA/3WRg0JjcW4mSO8Ur7UWkzuLvt2xtE8MmMd2CyP/SbYfhuxapvWNhk 7UCxs+JQuzKT/MXs7pgNVagjW1NebKQErqfj2L2sLiZPypKcEL8k56MNVGESfJSdL1AA 9qfaCHyETkye2mYUPmfBl1+rc67hs/OG/iv+lYnoXW5RZz+S9bv/ISLcy8fekyK3ffZH G7W4VuTJ3Q8gB6z1RxE9VigX/rKNDjsM180XaNemSVpP5feHnPLhEK54z+PBqdiwB9c/ STNeSWgt4SH86nwh+d8Ryokw9X/qVutA9FKlxoNKOxYXMGuR/fWgAXYd1JrkhChFFhBV oNhA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20130820; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=RGm7v+7rYTXhIqJ64iTj6un3XjznrBtPV6BylYg1O+U=; b=c1xNgT9P4/JgrNsp2QjXMpTc/4SCeKveorbTZ/RraM3xjrJrKCyFN5mmqkUs1futa4 hKpAoSGEqfBXLPC8CLgLRFp/CeYg178Sp/HX7gpvZ89nEVvmBMSKEaaRGflIZi96wuEC H5OcxRnVF2ImfODcqP4fQOI0KOg/Lj+/yQJPXla1NRyn7w9C1hUDF0e1FzUDCyGv762P a9Oqeut675O7XbxoSdQrdw0YZAaOKGQ5Y4nOCkGlD+PA45C1oNKrk7TAC/JPuNxuR0ja 8gk406LpOYe24GtpBODN8fKxaGLYL7e9+gTnAjaiMxQvRIQbrtJ+LwdX0JqpJUutcO8i iQAQ== X-Gm-Message-State: AE9vXwMfxbwJb1+pJmL0hiOIB6q9qnT/M7Lk7FSoteJyTCgNyAwIJtRlKiyUuLLo/W+hxdBLDn6CjmQ+0l1l6w== X-Received: by 10.194.85.18 with SMTP id d18mr15187787wjz.43.1472476502158; Mon, 29 Aug 2016 06:15:02 -0700 (PDT) MIME-Version: 1.0 Received: by 10.28.25.67 with HTTP; Mon, 29 Aug 2016 06:14:21 -0700 (PDT) In-Reply-To: References: From: rss rss Date: Mon, 29 Aug 2016 15:14:21 +0200 Message-ID: Subject: Re: Kafka and Flink's partitions To: user@flink.apache.org Cc: Kostas Kloudas , Aljoscha Krettek Content-Type: multipart/related; boundary=089e0103e05cda98de053b35a6b2 archived-at: Mon, 29 Aug 2016 13:15:14 -0000 --089e0103e05cda98de053b35a6b2 Content-Type: multipart/alternative; boundary=089e0103e05cda98dc053b35a6b1 --089e0103e05cda98dc053b35a6b1 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hello, thanks for the answer. 1. There is currently no way to avoid the repartitioning. When you do a > keyBy(), Flink will shuffle the data through the network. What you would > need is a way to tell Flink that the data is already partitioned. If you > would use keyed state, you would also need to ensure that the same hash > function is used for the partitions and the state. > Is it an assumption only or are some examples exist? Yesterday I wrote a question about incompatibility of keyed serializer in Flink with Kafka's deserializer. 2. Why do you assume that this would end up in one partition? Just assumption. I don't know ways how to check it. 3. You can also read old messages from a Kafka topic by setting the > "auto.offset.reset" to "smallest" (or "latest") and using a new "group.id > ". > Ok, I know about it. But "smallest" is a way to repeat test with same data. The question from my side in general. Is the implementation https://github.com/rssdev10/flink-kafka-streaming/blob/master/src/main/java= /FlinkStreamingConsumer.java appropriate to the schema in the first email? Regarding the example. This small autonomous test is based on DIMA's project. And in this form you can use it, If it may be useful. Thanks, best regards. 2016-08-29 13:54 GMT+02:00 Robert Metzger : > Hi rss, > > Concerning your questions: > 1. There is currently no way to avoid the repartitioning. When you do a > keyBy(), Flink will shuffle the data through the network. What you would > need is a way to tell Flink that the data is already partitioned. If you > would use keyed state, you would also need to ensure that the same hash > function is used for the partitions and the state. > > 2. Why do you assume that this would end up in one partition? > > 3. You can also read old messages from a Kafka topic by setting the > "auto.offset.reset" to "smallest" (or "latest") and using a new "group.id > ". > > I'll add Aljoscha and Kostas to the eMail. Maybe they can help with the > incorrect results of the windowing. > > Regards, > Robert > > > On Thu, Aug 25, 2016 at 8:21 PM, rss rss wrote: > >> Hello, >> >> I want to implement something like a schema of processing which is >> presented on following diagram. This is calculation of number of unique >> users per specified time with assumption that we have > 100k events per >> second and > 100M unique users: >> >> >> >> I have one Kafka's topic of events with a partitioner by hash(userId) % >> partitionsNum https://github.com/rssdev10/fl >> ink-kafka-streaming/blob/master/src/main/java/KafkaPartitioner.java. I >> have prepared a runnable example https://github.com/rssdev10/fl >> ink-kafka-streaming/blob/master/src/main/java/FlinkStreamingConsumer.jav= a >> >> And the project is available by https://github.com/rssdev10/fl >> ink-kafka-streaming/ . Also see this page about how to run data >> generator and run the test. >> >> Basic assumption. I need to calculate a number of unique identifiers, s= o >> I need to store them in a memory in Set structure but the size o= f >> this data structure is dozens GB. So I need to partitioning data by >> identifier to reduce size and collect only already calculated numbers pe= r >> specified time. E.g. every hour. >> >> Questions: >> >> 1. The logic of Flink is very hidden. Window operator requires keyed >> stream. Does it means that when I'm doing >> >> eventStream.keyBy(event -> event.partition(partNum)); >> >> with the same partitioner as used for Kafka then Flink saves primary >> partitions? I want to avoid any repartitioning. >> 2. Then I'm doing >> >> WindowedStream uniqUsersWin =3D >> userIdKeyed.timeWindow(Time.seconds(windowDurationTime)); >> >> DataStream uniqUsers =3D uniqUsersWin.trigger(Proc= essingTimeTrigger.create()) >> .fold(new UniqAggregator(), (FoldFunction) (accumulator, value) -> { >> accumulator.uniqIds.add(value.getUserId()); >> >> accumulator.registerEvent(value); >> >> return accumulator; >> }) >> >> does it mean that I have only one partition? >> 3. Next, I want to collect partial results of aggregation. I'm using >> a custom trigger https://github.com/rssdev10/fl >> ink-kafka-streaming/blob/master/src/main/java/CountOrTimeTrigger.java >> >> which provides firing on collected partial aggregates accordingly to = number >> of Kafka's partitions of by emergency time if the number of aggregate= s is >> not enough. And the following code for aggregation: >> >> AllWindowedStream combinedUniqNumStrea= m =3D >> uniqUsers >> .timeWindowAll(Time.seconds(emergencyTriggerTimeout)) >> .trigger(PurgingTrigger.of(CountOrTimeTrigger.of(part= Num))); >> >> combinedUniqNumStream >> .fold(new ProductAggregator(), >> (FoldFunction) = (accumulator, value) -> { >> accumulator.value +=3D value.value; >> >> accumulator.summarize(value); >> >> return accumulator; >> }) >> >> But sometime I see an incorrect number of unique identifiers probably >> because of skewing of the partial aggregates. This test generates not= more >> than 1000 identifiers. It is possible to see it when this test is ran= after >> preloading of messages to Kafka. >> >> >> PS: I found some information at http://data-artisans.com/kafka >> -flink-a-practical-how-to/ and https://www.elastic.co/blog/bu >> ilding-real-time-dashboard-applications-with-apache-flink- >> elasticsearch-and-kibana but unfortunately these articles doesn't answer >> how to build the specified schema. >> >> >> Cheers >> >> >> =E2=80=8B >> > > --089e0103e05cda98dc053b35a6b1 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hello, thanks for the answer.

1. There is currently no way to avoid th= e repartitioning. When you=20 do a keyBy(), Flink will shuffle the data through the network. What you=20 would need is a way to tell Flink that the data is already partitioned.=20 If you would use keyed state, you would also need to ensure that the=20 same hash function is used for the partitions and the state.

Is it an assumption only or are some examples exist= ? Yesterday I wrote a question about incompatibility of keyed serializer in= Flink with Kafka's deserializer.

2. Why do you assume that this would end up in one part= ition?

Just assumption. I don't know ways ho= w to check it.

3. = You can also read old messages from a Kafka topic by setting the=20 "auto.offset.reset" to "smallest" (or "latest"= ;) and using a new "grou= p.id".

Ok, I know about = it. But "smallest" is a way to repeat test with same data.

The question from my side in general. Is the implementation= https://github.com/rssdev10/flink= -kafka-streaming/blob/master/src/main/java/FlinkStreamingConsumer.java = appropriate to the schema in the first email?


<= div>Regarding the example. This small autonomous test is based on DIMA'= s project. And in this form you can use it, If it may be useful.

Thanks,
best regards.

2016-08-29 13:54 GMT+02:00 = Robert Metzger <rmetzger@apache.org>:
Hi rss,

Concerning your q= uestions:
1. There is currently no way to avoid the repartitionin= g. When you do a keyBy(), Flink will shuffle the data through the network. = What you would need is a way to tell Flink that the data is already partiti= oned. If you would use keyed state, you would also need to ensure that the = same hash function is used for the partitions and the state.

=
2. Why do you assume that this would end up in one partition?

3. You can also read old messages from a Kafka topic= by setting the "auto.offset.reset" to "smallest" (or &= quot;latest") and using a new "group.id".

I'll add Aljos= cha and Kostas to the eMail. Maybe they can help with the incorrect results= of the windowing.

Regards,
Robert
=


On Thu, Aug 25, 2016 at 8:21 PM= , rss rss <rssdev10@gmail.com> wrote:
Hello,

=
=C2=A0 I want to implement something like a schema of processing whic= h is presented on following diagram. This is calculation of number of uniqu= e users per specified time with assumption that we have > 100k events pe= r second and > 100M unique users:

=C2=A0

=C2=A0I h= ave one Kafka's topic of events with a partitioner by hash(userId) % pa= rtitionsNum=C2=A0 http= s://github.com/rssdev10/flink-kafka-streaming/blob/master/src/mai= n/java/KafkaPartitioner.java. I have prepared a runnable example <= a href=3D"https://github.com/rssdev10/flink-kafka-streaming/blob/master/src= /main/java/FlinkStreamingConsumer.java" target=3D"_blank">https://github.co= m/rssdev10/flink-kafka-streaming/blob/master/src/main/java/FlinkS= treamingConsumer.java

=C2=A0And the project is availa= ble by https://github.com/rssdev10/flink-kafka-streaming/ . = Also see this page about how to run data generator and run the test.
=C2=A0Basic assumption. I need to calculate a number of unique = identifiers, so I need to store them in a memory in Set<String> struc= ture but the size of this data structure is dozens GB. So I need to partiti= oning data by identifier to reduce size and collect only already calculated= numbers per specified time. E.g. every hour.

=C2= =A0Questions:
  1. The logic of Flink is very hidden. Window op= erator requires keyed stream. Does it means that when I'm doing
    eventStream.keyBy(event -> event.partition(partNu= m));with the same partitioner as used for Kafka then Flink sav= es primary partitions? I want to avoid any repartitioning.
  2. Then I&#= 39;m doing
    WindowedStream=
    <Event, Integer, TimeWindow> uniqUsersWin =3D
    userIdKeyed.= timeWindow(Time.seconds(windowD= urationTime));

    DataStream<ProductAggregator> uniqUsers = =3D uniqUsersWin.trigger(ProcessingTimeTrigger.create())
    .fold(new UniqAggregator(), (FoldFunction<Event, = UniqAggregator>) (accumulator, value) -> {
    accumulator= .uniqIds.add(= value.getUserId());

    accumulator.registerEvent(valu<= wbr>e);

    return accumulator;
    })
    does it mean that I have = only one partition?
  3. Next, I want to collect partial results of = aggregation. I'm using a custom trigger https://github.com/rssdev10/flink-kafka-stream= ing/blob/master/src/main/java/CountOrTimeTrigger.java which p= rovides firing on collected partial aggregates accordingly to number of Kaf= ka's partitions of by emergency time if the number of aggregates is not= enough. And the following code for aggregation:
    AllWindowedStream<ProductAggregator, TimeWin=
    dow> combinedUniqNumStream =3D
    uniqUsers
    .= timeWindowAll(Time.seconds(emer= gencyTriggerTimeout))
    .trigger(PurgingTrigger.of(CountOrTimeTrigger.of(partNum)));

    combinedUniqNumSt= ream
    .fold(n= ew ProductAggregator(),
    (FoldFunction<ProductA= ggregator, ProductAggregator>) (accumulator, value) -> {
    = accumulator.= value +=3D value.value;

    accumulator.summarize(value);

    = return accumulator;
    })
    But sometime I see an incorrect numbe= r of unique identifiers probably because of skewing of the partial aggregat= es. This test generates not more than 1000 identifiers. It is possible to s= ee it when this test is ran after preloading of messages to Kafka.


PS: I found some information at http://d= ata-artisans.com/kafka-flink-a-practical-how-to/ and https://www.elastic.= co/blog/building-real-time-dashboard-applications-with-apache-fli= nk-elasticsearch-and-kibana but unfortunately these articles doesn= 't answer how to build the specified schema.


Cheers
=


=E2=80=8B


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