While this is true, for a metrics workload it does not work great I have both seen and heard from others, mainly due to the fact it does not have an inverted index - so finding a small subset of metrics in a dataset of billions of metrics ends up taking significant time due to the scan required to find the timeseries matching the arbitrary number of dimensions specified to find the timeseries you're looking for.

If you're building it with a specific application and a concrete schema you can create which will result in fast queries and don't have requirements for arbitrary dimensions being specified for lookup, then yes it's great as a TSDB.

Prometheus, M3DB, etc all use an inverted index alongside the column store TSDB to help with metrics workloads.

Most practical applications using Clickhouse for metrics data store the metric index separately. What index you want really depends on the metric system, e.g. with graphite data you don't want an inverted index, you want a trie.

Yes I've seen that also work, it's a lot of stitching together things yourself and we had to put a lot of caching in front of the inverted index we were using, however definitely plausible. ClickHouse doesn't do any streaming of data between nodes as you scale up and down which was a big thing for us since we had large datasets and needed to rebalance when cluster expanded/shrunk.

With regards to trie vs inverted index for Graphite data, I'd actually still be inclined to say inverted index is better based on the amount of queries I saw at Uber with Graphite where people did `servers.*.disk.bytes-used` type queries which is way faster to do using an inverted index since you have a postings list for each part of the dot-separated metric name, rather than traversing a trie with thousands to tens of thousands of entries in index 1 host part of the Graphite name. This is what M3DB does[0].

[0]: https://github.com/m3db/m3/blob/b2f5b55e8313eb48f023e08f6d53...

Just to point out that there is inverted index implementation of graphite data working on clickhouse.

Regarding the auto-rebalance feature, I cannot much more agree with you. It's something that clickhouse definitely need to handle internally.

That's interesting, I had not heard of ClickHouse as a backend for Graphite with an inverted index. Let me know if you have any links to that.

I'm assuming this is an out of process inverted index used alongside ClickHouse? Or is it more of a secondary table contained by ClickHouse which can be searched to find the metrics, then the data is looked up?

The latter scales not as well with billions of unique metrics since it's always a scan across the unique metrics stored in the time window your query searches for (since any arbitrary dimensions can be specified, all must be evaluated). This is the drawback of PromHouse which is an implementation of Prometheus remote storage on top of ClickHouse - and the major reason why PromHouse was only ever a proof of concept rather than a production offering.

I also confirm that. Several companies have successfully transitioned their monitoring stack from graphite initial python implementation to a clickhouse based backend.

Not to bad-mouth Clickhouse but the original python implementation of graphite + carbon was setting the bar very low, though, and transitioning from there to anything would have increasing performances by orders of magnitude.

You should read the link below [1]. Even if it's not uber scale I suspect yandex to use something similar.

I agree that python implementation of graphite was not particularly fast but there was faster implementation in C that companies used first to significantly increase performance. Then coordination of storage backend becomes complex when you try to scale the initial design. This is where clickhouse really shine. It provide out of the box distributed storage with compaction, rollup and fast querying. The other layers are stateless, which means that they'll scale with your computing ressources.

M3DB is roughly doing the same thing as clickhouse but clickhouse is much more advanced database that has proven records of running at petabyte scale without a sweat. For example they now have tiered storage which means that you can store recent event in nvme and rollup to standard HDD...

[1]https://medium.com/avitotech/metrics-storage-how-we-migrated...

That is also my experience. Also in bencmark it is almost as fast as GPU analytical DBs or KDB+.