Timeseries Indexing at Scale
Note
This blog post was co-authored with May Lee and is cross-posted on the Datadog blog.
Datadog collects billions of events from millions of hosts every minute and that number keeps growing and fast. Our data volumes grew 30x between 2017 and 2022. On top of that, the kind of queries we receive from our users has changed significantly. Why? Because our customers have grown in sophistication: they run more complex stacks, want to monitor more data, and run more complex analyses. That, in turn, puts pressure on our timeseries data store.
Data stores have a number of tricks in their bag to offer good performance. One of the most critical ones is the judicious use of indices, a key data structure that can make queries fast and efficient, or unbearably slow. Over the years, our homegrown indices that were put in place in 2016 became a performance bottleneck for queries and a source of increased maintenance. We knew that we had to learn from these challenges and come up with something better.
This blog post provides an overview of the Datadog timeseries database and the challenges of timeseries indexing at scale. We’ll compare the performance and reliability of two generations of indexing services.