Tuesday, January 7, 2014

How HipChat Stores And Indexes Billions Of Messages Using ElasticSearch And Redis

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HipChat started in an unusual space, one you might not think would have much promise, enterprise group messaging, but as we are learning there is gold in them there enterprise hills. Which is why Atlassian, makers of well thought of tools like JIRA and Confluence, acquired HipChat in 2012.

And in a tale not often heard, the resources and connections of a larger parent have actually helped HipChat enter an exponential growth cycle. Having reached the 1.2 billion message storage mark they are now doubling the number of messages sent, stored, and indexed every few months.

That kind of growth puts a lot of pressure on a once adequate infrastructure. HipChat exhibited a common scaling pattern. Start simple, experience traffic spikes, and then think what do we do now? Using bigger computers is usually the first and best answer. And they did that. That gave them some breathing room to figure out what to do next. On AWS, after a certain inflection point, you start going Cloud Native, that is, scaling horizontally. And that’s what they did.

But there's a twist to the story. Security concerns have driven the development of an on-premises version of HipChat in addition to its cloud/SaaS version. We'll talk more about this interesting development in a post later this week.

While HipChat isn’t Google scale, there is good stuff to learn from HipChat about how they index and search billions of messages in a timely manner, which is the key difference between something like IRC and HipChat. Indexing and storing messages under load while not losing messages is the challenge.

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