Video of a conference talk by Burak Velioglu at Citus Con: An Event for Postgres. Abstract: Managing time series data at scale can be a challenge. PostgreSQL offers many powerful data processing features such as indexes, COPY, and SQL-but the high data volumes and ever-growing nature of time series data can cause your database to slow down over time.
Fortunately, Postgres has a built-in solution to this problem: Partitioning tables by time range. Partitioning with the Postgres declarative partitioning feature can help you speed up query and ingest times for your time series workloads. Though, you’ll still be limited by the memory, CPU, and storage resources of your Postgres server.
The good news is you can scale out your partitioned Postgres tables to handle enormous amounts of data by distributing the partitions across a cluster using Citus. This talk will guide you to using Postgres with Citus (and pg_cron) for time series data-effectively transforming PostgreSQL into a distributed time series database with Citus.
Burak Velioglu is a software engineer at Microsoft working in the Citus open source engineering team. Interested in distributed systems, machine learning, analytics, and anything related, Burak is a former researcher with an MSc in brain decoding.
► Video bookmarks:
⏩ 00:00 Introduction
⏩ 01:31 Timeseries data and workload
⏩ 03:39 Postgres built-in partitioning
⏩ 07:25 Easy partition management with Citus
⏩ 09:36 Citus Columnar
⏩ 14:44 Automated partition management with pg_cron
⏩ 18:16 Sharding & partitioning
⏩ 22:41 Demo
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