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        "author": "Mihai Bivol",
        "author_email": "mm.bivol@gmail.com",
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        "description": "Graphite-Anomaly-Detector\n=========================\n\nCommand line application that determines spikes in Graphite metrics for a given host and generates a CSV report\n\nUsage\n-----\n<pre>\nusage: generate_report.py [-h] -s SERVERS [SERVERS ...] -o OUTPUT [-l LIMIT]\n                          [-p PATTERN] [-t TIMEOUT]\n\nGenerates CSV report with spikes found on Graphite hosts\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -s SERVERS [SERVERS ...], --servers SERVERS [SERVERS ...]\n                        Servers queried by program (default: None)\n  -o OUTPUT, --output OUTPUT\n                        Output CSV file (default: [None])\n  -l LIMIT, --limit LIMIT\n                        Limit request count per host (default: [500])\n  -p PATTERN, --pattern PATTERN\n                        Glob pattern for metrics (default: ['*'])\n  -t TIMEOUT, --timeout TIMEOUT\n                        Timeout bethween requests for host (default: [1])\n</pre>\n\nFor example\n<pre>\n./generate_report.py -s dumdum:81 -o report.csv -l 2 -p *.cpu.* -t 42\n</pre>\n\nWill gather the first two metrics that match \\*.cpu.\\* with 42 seconds timeout bethween the requests.\nThe command will ouptut the results in report.csv\n\nResult\n------\n\nThe program creates a csv file ginven as argument for the --output option having 5 columns:\n\n| HOST    | METRIC     | TIME        | RELEVANCE | GRAPH_URL                               |\n|---------|------------|-------------|-----------|-----------------------------------------|\n| host:81 | cpu.0.wait | 4 Jul 2013  | .42       | http://host:81/render/?target=cpu.0.wait|\n\nDeveloping Detectors\n--------------------\nAll detectors should extend the Detector class and implement the detect_anomalies method.\n\nTo change the detector in the program, just change the DETECTOR=SpikeDetector() line with an instance\nof your detector.\n\nTesting Detectors\n-----------------\n\nFor testing you can use test_local.py.\n\nIt requires matplotlib for plotting the results.\n\ntest_local.py receives a Graphite json response for a given target and plots the following:\n * Original timeseries\n * Timeseries after running the detector\n * Detector results\n * A treshold line (you can remove it while testing)\n\nYou just have to replace SpikeDetector with your detector.\n\nFor testing large data, you can use the util.logger ```create_logfile``` function.\nIt creates a json file that can be viewed using view_log.py script that plots a graph\nsimmilar to the one yielded by test_local.py.",
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