3 ms·

It's legitimately fast. My company migrated our time-series data from InfluxDB to ClickHouse last year (I personally led this, in fact), and the performance di

It's legitimately 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.

deepsun 7y ago
Clickhouse is not really time-series database, it's more general analytical DB (e.g. also can handle strings, logs, user IPs). 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.
- dikei 7y ago
  Prometheus is not really a database, because the only operation allowed is query, you can't insert/delete data manually. For many use cases, that's not acceptable.
  - deepsun 7y ago
    That's why I mentioned VictoriaMetrics :) It supports push (using any protocol).
jstrong 7y ago
I think of influx performance as similar to pandas. It's fast enough for many things it was designed for, but it's pretty easy to hit a performance cliff. In particular, "select * from table" type queries are very slow, so don't expect to be pulling out large blocks of data to do analysis somewhere else. 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.

deepsun 7y ago

Clickhouse is not really time-series database, it's more general analytical DB (e.g. also can handle strings, logs, user IPs). 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.

dikei 7y ago
Prometheus is not really a database, because the only operation allowed is query, you can't insert/delete data manually. For many use cases, that's not acceptable.
- deepsun 7y ago
  That's why I mentioned VictoriaMetrics :) It supports push (using any protocol).

jstrong 7y ago

I think of influx performance as similar to pandas. It's fast enough for many things it was designed for, but it's pretty easy to hit a performance cliff. In particular, "select * from table" type queries are very slow, so don't expect to be pulling out large blocks of data to do analysis somewhere else. 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.