Return-Path: X-Original-To: apmail-storm-user-archive@minotaur.apache.org Delivered-To: apmail-storm-user-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 7E500112A9 for ; Tue, 8 Jul 2014 20:48:57 +0000 (UTC) Received: (qmail 64414 invoked by uid 500); 8 Jul 2014 20:48:56 -0000 Delivered-To: apmail-storm-user-archive@storm.apache.org Received: (qmail 64375 invoked by uid 500); 8 Jul 2014 20:48:55 -0000 Mailing-List: contact user-help@storm.incubator.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@storm.incubator.apache.org Delivered-To: mailing list user@storm.incubator.apache.org Received: (qmail 64365 invoked by uid 99); 8 Jul 2014 20:48:55 -0000 Received: from athena.apache.org (HELO athena.apache.org) (140.211.11.136) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 08 Jul 2014 20:48:55 +0000 X-ASF-Spam-Status: No, hits=1.5 required=5.0 tests=HTML_MESSAGE,RCVD_IN_DNSWL_LOW,SPF_PASS X-Spam-Check-By: apache.org Received-SPF: pass (athena.apache.org: domain of cody.a.ray@gmail.com designates 209.85.216.46 as permitted sender) Received: from [209.85.216.46] (HELO mail-qa0-f46.google.com) (209.85.216.46) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 08 Jul 2014 20:48:50 +0000 Received: by mail-qa0-f46.google.com with SMTP id v10so804287qac.33 for ; Tue, 08 Jul 2014 13:48:29 -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 :content-type; bh=mme4soX4mr8JTdT58eoAYHeBLMGW7oeDpivhy5aAzgI=; b=ND8eyNY4f31XR58fQjTeiHxZ6Twj5lNAtyZPz7PgAhHsyEoFVDdc4lbDCrANkMoiO6 5t6HH10o5J1/YavIXNuTHoMAPYeeSao4+Xv+Xqr7xHMlsl68Pw/VhG8HP0/1lVEiEQXG i/fr5c6dXkUBnrByHQkj8zfJe3OE9XL7PUJOxEfkeaYPMkMbY4CF8GxA0Xo8zJ7kZY9t pnQKNzcBq0YghicxVhTtHxEaVGrtdZ4BUKUovtY2E5U+Qxsl6tZ3t8UZ8xvOcPtVIE+V eETepB00/eIZLFQEXggaotIdizY8HoZXQbi6ZlNQjl6zMw/RbzZos7Uw3L4EIalDC2rl VOCw== X-Received: by 10.140.41.202 with SMTP id z68mr59456411qgz.37.1404852509229; Tue, 08 Jul 2014 13:48:29 -0700 (PDT) MIME-Version: 1.0 Received: by 10.229.118.199 with HTTP; Tue, 8 Jul 2014 13:48:09 -0700 (PDT) In-Reply-To: References: From: "Cody A. Ray" Date: Tue, 8 Jul 2014 15:48:09 -0500 Message-ID: Subject: Re: performance issues in time-series aggregation with Storm/Trident To: user@storm.incubator.apache.org Content-Type: multipart/related; boundary=001a11c11a32c74d6b04fdb4b612 X-Virus-Checked: Checked by ClamAV on apache.org --001a11c11a32c74d6b04fdb4b612 Content-Type: multipart/alternative; boundary=001a11c11a32c74d6804fdb4b611 --001a11c11a32c74d6804fdb4b611 Content-Type: text/plain; charset=UTF-8 How many Kafka partitions are you using with a 10-20MB fetch size? (I believe that batch size ~= # partitions * kafka fetch size). I added the initial shuffle after the spout so that I could have a much higher parallelism for the main transform stage (CPU-bound) than the spout stage (IO-bound). Is this not the right approach? Without this, they have the same parallelism. Though localOrShuffle might help here then? Yes, we monitor CPU and memory (but not network usage). CPU loads are fairly spiky (presumably spiking during the transform stage) but we generally have 80+% idle. All storm boxes have 4-6GB memory free. Although we don't have solid network usage monitoring, some simple iperf tests have shown that we can transfer 1G in a few seconds, IIRC. Will run a few tests with maxSpoutPending and see how that affects our throughput. -Cody On Tue, Jul 8, 2014 at 1:23 PM, Danijel Schiavuzzi wrote: > You should increase maxSpoutPending, as it enables pipelining of batches > inside the topology, thus increasing performance. As for the Kafka fetch > size, I've got the best throughput with 10-20MB sizes. The buffer size > didn't impact the throughput. > > Also, I believe the first shuffle() repartitioning operation, right after > the spout, is not needed, as it will only slow down processing by > transferring tuples between workers over the transport layer and the > network needlessly. It's best to avoid repartitioning operations and let > Trident pack the topology into as few bolts as possible. I've got a massive > speedup by removing unnecessary stream repartitionings (shuffle()s). In my > case, the overhead of repartitioning the stream (to enable increasing the > various tasks paralellism) was greater than the gain of > the increased paralellism of the tasks. Increasing task paralellism doesn't > always equal increased performance, it depends of course on the specific > case. > > Do you monitor the basic system stats like CPU, memory and network usage? > > On Monday, July 7, 2014, Cody A. Ray wrote: > >> Hi, >> >> We're doing time-series event aggregation with Storm/Trident, which I >> believe is a pretty common use case. However, we're running into >> performance issues when trying to transition our high-volume applications >> to it (up to 600k req/min peak). >> >> Source code: https://gist.github.com/codyaray/75533044fc8c0a12fa67 >> (Can't easily push a working branch to github since the JSON parser is in >> a private library.) >> >> Component Versions: >> - Kafka 0.8.0 >> - Storm 0.9.0.1 >> - Kafka Spout 0.2.0 from wurstmeister >> >> >> Hardware in EC2: >> - 3 zookeepers on c1.mediums >> - 2 kafkas on m1.larges >> - 3 storms on m1.larges and c3.xlarges (in another region) >> - 2 mongos on m1.large and m1.xlarges (in another region) >> >> Topology: >> - spout0 bolt (from Trident) with parallelism 2 (one per Kafka partition) >> - Transform bolt with parallelism 12 (= 3 worker hosts * 2 cores/host * 2 >> executors/30core) >> - 30s and 30m Aggregator bolts with parallelism 3 (one per worker host) >> >> Tuning Options: >> - Kafka Max Fetch Size: have tried various options from 256K to 20M >> - Kafka Buffer Size: have tried default (1M) as well as matching to Max >> Fetch Size >> - Max Spout Parallelism: left default of 1 >> >> If you look at the Storm UI, you'll notice that none of the bolts are >> over-capacity (>= 1). The highest capacity bolt is the Aggregator-30s which >> performs the persistentAggregate. >> >> [image: Inline image 3] >> (A larger full-screen snapshot of this UI view is attached.) >> >> But diving into this bolt shows that one of the 3 executors is almost >> unused, one is medium-used, and the other is heavily-used. So something may >> not be balanced well??? >> >> [image: Inline image 1] >> (A larger full-screen snapshot of this UI view is attached.) >> >> We've used the Nimbus Thrift API to put together a Graphite dashboard >> showing (what we hope are) the key metrics for this pipeline. >> >> [image: Inline image 2] >> (See attached for a bigger version of this graph). >> >> These graphs show rates as request messages (and later tuples) move >> through the system. The following list gives the Graphite targets shown in >> each graph. >> >> - Suro Send - number of messages coming from each Suro into Kafka >> - prod.suro.*mongo*.messages >> - Kafka Ingestion - number of messages coming into each Kafka >> Broker-Partition >> - prod.kafka.kafka*.requests.*.latest >> - Suro/Kafka/Storm Pipeline - total number of messages going through >> Suro/Kafka/Storm >> - alias(sum(prod.suro.*mongo*.messages),%22suro%20messages%22) >> - >> alias(sum(prod.kafka.kafka01*.requests.*.latest),%22kafka01%20latest%22) >> - >> alias(sum(prod.kafka.kafka02*.requests.*.latest),%22kafka02%20latest%22) >> - >> alias(sum(prod.storm.storm*.*.stats15.spout0.*.s1.emit.count),%22storm%20spout%22) >> - Kafka Spout Emitter - number of Messages Per Batch and Batch Count: >> - >> alias(divideSeries(sum(prod.storm.storm*.*.stats15.spout0.*.s1.emit.count),sum(prod.storm.storm*.6*.stats15.mastercoord-bg0.*.batch.emit.count)),%22Messages%20Per%20Batch%22) >> - >> alias(secondYAxis(prod.storm.storm*.6*.stats15.mastercoord-bg0.*.batch.emit.count),%22Batch%20Count%22) >> - Spout Output vs Transform Input: >> - >> alias(sum(prod.storm.storm*.*.stats15.spout0.*.s1.emit.count),%22Spout%20Output%22) >> - >> alias(sum(prod.storm.storm01*.*.stats15.Transform-Aggregator-30s-Transform-Aggregator-30m.*.spout0.s1.execute.count),%22Transform%20Executes%22) >> - >> alias(sum(prod.storm.storm01*.*.stats15.Transform-Aggregator-30s-Transform-Aggregator-30m.*.spout0.s1.ack.count),%22Transform%20Acks%22) >> - Transform Output vs Aggregator Input: >> - >> alias(sum(prod.storm.storm01*.*.stats15.Transform-Aggregator-30s-Transform-Aggregator-30m.*.s13.emit.count),%22Transform-30m%20Output%22) >> - >> alias(sum(prod.storm.storm01*.6*.stats15.Aggregator-30m.*.*-Trans*.s*.execute.count),%22Aggregate-30m%20Executes%22) >> - >> alias(sum(prod.storm.storm01*.6*.stats15.Aggregator-30m.*.*-Trans*.s*.ack.count),%22Aggregate-30m%20Acks%22) >> - >> alias(sum(prod.storm.storm01*.*.stats15.Transform-Aggregator-30s-Transform-Aggregator-30m.*.s7.emit.count),%22Transform-30s%20Output%22) >> - >> alias(sum(prod.storm.storm01*.6*.stats15.Aggregator-30s.*.*-Trans*.s*.execute.count),%22Aggregator-30s%20Executes%22) >> - >> alias(sum(prod.storm.storm01*.6*.stats15.Aggregator-30s.*.*-Trans*.s*.ack.count),%22Aggregator-30s%20Acks%22) >> - Stats 1.5 Mongo Ops: >> - >> alias(sum(prod.mongo.mongo43*.counters.update,prod.mongo.samongo99*.counters.update),%22writes%22) >> - >> alias(sum(prod.mongo.mongo43*.counters.query,prod.mongo.samongo99*.counters.query),%22reads%22) >> >> I'm not positive I'm interpreting/calculating all of these correctly, in >> particular, Messages Per Batch and Batch Count. Do these look right? >> >> As you can see in the above Transform Output vs Aggregate Input graph, >> the number of tuples processed by the 30m and the 30s aggregators differs. >> Since each input message is mapped to one 30m and one 30s message, I would >> expect these numbers to be roughly the same. Am I making an invalid >> assumption? What else could this be? >> >> Assuming the Kafka Spout Emitter graph is correct, you can see that there >> are periods of time that we emit nothing. From changing the tuning >> parameters (in particular, the Kafka maxFetchSize) and varying how much >> backlog is available in Kafka, we've noticed that with a large backlog and >> maxFetchSize that we'll see a large number of Messages Per Batch but many >> fewer Batches/min (e.g., lower Batch Count). At worst, this became one >> batch in an hour or more as Storm chugged away at the data. >> >> From the Mongo graph, you can see that we're not doing a ton of writes >> per minute; around 75k or so. We've used Mongo quite extensively before and >> seen much better write performance than this so we think its an artifact of >> how we're interacting with it. (In fact, this is a transition topology >> designed to move us off Mongo entirely.) >> >> So, in summary, we're having difficulty figuring out where our system is >> bottlenecking at high-volumes. >> >> If you've made it this far, thanks for listening! Any help or insight or >> ideas are appreciated. :) >> >> -Cody >> >> -- >> Cody A. Ray, LEED AP >> cody.a.ray@gmail.com >> 215.501.7891 >> > > > -- > Danijel Schiavuzzi > > E: danijel@schiavuzzi.com > W: www.schiavuzzi.com > T: +385989035562 > Skype: danijels7 > -- Cody A. Ray, LEED AP cody.a.ray@gmail.com 215.501.7891 --001a11c11a32c74d6804fdb4b611 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
How many Kafka partitions are you using with a 10-20MB fet= ch size? (I believe that batch size ~=3D # partitions * kafka fetch size).<= div>
I added the initial shuffle after the spout so that I co= uld have a much higher parallelism for the main transform stage (CPU-bound)= than the spout stage (IO-bound). Is this not the right approach? Without t= his, they have the same parallelism. Though localOrShuffle might help here = then?

Yes, we monitor CPU and memory (but not network usage).= CPU loads are fairly spiky (presumably spiking during the transform stage)= but we generally have 80+% idle. All storm boxes have 4-6GB memory free. A= lthough we don't have solid network usage monitoring, some simple iperf= tests have shown that we can transfer 1G in a few seconds, IIRC.

Will run a few tests with maxSpoutPending and see how t= hat affects our throughput.

-Cody
<= div class=3D"gmail_extra">

On Tue, Jul 8,= 2014 at 1:23 PM, Danijel Schiavuzzi <danijel@schiavuzzi.com><= /span> wrote:
You should increase maxSpoutPending, as it e= nables pipelining of batches inside the topology, thus increasing performan= ce. As for the Kafka fetch size, I've got the best throughput with 10-2= 0MB sizes. The buffer=C2=A0size didn't impact the throughput.

Also, I believe the first shuffle() repartitioning operation,=C2= =A0right after the spout, is not needed, as=C2=A0it will only slow down pro= cessing by transferring tuples between workers over the transport layer=C2= =A0and the network needlessly. It's best to avoid repartitioning operat= ions=C2=A0and let Trident pack the topology into as few bolts as possible. = I've got a massive speedup by removing unnecessary stream=C2=A0repartit= ionings=C2=A0(shuffle()s). In my case, the=C2=A0overhead of repartitioning= =C2=A0the stream (to enable=C2=A0increasing=C2=A0the various=C2=A0tasks=C2= =A0paralellism)=C2=A0was greater than the gain of the=C2=A0increased=C2=A0p= aralellism of the tasks.=C2=A0Increasing task paralellism doesn't alway= s=C2=A0equal=C2=A0increased performance, it depends of course on the specif= ic case.

Do you monitor the basic system stats like CPU, memory = and network usage?

On Monday, July 7, = 2014, Cody A. Ray <cody.a.ray@gmail.com> wrote:
Hi,

We're doing time-series event aggregation with Storm/Trident, wh= ich I believe is a pretty common use case. However, we're running into = performance issues when trying to transition our high-volume applications t= o it (up to 600k req/min peak).

(Can't easily push a working br= anch to github since the JSON parser is in a private library.)

Component Versions:
- Kafka 0.8.0
-= Storm=C2=A00.9.0.1
- Kafka Spout 0.2.0 from wurstmeister=

Hardware in EC2:
- 3 zookeepers on c1.mediums
- 2 = kafkas on m1.larges
- 3 storms on m1.larges and c3.xlarges (in an= other region)
- 2 mongos on m1.large and m1.xlarges (in another r= egion)=C2=A0

Topology:
- spout0 bolt (from Trident) with p= arallelism 2 (one per Kafka partition)
- Transform bolt with para= llelism 12 (=3D 3=C2=A0worker=C2=A0hosts * 2 cores/host * 2 executors/30cor= e)
- 30s and 30m Aggregator bolts with parallelism 3 (one per worker host= )

Tuning Options:
- Kafka Max Fetch Size= : have tried various options from 256K to 20M
- Kafka Buffer Size= : have tried default (1M) as well as matching to Max Fetch Size
- Max Spout Parallelism: left default of 1

If= you look at the Storm UI, you'll notice that none of the bolts are ove= r-capacity (>=3D 1). The highest capacity bolt is the Aggregator-30s whi= ch performs the persistentAggregate.

3D"Inline
(A larger f= ull-screen snapshot of this UI view is attached.)

= But diving into this bolt shows that one of the 3 executors is almost unuse= d, one is medium-used, and the other is heavily-used. So something may not = be balanced well???

3D"Inline
(A larg= er full-screen snapshot of this UI view is attached.)

<= /div>
We've used the Nimbus Thrift API to put together a Graphite dashbo= ard showing (what we hope are) the key metrics for this pipeline.

3D"Inline
(See attached for a b= igger version of this graph).

These graphs show ra= tes as request messages (and later tuples) move through the system. The fol= lowing list gives the Graphite targets shown in each graph.
  • Suro Send - number of messages coming from each Suro into Kafk= a
    • prod.suro.*mongo*.messages
  • Kafka Ingestion - num= ber of messages coming into each Kafka Broker-Partition
    • prod.kafka.kafka*.requests.*.latest
  • Suro/Kafka/Storm P= ipeline - total number of messages going through Suro/Kafka/Storm
    • <= li>alias(sum(prod.suro.*mongo*.messages),%22suro%20messages%22)
    • alias(sum(prod.kafka.kafka01*.requests.*.latest),%22kafka01%20latest%22)
    • alias(sum(prod.kafka.kafka02*.requests.*.latest),%22kafka02%20latest%22= )
    • alias(sum(prod.storm.storm*.*.stats15.spout0.*.s1.emit.count),%22= storm%20spout%22)
  • Kafka Spout Emitter - number of Messages Per= Batch and Batch Count:
    • alias(divideSeries(sum(prod.storm.storm*.*.stats15.spout0.*.s1.emit= .count),sum(prod.storm.storm*.6*.stats15.mastercoord-bg0.*.batch.emit.count= )),%22Messages%20Per%20Batch%22)
    • alias(secondYAxis(prod.storm.storm= *.6*.stats15.mastercoord-bg0.*.batch.emit.count),%22Batch%20Count%22)
  • Spout Output vs Transform Input:
    • alias(sum(prod.storm.= storm*.*.stats15.spout0.*.s1.emit.count),%22Spout%20Output%22)
    • alia= s(sum(prod.storm.storm01*.*.stats15.Transform-Aggregator-30s-Transform-Aggr= egator-30m.*.spout0.s1.execute.count),%22Transform%20Executes%22)
    • alias(sum(prod.storm.storm01*.*.stats15.Transform-Aggregator-30s-Transf= orm-Aggregator-30m.*.spout0.s1.ack.count),%22Transform%20Acks%22)
  • Transform Output vs Aggregator Input:
    • alias(sum(prod.s= torm.storm01*.*.stats15.Transform-Aggregator-30s-Transform-Aggregator-30m.*= .s13.emit.count),%22Transform-30m%20Output%22)
    • alias(sum(prod.storm= .storm01*.6*.stats15.Aggregator-30m.*.*-Trans*.s*.execute.count),%22Aggrega= te-30m%20Executes%22)
    • alias(sum(prod.storm.storm01*.6*.stats15.Aggregator-30m.*.*-Trans*.s*.a= ck.count),%22Aggregate-30m%20Acks%22)
    • alias(sum(prod.storm.storm01*= .*.stats15.Transform-Aggregator-30s-Transform-Aggregator-30m.*.s7.emit.coun= t),%22Transform-30s%20Output%22)
    • alias(sum(prod.storm.storm01*.6*.stats15.Aggregator-30s.*.*-Trans*.s*.e= xecute.count),%22Aggregator-30s%20Executes%22)
    • alias(sum(prod.storm= .storm01*.6*.stats15.Aggregator-30s.*.*-Trans*.s*.ack.count),%22Aggregator-= 30s%20Acks%22)
  • Stats 1.5 Mongo Ops:
    • alias(sum(prod.mongo.mongo43*.cou= nters.update,prod.mongo.samongo99*.counters.update),%22writes%22)
    • a= lias(sum(prod.mongo.mongo43*.counters.query,prod.mongo.samongo99*.counters.= query),%22reads%22)
I'm not positive I'm interpreting/calculating = all of these correctly, in particular, Messages Per Batch and Batch Count. = Do these look right?

As you can see in the above T= ransform Output vs Aggregate Input graph, the number of tuples processed by= the 30m and the 30s aggregators differs. Since each input message is mappe= d to one 30m and one 30s message, I would expect these numbers to be roughl= y the same. Am I making an invalid assumption? What else could this be?

Assuming the Kafka Spout Emitter graph is correct, you = can see that there are periods of time that we emit nothing. From changing = the tuning parameters (in particular, the Kafka maxFetchSize) and varying h= ow much backlog is available in Kafka, we've noticed that with a large = backlog and maxFetchSize that we'll see a large number of Messages Per = Batch but many fewer Batches/min (e.g., lower Batch Count). At worst, this = became one batch in an hour or more as Storm chugged away at the data.

From the Mongo graph, you can see that we're not do= ing a ton of writes per minute; around 75k or so. We've used Mongo quit= e extensively before and seen much better write performance than this so we= think its an artifact of how we're interacting with it. (In fact, this= is a transition topology designed to move us off Mongo entirely.)

So, in summary, we're having difficulty figuring ou= t where our system is bottlenecking at high-volumes.

If you've made it this far, thanks for listening! Any help or insigh= t or ideas are appreciated. :)

-Cody

--
Co= dy A. Ray, LEED AP
cody.a.ray@gmail.com
215.501.7891


--
Danijel Schiavuzzi

E: danijel@schiavuzzi.com
W: www.schiavuzzi.com
T: +385989035562
Skype: danijels7




--
Cody A.= Ray, LEED AP
= cody.a.ray@gmail.com
215.501.7891
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