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From Gary Tully <gary.tu...@gmail.com>
Subject Re: Scaleability problems with queue subscriptors
Date Wed, 04 May 2011 21:39:11 GMT
have you tried to use prefetch=0 on the work queue, so the next
message will not be dispatched till you issue another receive call
rather than when the ack occurs.

On 4 May 2011 21:29, Josh Carlson <jcarlson@e-dialog.com> wrote:
> Hi Gary,
>
> Thanks for the response. We've decided it would be easy for us to partition our consumers
such that they each consumer operates on only one queue. However, the model we are using retrieves
a message from one queue (the job queue), then grabs something to do from another queue (the
work queue), once it retrieves the message from the work queue it acknowledges the job queue
and goes and does its work. However, since another message is dispatched once the ack is done
and the work can take a long time (potentially infinite) we unsubscribe.  Subsequently, once
the work is done the consumer needs to go subscribe and retrieve another message.
>
> Switching to one queue helps when there is no or few messages. However, it is not scaling
when there are plenty of messages due to the way we need to subscribe/unsubscribe. Do you
have any suggestions on how we might be able to scale this given our current architecture?
>
> -Josh
>
>> -----Original Message-----
>> From: Gary Tully [mailto:gary.tully@gmail.com]
>> Sent: Friday, April 29, 2011 6:27 PM
>> To: users@activemq.apache.org
>> Subject: Re: Scaleability problems with queue subscriptors
>>
>> setting up a consumer is a little expensive, have a look at using a
>> composite destination so that you can subscribe to all destinations at
>> once.
>> Also, there is a delay between new consumer registration and async
>> dispatch, so waiting a few seconds before unsubscribe is necessary.
>>
>> http://activemq.apache.org/composite-destinations.html
>>
>> On 28 April 2011 23:41, Josh Carlson <jcarlson@e-dialog.com> wrote:
>> > We are using a shared file system Master/Slave for the broker. Version
>> 5.4.2. Our clients use the STOMP protocol. We use client
>> acknowledgements and communicate synchronously with the broker (using
>> receipts). We set prefetch to 1 in our subscriptions. Our clients
>> iterate over several queues, subscribing, checking for messages (timeout
>> of 50ms), and if one isn't available it un-subscribes and goes to the
>> next queue. There are almost always cases where there are no messages in
>> the queues. We ran into a problem where our application slowed down to a
>> crawl when we deployed additional clients and I've narrowed it down to
>> the fact that most of the time when we subscribed to the queue and then
>> asked if a message was ready it wouldn't be even though there were
>> messages in the queue. My assumption is that it is taking some time to
>> dispatch the message.
>> >
>> > Is there some configuration parameters I might want to set to help
>> with this problem? Or is this type of use just not going to scale?
>> >
>> > Here is some benchmark data. Each test, creates N consumers, but
>> before they are allowed to start it enqueues 50*N messages for the
>> consumer into *one* queue. The first set of metrics is for the case
>> where the consumers are iterating over 6 different queues (even though
>> there is only data in one). The second set of metrics we ONLY have 1
>> queue ... in this case the client only subscribes and un-subscribes once
>> except in the case where a message 'isn't ready' in that 50ms (in which
>> case it re-subscribes to the same queue). The metrics capture the entire
>> process. getNextMessage, iterates over the queues, doing the
>> subscribes/un-subscribes, receipts etc ...
>> >
>> > Note that in the 6 queue case time degrades once you have 100
>> consumers. In the other case it degrades after 100 but we never see a
>> Median greater than 206ms.
>> >
>> > TEST Case 6 Queues ... 5 of which are empty (note that in this first
>> case since 5 queues are empty one expects at least 250ms to poll those 5
>> empty queues). Times are in seconds.
>> >
>> > Number of Consumers 1. Muliple Queues
>> > Min: 0.349334999918938
>> > Max: 0.368788999971002
>> > Mean: 0.350222800001502
>> > Median: 0.349644500005525
>> > Std Dev: 0.00271797410606451
>> > Starting test for consumer count 10
>> >
>> > Number of Consumers 10. Muliple Queues
>> > Min: 0.349282000097446
>> > Max: 0.394184999982826
>> > Mean: 0.353602201999165
>> > Median: 0.352992500003892
>> > Std Dev: 0.00542072612850504
>> > Starting test for consumer count 50
>> >
>> > Number of Consumers 50. Muliple Queues
>> > Min: 0.315161000005901
>> > Max: 0.425882000010461
>> > Mean: 0.360078899599938
>> > Median: 0.359610499988775
>> > Std Dev: 0.00788422976924438
>> > Starting test for consumer count 75
>> >
>> > Number of Consumers 75. Muliple Queues
>> > Min: 0.342441000044346
>> > Max: 0.66088400001172
>> > Mean: 0.401721995466513
>> > Median: 0.396242500049994
>> > Std Dev: 0.0404559664668615
>> > Starting test for consumer count 100
>> >
>> > Number of Consumers 100. Muliple Queues
>> > Min: 0.352722999989055
>> > Max: 3.99510599998757
>> > Mean: 0.563622044800525
>> > Median: 0.494796500017401
>> > Std Dev: 0.413950797976057
>> > Starting test for consumer count 300
>> >
>> > Number of Consumers 300. Muliple Queues
>> > Min: 0.361888999934308
>> > Max: 5.53048999991734
>> > Mean: 1.91027370266765
>> > Median: 1.8000390000525
>> > Std Dev: 0.489824211293863
>> > Starting test for consumer count 600
>> >
>> > Number of Consumers 600. Muliple Queues
>> > Min: 0.335149999940768
>> > Max: 10.6164910000516
>> > Mean: 4.52802392866641
>> > Median: 4.35808100004215
>> > Std Dev: 0.840368954779232
>> > Starting test for consumer count 900
>> >
>> > Number of Consumers 900. Muliple Queues
>> > Min: 0.639438000041991
>> > Max: 18.2733670000453
>> > Mean: 8.00563488822206
>> > Median: 7.6759294999647
>> > Std Dev: 1.38340937172684
>> > Starting test for consumer count 1200
>> >
>> > Number of Consumers 1200. Muliple Queues
>> > Min: 0.474138000048697
>> > Max: 31.5018520000158
>> > Mean: 12.8169781057334
>> > Median: 12.2411614999873
>> > Std Dev: 2.45701978986895
>> > Starting test for consumer count 1500
>> >
>> > Number of Consumers 1500. Muliple Queues
>> > Min: 3.1234959999565
>> > Max: 48.7995179999853
>> > Mean: 18.8858608815866
>> > Median: 17.5380175000173
>> > Std Dev: 4.1516799330252
>> > Starting test for consumer count 1800
>> >
>> > Number of Consumers 1800. Muliple Queues
>> > Min: 4.99818900006358
>> > Max: 73.2436839999864
>> > Mean: 27.1358068585671
>> > Median: 25.4123435000074
>> > Std Dev: 6.30049000845097
>> > Starting test for consumer count 2400
>> >
>> > Number of Consumers 2400. Muliple Queues
>> > Min: 0.319424999994226
>> > Max: 114.78910699999
>> > Mean: 46.0846290592237
>> > Median: 44.3440699999919
>> > Std Dev: 10.2871979782358
>> >
>> > TEST Case only 1 queue
>> >
>> > Number of Consumers 1. Only One Queue
>> > Min: 0.0413880000123754
>> > Max: 0.0445370000088587
>> > Mean: 0.0416983800008893
>> > Median: 0.041657000023406
>> > Std Dev: 0.00042437742781418
>> > Starting test for consumer count 10
>> >
>> > Number of Consumers 10. Only One Queue
>> > Min: 0.0409169999184087
>> > Max: 0.0494429999962449
>> > Mean: 0.0419903019983321
>> > Median: 0.0417659999802709
>> > Std Dev: 0.000839524388489985
>> > Starting test for consumer count 50
>> >
>> > Number of Consumers 50. Only One Queue
>> > Min: 0.00652100006118417
>> > Max: 0.0843779999995604
>> > Mean: 0.0431237947992515
>> > Median: 0.0423434999538586
>> > Std Dev: 0.00470470800328101
>> > Starting test for consumer count 75
>> >
>> > Number of Consumers 75. Only One Queue
>> > Min: 0.00334199995268136
>> > Max: 0.120109000010416
>> > Mean: 0.0456681223996294
>> > Median: 0.0435704999836161
>> > Std Dev: 0.00729394094656864
>> > Starting test for consumer count 100
>> >
>> > Number of Consumers 100. Only One Queue
>> > Min: 0.00263900007121265
>> > Max: 0.206331999972463
>> > Mean: 0.051723164400761
>> > Median: 0.0513750000391155
>> > Std Dev: 0.0225837245735077
>> > Starting test for consumer count 300
>> >
>> > Number of Consumers 300. Only One Queue
>> > Min: 0.00258900003973395
>> > Max: 1.01170199993066
>> > Mean: 0.138241231733017
>> > Median: 0.136385999969207
>> > Std Dev: 0.0863229692434055
>> > Starting test for consumer count 600
>> >
>> > Number of Consumers 600. Only One Queue
>> > Min: 0.00214999995660037
>> > Max: 3.27785699989181
>> > Mean: 0.274939405133063
>> > Median: 0.256795499997679
>> > Std Dev: 0.237695097382708
>> > Starting test for consumer count 900
>> >
>> > Number of Consumers 900. Only One Queue
>> > Min: 0.00206800003070384
>> > Max: 31.7313950000098
>> > Mean: 0.5553230254
>> > Median: 0.338199999998324
>> > Std Dev: 1.14882073602057
>> > Starting test for consumer count 1200
>> >
>> > Number of Consumers 1200. Only One Queue
>> > Min: 0.00257100001908839
>> > Max: 49.8629720000317
>> > Mean: 0.912980378683317
>> > Median: 0.393762999970932
>> > Std Dev: 2.87091387484458
>> > Starting test for consumer count 1500
>> >
>> > Number of Consumers 1500. Only One Queue
>> > Min: 0.00201100006233901
>> > Max: 74.3607440000633
>> > Mean: 1.19311908142647
>> > Median: 0.205018000095152
>> > Std Dev: 4.4037236439348
>> > Starting test for consumer count 1800
>> >
>> > Number of Consumers 1800. Only One Queue
>> > Min: 0.00196300004608929
>> > Max: 84.4792379999999
>> > Mean: 1.29789674880008
>> > Median: 0.117239500046707
>> > Std Dev: 5.19232074252423
>> > Starting test for consumer count 2400
>> >
>> > Number of Consumers 2400. Only One Queue
>> > Min: 0.00200599990785122
>> > Max: 124.155756999971
>> > Mean: 1.77886690554984
>> > Median: 0.101840000017546
>> > Std Dev: 8.38169615533614
>> >
>> >
>>
>>
>>
>> --
>> http://blog.garytully.com
>> http://fusesource.com
>



-- 
http://blog.garytully.com
http://fusesource.com

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