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Banshee is a real-time anomalies(outliers) detection system for periodic metrics. We are using it to monitor our website and rpc services intefaces, including called frequency, response time and exception calls. Our services send statistics to statsd, statsd aggregates them every 10 seconds and broadcasts the results to its backends including banshee, banshee analyzes current metrics with history data, calculates the trending and alerts us if the trending behaves anomalous. For example, we have an api named get_user, this api's response time (in milliseconds) is reported to banshee from statsd every 10 seconds: Banshee will catch the latest metric 300 and report it as an anomaly. Why don't we just set a fixed threshold instead (i.e. 200ms)? This may also works but it is boring and hard to maintain a lot of thresholds. Banshee will analyze metric trendings automatically, it will find the "thresholds" automatically. 1. Designed for periodic metrics. Reality metrics are always with periodicity, banshee only peeks metrics with the same "phase" to detect. 2. Multiple alerting rule configuration options, to alert via fixed-thresholds or via anomalous trendings. 3. Coming with anomalies visualization webapp and alerting rules admin panels. 4. Require no extra storage services, banshee handles storage on disk by itself. 1. Go >= 1.5. 2. Node and gulp. 1. Clone the repo. 2. Build binary via `make`. 3. Build static files via `make static`. Usage: Flags: See package config. In order to forward metrics to banshee from statsd, we need to add the npm module statsd-banshee to statsd's banckends: 1. Install statsd-banshee on your statsd servers: 2. Add module statsd-banshee to statsd's backends in config.js: Require bell.js v2.0+ and banshee v0.0.7+: Banshee have 4 compontents and they are running in the same process: 1. Detector is to detect incoming metrics with history data and store the results. 2. Webapp is to visualize the detection results and provides panels to manage alerting rules, projects and users. 3. Alerter is to send sms and emails once anomalies are found. 4. Cleaner is to clean outdated metrics from storage. See package alerter and alerter/exampleCommand. Via fabric( See docs for more. Just pull the latest code: Note that the admin storage sqlite3 schema will be auto-migrated. 1. Detection algorithms, see package detector. 2. Detector input net protocol, see package detector. 3. Storage, see package storage. 4. Filter, see package filter. MIT (c) eleme, inc.


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Banshee is a real-time anomalies(outliers) detection system for periodic metrics.

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  1. Go >= 1.5.
  2. Node and gulp.


  1. Clone the repo.
  2. Build binary via make.
  3. Build static files via make static.





>>> import numpy as np
>>> x = np.array([40, 52, 63, 44, 54, 43, 67, 54, 49, 45, 48, 54, 57, 43, 58])
>>> mean = np.mean(x)
>>> std = np.std(x)
>>> (80 - mean) / (3 * std)
1.2608052883472445 # anomaly, too big
>>> (20 - mean) / (3 * std)
-1.3842407711224991 # anomaly, too small


Statsd Backend

Migrate from bell



MIT Copyright (c) 2015 - 2016 Eleme, Inc.


Last updated on 03 Mar 2016

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