Videos Archive - PrestoDB

Customer-Facing Presto at Rippling – Andy Li, Rippling

Presto is used for a variety of cases, but tends to be used for larger scale analytical queries. We have been transitioning to using Presto to power our data platform and customer-facing scripting language, RQL (Rippling Query Language) to run arbitrary customer queries to power core products. Presto helps enable diverse, federated querying at scale. In this talk, Andy will cover where Presto sits in Rippling’s ecosystem as a core query layer, our collaboration and contributions for closer integration with Apache Pinot, and learnings on using Presto to handle a large variety of query patterns.

Real Time Analytics at Uber with Presto-Pinot

In this talk, seasoned engineers at Uber will walk through the real time analytics use cases at Uber and the work they have done on the Presto architecture and the Presto-Pinot connector to address them.

HermesDB – Integrated Presto with a lucene-based Query Engine – Yue Long, Tencent

HermesDB is the next generation of OLAP engine at Tencent with the architecture featuring separation of storage and calculation. HermesDB characterizes efficient indexing files in storage data, equipping with customized Presto as the core query engine. With the help of Presto connector, HermesDB could not only support full ANSI syntax but also ultilize Apache Lucene as underlying computer core. Besides, we are in the progress of improving the end-to-end performance with the newly released Java Vector APIs, acclecerating different kinds of complex computations with SIMD instructions. According to the benchmark(SSB) we have, HermesDB outperformances other mainstream C++ based MPP engines.

Presto at Tencent at Scale: Usability Extension, Stability Improvement and Performance Optimization – Junyi Huang & Pan Liu

Presto has been adopted at Tencent as scale to serve scenarios of ad-hoc queries and interactive queries for different business units. In this talk, we’d like to share our practice of Presto in production. In details, we’ll talk about our works to further improve the stability, extend the usability, and optimize the performance of Presto. The works all together make Presto better fit in our production environment, which we think will also benefit the community.

Realtime Analytics with Presto and Apache Pinot – Xiang Fu

In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this talk, we will explore how to get the best of both worlds using Apache Pinot and Presto: 1. How people do analytics today to trade-off Latency and Flexibility: Comparison over analytics on raw data vs pre-join/pre-cube dataset. 2. Introduce Apache Pinot as a column store for fast real-time data analytics and Presto Pinot Connector to cover the entire landscape. 3. Deep dive into Presto Pinot Connector to see how the connector does predicate and aggregation push down. 4. Benchmark results for Presto Pinot connector.

Panel: The Presto Ecosystem

The Presto Ecosystem – Moderated by Dipti Borkar, Ahana; Maxime Beauchemin, Preset; Vinoth Chandar, Apache Hudi; Kishore Gopalakrishna, Apache Pinot & James Sun, Facebook, Inc.