Videos Archive - PrestoDB

Presto on Spark – Facebook – Virtual Meetup

At Facebook, we have spent the past several years in independently building and scaling both Presto and Spark to Facebook scale batch workloads. It is now increasingly evident that there is significant value in coupling Presto’s state-of-art low-latency evaluation with Spark’s robust and fault tolerant execution engine. In this talk, we’ll take a deep dive in Presto and Spark architecture with a focus on key differentiators (e.g., disaggregated shuffle) that are required to further scale Presto.

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 On Spark: Scaling not Failing with Spark – Ariel Weisberg, Meta & Shradha Ambekar, Intuit

Presto on Spark is an integration between Presto and Spark that leverages Presto’s compiler/evaluation as a library and Spark’s large scale processing capabilities. It enables a unified SQL experience between interactive and batch use cases. A unified option for batch data processing and ad hoc is very important for creating the experience of queries that scale instead of fail without requiring rewrites between different SQL dialects. In this session, we’ll talk about Presto On Spark architecture, why it matters and its implementation/usage at Intuit.