Building a scalable time-series database on PostgreSQL
Michael J. Freedman is a Professor in the Computer Science Department at Princeton University, as well as the co-founder and CTO of Timescale, building an open-source database that scales out SQL for time-series data. His work broadly focuses on distributed systems, networking, and security, and has led to commercial products and deployed systems reaching millions of users daily. Honors include a Presidential Early Career Award (PECASE), Sloan Fellowship, NSF CAREER Award, ONR Young Investigator Award, DARPA CSSG membership, and multiple award publications.
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Today everything is instrumented, generating more and more time-series data streams that need to be monitored and analyzed. When it comes to storing this data, many developers often start with some well-trusted system like PostgreSQL, enjoying the convenience of having their data in one place, with time-series data stored alongside relational data and queried together using SQL. But when their data hits a certain scale, many give up Postgres' query power and ecosystem by migrating to some NoSQL or other "modern" time-series architecture. They face the traditional trade-off: query power or scale, and effectively silo their data.
In this talk, I describe why this perceived trade-off isn't necessary, and how we've built an efficient, scalable time-series database engineered up from PostgreSQL. In particular, the nature of time-series workloads one finds in DevOps, monitoring, IoT, finance, and elsewhere -- inserting new data about recent events -- presents very different demands than general transactional (OLTP) workloads. We've architected our time-series database to take advantage of and embrace these differences.
The system architecture automatically partitions data across both time and space, even though it exposes the illusion of a single continuous table -- a hypertable -- across all of your data spread across one or many servers. Its distributed query optimizations both hide the fact that users are interacting with many “chunks” of data, which are right-sized by volume and time constraints, and minimize which and how chunks are accessed to answer queries. In fact, the database supports all-of-SQL (not "SQL-like") against this hypertable (e.g., secondary indexes, rich query predicates and group bys, aggregations, windowing functions, CTEs, JOINs).
Through performance benchmarks, I show how the database scales much better than PostgreSQL, even on a single node. In particular, by appropriately sizing chunks, it avoids the "performance cliff" that vanilla PostgreSQL experiences once reaching table sizes of 10s-100s of millions of rows, while offering some compelling query performance improvements.
The database is implemented as a PostgreSQL extension, released under the Apache 2 license. A single-node beta release is available on GitHub, with the clustered version under development.
- 2017 July 14 14:30
- 50 min
- PGConf Local: Philly 2017