Citus Blog

I recently gave a talk about the Citus extension to Postgres at the Warsaw PostgreSQL Users Group. Unfortunately, I did not get to go in person to beautiful Warsaw, but it was still a nice way to interact with the global Postgres community and talk about what Citus is, how it works, and what it can do for you.

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Materialized views were a long awaited feature within Postgres for a number of years. They finally arrived in Postgres 9.3, though at the time were limited. In Postgres 9.3 when you refreshed materialized views it would hold a lock on the table while they were being refreshed. If your workload was extremely business hours based this could work, but if you were powering something to end-users this was a deal breaker. In Postgres 9.4 we saw Postgres achieve the ability to refresh materialized views concurrently. With this we now have fully baked materialized view support, but even still we’ve seen they may not always be the right approach.

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Many companies generate large volumes of time series data from events happening in their application. It’s often useful to have a real-time analytics dashboard to spot trends and changes as they happen. You can build a real-time analytics dashboard on Postgres by constructing a simple pipeline:

1. Load events into a raw data table in batches
2. Periodically aggregate new events into a rollup table
3. Select from the rollup table in the dashboard

For large data streams, Citus (an open source extension to Postgres that scales out Postgres horizontally) can scale out each of these steps across all the cores in a cluster of Postgres nodes.

One of the challenges of maintaining a rollup table is tracking which events have already been aggregated—so you can make sure that each event is aggregated exactly once. A common technique to ensure exactly-once aggregation is to run the aggregation for a particular time period after that time period is over. We often recommend aggregating at the end of the time period for its simplicity, but you cannot provide any results before the time period is over and backfilling is complicated.

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Citus scales out Postgres for a number of different use cases, both as a system of record and as a system of engagement. One use case we’re seeing implemented a lot these days: using the Citus database to power customer-facing real-time analytics dashboards, even when dealing with billions of events per day. Dashboards and pipelines are easy to handle when you’re at 10 GB of data, as you grow even basic operations like a count of unique users require non-trivial engineering work to get performing well.

Citus is a good fit for these types of event dashboards because of Citus’ ability to ingest large amounts of data, to perform rollups concurrently, to mix both raw unrolled-up data with pre-aggregated data, and finally to support a large number of concurrent users. Adding all these capabilities together, the Citus extension to Postgres works well for end users where a data warehouse may not work nearly as well. We’ve talked some here about various parts of building a real-time customer facing dashboard, but today we thought we’d go one step further and give you a guide for doing it end to end.

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HyperLogLog is an awesome approximation algorithm that addresses the distinct count problem. I am a big fan of HyperLogLog (HLL), so much so that I already wrote about the internals and how HLL solves the distributed distinct count problem. But there’s more to talk about, including HLL with rollup tables.

Rollup Tables and Postgres

Rollup tables are commonly used in Postgres when you don’t need to perform detailed analysis, but you still need to answer basic aggregation queries on older data.

With rollup tables, you can pre-aggregate your older data for the queries you still need to answer. Then you no longer need to store all of the older data, rather, you can delete the older data or roll it off to slower storage—saving space and computing power.

Let’s walk through a rollup table example in Postgres without using HLL.

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