For longer-scale timeseries I still recommend Druid as the go-to. Mainly because if you make use of it's ahead-of-time aggregations (which you can do for real-time or scale-out batch ingestion) then your ad-hoc queries can execute extremely quickly even over very large datasets.
Druid only really has 1 downside, which is it's still a bit of a pain to setup. It's gotten a ton ton better in recent times and I have been contributing changes to make it work better out of the box with common big data tooling like Avro.
For performance it's the top dog except for really naive queries that are dominated by scan performance. For those you are best off with Clickhouse, it's vectorized query engine is extremely fast for simpler/scan heavy workloads.
Druid only really has 1 downside, which is it's still a bit of a pain to setup. It's gotten a ton ton better in recent times and I have been contributing changes to make it work better out of the box with common big data tooling like Avro.
For performance it's the top dog except for really naive queries that are dominated by scan performance. For those you are best off with Clickhouse, it's vectorized query engine is extremely fast for simpler/scan heavy workloads.