Citus Blog

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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