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.

Presto on AWS using Ahana Cloud at Cartona – Omar Mohamed, Cartona

Presto on AWS using Ahana Cloud at Cartona – Omar Mohamed, Cartona

Cartona is one of the fastest growing B2B e-commerce marketplaces in Egypt that connects retailers with suppliers, wholesalers, and production companies. We needed to federate across multiple data sources, including transactional databases like Postgres and AWS S3 data lake. In this session, we’ll talk about how Presto allows us to join across all of these data sources without having to copy or ingest data – it’s all done in place. In addition, we’ll talk about how we were up and running in less than an hour with the Ahana Cloud managed service. It gives us the power of Presto and the ease of use without the need to manage it or have deep skills to deploy and operate it.