Common Sub Expression Optimization at Facebook

Common Sub Expression Optimization at Facebook

In complex analytics queries, we often see repeated expressions, for example parsing the same JSON column but extracting different fields, elaborate CASE statement with common predicates and different ones. Previously, Presto will compute the same expression many times as they appear in query. With common sub expression optimization, we would only evaluate the same expression once within the same project operator or filter operator. In our workload, we’ve seen 3x improvements on certain queries with expensive common sub expressions like JSON_PARSE. Microbenchmark also shows a consistent ~10% performance improvement for simple common sub-expressions like x + y. In this talk, we will talk about how this is implemented.

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.