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Presto: A Decade of SQL Analytics at Meta
Yutian Sun (Meta (United States)), Tim Meehan (Meta (United States)), Rebecca Schlussel (Meta (United States)), Wenlei Xie (Meta (United States)), Masha Basmanova (Meta (United States)), Orri Erling (Meta (United States)), Andrii Rosa (Meta (United States)), Shixuan Fan (Meta (United States)), Rongrong Zhong (Meta (United States)), Arun Thirupathi (Meta (United States)), Nikhil Collooru (Meta (United States)), Ke Wang (Meta (United States)), Sameer Agarwal (Meta (United States)), Arjun Gupta (Meta (United States)), Dionysios Logothetis (Meta (United States)), Kostas Xirogiannopoulos (Meta (United States)), Amit Dutta (Meta (United States)), Varun Gajjala (Meta (United States)), Rohit Jain (Meta (United States)), Ajay Palakuzhy (Meta (United States)), Prithvi Pandian (Meta (United States)), Sergey Pershin (Meta (United States)), Abhisek Saikia (Meta (United States)), Pranjal Shankhdhar (Meta (United States)), Neerad Somanchi (Meta (United States)), Swapnil Tailor (Meta (United States)), Jialiang Tan (Meta (United States)), Sreeni Viswanadha (Meta (United States)), Zac Wen (Meta (United States)), Biswapesh Chattopadhyay (Meta (United States)), Bin Fan, Deepak Majeti, Aditi Pandit
Résumé : Presto is an open-source distributed SQL query engine that supports analytics workloads involving multiple exabyte-scale data sources. Presto is used for low-latency interactive use cases as well as long-running ETL jobs at Meta. It was originally launched at Meta in 2013 and donated to the Linux Foundation in 2019. Over the last ten years, upholding query latency and scalability with the hyper growth of data volume at Meta as well as new SQL analytics requirements have raised impressive challenges for Presto. A top priority has been ensuring query reliability does not regress with the shift towards smaller, more elastic container allocation, which requires queries to run with substantially smaller memory headroom and can be preempted at any time. Additionally, new demands from machine learning, privacy, and graph analytics have driven Presto maintainers to think beyond traditional data analytics. In this paper, we discuss several successful evolutions in recent years that have improved Presto latency as well as scalability by several orders of magnitude in production at Meta. Some of the notable ones are hierarchical caching, native vectorized execution engines, materialized views, and Presto on Spark. With these new capabilities, we have deprecated or are in the process of deprecating various legacy query engines so that Presto becomes the single piece to serve interactive, ad-hoc, ETL, and graph processing workloads for the entire data warehouse.