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Get started with Grafana and Prometheus

Fri, 03 Apr 2026 19:43:06 +0000

Get started with Grafana and Prometheus

Prometheus is an open source monitoring system for which Grafana provides out-of-the-box support. This topic walks you through the steps to create a series of dashboards in Grafana to display system metrics for a server monitored by Prometheus.

Tip
Check out our Prometheus Learning Journeys.

Connect to a Prometheus data source in Grafana Cloud to visualize your metrics directly from where they are stored.

Send metrics to Grafana Cloud using Prometheus remote write to explore Grafana Cloud without making significant changes to your existing configuration.

Grafana and Prometheus:

Download Prometheus and Node exporter
Install Prometheus Node exporter
Install and configure Prometheus
Configure Prometheus for Grafana
Check Prometheus metrics in Grafana Explore view
Start building dashboards

Download Prometheus and Node exporter

Download the following components:

Like Grafana, you can install Prometheus on many different operating systems. Refer to the Prometheus download page to see a list of stable versions of Prometheus components.

Install Prometheus Node exporter

Install Node exporter on all hosts you want to monitor. This guide shows you how to install it locally.

Prometheus Node exporter is a widely used tool that exposes system metrics. For instructions on installing Node exporter, refer to the Installing and running the Node exporter section in the Prometheus documentation.

When you run Node exporter locally, navigate to http://localhost:9100/metrics to check that it is exporting metrics.

Note
The instructions in the referenced topic are intended for Linux users. You may have to alter the instructions slightly depending on your operating system. For example, if you are on Windows, use the windows_exporter instead.

Install and configure Prometheus

After downloading Prometheus, extract it and navigate to the directory.
```
tar xvfz prometheus-*.tar.gz
cd prometheus-*
```
Locate the prometheus.yml file in the directory.
Modify the Prometheus configuration file to monitor the hosts where you installed Node exporter.

By default, Prometheus looks for the file prometheus.yml in the current working directory. This behavior can be changed via the --config.file command line flag. For example, some Prometheus installers use it to set the configuration file to /etc/prometheus/prometheus.yml.

The following example shows you the code you should add. Notice that static configs targets are set to ['localhost:9100'] to target node-explorer when running it locally.

 # A scrape configuration containing exactly one endpoint to scrape from Node exporter running on a host:
 scrape_configs:
     # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.
     - job_name: 'node'

     # metrics_path defaults to '/metrics'
     # scheme defaults to 'http'.

       static_configs:
       - targets: ['localhost:9100']

Start the Prometheus service:

 ./prometheus --config.file=./prometheus.yml

Confirm that Prometheus is running by navigating to http://localhost:9090.

You can see that the Node exporter metrics have been delivered to Prometheus. Next, the metrics will be sent to Grafana.

Configure Prometheus for Grafana

When running Prometheus locally, there are two ways to configure Prometheus for Grafana. You can use a hosted Grafana instance at Grafana Cloud or run Grafana locally.

This guide describes configuring Prometheus in a hosted Grafana instance on Grafana Cloud.

Because you are running your own Prometheus instance locally, you must remote_write your metrics to the Grafana.com Prometheus instance. Grafana provides code to add to your prometheus.yml config file. This includes a remote write endpoint, your user name and password.

Add the following code to your prometheus.yml file to begin sending metrics to your hosted Grafana instance.

remote_write:
- url: <https://your-remote-write-endpoint>
  basic_auth:
    username: <your user name>
    password: <Your Grafana.com API Key>

Note
To configure your Prometheus instance to work with Grafana locally instead of Grafana Cloud, install Grafana here and follow the configuration steps listed here.

Troubleshooting

These are some of the troubleshooting steps you can try if Prometheus isn’t running or functioning as expected. The steps provided have been selected based on the Learning Journeys we offer for Prometheus. If you’d like to explore further, check out the Prometheus Learning Journey if you want to visualize data in Grafana Cloud without sending or storing data in Grafana Cloud, such as for local retention needs. Alternatively, if you already have a Prometheus setup and want to explore Grafana Cloud without making significant changes, visit the Prometheus remote write learning journey.

1. Checking if Prometheus is running

If the Prometheus web UI is inaccessible (e.g., “Connection refused” error in the browser) or Prometheus queries fail (e.g., errors in Grafana like “Data source unavailable” or “No data points”), a good place to start is confirming that the Prometheus process and service are running.

You can do this by checking the system process or verifying the service status:

Linux

sudo systemctl status prometheus

Shows whether the process is running and if the service is active.

MacOS

pgrep prometheus

Returns the process ID (PID) if Prometheus is running.

Windows (PowerShell)

Get-Process -Name prometheus -ErrorAction SilentlyContinue

Checks if the Prometheus process is running by name.

2. If Prometheus is not running

Start by checking for common causes:

Check for port conflicts.

Prometheus runs on port 9090 by default. If another process is using this port, Prometheus may fail to start. You can check for port conflicts with:

Linux & MacOS

lsof -i :9090

Windows (PowerShell)

netstat -ano | findstr :9090

Shows if another process is using port 9090.

Verify the Prometheus binary placement: ensure Prometheus binaries (prometheus and promtool) are correctly installed.

Linux & MacOS

ls /usr/local/bin/prometheus /usr/local/bin/promtool

If missing, move them to /usr/local/bin or a directory in your system’s PATH.

Check if Prometheus is in the path.

which prometheus
which promtool

If there’s no output, the binaries are not in the system PATH.

Ensure configuration & data files are in place.

Linux & MacOs

ls /etc/prometheus /var/lib/prometheus
ls /etc/prometheus/prometheus.yml

Makes sure that Prometheus has its necessary configuration and data storage directories.

Check permissions: If Prometheus is running as a dedicated user, ensure the correct ownership:

sudo chown -R prometheus:prometheus /etc/prometheus /var/lib/prometheus

(Optional) Secure Prometheus by creating a dedicated user

sudo useradd --no-create-home --shell /bin/false prometheus

Recommended for security: runs Prometheus as a non-login user.

3. Checking if Prometheus is running as a service

If Prometheus is running as a process, check whether it is properly set up and managed as a service to ensure it restarts automatically after reboots or failures.

Check Prometheus service status

Linux

systemctl status prometheus.service

Windows

sc query prometheus

MacOs

pgrep prometheus

If the service is inactive (dead) or stopped, proceed to the next steps.

4. If Prometheus is not running as a service

If Prometheus is not running as a managed service, ensure it is correctly configured and can restart automatically.

Verify service configuration (Linux & MacOs)

Check the service unit file to ensure correct paths:

sudo nano /etc/systemd/system/prometheus.service

Look for the ExecStart line:

ExecStart=/usr/local/bin/prometheus --config.file=/etc/prometheus/prometheus.yml --storage.tsdb.path=/var/lib/prometheus/

Ensure:
- The binary path (/usr/local/bin/prometheus) is correct.
- The configuration file (/etc/prometheus/prometheus.yml) is in place.
- The storage path (/var/lib/prometheus/) exists.

Restart and enable Prometheus service (Linux & MacOs)

sudo systemctl daemon-reload
sudo systemctl enable prometheus
sudo systemctl start prometheus
sudo systemctl status prometheus

Check Prometheus health status

After restarting, verify if Prometheus is responsive:

curl -s http://localhost:9090/-/ready

If successful, this confirms Prometheus is ready to serve requests.

Restart Prometheus service (Windows)

If running as a Windows service, restart it:

net stop prometheus
net start prometheus

5. Checking if Prometheus is capturing metrics

If you installed Node exporter to expose your system metrics, you can check if Prometheus is capturing metrics by sending a request to the /metrics endpoint.

curl http://localhost:9090/metrics

It should return a number of metrics and metadata about the metrics being exposed.

Check Prometheus metrics in Grafana Metics Drilldown

In your Grafana instance, go to the Drilldown view and experience query-less browsing of Prometheus-compatible metrics.

Start building dashboards

Now that you have a curated list of queries, create dashboards to render system metrics monitored by Prometheus. When you install Prometheus and Node exporter or windows_exporter, you will find recommended dashboards for use.

The following image shows a dashboard with three panels showing some system metrics.

To learn more:

Grafana documentation: Prometheus data source
Prometheus documentation: What is Prometheus?

Get started with Grafana and InfluxDB

Fri, 03 Apr 2026 19:43:06 +0000

Get started with Grafana and InfluxDB

InfluxDB is an open-source time series database (TSDB) developed by InfluxData. It is optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, IoT sensor data, and real-time analytics.

Get InfluxDB

You can download InfluxDB and install it locally or you can sign up for InfluxDB Cloud. Windows installers are not available for some versions of InfluxDB.

Install other InfluxDB software

Install Telegraf. This tool is an agent that helps you get metrics into InfluxDB. For more information, refer to Telegraf documentation.

If you chose to use InfluxDB Cloud, then you should download and install the InfluxDB Cloud CLI. This tool allows you to send command line instructions to your cloud account. For more information, refer to Influx CLI documentation.

Get data into InfluxDB

If you downloaded and installed InfluxDB on your local machine, then use the Quick Start feature to visualize InfluxDB metrics.

If you are using the cloud account, then the wizards will guide you through the initial process. For more information, refer to Configure Telegraf.

Note for Windows users:

Windows users might need to make additional adjustments. Look for special instructions in the InfluxData documentation and Using Telegraf on Windows blog post. The regular system monitoring template in InfluxDB Cloud is not compatible with Windows. Windows users who use InfluxDB Cloud to monitor their system will need to use the Windows System Monitoring Template.

Add your InfluxDB data source to Grafana

You can have more than one InfluxDB data source defined in Grafana.

Follow the general instructions to add a data source.
Decide if you will use InfluxQL or Flux as your query language.
- Configure the data source for your chosen query language. Each query language has its own unique data source settings.
- For querying features specific to each language, see the data source’s query editor documentation.

InfluxDB guides

InfluxDB publishes guidance for connecting different versions of their product to Grafana.

InfluxDB OSS or Enterprise 1.8+. To turn on Flux, refer to Configure InfluxDB. Select your InfluxDB version in the upper right corner.
InfluxDB OSS or Enterprise 2.x. Refer to Use Grafana with InfluxDB. Select your InfluxDB version in the upper right corner.
InfluxDB Cloud. Refer to Use Grafana with InfluxDB Cloud.

Important tips

Make sure your Grafana token has read access. If it doesn’t, then you’ll get an authentication error and be unable to connect Grafana to InfluxDB.
Avoid apostrophes and other non-standard characters in bucket and token names.
If the text name of the organization or bucket doesn’t work, then try the ID number.
If you change your bucket name in InfluxDB, then you must also change it in Grafana and your Telegraf .conf file as well.

Add a query

This step varies depending on the query language that you selected when you set up your data source in Grafana.

InfluxQL query language

In the query editor, click select measurement.

Grafana displays a list of possible series. Click one to select it, and Grafana graphs any available data. If there is no data to display, then try another selection or check your data source.

Flux query language

Create a simple Flux query.

Add a panel.
In the query editor, select your InfluxDB-Flux data source. For more information, refer to Queries.
Select the Table visualization.
In the query editor text field, enter buckets() and then click outside of the query editor.

This generic query returns a list of buckets.

You can also create Flux queries in the InfluxDB Explore view.

In your browser, log in to the InfluxDB native UI (OSS is typically something like http://localhost:8086 or for InfluxDB Cloud use: https://cloud2.influxdata.com).
Click Explore to open the Data Explorer.
The InfluxDB Data Explorer provides two mechanisms for creating Flux queries: a graphical query editor and a script editor. Using the graphical query editor, create a query. It will look something like this:
Click Script Editor to view the text of the query, and then copy all the lines of your Flux code, which will look something like this:
In Grafana, add a panel and then paste your Flux code into the query editor.
Click Apply. Your new panel should be visible with data from your Flux query.

Check InfluxDB metrics in Grafana Explore

In your Grafana instance, go to the Explore view and build queries to experiment with the metrics you want to monitor. Here you can also debug issues related to collecting metrics.

Start building dashboards

There you go! Use Explore and Data Explorer to experiment with your data, and add the queries that you like to your dashboard as panels. Have fun!

Here are some resources to learn more:

Grafana documentation: InfluxDB data source
InfluxDB documentation: Comparison of Flux vs InfluxQL

Get started with Grafana and MS SQL Server

Fri, 03 Apr 2026 19:43:06 +0000

Get started with Grafana and MS SQL Server

Microsoft SQL Server is a popular relational database management system that is widely used in development and production environments. This topic walks you through the steps to create a series of dashboards in Grafana to display metrics from a MS SQL Server database.

Download MS SQL Server

MS SQL Server can be installed on Windows or Linux operating systems and also on Docker containers. Refer to the MS SQL Server downloads page, for a complete list of all available options.

Install MS SQL Server

You can install MS SQL Server on the host running Grafana or on a remote server. To install the software from the downloads page, follow their setup prompts.

If you are on a Windows host but want to use Grafana and MS SQL data source on a Linux environment, refer to the WSL to set up your Grafana development environment blog post. This will allow you to leverage the resources available in grafana/grafana GitHub repository. Here you will find a collection of supported data sources, including MS SQL Server, along with test data and pre-configured dashboards for use.

Add the MS SQL data source

There are several ways to authenticate in MSSQL. Start by:

Click Connections in the left-side menu and filter by mssql.
Select the Microsoft SQL Server option.
Click Create a Microsoft SQL Server data source in the top right corner to open the configuration page.
Select the desired authentication method and fill in the right information as detailed below.
Click Save & test.

General configuration

Name	Description
`Name`	The data source name. This is how you refer to the data source in panels and queries.
`Host`	The IP address/hostname and optional port of your MS SQL instance. If port is omitted, the default 1433 will be used.
`Database`	Name of your MS SQL database.

SQL Server Authentication

Name	Description
`User`	Database user’s login/username.
`Password`	Database user’s password.

Windows Active Directory (Kerberos)

Below are the four possible ways to authenticate via Windows Active Directory/Kerberos.

Note
Windows Active Directory (Kerberos) authentication is not supported in Grafana Cloud at the moment.

Method	Description
Username + password	Enter the domain user and password
Keytab file	Specify the path to a valid keytab file to use that for authentication.
Credential cache	Log in on the host via `kinit` and pass the path to the credential cache. The cache path can be found by running `klist` on the host in question.
Credential cache file	This option allows multiple valid configurations to be present and matching is performed on host, database, and user. See the example JSON below this table.

[
  {
    "user": "grot@GF.LAB",
    "database": "dbone",
    "address": "mysql1.mydomain.com:3306",
    "credentialCache": "/tmp/krb5cc_1000"
  },
  {
    "user": "grot@GF.LAB",
    "database": "dbtwo",
    "address": "mysql2.gf.lab",
    "credentialCache": "/tmp/krb5cc_1000"
  }
]

For installations from the grafana/grafana repository, gdev-mssql data source is available. Once you add this data source, you can use the Datasource tests - MSSQL dashboard with three panels showing metrics generated from a test database.

Optionally, play around this dashboard and customize it to:

Create different panels.
Change titles for panels.
Change frequency of data polling.
Change the period for which the data is displayed.
Rearrange and resize panels.

Start building dashboards

Now that you have gained some idea of using the pre-packaged MS SQL data source and some test data, the next step is to setup your own instance of MS SQL Server database and data your development or sandbox area.

To fetch data from your own instance of MS SQL Server, add the data source using instructions in Step 4 of this topic. In Grafana Explore build queries to experiment with the metrics you want to monitor.

Once you have a curated list of queries, create dashboards to render metrics from the SQL Server database. For troubleshooting, user permissions, known issues, and query examples, refer to Using Microsoft SQL Server in Grafana.