Videos Archive - PrestoDB

Velociraptor – The Next Generation of RaptorX – Vladimir Rodionov, Carrot Cache

Vladimir Rodionov, founder of Carrot Cache will present the Velociraptor – the next evolution of PrestoDB hierarchical caching framework RaptorX. Velociraptor enables efficient data and meta-data caching well beyond RaptorX limits in terms of number of data files (multi-billions), number of table partitions (multi-millions) and number of table columns (multi-thousands). Velociraptor replaces all five RaptorX caches (Hive meta-data, file list, query result fragments, ORC/Parquet meta-data and data I/O) with a scalable solution, based on Carrot Cache, which does not pollute JVM heap memory, does not affect Java Garbage Collector, keeps all data and meta-data off Java heap memory or on disk and can scale well beyond server’s physical RAM limit. Velociraptor supports server restart, by quickly saving and loading data to/from disk for automatic cache warm up.

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.

After RaptorX: Improve Performance Understanding and Workload Analysis in Presto – Ke Wang & Bin Fan

RaptorX, an umbrella project presented in PrestoCon Day in March, enabled the Presto interactive fleet in Facebook to reduce latency by 10x, based on a set of architectural improvements and optimizations with hierarchical caching. This presentation provides an update on the follow-up enhancement. Bin Fan from Alluxio will talk about the exploration of a probabilistic algorithm in Alluxio caching to estimate cache working set and the implementation of shadow cache Ke Wang from Facebook will talk about how shadow cache is used to understand the system bottleneck for better resource allocation and query routing decisions. She will also cover a recent improvement in collecting and aggregating per-query runtime statistics on the Presto engine to better understand the time breakdown, resource usage breakdown and cache hit rate on a per-query basis, which can help identify areas of improvement.

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.

RaptorX: Building a 10X Faster Presto – James Sun, Facebook, Inc

RaptorX is an internal project name aiming to boost query latency significantly beyond what vanilla Presto is capable of. For this session, we introduce the hierarchical cache work including Alluxio data cache, fragment result cache, etc. Cache is the key building block for RaptorX. With the support of the cache, we are able to boost query performance by 10X. This new architecture can beat performance oriented connectors like Raptor with the added benefit of continuing to work with disaggregated storage.

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.

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.