Building Observability Tools At Scale

Kevin Lew and Sangeeta Narayanan give us some lessons learned from building logging and monitoring solutions at Netflix:

We started our tooling efforts with providing visibility into device and server logs, so that our users can go to one tool instead of having to use separate data-specific tools or logging into servers. Providing visibility into logs is valuable because log messages include important contextual information, especially when errors occur.

However, at some point in our business growth, storing device and server logs didn’t scale because the increasing volume of log data caused our storage cost to balloon and query times to increase. Besides reducing our storage retention time period, we addressed scalability by implementing a real-time stream processing platform called Mantis. Instead of saving all logs to persistent storage, Mantis enables our users to stream logs into memory, and keep only those logs that match SQL-like query criteria. Users also have the choice to transform and save matching logs to persistent storage. A query that retrieves a sample of playback start events for the Apple iPad is shown in the following screenshot:

It’s an interesting post.

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