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But this also means your users can generate their own queries, possibly doing some weird stuff taking down the db, so I assume it's more for "internal tools"?
No matter your size unless you have a trivial amount of data, if you expose a full SQL query language you can be hit be a DOS attack pretty trivially.
This ignores that row level security is also not enough on its own to implement an even moderately capable level of access controls.
It’s just like all the other postgres extensions named “pg_foo”, and the clear and obvious choice for “foo” in this case is “clickhouse”.
Unless this is some bad joke that has flown over my head.
Our main focus is comprehensive pushdown capabilities. It was very surprising to see how much the Postgres FDW framework has evolved over the years and the number and types of hooks it now provides for push down.
We’ve made notable progress over the last few months, including support for pushdown of custom aggregations and SEMI JOINs/basic subqueries. Fourteen of twenty-two TOCH queries are now fully pushdownable.
We’ll be doubling down to add pushdown support for much more complex queries, CTEs, window functions, and more. More on th e future here - https://github.com/ClickHouse/pg_clickhouse?tab=readme-ov-fi...
All with the goal of enabling customer to build fast analytics from the Postgres layer itself but still using the power of ClickHouse!
That would be incredible! So many times I want to reach for ClickHouse but whatever company I'm at has so much inertia built into PG.
And yes I'm aware of PeerDB or whatever that project is called. This is still helpful.
With respect to data replication, it gets really hard and has its challenges as data sizes grow - reliably moving tens of terabytes at speed, handling intricate quirks around replication slots, enterprise-grade observability etc. PeerDB/ClickPipes is designed to solve these problems. I wrote a blog post covering this in more detail here: https://clickhouse.com/blog/postgres-cdc-year-in-review-2025
That said, point taken - we will ensure query and app migration is seamless as well and reduce friction in integrating Postgres and ClickHouse. pg_clickhouse is a step in that direction! :)
Our CH wrapper is actively maintained, with push down, parameterized views, and async streaming: https://supabase.github.io/wrappers/catalog/clickhouse/
We see a lot of companies choosing CH with PG - it’s fantastic
what would you recommend and why?
I understand part of the interest of pg_clickhouse is to be able to use "pre-existing Postgres queries" on an analytical database without having to change anything, so if I am building my database now and have no legacy, would pg_clickhouse make sense, or should I do analytics differently?
Also, would you have some kind of tutorial / sample setup of a typical business application in Postgres and kind of replication in clickhouse to make analytics queries? so I can see how Clickhouse would be typically used?
Now, coming to your question about replication: you can use PeerDB (acquired by ClickHouse https://github.com/PeerDB-io/peerdb), which is laser-focused and battle-tested at scale for Postgres-to-ClickHouse replication. Once the data is replicated into ClickHouse, you can start querying those tables from within Postgres using pg_clickhouse. In ClickHouse Cloud, we offer ClickPipes for Postgres CDC/replication, which is a managed service version of PeerDB and is tightly integrated. Now there could be non-transcational tables that you can directly ingest to ClickHouse and still query using pg_clickhouse.
So TL;DR: Postgres for OLTP; ClickHouse for OLAP; PeerDB/ClickPipes for data replication; pg_clickhouse as the unified query layer. We are actively working on making this entire stack tightly integrated so that building real-time apps becomes seamless. More on that soon! :)
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?
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 :)
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?
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 and addresses 100s of footguns. 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. ;) :)
I've been really happy with DuckLake, happy to answer any questions about it.
DuckDB has always felt easier to use vs. Clickhouse for me, but both are great options. If I were you, I'd try both options for a few hours with your use case and pick the one that feels better.
ClickHouse’s bread and butter is real-time analytics for customer-facing applications, which often come with demanding concurrency and latency requirements.
Ack, totally makes sense that both are amazing technologies - you could try both and test them at the scale your real-time application may reach, and then choose the technology that best fits your needs. :)
You keep like 1 year's worth of data in your "business database", and then archive the rest in S3 with parquet and query with DuckDB ?
And if you want to sync everything, even "current data", to do datascience/analytics, can you just write the recent data (eg the last week of data or whatever) in S3 every hours/days to get relatively up-to-date data? And doesn't that cause the S3 data to grow needlessly (eg does it replace, rather than store an additional copy of recent data each hour?)
Do you have kind of "starter project" for a Postgres + DuckLake integration that I could look at to see how it's used in practice, and how it makes some operations easier?
```
meltano run tap-postgres target-ducklake
```
0 - https://www.notion.so/luabase/Postgres-to-DuckLake-example-2...
Note that I work for the company that built timescale (Tiger Data). Clickhouse is cool though, just throwing another option into the ring.
Tbf in terms of speed Clickhouse pulls ahead on most benchmark, unless you want to join a lot with your postgres data directly then you might benefit from having everything in one place. And of course you avoid the sync overhead.
Despite that, man it is really nice to be able to join your non-timeseries data in your queries (perhaps the fdw will allow this for clickhouse? I need to look into that). If you don't have to deal with the operations side too much and performance isn't a problem, Timescale is really nice.
I'd like to be able to use that for alert detection, etc, and some dashboard metrics, so I was thinking that it was the kind of perfect use-case for timescale, but because I haven't been using it yet "at scale" (not deployed yet) I don't know how it will behave
How do you do JOINs with business data for Clickhouse then? Do you have to do some kind of weird process where you query CH, then query Postgres, then join "manually" in your backend?
I actually have a blogpost on my experience with it here: https://www.wkrp.xyz/a-small-time-review-of-timescaledb/ that goes into a bit more detail as to my use case and issues I experienced. I'm actually half-way through writing the follow up using Clickhouse.
As detailed in the blog post, my data is all MMO video game stats such as item drops. With Timescale, I was able to join an "items" table with information such as the item name and image url in the same query as the "item_drops" table. This way the data includes everything needed for presentation. To accomplish the same in clickhouse, I create an "items" table and an "items_dict" dictionary (https://clickhouse.com/docs/sql-reference/dictionaries) that contains the same data. The Clickhouse query then JOINs the item_dict against item_drops to achieve the same thing.
If you know the shape of your data, you can probably whip up some quick scripts for generating fake versions and inserting into Timescale to get a feel for storage and query performance.
Picking the best solution for your concrete workload (and your future demands) should be equally important to the implementation effort, to avoid that you run into walls later on. At least as long as data volume, query complexity or concurrency scalability can be challenges.
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