Building a Scalable Postgres Metrics Backend Using the Citus Extension

If you haven't already seen it, Dan from Heap Analytics has a great talk about how they use Postgres and Citus.

https://www.youtube.com/watch?v=NVl9_6J1G60

We[0] just recently moved from Hbase to Postgres w/ Citus. Still not fully settled in, but got incredible support from the Citus team (seriously amazing guys) and things are looking much brighter than before.

On a 8 worker nodes (16 cores, 80GB ram, 3TB SSD), we're at around 20k inserts/second, while most of the selects are running at around 100ms (besides some counts and complex queries).

If you have a lot of data and know Postgres, give it a chance.

[0] https://pexe.so

If you're looking for a metrics backend built on traditional Postgres, check out Tgres.

http://grisha.org/blog/2016/07/29/state-of-tgres-2016/

For anyone with questions about how to properly shard a table, how to fit a problem to a distribution strategy, or general questions about deployment, Citus has a Slack channel which is active and extremely welcoming. You can request an invite here: https://slack.citusdata.com/

Disclaimer: don't work there, but on the Slack and the Citus team is nothing short of awesome.

I had a look at Citus when evaluating time series databases and the documents said that work was still under way on masterless setup for faster ingestion (up to 500k metrics /sec).

https://docs.citusdata.com/en/v5.2/performance/scaling_data_...

If that has changed I'd add it to my table.

https://docs.google.com/spreadsheets/d/1sMQe9oOKhMhIVw9WmuCE...