But anyways, a fun test I ran was "load 4.5 million samples of Beijing hourly air quality data (ozone, S02, PM2.5, etc) from May 2013 to May 2017." (using Bento to turn the csv into json to fire at the API, with some batching and 4 http connections to Victoriametrics )
VictoriaMetrics accepts all of this in 54 seconds, network traffic shows between 9 and 14MB per second , and the data is available several seconds later (it's definitely a noticeable delay for me before it shows up in the VictoriaMetrics UI). Server is an old passively cooled thin client with a quad core AMD GX-415GA (circa 2013) and DDR3 ram.
So, 4.5M data points, 11 timeseries. Docker volume says it's under 14MB in size.
In my regular day-to-day telemetry (separate container) the volume is 99MB in size, it has 11,747 timeseries, 18,689 total samples, but I only keep a few days of data retention.
I think VictoriaMetrics is great, and I have found their documentation is extensive and easy to follow.
But anyways, a fun test I ran was "load 4.5 million samples of Beijing hourly air quality data (ozone, S02, PM2.5, etc) from May 2013 to May 2017." (using Bento to turn the csv into json to fire at the API, with some batching and 4 http connections to Victoriametrics )
VictoriaMetrics accepts all of this in 54 seconds, network traffic shows between 9 and 14MB per second , and the data is available several seconds later (it's definitely a noticeable delay for me before it shows up in the VictoriaMetrics UI). Server is an old passively cooled thin client with a quad core AMD GX-415GA (circa 2013) and DDR3 ram.
So, 4.5M data points, 11 timeseries. Docker volume says it's under 14MB in size.
In my regular day-to-day telemetry (separate container) the volume is 99MB in size, it has 11,747 timeseries, 18,689 total samples, but I only keep a few days of data retention.
I think VictoriaMetrics is great, and I have found their documentation is extensive and easy to follow.