It seems it's pretty crowded
It seems it's pretty crowded
> It turns out you can accomplish quite a lot with 4,709 lines of Go code! How about a full time-series database implementation, robust enough to be run in production for a year where it stored 2.1 trillion data points, and supporting 119M queries per second (53M inserts per second) in a four-node cluster? Statistical queries over the data complete in 100-250 ms while summarizing up to 4 billion points. It’s pretty space-efficient too, with a 2.9x compression ratio. At the heart of these impressive results, is a data structure supporting a novel abstraction for time-series data: a time partitioning, copy-on-write, version-annotated, k-ary tree.
That is why I am always looking forward to new things such as this or BTrDB. There is still a lot of room for improvement everywhere.
For example if you want to store timeseries data long term and already use HBase then OpenTSDB is a good choice.
On the other hand if you want to do monitoring that's simple and dependable in an emergency with querying, graphing and alerting over short/medium-term data, then Prometheus would a good choice.
This in particular, but your entire post, would be a great addition to the Prometheus front page, or top of the documentation section. This wasn't clear to me initially, speaking as someone who evaluated prometheus a few months ago.
Ignoring that as it's planned work, the typical considerations are more around availability vs. consistency and that we're a metrics system focused on operations rather than a event store. Most of that's already covered in our docs. See https://prometheus.io/docs/introduction/overview/#when-does-... and https://prometheus.io/docs/introduction/faq/