Performance Tuning Apache Storm at Keen IO
Tuesday, March 8, 2016 at 8:56AM 
Hi, I'm Manu Mahajan and I'm a software engineer with Keen IO's Platform team. Over the past year I've focused on improving our query performance and scalability. I wanted to share some things we've learned from this experience in a series of posts.
Today, I'll describe how we're working to guarantee consistent performance in a multi-tenant environment built on top of Apache Storm.
tl;dr we were able to make query response times significantly more consistent and improve high percentile query-duration by 6x by making incremental changes that included isolating heterogenous workloads, making I/O operations asynchronous, and using Storm’s queueing more efficiently.
High Query Performance Variability
Keen IO is an analytics API that allows customers to track and send event data to us and then query it in interesting ways. We have thousands of customers with varying data volumes that can range from a handful of events a day to upwards of 500 million events per day. We also support different analysis types like counts, percentiles, select-uniques, funnels, and more, some of which are more expensive to compute than others. All of this leads to a spectrum of query response times ranging from a few milliseconds to a few minutes.
The software stack that processes these queries is built on top of Apache Storm (and Cassandra and many other layers). Queries run on a shared storm cluster and share CPU, memory, IO, and network resources. An expensive query can easily consume physical resources and slow down simpler queries that would otherwise be quick.
If a simple query takes a long time to execute it creates a really bad experience for our customers. Many of them use our service to power real-time dashboards for their teams and customers, and nobody likes to wait for a page while it's loading.
