Rob Skillington

The cardinality of monitoring data we are collecting today continues to rise, in no small part due to the ephemeral nature of containers and compute platforms like Kubernetes. Querying a flat dataset comprised of an increasing number of metrics requires searching through millions and in some cases billions of metrics to select a subset to display or alert on. The ability to use wildcards or regex within the tag name and values of these metrics and traces are becoming less of a nice-to-have feature and more useful for the growing popularity of ad-hoc exploratory queries.

In this talk we will look at how Prometheus introduced the concept of a reverse index existing side-by-side with a traditional column based TSDB in a single process. We will then walk through the evolution of M3’s metric index, starting with ElasticSearch and evolving over the years to the current M3DB reverse index. We will give an in depth overview of the alternate designs and dive deep into the architecture of the current distributed index and the optimizations we’ve made in order to fulfill wildcards and regex queries across billions of metrics.

The world in which we monitor software is growing more complex every year. There are increasingly more ways to run server-side software, with many more independent services and more points of failures, the list goes on! On the plus side, there’s a lot of great tools and patterns being developed to try and make things simple to assess and understand. This talk covers how metrics and monitoring can be leveraged in a variety of different ways, auto-discovering applications and their usage of databases, caches, load balancers, etc, setting up and tearing down dashboards and monitoring automatically for services and instances, and more.

We’ll also talk about how you can accomplish all this with a global view of your systems using both Prometheus and Graphite with M3, our open source metrics platform. We’ll take a deep dive look at how we use M3DB, distributed aggregation with the M3 aggregator and the M3 Kubernetes operator to horizontally scale a metrics platform in a way that doesn’t cost outrageous amounts to run with a system that’s still sane to operate with petabytes of metrics data.