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.

Presto is an open source distributed query engine used widely at Facebook, Uber, Twitter, Pinterest, and many other internet companies. Since open sourced in 2013, the Presto community has made several rounds of design and implementations, to support a variety of use cases, including interactive analytics, real time reporting and dashboard, ETL workloads, A/B testing, monitoring and alerts, etc. In this paper, we'd like to introduce some of the most important features and performance improvements the open source Presto community made in recent years, which enables companies running Presto at scale, supporting millions of queries per day, with hundreds of thousands of machines. Specifically, how Presto provides unified SQL on heterogeneous storage systems without data copy; how Presto deals with complex data, including nested columnar data and schema evolution; How Presto supports geospatial queries efficiently, and how file list cache works in Presto. We also talk about cluster federation, and Presto on cloud. Experimental results and our production experience could help others running interactive SQL systems at scale.

Presto is an open source distributed query engine that supports much of the SQL analytics workload at Facebook. Presto is designed to be adaptive, flexible, and extensible. It supports a wide variety of use cases with diverse characteristics. These range from user-facing reporting applications with sub-second latency requirements to multi-hour ETL jobs that aggregate or join terabytes of data. Presto's Connector API allows plugins to provide a high performance I/O interface to dozens of data sources, including Hadoop data warehouses, RDBMSs, NoSQL systems, and stream processing systems. In this paper, we outline a selection of use cases that Presto supports at Facebook. We then describe its architecture and implementation, and call out features and performance optimizations that enable it to support these use cases. Finally, we present performance results that demonstrate the impact of our main design decisions.

An important omission in current development practice for business process (or workflow) management systems is modeling of data & access for a business process, including relationship of the process data and the persistent data in the underlying enterprise database(s). This paper develops and studies a new approach to modeling data for business processes: representing data used by a process as a hierarchically structured business entity with (i) keys, local keys, and update constraints, and (ii) a set of data mapping rules defining exact correspondence between entity data values and values in the enterprise database. This paper makes the following technical contributions: (1) A data mapping language is formulated based on path expressions, and shown to coincide with a subclass of the schema mapping language Clio. (2) Two new notions are formulated: Updatability allows each update on a business entity (or database) to be translated to updates on the database (or resp. business entity), a fundamental requirement for process implementation. Isolation reflects that updates by one process execution do not alter data used by another running process. The property provides an important clue in process design. (3) Decision algorithms for updatability and isolation are presented, and they can be easily adapted for data mappings expressed in the subclass of Clio.