Install Prometheus

This is a simple doc to install, configure, and use a simple Prometheus instance.
Download and run Prometheus locally, configure it to scrape itself and an example application, and then work with queries, rules, and graphs to use collected time series data.

Installation

Using pre-compiled binaries
There are precompiled binaries for most official Prometheus components.
Download the latest release of Prometheus for your platform, then extract and run it:
tar xvfz prometheus-*.tar.gz
cd prometheus-*

Docker Installation

All Prometheus services are available as Docker images on Quay.io or Docker Hub.
Running Prometheus on Docker is as simple as docker run -p 9090:9090 prom/prometheus.
This starts Prometheus with a sample configuration and exposes it on port 9090.
The Prometheus image uses a volume to store the actual metrics.
For production deployments, it is highly recommended to use a named volume to ease managing the data on Prometheus upgrades.
To provide your configuration, there are several options. Here are two examples.

Volumes & bind-mount

Bind-mount your prometheus.yml from the host by running:

docker run \
    -p 9090:9090 \
    -v /path/to/prometheus.yml:/etc/prometheus/prometheus.yml \
    prom/prometheus

Or bind-mount the directory containing prometheus.yml onto /etc/prometheus by running:

docker run \
    -p 9090:9090 \
    -v /path/to/config:/etc/prometheus \
    prom/prometheus

Save your Prometheus data

Prometheus data is stored in /prometheus dir inside the container, so the data is cleared every time the container gets restarted. To save your data, you need to set up persistent storage (or bind mounts) for your container.
Run Prometheus container with persistent storage:

# Create a persistent volume for your data
docker volume create prometheus-data
# Start Prometheus container
docker run \
    -p 9090:9090 \
    -v /path/to/prometheus.yml:/etc/prometheus/prometheus.yml \
    -v prometheus-data:/prometheus \
    prom/prometheus

Custom image

To avoid managing a file on the host and bind-mounting it, the configuration can be baked into the image. This works well if the configuration itself is rather static and the same across all environments.

For this, create a new directory with a Prometheus configuration and a Dockerfile like this:

FROM prom/prometheus
ADD prometheus.yml /etc/prometheus/

Now build and run it:
docker build -t my-prometheus .
docker run -p 9090:9090 my-prometheus
A more advanced option is to render the configuration dynamically on start with some tooling or even have a daemon update it periodically.

Starting Prometheus

To start Prometheus with your newly created configuration file, change to the directory containing the Prometheus binary and run:

# Start Prometheus.
# By default, Prometheus stores its database in ./data (flag --storage.tsdb.path).
./prometheus --config.file=prometheus.yml

Prometheus should start up. You should also be able to browse a status page about itself at localhost:9090.
Give it a couple of seconds to collect data about itself from its own HTTP metrics endpoint.
You can also verify that Prometheus is serving metrics about itself by navigating to its metrics endpoint: localhost:9090/metrics

Using the expression browser

Explore data that Prometheus has collected about itself.
To use Prometheus's built-in expression browser, navigate to http://localhost:9090/graph and choose the "Table" view within the "Graph" tab.
As you can gather from localhost:9090/metrics, one metric that Prometheus exports about itself is named prometheus_target_interval_length_seconds (the actual amount of time between target scrapes).
Enter the below into the expression console and then click "Execute":
prometheus_target_interval_length_seconds

This should return several different time series (along with the latest value recorded for each), each with the metric name prometheus_target_interval_length_seconds, but with different labels.
These labels designate different latency percentiles and target group intervals.
If we are interested only in 99th-percentile latencies, we could use this query:
prometheus_target_interval_length_seconds{quantile="0.99"}

To count the number of returned time series, you could write:
count(prometheus_target_interval_length_seconds)

Using the graphing interface

To graph expressions, navigate to http://localhost:9090/graph and use the "Graph" tab.

Enter the following expression to graph the per-second rate of chunks being created in the self-scraped Prometheus:
rate(prometheus_tsdb_head_chunks_created_total[1m])
Try the graph range parameters and other settings.

Starting up some sample targets

Add additional targets for Prometheus to scrape.
The Node Exporter is used as an example target, for more information on using it see these instructions.

tar -xzvf node_exporter-*.*.tar.gz
cd node_exporter-*.*

# Start 3 example targets in separate terminals:
./node_exporter --web.listen-address 127.0.0.1:8080
./node_exporter --web.listen-address 127.0.0.1:8081
./node_exporter --web.listen-address 127.0.0.1:8082

You should now have example targets listening on http://localhost:8080/metrics, http://localhost:8081/metrics, and http://localhost:8082/metrics.

Configure Prometheus to monitor the sample targets

Now we will configure Prometheus to scrape these new targets. Let's group all three endpoints into one job called node. We will imagine that the first two endpoints are prod targets, while the third one represents a dev instance. To model this in Prometheus, we can add several groups of endpoints to a single job, adding extra labels to each group of targets. In this example, we will add the group="suyash" label to the first group of targets, while adding group="suyi" to the second.

Add the following job definition to the scrape_configs section in your prometheus.yml and restart your Prometheus instance:

scrape_configs:
  - job_name:       'node'

    # Override the global default and scrape targets from this job every 5 seconds.
    scrape_interval: 5s

    static_configs:
      - targets: ['localhost:8080', 'localhost:8081']
        labels:
          group: 'suyash'

      - targets: ['localhost:8082']
        labels:
          group: 'suyi'

Go to the expression browser and verify that Prometheus now has information about the time series that these example endpoints expose, such as node_cpu_seconds_total.

Configure rules for aggregating scraped data into new time series.
Though not a problem in our example, queries that aggregate over thousands of time series can get slow when computed ad-hoc. To make this more efficient, Prometheus can prerecord expressions into new persisted time series via configured recording rules. Let's say we are interested in recording the per-second rate of CPU time (node_cpu_seconds_total) averaged over all CPU per instance as measured over a window of 5 minutes. We could write this as:

avg by (job, instance, mode) (rate(node_cpu_seconds_total[5m]))

To record the time series resulting from this expression into a new metric called job_instance_mode:node_cpu_seconds:avg_rate5m, create a file with the following recording rule and save it as prometheus.rules.yml:

groups:
- name: cpu-node
  rules:
  - record: job_instance_mode:node_cpu_seconds:avg_rate5m
    expr: avg by (job, instance, mode) (rate(node_cpu_seconds_total[5m]))

To make Prometheus pick up this new rule, add a rule_files statement in your prometheus.yml. The config should now look like this:

global:
  scrape_interval:     15s # By default, scrape targets every 15 seconds.
  evaluation_interval: 15s # Evaluate rules every 15 seconds.

  # Attach these extra labels to all time-series collected by this Prometheus instance.
  external_labels:
    monitor: 'codelab-monitor'

rule_files:
  - 'prometheus.rules.yml'

scrape_configs:
  - job_name: 'prometheus'

    # Override the global default and scrape targets from this job every 5 seconds.
    scrape_interval: 5s

    static_configs:
      - targets: ['localhost:9090']

  - job_name:       'node'

    # Override the global default and scrape targets from this job every 5 seconds.
    scrape_interval: 5s

    static_configs:
      - targets: ['localhost:8080', 'localhost:8081']
        labels:
          group: 'suyash'

      - targets: ['localhost:8082']
        labels:
          group: 'suyi'

Restart Prometheus with the new configuration and verify that a new time series with the metric name job_instance_mode:node_cpu_seconds:avg_rate5m is now available by querying it through the expression browser or graphing it.

Reloading configuration

As mentioned in the configuration documentation a Prometheus instance can have its configuration reloaded without restarting the process by using the SIGHUP signal. If you're running on Linux this can be performed by using kill -s SIGHUP 11232, where 11232 is your Prometheus PID.

Shutting down your instance

While Prometheus does have recovery mechanisms in the case that there is an abrupt process failure it is recommended to use the SIGTERM signal to cleanly shut down a Prometheus instance. If you're running on Linux this can be performed by using kill -s SIGTERM , replacing with your Prometheus process ID.

Ref: https://prometheus.io/docs/prometheus/latest/getting_started/