Presto on AWS Journey at Twilio – Lesson Learned and Optimization – Aakash Pradeep & Badri Tripathy

Presto on AWS Journey at Twilio – Lesson Learned and Optimization – Aakash Pradeep & Badri Tripathy

Twilio as a leader in cloud communication platforms is very heavy on data and data-based decision making. Most data related use cases are currently powered by the Presto engine. Two years back we started the Journey with Presto in Twilio and today the system has scaled to a multi-PB data lakehouse and supports more than 75k queries per day. In this journey, we learned a lot about how to effectively operationalize Presto on AWS and some of the tricks to have better query reliability, query performance, guard-railing the clusters and save cost. With this talk, we want to share this experience with the community.

Presto Query Analysis for Data Layout Formatting and Query Result Caching – Gurmeet Singh, Uber

Presto Query Analysis for Data Layout Formatting and Query Result Caching – Gurmeet Singh, Uber

In this talk, I will be talking about a microservice that we have built at Uber to be able to analyze Presto queries. The Presto Query Engine does not provide endpoints for query analysis purposes. One has to either execute the query or gather insights from the query explain plan. In this talk, I will talk about 1. The work that we had to do to do the query analysis in a microservice using Presto as a library. 2. Doing predicate analysis on the queries to come up with data formatting recommendations in order to improve query performance. 3. Using the analysis service for query result cache invalidation. The analysis figures out whether the results from a previous run of the query are still valid and can be reused.