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        "description": "==========\ngrafanalib\n==========\n\n.. image:: https://circleci.com/gh/weaveworks/grafanalib.svg?style=shield\n    :target: https://circleci.com/gh/weaveworks/grafanalib\n\nDo you like `Grafana <http://grafana.org/>`_ but wish you could version your\ndashboard configuration? Do you find yourself repeating common patterns? If\nso, grafanalib is for you.\n\ngrafanalib lets you generate Grafana dashboards from simple Python scripts.\n\nWriting dashboards\n==================\n\nThe following will configure a dashboard with a single row, with one QPS graph\nbroken down by status code and another latency graph showing median and 99th\npercentile latency:\n\n.. code-block:: python\n\n  from grafanalib.core import *\n\n\n  dashboard = Dashboard(\n    title=\"Frontend Stats\",\n    rows=[\n      Row(panels=[\n        Graph(\n          title=\"Frontend QPS\",\n          dataSource='My Prometheus',\n          targets=[\n            Target(\n              expr='sum(irate(nginx_http_requests_total{job=\"default/frontend\",status=~\"1..\"}[1m]))',\n              legendFormat=\"1xx\",\n              refId='A',\n            ),\n            Target(\n              expr='sum(irate(nginx_http_requests_total{job=\"default/frontend\",status=~\"2..\"}[1m]))',\n              legendFormat=\"2xx\",\n              refId='B',\n            ),\n            Target(\n              expr='sum(irate(nginx_http_requests_total{job=\"default/frontend\",status=~\"3..\"}[1m]))',\n              legendFormat=\"3xx\",\n              refId='C',\n            ),\n            Target(\n              expr='sum(irate(nginx_http_requests_total{job=\"default/frontend\",status=~\"4..\"}[1m]))',\n              legendFormat=\"4xx\",\n              refId='D',\n            ),\n            Target(\n              expr='sum(irate(nginx_http_requests_total{job=\"default/frontend\",status=~\"5..\"}[1m]))',\n              legendFormat=\"5xx\",\n              refId='E',\n            ),\n          ],\n          yAxes=[\n            YAxis(format=OPS_FORMAT),\n            YAxis(format=SHORT_FORMAT),\n          ],\n          alert=Alert(\n            name=\"Too many 500s on Nginx\",\n            message=\"More than 5 QPS of 500s on Nginx for 5 minutes\",\n            alertConditions=[\n              AlertCondition(\n                Target(\n                  expr='sum(irate(nginx_http_requests_total{job=\"default/frontend\",status=~\"5..\"}[1m]))',\n                  legendFormat=\"5xx\",\n                  refId='A',\n                ),\n                timeRange=TimeRange(\"5m\", \"now\"),\n                evaluator=GreaterThan(5),\n                operator=OP_AND,\n                reducerType=RTYPE_SUM,\n              ),\n            ],\n          )\n        ),\n        Graph(\n          title=\"Frontend latency\",\n          dataSource='My Prometheus',\n          targets=[\n            Target(\n              expr='histogram_quantile(0.5, sum(irate(nginx_http_request_duration_seconds_bucket{job=\"default/frontend\"}[1m])) by (le))',\n              legendFormat=\"0.5 quantile\",\n              refId='A',\n            ),\n            Target(\n              expr='histogram_quantile(0.99, sum(irate(nginx_http_request_duration_seconds_bucket{job=\"default/frontend\"}[1m])) by (le))',\n              legendFormat=\"0.99 quantile\",\n              refId='B',\n            ),\n          ],\n          yAxes=single_y_axis(format=SECONDS_FORMAT),\n        ),\n      ]),\n    ],\n  ).auto_panel_ids()\n\nThere is a fair bit of repetition here, but once you figure out what works for\nyour needs, you can factor that out.\nSee `our Weave-specific customizations <grafanalib/weave.py>`_ for inspiration.\n\nGenerating dashboards\n=====================\n\nIf you save the above as ``frontend.dashboard.py`` (the suffix must be\n``.dashboard.py``), you can then generate the JSON dashboard with:\n\n.. code-block:: console\n\n  $ generate-dashboard -o frontend.json frontend.dashboard.py\n\nInstallation\n============\n\ngrafanalib is just a Python package, so:\n\n.. code-block:: console\n\n  $ pip install grafanalib\n\nSupport\n=======\n\nThis library is in its very early stages. 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