In this This manual , we will show you the different options for fetching metrics from Checkmk to push this data to a 3rd party tool.
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With this example, you will receive all perfdata for a specific service/host. You can use livestatus for that.
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OMD[mysite]:~$ lq "GET services\nFilter: host_name = mysite\nFilter: service_description = Filesystem /\nColumns: host_name\nColumns: service_description\nColumns: rrddata:m1:fs_used.max,1024,/:1614839543:1614929543:30\nOutputFormat: python" [[u"mysite",u"Filesystem /",[1614839400,1614931200,1800,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,333.982,334.178,334.465,334.638,334.773,334.923,335.082,335.249,335.361,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,None,336.493,336.673]]] |
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Scroll down this page and look for Service Metrics. You will see the Metric IDs on the right-hand side like below:
Query via Web-API
| Checkmk Version | Web or Rest API |
|---|---|
| 2.0.0p* | Web-API |
| 2.1.0p* | Web-API |
| 2.2.0p* | REST-API |
Custom graph (Web-API)
With this example, you can query the metrics of a custom graph in Checkmk
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With this example, you can query the metrics of a forecast graph in Checkmk.
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root@linux:~# curl -k "https://mylinuxhost/mysite/check_mk/webapi.py?action=get_graph&_username=automation&_secret=mysecret&request_format=python&output_format=python" -d 'request={"specification":["forecast","forecast_graph_4"],"data_range":{"time_range":[1616062956,1616513102]}}'
{'result_code': 0, 'result': {'start_time': 1616504400, 'end_time': 1616527800, 'step': 1800, 'curves': [{'line_type': 'area', 'color': '#FFFFFF', 'title': 'CPU utilization - lower edge', 'rrddata': [33.345051190851265, 37.67683226938787, 44.50125333428577, 53.2724501059588, 60.44537606761632, 66.75387999685792, 72.64393851155472, 78.29311232060269, 83.75780489204897, 89.06739515972093, 94.1829539622409, 99.15563480780335, 104.09381440605844]}, {'line_type': 'stacked', 'color': '#F4E750', 'title': 'CPU utilization - confidence interval', 'rrddata': [11.100249458703228, 10.146064241405181, 10.102262795192633, 11.3949147901306, 16.27542796258677, 22.884785199874802, 30.331033266252426, 38.25905074392767, 46.55603069680629, 55.163215257233624, 64.15846274796492, 73.43946615261119, 82.78947205187224]}, {'line_type': 'line', 'color': '#0000FF', 'title': 'CPU utilization - original', 'rrddata': [42.2174, 40.4999, 47.7736, 60.8701, None, None, None, None, None, None, None, None, None]}, {'line_type': 'line', 'color': '#FF0000', 'title': 'CPU utilization - fit & forecast', 'rrddata': [38.89517592020288, 42.749864390090465, 49.552384731882086, 58.969907501024096, 68.58309004890971, 78.19627259679532, 87.80945514468094, 97.42263769256652, 107.03582024045211, 116.64900278833774, 126.26218533622335, 135.87536788410895, 145.48855043199455]}]}} |
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