Buckle up, for the next post in the Trino Summit 2022 recap series. In this post, we’re covering the talk given by Lyft engineers, Charles and Ritesh, on how they have not only scaled Trino as adoption grew, but with less nodes and more effective usage. They also started moving to utilizing Trino more for ETL rather than just interactive analytics. Get ready for a smooth ride as Lyft brings you large scale ETL with Trino.
Recap #
Lyft uses Trino to perform ETL jobs reading 10 petabytes of data per day and writing 100 terabytes per day. They run 250,000 queries per day, with around 2,000 unique users. This requires approximately 750 EC2 instances scaling up or down with an autoscaler. Over 90 percent of queries complete within a one to three minutes.
In the last year, Lyft cut their number of Trino nodes in half, while increasing their workloads. This is possible due to recent improvements in Trino and upgrades in Java versions. Lyft is not using fault-tolerant execution, but has started seeing interest in using Trino for ETL jobs due to the faster turnaround. Some issues Lyft has faced has been around how resource hungry Trino is, as well as, the issue where the coordinator can be a single point of failure for queries executing on a cluster.
Lyft was one of the earliest companies to really push using Trino for ETL use cases. They built custom best effort rollback code in Apache Airflow. If a query fails, the operation reverts to the state before the operation began. Lyft runs four Trino clusters split by the type of workload used on that cluster. The best practices are careful usage around broadcast joins, query sharding, and scaling writers for ETL loads.
One final point Lyft pointed out is keeping up with the rapid release cycle of Trino was a challenge. Lyft showcases their regression testing using their query replay framework. This session is a smooth five out of five ride. Enjoy!
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