3 ms·
The one thing I really want (which apparently Monarch has) is histogram retention. I'm often called upon to summarize service latency as global p50 and p95, and
by jldugger 6y ago
The one thing I really want (which apparently Monarch has) is histogram retention. I'm often called upon to summarize service latency as global p50 and p95, and at the sheer volume of data we have, we aggregate that metric. Thus I am left calculating an average of p95s, which isn't super useful.
To the best of my knowledge, nothing else in the market does that.
- jeffbee 6y agoStackdriver/Google Cloud monitoring is backed by Monarch, so if you want the flavor of a Monarch distribution-valued metric, see the docs: https://cloud.google.com/monitoring/api/ref_v3/rest/v3/TypedValue#Distribution https://cloud.google.com/monitoring/api/ref_v3/rest/v3/Typed... Since the distribution is represented by a CDF of buckets, there's no guarantee that you'll get an accurate representation of the median or any other quantile. On the other hand you'll get an exact average.
- scottlamb 6y agoMonarch has distributions with predetermined bucket boundaries. These are indeed very useful. Pet peeve: it can calculate and graph something it calls a quantile. But if the value is in the middle of a large bucket, it will just interpolate or something and the result will be terribly misleading. It'd be much better if it gave lower/upper bounds. I try to use distributions via questions of the form "what fraction of values are less than / greater than N [which I've verified is a bucket boundary]?". This gives you an answer you can trust.
- pkhuong 6y agoCirconus (https://www.circonus.com/ https://www.circonus.com/) supports both recording histogram directly, and merging histograms for analysis. IIUC, it also supports first-class timeseries data similarly to Monarch, where each data point has a high precision timestamp that does not have to align with other timeseries in the data set.