I suppose it depends what you're going to let your user do, but OLAPs in general and Clickhouse in particular don't do well under row-oriented workloads, as described in the post here. I'm imagining users primarily operating on small numbers of rows and sometimes making updates to or deleting them, a worst-case scenario for Clickhouse but best-case for an OLTP like Postgres.
Ah totally. Thanks for sharing your thoughts! In my case I’m evaluating clickhouse as a source of truth for customer telemetry data. Totally agree about the OLTP limitations.
(Remember that clickhouse is not reliable. It doesn’t pretend to be.
Clickhouse is great for lots of common query workloads, but if losing your data would be a big deal then it makes a lot of sense to have your data in a reliable and backed up place (eg timescale or just s3 files or whatever) too.
Of course lots of times people chuck stuff into clickhouse and it’s fine if they lose a bit sometimes. YMMV.)
I have not found this to be the case. Like any system you need to take precautions (replicas and sharding) to ensure no data loss, but I didn't find that to be challenging. In what way have you found ClickHouse particularly risky in this way?
It’s basic computer science. Clickhouse doesn’t fsync etc.
Clickhouse (and other systems with the same basic architecture, like elastic search and, shudder, mongodb) work very well on happy path. They are not advertising themselves as ACID.
