Videos Archive - PrestoDB

Shared Foundations Of Composable Data Systems – Biswapesh Chattopadhyay, Google

Data processing systems have evolved significantly over the last decade, driven by various factors such as the advent of cloud computing, increasingly complexity of applications such as ML, HTAP, Streaming, Observability and Graph processing. However, historically, these frameworks have evolved independently, leading to significant fragmentation of the stack. In this talk, I will talk about how this has evolved in the open source and at Meta, and how we are solving this problem through the Shared Foundations effort, leading to composable systems. This has resulted in significantly better performance, more features, higher engineering velocity and a more consistent user experience.

Presto & the Foundations of Open Lake House: Trends & Opportunities – Biswapesh Chattopadhyay, Meta

Building open and shared foundational tech to build a lake house architecture can provide the best-of-breed user experience across the Analytics and ML domains and potentially beyond. In this talk, Biswa will share examples drawn from the evolution of the data stack at Meta over the last few years including efforts towards dialect unification (Sapphire aka Presto-on-Spark and Xstream-IE streaming engine efforts), eval unification (using Velox as the base), eliminating the need for data duplication for interactive analytics by building smart caching (RaptorX), building a best-of-breed file format that works across Analytics and ML (Alpha), and building an open source ML data pre-proc engine (TorchArrow) which shares the core dialect and eval components with Presto.

Open Source Data Lake Analytics: Trends and Opportunities – Biswapesh Chattopadhyay, Meta

Open source data analytics is undergoing an interesting transformation as the industry rapidly evolves around it. Accelerating migration to the cloud, the rise of immensely well funded proprietary vendors, fast evolving needs of the users all contribute to this. This talk goes into detail about the trends and opportunities in the OSS data analytics space, and a call to action on how this space can stay relevant.

Disaggregated Coordinator Production Rollout – Swapnil Tailor, Meta

We would like to talk about disaggregated coordinator production rollout with some stats. Also we can inform community about changes needed to support deployment of disaggregated coordinator.

Disaggregated Coordinator – Swapnil Tailor, Facebook

In the existing Presto architecture, single coordinator has become a bottleneck in a number of ways for cluster scalability. – With an increasing number of workers, the coordinator has the potential of slow down due to a high number of tasks. – In high QPS use cases, we have found workers can become starved of splits by excessive CPU being spend on task updates in coordinator. – Also with single coordinator, we have an upper limit on the worker pool because of above-mentioned reasons. To overcome with this challenges, we are coming up with a new architecture which supports multiple coordinators in a single cluster.