Quick Stats – Runtime ANALYZE for Better Query Plans – Anant Aneja, Ahana

Quick Stats – Runtime ANALYZE for Better Query Plans – Anant Aneja, Ahana

An optimizer’s plans are only as good as the estimates available for the tables its querying. For queries over recently ingested data that is not yet ANALYZE-d to update table or partition stats, the Presto optimizer flies blind; it is unable to make good query plans and resorts to syntactic join orders. To solve this problem, we propose building ‘Quick Stats’ : By utilizing file level metadata available in open data lake formats such as Delta & Hudi, and by examining stats from Parquet & ORC footers, we can build a representative stats sample at a per partition level. These stats can be cached for use be newer queries, and can also be persisted back to the metastore. New strategies for tuning these stats, such as sampling, can be added to improve their precision.

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.