I'm very likely just looking in the wrong place, though. Is there documentation on how we should be getting data in to Prometheus? I found the general writeup at, but it doesn't actually specify what the right way to go about adding metrics is.Is that right? (It's not necessarily a deal killer in general, but it might limit us in certain use cases.) My understanding is that the retention in Prometheus is currently limited to 1 year.Prometheus solves both of these, as far as I can tell, which is great! Two questions: For example, if we're recording the 75th percentile of a given metric, and then ,we try to aggregate it, we actually end up with a value that's not actually representative. For some types of information, the way that graphite does aggregation actually breaks the metrics.In some cases, we may want to tag with certain info - for example, country code. For example, we collect a bunch of performance timing metrics from users. Tagging metrics to allow for certain types of filtering.There were two specific use cases that I/we had in mind: Support logging to syslog with the -syslog runtime option.Support 0.9.x backends in 1.0.0 cluster.Readers are more resilient to the loss of a single backend.Cluster servers can now communicate over HTTPS when INTRACLUSTER_HTTPS is enabled.Configurable in content and layout Realtime reporting using Graphite and. ![]() This is useful when running multiple WSGI workers. The rich reports with the opportunity to time-slice them and to group requests. Python’s own log rotation can be disabled using the LOG_ROTATION setting.Brand new clustering implementation using a pool of worker threads and persistent connections to backends.Other notable changes that may affect our upgrading: +timeSlice +verticalLine +weightedAverage +removeBetweenPercentile +removeEmptySeries +offsetToZero +pow +powSeries +reduceSeries +integralByInterval +interpolate +invert +linearRegression +linearRegressionAnalysis ![]() +exponentialMovingAverage +fallbackSeries
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