Building Modern Data Lakes for Analytics Using Object Storage – Satish Ramakrishnan, MinIO

Building Modern Data Lakes for Analytics Using Object Storage – Satish Ramakrishnan, MinIO

The modern data lake is distributed, unstructured and demands performance and scale – or better stated, performance at scale. Modern object stores are the ideal platform to pair with MPP query engines like Presto – particularly as the scale reaches tens or hundreds of petabytes with tens to hundreds of concurrent queries. In this talk, Satish Ramakrishnan will outline the better together attributes of the two technologies with a focus on the most sophisticated modern object storage features – from throughput optimizations, multi-cloud capabilities, cross-cloud active active replication and lifecycle management. Participants will come away with a reference architecture suited to query processing at object scale.

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.