[oulipo2]

I'm using Postgres as my base business database, and thinking now about linking it to either DuckDb/DuckLake or Clickhouse...

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?

[exagolo]

If not having to adjust queries is a major driver for your considerations, then I would highly recommend looking at SQLGlot (https://github.com/tobymao/sqlglot), a transpiler that makes you (more) independent of query dialects. They already support 30 dialects (big vendors such as Snowflake, Databricks, BigQuery, but also loads of the specialists such as ClickHouse, SingleStore or Exasol). Repo is maintained extremely well.

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.

[jascha_eng]

Depending on your workload you might also be able to use Timescale to have very fast analytical queries inside postgres directly. That avoids having to replicate the data altogether.

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.

[oulipo2]

I'm indeed already using Timescaledb, I was wondering if I would really gain something from adding clickhouse

[wkrp]

I was using Timescale for a small project of mine and eventually switched to Clickhouse. While there was a 2-4x disk space reduction, the major benefits have operational (updates & backups). The documentation is much better since Timescale's mixes their cloud product documentation in, really muddying the water.

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.

[oulipo2]

Can you tell me more about why timescale doesn't perform in your opinion? My use case for timescale would be to gather my IoT telemetry data (perhaps 20/100 points per second) and store eg 1 year worth of it to do some analysis and query some past data, then offload that to parquet files on S3 for older data

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?

[wkrp]

I was a little unclear, I think Timescale performs quite well. Just that in my (very limited) experience, Clickhouse performs better on the same data.

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.

[saisrirampur]

More on use-cases involving TimescaleDB replication/migration to ClickHouse https://clickhouse.com/blog/timescale-to-clickhouse-clickpip...

[mritchie712]

We released a meltano target for DuckLake[0]. dlt has one now too. Pretty easy to sync pg -> ducklake.

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.

0 - https://www.definite.app/blog/target-ducklake

[saisrirampur]

I love DuckDB from a product perspective and appreciate the engineering excellence behind it. However, DuckDB was primarily built for seamless for in-process analytics, data science, data-preparation/ETL workloads than real-time customer facing analytics.

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. :)

[Ritewut]

I tested DuckDB and even Motherduck and this was my takeaway. Square hole, round peg situation.

[oulipo2]

Nice, what would be your typical setup?

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?

[mritchie712]

Once you have meltano installed, it's just be:

    ```
    meltano run tap-postgres target-ducklake
    ```

Setting up meltano would be a bit more involved[0]

0 - https://www.notion.so/luabase/Postgres-to-DuckLake-example-2...

[saisrirampur]

Great question! If you’re starting a greenfield application, pg_clickhouse makes a lot of sense since you’ll be using a unified query layer for your application.

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 with ClickHouse. 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! :)

[oulipo2]

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?

[saisrirampur]

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 :)

[oulipo2]

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?

[saisrirampur]

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. ;) :)

[oulipo2]

Nice! Looking forward to reading it!