How Many Queues Are Best For Max Performance? RabbitMQ

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How Many Queues Are Best For Max Performance? RabbitMQ

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A generally useful question posed by Charming asks how many queues one should use in RabbitMQ for the maximum message passing throughput/performance.  I thought I'd distill the answers by Brian Kelly and RobotEyes here for anyone who's worried about their performance with RabbitMQ.  And I'll bet that some of these pointers are relevant to other message queues as well:

From the RabbitMQ blog:

RabbitMQ's queues are fastest when they're empty. When a queue is empty, and it has consumers ready to receive messages, then as soon as a message is received by the queue, it goes straight out to the consumer. In the case of a persistent message in a durable queue, yes, it will also go to disk, but that's done in an asynchronous manner and is buffered heavily. The main point is that very little book-keeping needs to be done, very few data structures are modified, and very little additional memory needs allocating.

From the rabbitmq-discuss mailing group:

  • Use a larger prefetch count. Small values hurt performance.
  • A topic exchange is slower than a direct or a fanout exchange.
  • Make sure queues stay short. Longer queues impose more processing overhead.
  • If you care about latency and message rates then use smaller messages. Use an efficient format (e.g. avoid XML) or compress the payload.
  • Experiment with HiPE, which helps performance.
  • Avoid transactions and persistence. Also avoid publishing in immediate or mandatory mode. Avoid HA. Clustering can also impact performance.
  • You will achieve better throughput on a multi-core system if you have multiple queues and consumers.
  • Use at least v2.8.1, which introduces flow control. Make sure the memory and disk space alarms never trigger.
  • Virtualisation can impose a small performance penalty.
  • Tune your OS and network stack. Make sure you provide more than enough RAM. Provide fast cores and RAM.

Hope that gives you some ideas for improving your queueing performance.


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