Am I missing something here, or is the only technical detail in the post "a trillion rows is a lot of data, maybe trying trinio will work this time"?

It feels like a lot of churn and not a lot of measurements or engineering.

> Five months ago, we rebuilt our warehouse on DuckDB instead of ClickHoue

They switched from ClickHouse to DuckDB five months ago, then switched from DuckDB to their own Trino derivative?

The article then ends before saying if the new setup solved their problems.

It’s great that we can now make these migrations with little downtime (I assume?) and try different things, but I wish we could have read more about what testing they did before jumping. The fact that they jumped to DuckDB without realizing it wasn’t going to work at their scale and spent only a few months with it before jumping again is not convincing.

I hoe there’s more to this story that just didn’t make it into the blog posts. As written it’s giving me flashbacks to some teams I worked with who changed tech stacks every six months because they liked migrating more than tuning and maintaining.

> Five months ago, we rebuilt our warehouse [1] on DuckDB instead of ClickHouse.

Why did they write this post if they had not even allowed things to settle. It sounds like someone wanted to claim victory (and promo?)

https://posthog.com/blog/why-we-rebuilt-our-data-warehouse

Sometimes a company just isn't able to manage a data warehouse; I've worked for a few.

I even phone screened with this one but couldn't cover their crazy quilt of requirements; at that time they were also looking for someone to rewrite the clickhouse optimizer, and they were stuck trying to reshard their cluster, which should take an afternoon.

I still like the company because their business prop reminds me of what we were doing in 2009, a kind of federated analytics organization on a centralized data warehouse that enables a lot of tools to be developed quickly at large scale.

But goddammit they keep picking these half-working platforms and flip-flopping from one to another. We were crushing millions of queries against 2T-row tables on Vertica in 2010, and it's only gotten better for customer-facing data apps, which is what Posthog is. The company doesn't seem to have a baseline of what to expect; the market is too saturated with slow tools that lower expectations.

I wonder if they have considered moving back to ClickHouse. ClickHouse Iceberg support has improved a lot over the past few months. You can easily run stateless ClickHouse on top of Iceberg. This solves the tenancy issue, and it can easily handle petabytes scale [1].

[1] https://clickhouse.com/blog/jump-trading-uses-clickhouse-wit...

The 2026 equivalent of 2016's "Why Uber Engineering Switched from Postgres to MySQL" ! https://www.uber.com/us/en/blog/postgres-to-mysql-migration/

Haven't seen Posthog's website in a while. Man, it's hideous.

The key insight I am getting from this is that as migrations get easier to do with agents, there will be a lot more of them.

But more importantly a lot of them will happen because we will all get more trigger happy to start one. On the other hand I also don't know if at scales like PostHog, a successful migration can be tested without doing all of it.

This reminds me so much of when we would bring a new marketing leader and they would plant their flag by nuking all of the existing marketing automation processes to bring in theirs, with no net impact on the business. Bike shedding.

Is Quack, the DuckDB client-server protocol, something that could have helped them with the scaling problems they encountered?

I would have appreciated more technical details with methodologies and explicit failures observed. Oh and a human voice would be nice for a change.

> Customers ran more complex queries than we expected

Did the queries change after DuckDB was introduced? Did they do a study of query patterns that could have anticipated this ahead of time?

> Predicting CPU and memory for a specific query was even harder.

What attempts / methodologies were used to try this? What was hard? What failed?

> We'd rather have contributed to the existing community and helped it grow. But we couldn't land changes as fast as we needed, so we ended up carrying patches in a fork

This isn't a BAD approach per se. IME it's common (and necessary) for the large scale users of a platform to maintain a fork for their needs and work with the community and the maintainers to upstream changes when/if the community is ready to accept. That's called influencing. On the other hand, if you toss of a few slop grenades to the community and they reject you shouldn't be surprised. (Not suggesting that's what happening here but the defeatest conclusion may imply it?)

> Missing reconnects and managing TTLs and idle connections at scale is hard

Wonder if they tried pgdog?

I'm surprised at this. That is a lot of engineering effort without revealing why they chose they own format and chose to query it via Trino.

Feels off that only 5 months ago the company just migrated to DuckDB only to now move to something else.

Who did the growth estimation of the data? This looks like an example of bad planning.

Interestingly, it seems that a data lake is the hot new thing to build in house. Hell, we did it too. If you don't mind, I wrote up exactly what these new data lakes tend to look like: https://telemetrymachine.com/blog/the-new-state-of-the-art-i...

The git repo [0] has a lot more of the technical details.

[0] https://github.com/PostHog/hoglake

This post is pointless. Tells us how it went, show performance numbers, show benefits, resource usage, ease of use, etc.

One thing I really appreciate about CloudFlare blog posts is that often they show their work, by showing what tickets they opened, what improvements they land upstream. With this march across clickhouse, duckdb, now trino, I'd love to know what if any places the team worked with upstream, what improvements got tried.

I'm pretty ok with the march across products myself. It felt like the big shift was from shared giant clickhouse to lots of duckdbs. I wonder what juggling many more clickhouses would have been like. Excited and hopeful to here more back on Trini, with hope!

It's wild how many really really good data stacks are out there. Apache has been collecting some absolutely stellar projects. And it feels like the rest of world meaning everyone but China is kind of sleeping on a lot of these options. Maybe it's just that these projects often go out of their way to have a place for emblems, for who is using the project, but a lot of these have pretty wide ranges of companies using them.

I do wish we were still in an age of company blogging, where people did talk more to the tech getting played with, talk to what's cooking in the shop. It's great to have these outreaches. There's so much tech and we are just so quiet about it! There's so much less signal to go off of, so much less shared awareness than the aughts and 10's.

after 3 months, how we switched back to forked clickhouse.

This idiotic churn used to be restricted to front end devs and backend folks were spared. Thanks for Agents now every part of the stack is going to be in constant churn thanks to resume builders. At no point in the article did you get a sense they have a handle on what their problem is and why a certain technology and architecture would be a better fit. Just try it out, churn out, try it out.

Translation: frontier models are so good that we can rewrite our own warehouse infra.

HogLake is quite the name ..

6 months from now: We are moving our warehouse off of Trino + HogLake to newer technology XXX.

This is ridiculous. Why can you not foresee such issues? Do you not do testing with mock data at the size you need? Migrating production between stacks so often means you are not getting new features in. This is pure churn.

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