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0 pointsoulipo26mo ago0 comments

Nice! Right now I'm using Timescaledb, do you think it makes sense to move to a Postgres+CH setup instead? or only if I hit the limit of timescaledb?

Also what would be the benefit for me of querying clickhouse from Postgres, rather than directly through my backend via an ORM/SDK? is that because it would allow me to do JOINs?

What would be the typical setup if I want to JOIN analytical data (eg my IoT device readings) from CH with some business data (eg the user owning the device) from my Postgres? Would I replicate that business data to CH to do the join there, or would that be typically the exact use-case for pg_clickhouse?

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saisrirampur6mo ago· 2 in thread

Great questions! ClickHouse is a purpose-built analytical database with thousands of optimizations for analytics, which is why it’s typically faster and more scalable than TimescaleDB. Here’s a post that covers real scenarios where users have moved workloads from Timescale to ClickHouse: https://clickhouse.com/blog/timescale-to-clickhouse-clickpip...

If your operational (OLTP) tables are reasonably big, the recommended approach is to replicate them into ClickHouse and let ClickHouse handle the joins. This avoids cross-database joins and lets the execution be pushed fully into ClickHouse. You can use ClickPipes/PeerDB to make that super easy. https://clickhouse.com/docs/integrations/clickpipes/postgres... https://clickhouse.com/docs/integrations/clickpipes/postgres

Where pg_clickhouse fits: If you’re already using Postgres for OLTP and want to offload analytics to ClickHouse without rewriting your app, the pg_clickhouse extension helps. It lets you run OLTP and OLAP queries from Postgres, while pushing the analytical queries—and their joins—down to ClickHouse, where the replicated data lives. Going native i.e. querying ClickHouse directly for OLAP will be the most optimal and is recommended if your analytics is advanced/complex. We will be evolving pg_clickhouse over the coming months to support pushdown for more and more complex/advanced queries :)

oulipo2OP6mo ago

Very interesting! So right now I'm developing the backend, so I can still move analytics to CH, but I'm still wondering whether it would make sense because it might not be so large that it requires it (eg 50G/year of data I'd say)

And on the other hand, I can imagine that there could be plenty of footguns with replication to another database (not instant, what about schema changes, backfills, what if some database is shutdown for update while replicating, etc), so I'm a bit cautious about having a complex setup right now

Would you have some basic examples of a "mini-backend" Postgres+Clickhouse replication, using docker-compose + Typescript/Python or something, so I could play with it and take a look at what could be the operational complexity?

saisrirampur6mo ago

You should just give it a shot in 10-15 min and see how it looks with ClickHouse. We made it that simple with ClickPipes :). Don’t intend to sell here, but it is as simple as signing up for trial on ClickHouse Cloud and clicking a few buttons and start seeing PG data getting synced.

In regards to footguns with replication, totally understand you being cautious. Last 2 years at PeerDB/ClickPipes was laser focused on just Postgres CDC to provide a dead simple yet highly reliable experience. The product has 100s of features, addresses 100s of footguns and actively being enhanced. Sharing some customers using this production https://clickhouse.com/blog/postgres-cdc-year-in-review-2025... You should give it a shot to see how easy it is. :)

In regards to sample apication, here is one, https://github.com/ClickHouse/HouseClick It showcases PG + CH stack. We just merged a PR to integrate pg_clickhouse too. The good news is that, there is a blog planned in a couple of weeks which showcases a tightly integrated experience PG +CH with CDC and pg_clickhouse, all in OSS. It will have docker-compose too. Your question adds up to what we are thinking next, I couldn’t resist myself to reveal it. ;) :)

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