But if you have a lot of time-series metrics (only numbers), you might be better with specialized time-series databases like Prometheus + VictoriaMetrics with Grafana for visualizing it.
However, influx, being schemaless, can be outstanding for rapidly prototyping ephemeral metrics, as adding a new measurement is zero-cost (just start writing it). It also plays great with grafana for building dashboards. Finally, I much prefer the ergonomics enhancements of the influx query language (v1, the v2 "flux" language looks terrible to me personally), particularly how duration strings are a first-class datatype ("group by time(1h)").
Interested to hear more about clickhouse performance, haven't had a chance to use it for anything where performance would matter significantly, although am aware it can be very fast.
My company migrated our time-series data from InfluxDB to ClickHouse last year (I personally led this, in fact), and the performance difference is night and day.
While I liked a lot of what Influx could do, it was also nonstandard in bizarre ways (Clickhouse behaves more like a subset of SQL), sometimes shockingly immature, and despite appearing fast when we first started using it, so slow that it was a considerable bottleneck.