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You can read an [extended introduction to anode here](http://blog.gopheracademy.com/birthday-bash-2014/using-go-for-anomaly-detection/).\n\nData analysis starts with getting to know your data. The overarching goal of anode is to advance the open-source offerings for metrics analysis by creating a proving ground for different approaches.\n\nAnode ships with a Graphite input plugin, capable of fetching a named metric from graphite and streaming updates to registered analysis plugins. Each analysis plugin then streams its result to output plugins. \n\nJust learning about metrics analysis? Have a look at this [excellent talk on anomaly detection for metrics](http://www.metaforsoftware.com/monitorama-pdx-simple-math-for-anomaly-detection/).\n\nExample\n-------\n\nA [three sigma](http://www.encyclopediaofmath.org/index.php/Three-sigma_rule) analyzer is included. This works best for data with a normal (gaussian) distribution.\n\n```\ngo get github.com/mattrco/anode\nanode -metric=app.latency\n```\n\n(Other flags exist; [see here](https://github.com/mattrco/anode/blob/master/main.go#L21)).\n\nThis will fetch and process the last 24 hours of data. New metrics will appear in graphite under `anode.threesig` which you can then plot alongside your existing metric. The screenshot below shows the original metric (lilac) with anomalous values highlighted in orange. \n\n![Screenshot of Three Sigma analyzer](https://raw.githubusercontent.com/mattrco/anode/master/threesigma.png)\n\nContributions\n-------------\n\nContributions welcome! Please open an issue to discuss what you'd like to work on.\n\nRelated projects and acknowledgement\n------------------------------------\n\n[mozilla-services/heka](https://github.com/mozilla-services/heka)\n\nHeka is a more general data collection and processing system, processing much more than time series. Its data pipeline inspired anode's architecture.\n\n[etsy/skyline](https://github.com/etsy/skyline)\n\nSkyline is an anomaly detection system for time series data.\n\nKey differences:\n\n* Skyline is production-ready and more work to get set up\n* Fairly rigid decision logic for determining whether latest updates to a series are anomalous. Algorithms each return a boolean and majority vote wins. There's no way to apply different analyses to different metrics, which is a key design point of anode.\n\nCopyright Notice and License\n----------------------------\n\nCopyright 2014 Matthew (\"Matt\") Cottingham\n\nLicensed under the Apache License, Version 2.0 (the \"License\"); you may not use this software except in compliance with the License. You may obtain a copy of the License at\n\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmattrco%2Fanode","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmattrco%2Fanode","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmattrco%2Fanode/lists"}