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 5892F200C49 for ; Fri, 17 Mar 2017 17:35:55 +0100 (CET) Received: by cust-asf.ponee.io (Postfix) id 56608160B80; Fri, 17 Mar 2017 16:35:55 +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 B5818160B70 for ; Fri, 17 Mar 2017 17:34:45 +0100 (CET) Received: (qmail 76440 invoked by uid 500); 17 Mar 2017 16:34:28 -0000 Mailing-List: contact user-help@cassandra.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@cassandra.apache.org Delivered-To: mailing list user@cassandra.apache.org Received: (qmail 76430 invoked by uid 99); 17 Mar 2017 16:34:28 -0000 Received: from pnap-us-west-generic-nat.apache.org (HELO spamd2-us-west.apache.org) (209.188.14.142) by apache.org (qpsmtpd/0.29) with ESMTP; Fri, 17 Mar 2017 16:34:28 +0000 Received: from localhost (localhost [127.0.0.1]) by spamd2-us-west.apache.org (ASF Mail Server at spamd2-us-west.apache.org) with ESMTP id 1967A1A01CE for ; Fri, 17 Mar 2017 16:34:28 +0000 (UTC) X-Virus-Scanned: Debian amavisd-new at spamd2-us-west.apache.org X-Spam-Flag: NO X-Spam-Score: 1.879 X-Spam-Level: * X-Spam-Status: No, score=1.879 tagged_above=-999 required=6.31 tests=[DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, HTML_MESSAGE=2, RCVD_IN_DNSWL_NONE=-0.0001, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, SPF_PASS=-0.001] autolearn=disabled Authentication-Results: spamd2-us-west.apache.org (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mx1-lw-us.apache.org ([10.40.0.8]) by localhost (spamd2-us-west.apache.org [10.40.0.9]) (amavisd-new, port 10024) with ESMTP id mOX0HBB10j1a for ; Fri, 17 Mar 2017 16:34:25 +0000 (UTC) Received: from mail-yw0-f171.google.com (mail-yw0-f171.google.com [209.85.161.171]) by mx1-lw-us.apache.org (ASF Mail Server at mx1-lw-us.apache.org) with ESMTPS id E2EBB5F46F for ; Fri, 17 Mar 2017 16:34:24 +0000 (UTC) Received: by mail-yw0-f171.google.com with SMTP id v198so56643561ywc.2 for ; Fri, 17 Mar 2017 09:34:24 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to; bh=MWQrGDQF8s7GtTkFAtZ1W1Jia2P06mt9GtKQPXQQitA=; b=E2VGqC0QqA5230zxPbWlzBlccGG9H4Xp71lZ9mlk4JOQ54cuC5uaewR+3rs39Ous9H Bs5UEK5pkcDUTNqY0nnsUQJ5RV70T41bXwzCUPOGJbCGVtx7I50GUWj+m8xeWz/mNVuw gMmbuKrk+cUfX7gndAKeWD4wF2wn0TqApoYke5wLc1pPakWeZWIiskzgzDHekRRjz9nZ ckm9IQDDKQI6D9Ij5wvyGV67KMSHC+65EOysMJocSsR9GXTUPP9x1PkDmgB+9FZBu78R O8PMXqAHjIN6sh7Lfs4MLux+hIF/dFkc8hym9zZ2Tu3x40HSKWgbHeNErbOLb+m1YxMb RGYA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to; bh=MWQrGDQF8s7GtTkFAtZ1W1Jia2P06mt9GtKQPXQQitA=; b=EX0p/Eiu6AAZnOjmd5Ac+R8ECSZJ+hVa66hPan95Kx2QG4A8OVy2gFeJjTkUFt+RMu 4NiFK3Kaw3ECl8ryUex3pzNLOyawH/5biGYj0AwU1usJbawnrdHi8yfFwZL25R8YY2su 8HQdidMC62bCVz7cd95d+hBvSz70ezjntmeSkh0LWgFSRnCFUEHq56qG+Mf4Ra6JTPNm zBd/PZvnuvZe4Oo6LNOzTRtW8cUZxPGOK1j3h3LzHyDe1gqIXTfC8B7HBzi90jpYbgRa b69ufEKm8PvSYcaOE6S5MzL1AhQ7rLDriF/Lwfb9m2ZzPdd6UA7xwsPNHZkTmSAmT+vQ Nkgw== X-Gm-Message-State: AFeK/H1WrVvU5VW9FMLHjAlo14TMly0u2UlgHxyswoTwk67UUqx/XQBzmyaBwpiaX648ee/e8+39MD+6Z5wRnQ== X-Received: by 10.37.165.67 with SMTP id h61mr7713711ybi.153.1489768009766; Fri, 17 Mar 2017 09:26:49 -0700 (PDT) MIME-Version: 1.0 Received: by 10.37.203.139 with HTTP; Fri, 17 Mar 2017 09:26:49 -0700 (PDT) In-Reply-To: References: <9DF3C015-A617-4262-B501-E0B44195FB9C@gmail.com> From: S G Date: Fri, 17 Mar 2017 09:26:49 -0700 Message-ID: Subject: Re: How can I scale my read rate? To: user@cassandra.apache.org Content-Type: multipart/related; boundary=94eb2c19fa5005d57c054aefa5c9 archived-at: Fri, 17 Mar 2017 16:35:55 -0000 --94eb2c19fa5005d57c054aefa5c9 Content-Type: multipart/alternative; boundary=94eb2c19fa5005d578054aefa5c8 --94eb2c19fa5005d578054aefa5c8 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Thank you all for the nice pointers. I am using 22 machines each running 60 threads as reading clients. Each client has 8 cores and 23 GB RAM. On the Cassandra side, I bumped up the machines from 2 core, 5.5gb nodes to 8 core, 24 gb nodes and the performance improved from 15,000 to 35,000 reads/second. Now it is stuck again at this throughput. I have tried using G1 garbage collector and played with increasing/decreasing threads and machines on the clients reading Cassandra= . No effect. Strangest part is this: It seems adding more nodes to Cassandra cluster has no effect ! I had 12 machines a while ago and when I bumped the cluster size to 16 nodes (33% bigger), there was zero improvement on the read performance. Is the datastax client smart enough to route a get-by-primary-key query to the right node always? Or does the Cassandra nodes redirect queries to the right node after they find a right token-range? If its the latter, then it seems a bigger cluster size's advantages are being offset by an increasing number of redirects between the Cassandra nodes. I tried to look at Cassandra JMX metrics but there are way too many of them= . What metrics should I look into? The tablehistogram for the table being queried looks awesome. =E2=80=8B How do I diagnose this system for a faulty setting? What are the usual suspects in such cases? Thanks SG On Thu, Mar 16, 2017 at 8:11 AM, Patrick McFadin wrote= : > My general rule of thumb when it comes to stress clients to cluster under > load is around 2:1. So with a 12 node cluster, 24 stress clients. As Eric > stated, monitoring your stress clients is critical to making sure you hav= e > adequate load being produced. > > Patrick > > On Wed, Mar 15, 2017 at 3:50 PM, Carlos Rolo wrote: > >> Plus is one of the most documented questions available... >> >> Regards, >> >> Carlos Juzarte Rolo >> Cassandra Consultant / Datastax Certified Architect / Cassandra MVP >> >> Pythian - Love your data >> >> rolo@pythian | Twitter: @cjrolo | Skype: cjr2k3 | Linkedin: >> *linkedin.com/in/carlosjuzarterolo >> * >> Mobile: +351 918 918 100 <+351%20918%20918%20100> >> www.pythian.com >> >> On Wed, Mar 15, 2017 at 10:29 PM, James Carman < >> james@carmanconsulting.com> wrote: >> >>> Come on, man. That's not called for. Someone is coming here to the >>> community for help. Let's be courteous, please. >>> >>> On Wed, Mar 15, 2017 at 5:12 PM daemeon reiydelle >>> wrote: >>> >>>> with that brain damaged memory setting yes. >>>> >>>> >>>> *.......* >>>> >>>> >>>> >>>> *Daemeon C.M. ReiydelleUSA (+1) 415.501.0198 <(415)%20501-0198>London >>>> (+44) (0) 20 8144 9872 <+44%2020%208144%209872>* >>>> >>>> On Tue, Mar 14, 2017 at 3:45 PM, S G wrote= : >>>> >>>> Hi, >>>> >>>> I am trying to scale my read throughput. >>>> >>>> I have a 12 node Cassandra cluster, each node has: >>>> RAM: 5.5gb >>>> Disk: 64gb >>>> C* version: 3.3 >>>> Java: 1.8.0_51 >>>> >>>> The cluster has 2 tables - each with 2 million rows. >>>> Partition keys for all these rows are unique. >>>> >>>> I am stress-reading it from a 15-node client cluster. >>>> Each read-client has 40 threads, so total 600 read-threads from 12 >>>> machines. >>>> Each read query is get-by-primary-key where keys are read randomly fro= m >>>> a file having all primary-keys. >>>> >>>> >>>> I am able to get only 15,000 reads/second from the entire system. >>>> Is this the best read performance I can expect? >>>> Are there any benchmarks I can compare against? >>>> >>>> >>>> I tried setting bloom_filter_fp_chance from 0.01 to 0.0001 and caching >>>> to {'keys': 'ALL', 'rows_per_partition': 'ALL'} but it had no effect. >>>> >>>> I have also captured few JMX metric graphs as follows: >>>> [image: Cassandra-Bulk-Read-Metrics.png] >>>> =E2=80=8B >>>> Garbage collection metrics did not look very interesting, hence I have >>>> not added the same. >>>> >>>> Any ideas what more I can do to debug the system and increase its read >>>> throughput? >>>> >>>> Thanks >>>> SG >>>> >>>> >>>> >>>> On Sat, Mar 11, 2017 at 2:33 PM, Jeff Jirsa wrote: >>>> >>>> 5.5G of ram isn't very much for a jvm based database. Having fewer >>>> instances with more ram will likely give you better performance. >>>> >>>> Also, this is probably a better question for the user list than the De= v >>>> list >>>> >>>> -- >>>> Jeff Jirsa >>>> >>>> >>>> > On Mar 11, 2017, at 1:20 PM, S G wrote: >>>> > >>>> > Hi, >>>> > >>>> > I have a 9 node cassandra cluster, each node has: >>>> > RAM: 5.5gb >>>> > Disk: 64gb >>>> > C* version: 3.3 >>>> > Java: 1.8.0_51 >>>> > >>>> > The cluster stores about 2 million rows and partition keys for all >>>> these >>>> > rows are unique. >>>> > >>>> > I am stress-reading it from a 12-node client cluster. >>>> > Each read-client has 50 threads, so total 600 read-threads from 12 >>>> machines. >>>> > Each read query is get-by-primary-key where keys are read randomly >>>> from a >>>> > file having all primary-keys. >>>> > >>>> > >>>> > I am able to get only 15,000 reads/second from the entire system. >>>> > Is this the best read performance I can expect? >>>> > Are there any benchmarks I can compare against? >>>> > >>>> > >>>> > I tried setting bloom_filter_fp_chance from 0.01 to 0.0001 and >>>> caching to >>>> > {'keys': 'ALL', 'rows_per_partition': 'ALL'} but it had no effect. >>>> > >>>> > Thx, >>>> > SG >>>> >>>> >>>> >>>> >> >> -- >> >> >> >> > --94eb2c19fa5005d578054aefa5c8 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Thank you all for the nice pointers.
I am using 22 mac= hines each running 60 threads as reading clients.
Each client has= 8 cores and 23 GB RAM.

On the Cassandra side, I b= umped up the machines from 2 core, 5.5gb nodes to 8 core, 24 gb nodes and t= he performance improved from 15,000 to 35,000 reads/second.

<= /div>
Now it is stuck again at this throughput.

I have tried using G1 garbage collector and played with increasing/decrea= sing threads and machines on the clients reading Cassandra.
No ef= fect.

Strangest part is this:
It seems a= dding more nodes to Cassandra cluster has no effect !
I had 12 ma= chines a while ago and when I bumped the cluster size to 16 nodes (33% bigg= er), there was zero improvement on the read performance.
Is the d= atastax client smart enough to route a get-by-primary-key query to the righ= t node always?
Or does the Cassandra nodes redirect queries to th= e right node after they find a right token-range?
If its the latt= er, then it seems a bigger cluster size's advantages are being offset b= y an increasing number of redirects between the Cassandra nodes.
=
I tried to look at Cassandra JMX metrics but there are way t= oo many of them.
What metrics should I look into?

<= /div>

The tablehistogram for the table being queried loo= ks awesome.

=E2=80=8B
How do I diagnose this s= ystem for a faulty setting?
What are the usual suspects in such c= ases?

Thanks
SG




On Thu, Mar 16, 2017 at 8:11 AM, Patrick McFadin <pmcfad= in@gmail.com> wrote:
My general rule of thumb when it comes to stress clients to clus= ter under load is around 2:1. So with a 12 node cluster, 24 stress clients.= As Eric stated, monitoring your stress clients is critical to making sure = you have adequate load being produced.=C2=A0

Patrick

On Wed, Mar 15, 2017 at 3:50 PM, Carlos Rolo <rolo@pythian= .com> wrote:
Plus is one of the most documented questions available...
=

Regards,
<= br>
Carlos Juzarte Rolo
Cassandra Consultant / Datastax= Certified Architect / Cassandra MVP
=C2=A0
Pythian= - Love your data

rolo@pythian | Twitter: @cjrolo = | Skype: cjr2k3 | Linkedin: linkedin.com/in/carlosju= zarterolo

On Wed, Mar 15, 2017 at 10:29 PM, James Carm= an <james@carmanconsulting.com> wrote:
Come on, man.=C2=A0 That's not c= alled for.=C2=A0 Someone is coming here to the community for help.=C2=A0 Le= t's be courteous, please.

On Wed, Mar 15, 2017 at 5:12 PM daemeon reiydelle <daemeonr@gmail.com>= wrote:
with that brain damaged memory setting yes= .


.......


Daemeon C.M. Reiydelle
U= SA (+1) 415.501.0198
London (+44)= (0) 20 8144 9872


On Tue, Mar 14, 2017 at 3:45= PM, S G <sg.online.email@gmail.com> wrote:
Hi,

I am tr= ying to scale my read throughput.

I have a 12 node Cassandra cluster, each node has:
RAM: 5.5gb
Disk: 64gb
C*= version: 3.3
Java: 1.8.0_51=

The cluster has 2 tables - each with 2 m= illion rows.
Partition keys = for all these rows are unique.

I am str= ess-reading it from a 15-node client cluster.
Each read-client has 40 threads, so total 600 read-thread= s from 12 machines.
Each rea= d query is get-by-primary-key where keys are read randomly from a file havi= ng all primary-keys.


=
I am able to get only 15,000 read= s/second from the entire system.
Is this the best read performance I can expect?
Are there any benchmarks I can compare against?


I tried setting bloom_filter_fp_chance from 0.01 to 0.00= 01 and caching to {'keys': 'ALL', 'rows_per_partition&#= 39;: 'ALL'} but it had no effect.

I have also captured few JMX metric graphs as f= ollows:
3D"Cassandra-Bulk-Read-Metrics.png"=E2=80=8B
Garbage collection metrics d= id not look very interesting, hence I have not added the same.

Any ideas what more I can do to = debug the system and increase its read throughput?

Thanks
SG



On Sa= t, Mar 11, 2017 at 2:33 PM, Jeff Jirsa <jjirsa@gmail.co= m> wrote:
5.5G of= ram isn't very much for a jvm based database. Having fewer instances w= ith more ram will likely give you better performance.

Also, this is probably a better question for the user list than the Dev lis= t

--
Jeff Jirsa


> On Mar 11, 2017, at 1:20 PM, S G <sg.online.email@gmail.com> w= rote:
>
> Hi,
>
> I have a 9 node cassandra cluster, each node has:
> RAM: 5.5gb
> Disk: 64gb
> C* version: 3.3
> Java: 1.8.0_51
>
> The cluster stores about 2 million rows and partition keys for all the= se
> rows are unique.
>
> I am stress-reading it from a 12-node client cluster.
> Each read-client has 50 threads, so total 600 read-threads from 12 mac= hines.
> Each read query is get-by-primary-key where keys are read randomly fro= m a
> file having all primary-keys.
>
>
> I am able to get only 15,000 reads/second from the entire system.
> Is this the best read performance I can expect?
> Are there any benchmarks I can compare against?
>
>
> I tried setting bloom_filter_fp_chance from 0.01 to 0.0001 and caching= to
> {'keys': 'ALL', 'rows_per_partition': 'ALL= '} but it had no effect.
>
> Thx,
> SG




--





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