Does anyone use clickhouse in production? I was initially pretty impressed but when I really put it through its paces I could OoM it as soon as I actually started querying non-trivial amounts of data:
Unless you try to join tables in it, in which case it will immediately explode.
More seriously, it's a columnar data store, not a relational database. It'll definitely pretend to be "postgres but faster", but that's a very thin and very leaky facade. You want to do massively a complex set of selects and conditional sums over one table with 3b rows and tb of data? You'll get a result in tens of seconds without optimization. You want to join two tables that postgres could handle easily? You'll OOM a machine with TB of memory.
So: good for very specific use cases. If you have those usecases, it's great! If you don't, use something else. Many large companies have those use cases.
Could you explain why you don't think ClickHouse is relational? The storage is an implementation detail. It affects how fast queries run but not the query model. Joins have already improved substantially and will continue to do so in future.
They've made strides in the last year or two to implement more join algorithms, and re-order your joins automatically (including whats on the "left" and "right" of the join, relating to performance of the algorithm).
Their release notes cover a lot of the highlights, and they have dedicated documentation regarding joins[1]. But we've made improvements by an order-of-magnitude before by just reordering our joins to align with how ClickHouse processes them.
p.s., It's also possible to break ClickHouse as you demonstrated. It used to be a lot easier.
When you use INSERT ... SELECT in ClickHouse you do need to pay attention to the generated table partitions, as they coexist in memory before flushing to storage. The usual approach is to break up the insert into chunks so you can control how many parts are generated or to adjust the partitioning in the target table.
It's possible the problem might be somehow related to this behavior but that's just conjecture. It's usually pretty easy to work around. Meanwhile if it's a bug it will probably get fixed quickly.
It’s important to structure your tables and queries in a way that aligns with the ordering keys, in order to optimize how much data needs to be loaded into RAM. You absolutely CANNOT just replicate your existing postgres DB and its primary keys or whatever over to CH. There are tricks like projections and incremental materialized views that can help to get the appropriate “lenses” for your queries. We use incremental MVs to, for example, continuously aggregate all-time stats about tens of billions of records. In general, for CH, space is cheap and RAM is expensive, so it’s better to duplicate a table’s data with a different ordering key than to make an inefficient query.
As long as the queries align with the ordering keys, it is insanely fast and able to enable analytics queries for truly massive amounts of data. We’ve been very impressed.
Depending on your use case, an incremental materialized view can also be really effective: when new rows for one table come in, query for related rows in a secondary table and populate the combination into a MV for efficient querying.
You can also specify specific join strategies for queries, but we haven’t had as much luck with that so far.
The JOIN thing is definitely the biggest pain point, though, I’ll not debate that at all.
I see you also created similar issues in Polars: https://github.com/pola-rs/polars/issues/17932 and DuckDB: https://github.com/duckdb/duckdb/issues/17066
ClickHouse has a built-in memory tracker, so even if there is not enough memory, it will stop the query and send an exception to the client, instead of crashing. It also allows fair sharing of memory between different workloads.
You need to provide more info on the issue for reproduction, e.g., how to fill the tables. 16 GB of memory should be enough even for a CROSS JOIN between a 10 billion-row and a 100-row table, because it is processed in a streaming fashion without accumulating a large amount of data in memory. The same should be true for a merge join.
However, there are places when a large buffer might be needed. For example, if you insert data into a table backed by S3 storage, it requires a buffer that can be in the order of 500 MB.
There is a possibility that your machine has 16 GB of memory, but most of it is consumed by Chrome, Slack, or Safari, and not much is left for ClickHouse server.
I do want to get a better reproduction on CH because it seems like it's an interplay between the INSERT INTO...SELECT. It's just a bit of work to generate synthetic data with the same profile as my production data (for what it's worth I did put quite a bit of effort into following the doc guidelines for dealing with low-memory machines).
Clickhouse is great, but like any database if you run it at scale someone must tend to it.