Legacy StatsD pipelines are not equipped to handle the volume of data emitted by today’s applications, causing inaccuracies and limitations when monitoring StatsD metrics. That’s why more organizations are turning to Circonus to modernize their approach to StatsD monitoring. Circonus’ powerful IRONdb time-series database and patented log linear OpenHistograms ingest an unlimited volume of highly granular data and efficiently store it at low cost for years using large scale data compression. As a result, users can replace pre-calculated aggregations required by legacy solutions with arbitrary on-demand analysis, enable Metrics 2.0 tagging, simplify their StatsD metrics pipelines to reduce resources, and scale indefinitely without sacrificing performance.
Empower SRE and software development teams to dynamically create SLOs
- Simplify defining and measuring SLOs by dynamically computing aggregations from raw data on-demand, after ingestion, using Circonus’ OpenHistograms, which can collect and efficiently store all source data.
- Enable various teams to set their own SLOs rather than be forced to share the same pre-calculated aggregations that only a subset of the team can use.
- Calculate more advanced aggregates such as arbitrary quantiles, percentiles, inverse quantiles and inverse percentiles — all without needing to manage and store additional metrics.