prometheus/docs/getting_started.md

---
title: Getting started
sort_rank: 1
---

This guide is a "Hello World"-style tutorial which shows how to install,
configure, and use a simple Prometheus instance. You will download and run
Prometheus locally, configure it to scrape itself and an example application,
then work with queries, rules, and graphs to use collected time
series data.

## Downloading and running Prometheus

[Download the latest release](https://prometheus.io/download) of Prometheus for
your platform, then extract and run it:

```bash
tar xvfz prometheus-*.tar.gz
cd prometheus-*
```

Before starting Prometheus, let's configure it.

## Configuring Prometheus to monitor itself

Prometheus collects metrics from _targets_ by scraping metrics HTTP
endpoints. Since Prometheus exposes data in the same
manner about itself, it can also scrape and monitor its own health.

While a Prometheus server that collects only data about itself is not very
useful, it is a good starting example. Save the following basic
Prometheus configuration as a file named `prometheus.yml`:

```yaml
global:
  scrape_interval:     15s # By default, scrape targets every 15 seconds.

  # Attach these labels to any time series or alerts when communicating with
  # external systems (federation, remote storage, Alertmanager).
  external_labels:
    monitor: 'codelab-monitor'

# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself.
scrape_configs:
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
  - job_name: 'prometheus'

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

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

For a complete specification of configuration options, see the
[configuration documentation](configuration/configuration.md).

## Starting Prometheus

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

```bash
# 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 to a status page
about itself at [localhost:9090](http://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](http://localhost:9090/metrics)

## Using the expression browser

Let us 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](http://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 a number of 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)
```

For more about the expression language, see the
[expression language documentation](querying/basics.md).

## Using the graphing interface

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

For example, 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])
```

Experiment with the graph range parameters and other settings.

## Starting up some sample targets

Let's 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.](https://prometheus.io/docs/guides/node-exporter/)

```bash
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 production targets, while the third one represents a
canary 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="production"` label to the first group of
targets, while adding `group="canary"` to the second.

To achieve this, add the following job definition to the `scrape_configs`
section in your `prometheus.yml` and restart your Prometheus instance:

```yaml
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: 'production'

      - targets: ['localhost:8082']
        labels:
          group: 'canary'
```

Go to the expression browser and verify that Prometheus now has information
about 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 cpus per instance (but preserving the `job`, `instance` and `mode`
dimensions) as measured over a window of 5 minutes. We could write this as:

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

Try graphing this expression.

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`:

```yaml
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:

```yaml
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 timeseries 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: 'production'

      - targets: ['localhost:8082']
        labels:
          group: 'canary'
```

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](configuration/configuration.md) 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 <PID>`, replacing `<PID>` with your Prometheus
process ID.

## Shutting down your instance gracefully.

While Prometheus does have recovery mechanisms in the case that there is an
abrupt process failure it is recommended to use signals or interrupts for a
clean shutdown of a Prometheus instance. On Linux, this can be done by sending
the `SIGTERM` or `SIGINT` signals to the Prometheus process. For example, you
can use `kill -s <SIGNAL> <PID>`, replacing `<SIGNAL>` with the signal name
and `<PID>` with the Prometheus process ID. Alternatively, you can press the
interrupt character at the controlling terminal, which by default is `^C` (Control-C).