11–20 of 26 posts
https://clickhouse.com/blog/building-a-logging-platform-with...
(Full disclosure: I work for ClickHouse and love it here!)
But it didn't work out.
One of the reasons is LogFire allows the users to fetch the service data with arbitrary SQL queries.
So they had to build their own backend in rust, on top of DataFusion.
I used ClickHouse myself and it's been nice, but it's easy when you get to decide what schema you need yourself. For small to medium needs, this plus Grafana works well.
But I must admit that the plug and play aspect of great services like Sentry or LogFire make it so easy to setup it's tempting to skip the whole self hosting. They are not that expensive (unlike datadog), and maintaining your observability code is not free.
I see lot of hype around ClickHouse these days. Few years ago I remember TimescaleDB making the rounds, arguably being predecessor for this sort of "observability on SQL" thinking. The article has short paragraph mentioning Timescale, but unfortunately it doesn't really go into comparing it to ClickHouse. How does HN see the situation these days, is ClickHouse simply overtaking Timescale on all axis? That sounds bit…
Such a PITA. Unless you have a dedicated team to handle observability, you are in for pain, no matter the tech stack you use.
That's not truth. There are solutions for logging, which are very easy to setup and operate. For example, VictoriaLogs [1] (I'm its' author). It is designed from the grounds up to be easy to configure and use. It contains a single self-contained executable without external dependencies, which runs optimally on any hardware starting from Raspberry Pi and ending with a monster machine containing hundreds of CPU cores a…
"But if you will use it, keep in mind that [InfluxDB] uses Bolt as its data backend."
Simply not true. The author seems to have confused the storage that Raft consensus uses for metadata with that used for the time series data. InfluxDB has its own custom data storage layer for time series data, and has had so for many years. A simple glance at the InfluxDB docs would make this clear.
(I was once part of the core database team at InfluxDB and have edited my comment for clarity.)
Another project I want to give shout out to is Databend. It's built around the idea of storing your data at S3-compatible storage as Parquet files, and querying as SQL or other protocol. Like many popular Data Lake solutions, but it's open-source and written in Rust, which means quite easy to extend for many who know it already.
Interestingly, I recently interviewed Samuel Colvin, Pydantic's author, and he said when designing his observability Saas called LogFire, he tried multiple backends, including ClickHouse. But it didn't work out. One of the reasons is LogFire allows the users to fetch the service data with arbitrary SQL queries. So they had to build their own backend in rust, on top of DataFusion. I used ClickHouse myself and it's bee…
Currently collecting just exception data from services to GlitchTip (Sentry fork), seems most valuable sysadmin-wise while having most security etc. concerns outsourced to managed hosting companies.
Was left curious what anomaly detection methods Elastic has built-in would take to DIY https://www.elastic.co/guide/en/machine-learning/current/ml-...> with data frame / statistics / ML libraries (Clojure Noj).
Interestingly, I recently interviewed Samuel Colvin, Pydantic's author, and he said when designing his observability Saas called LogFire, he tried multiple backends, including ClickHouse. But it didn't work out. One of the reasons is LogFire allows the users to fetch the service data with arbitrary SQL queries. So they had to build their own backend in rust, on top of DataFusion. I used ClickHouse myself and it's bee…
What kinds of SQL queries could ClickHouse not handle? Were the limitations about expressivity of queries, performance, or something else? I'm considering using CH for storing observability (particularly tracing) data, so I'm curious about any footguns or other reasons it wouldn't be a good fit.
E.G: Clickhouse interval support, which is an important type for observability, was lacking. You couldn't subtract datetimes to get an interval. If you'd compared 2 milliseconds intervals to one second ones, it wouldn't look at the unit and would say 2 ms is bigger, etc. So he had to go to the dev team, and after enough back and forth, instead of fixing it, they decided to return an error and he had to insist for a long time until they actually implemented a proper solution.
Quoting him "But like these endless issues with ClickHouse's flavor of SQL were problematic."
Another problem seemed to be that to benefit from very big scaling with things like data in Parquet at rest + local cache meant basically leaking all your money to AWS because the self-hosted version didn't expose a way to do that yourself. Click house scales fine at my size, so I can only trust him on that front since I'm nowhere that big.
Funnily after that, they moved to TimeScale, and the perfs wouldn't work for their use case.
They landed on DataFusion after a lot of trials and errors.
But really interesting perspective on the whole thing, you can see he is kinda obsessed with the user experience. The guy wrote a popular marshmallow alternative, 2 popular celery alternative and one watchdog popular alternative, all FOSS.
These kind of people are the source of all imposter syndrome in the world.
I'll publish that video next week on Bite Code if I can. If I can't, it will have to wait 3 weeks cause I'm leaving for a bit. But Charlie Marsh's one (uv's author) is up, if you are into overachievers.