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Scaling DISQUS to 75 Million Comments and 17,000 RPS

General Chicken

— 1 min read

This presentation and video by Jason Yan and David Cramer discusses how they scaled DISQUS, a comments as a service service for easily adding comments to your site and connecting communities. The presentation is very good, so here are just a few highlights:

  • Traffic: 17,000 requests/second peak; 450,000 websites; 15 million profiles; 75 million comments; 250 million visitors; 40 million monthly users / developer.
  • Forces: unpredictable traffic patterns because of celebrity gossip and events like disasters; discussion never expire which means they can't fit in memory; must always be up.
  • Machines: 100 servers; 30% web servers (Appache + mod_wsgi); 10% databases (PostgreSQL); 25% cache servers (memcached); 20% load balancing / high availability (HAProxy + heartbeat); 15% Utility servers (Python scripts).
  • Architecture: Requests are load balanced across an Apache cluster. Apache talks to memcached, HAProxy/pgbouncer to handle connection pooling to the database, and a central queue service.
  • Strategies: make sure indexes fit in memory; log slow queries; use connection pooling; the data model consists of user, forum, thread, post; partitions horizontally (Disqus, Your blog, etc) and vertically (forums, posts, users, sentry) at application level; joins performed in Python; Hudson is used for continuous integration; Redmine is used for bug tracking; extensive test suite; feature switches are used to turn off features; isolate slow functions from transactions; use autocommit for read slaves; a queue is used for low priority tasks; Django QuerySet caching is turned off to save memory.

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