[DM-15852] Introduction to Prometheus #1
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jonathansick
approved these changes
Oct 9, 2018
LICENSE
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| MIT License | |||
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| Copyright (c) 2018 LSST SQuaRE | |||
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Better to say
Copyright (c) 2018 Association of Universities for Research in Astronomy
| 3. Using the virtualenv in the Jupyter notebook | ||
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| ``` | ||
| python -m ipykernel install --user --name=prometheus-intro |
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huh, I hadn't seen something like this before. What's the purpose of having this kernel?
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This way I install the python environment in the notebook, so that the python packages installed in the virtualenv environment are also available in the notebook.
prometheus-intro.ipynb
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| "source": [ | ||
| "Prometheus is a pull-based monitoring system, which means that a Prometheus client exposes metrics via an HTTP endpoint, and the Prometheus server scrapes them periodically.\n", | ||
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| "Prometheus support different [types of metrics](https://prometheus.io/docs/concepts/metric_types/). A \"metric value\" in Prometheus is called a sample. A single Prometheus server can ingest millions of samples per second. The result is a large number of stored time series, which are uniquely identified by a [metric name]() and a set of labels. The Prometheus project implements its own Time Series DB and query language (PromQL) in Go.\n", |
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metric name looks like an empty link.
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Thank you @jonathansick for reviewing this. |
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In this notebook we introduce the main concepts of Prometheus. We run a Prometheus server, and look at a few metrics to illustrate the Prometheus interface and the PromQL query language.
Then we use the Prometheus python client to instrument a small app and expose metrics to the Prometheus server.
Finally we exemplify alerting with Prometheus.