https://github.com/rcrowley/go-metrics is one I've came across while researching potential Prometheus integrations, it seems to support a number of backends including Graphite and InfluxDB.
One challenge with that model of metrics is that it assumes that the monitoring system doesn't have much ability to work with samples, so it makes up for this by calculating rates and quantiles in the client. This isn't as accurate as doing it in the server, and some instrumentation systems don't allow you to extract out the raw data to allow you to do it in the server.
For example say you've a 1-minute rate exported/scraped every minute. If you miss a push/scrape you lose all information about that minute. Similarly if you're delayed by a second, you'll miss a spike just after the previous push/scrape. If instead you expose a monotonically increasing counter and calculate a rate over that in the server, you don't lose that data.
One challenge with that model of metrics is that it assumes that the monitoring system doesn't have much ability to work with samples, so it makes up for this by calculating rates and quantiles in the client. This isn't as accurate as doing it in the server, and some instrumentation systems don't allow you to extract out the raw data to allow you to do it in the server.
For example say you've a 1-minute rate exported/scraped every minute. If you miss a push/scrape you lose all information about that minute. Similarly if you're delayed by a second, you'll miss a spike just after the previous push/scrape. If instead you expose a monotonically increasing counter and calculate a rate over that in the server, you don't lose that data.