Videos Archive - PrestoDB

Predicting Resource Usages of Future Queries Based on 10M Presto Queries at Twitter

Here, Chunxu and Beinan would like to share what they have learned in developing a highly-scalable query predictor service through applying machine learning algorithms to ~10 million historical Presto queries to classify queries based on their CPU times and peak memory bytes. At Twitter, this service is helping to improve the performance of Presto clusters and provide expected execution statistics on Business Intelligence dashboards.

A Tour of Presto Iceberg Connector – Beinan Wang, Alluxio & Chunxu Tang, Twitter

Apache Iceberg is an open table format for huge analytic datasets. The Presto Iceberg connector consolidates the SQL engine and the table format, to empower high-performant data analytics. Here, Beinan and Chunxu would like to discuss and share the architectural design of the Presto Iceberg connector, advanced Iceberg feature support (such as native iceberg connector, row-level deletion, and iceberg v2 support), and the future roadmap.

Presto and Apache Iceberg – Chunxu Tang, Twitter

Apache Iceberg is an open table format for huge analytic datasets. At Twitter, engineers are working on the Presto-Iceberg connector, aiming to bring high-performance data analytics on Iceberg to the Presto ecosystem. Here, Chunxu would like to share what they have learned during the development, hoping to shed light on the future work of interactive queries.

How Carbon uses PrestoDB in the Cloud with Ahana to Power its Real-time Customer Dashboards

Carbon is a real-time revenue management platform that consolidates revenue and audience analytics, data management, and yield operations into a single solution. Real-time analytics is super critical – their customers rely on real-time data to make revenue decisions. After facing issues around performance, visibility & ease of use, and serverless pricing model with AWS Athena, the team moved to a managed service for PrestoDB in the cloud – Ahana Cloud – to power their customer-facing dashboards. In this session, Jordan will discuss some of the reasons the team moved from AWS Athena to a managed PrestoDB on Intel-optimized AWS instances. He will also dive into their current architecture that includes an Ahana-managed Hive Metastore along with Apache ORC file format and an S3-based data lake. Last, he’ll share some performance benchmarks and talk about what’s next for PrestoDB at Carbon.

Level 101 for Presto: What is PrestoDB?

In Level 101, you’ll get an overview of Presto, including: A high level overview of Presto & most common use cases The problems it solves and why you should use it A live, hands-on demo on getting Presto running on Docker Real world example: How Twitter uses Presto at scale

Level 102 for Presto: Getting Started with PrestoDB

In this tech talk, we’ll build on what we learned in Level 101 and show you how easy it is to get started with PrestoDB. We’ll also dive deeper into key features and connectors.

Level 103 for Presto: Deep Dive into the PrestoDB architecture at Twitter

In this tech talk, we’ll take a look at the PrestoDB architecture in more technical depth. Next, we’ll have the Presto engineers at Twitter share their PrestoDB use case and architecture which includes several large Presto clusters with over 2,000 nodes.