PrestoDB in HPE Ezmeral Unified Analytics – Milind Bhandarkar, HPE

PrestoDB in HPE Ezmeral Unified Analytics – Milind Bhandarkar, HPE


HPE Ezmeral Unified Analytics is an end-to-end data & AI/ML platform that consists of several popular open-source frameworks for data engineering, data analytics, data science, & ML engineering in a well-integrated packaging. These open-source frameworks include Apache Spark, Apache Airflow, Apache Superset, PrestoDB, MLFlow, Kubeflow, and Feast. This platform is built atop Kubernetes and provides built in security. In this talk we will focus on the role of PrestoDB in Unified Analytics as a fast SQL query engine, and also as a secure data access layer. We will discuss some of our value-additions to PrestoDB, such as a distributed memory-centric columnar caching layer that provides both explicit and transparent caching for dataset fragments, often leading to 3x to 4x query performance. We will conclude by proposing to make caching pluggable in PrestoDB and discussing future directions.

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.