Deploy on Linux

Thu, 09 Apr 2026 16:01:48 +0000

Deploy on Linux

This guide provides a step-by-step process for installing Tempo on Linux. It assumes you have access to a Linux system and the permissions required to deploy a service with network and file system access. At the end of this guide, you will have deployed a single Tempo instance on a single node.

These instructions focus on a monolithic installation. You can also run Tempo in distributed mode by deploying multiple binaries and using a distributed configuration.

Before you begin

To follow this guide, you need:

A running Grafana instance (refer to the installation instructions)
An Amazon S3 compatible object store
Git, Docker, and docker-compose plugin installed to test Tempo

System requirements

This configuration is an example you can use as a starting point. You may need to have more resources for your system than the minimum specifications listed below. Additional adjustments will be necessary for a production environment.

You must have the permissions required to deploy a service with a network and file system access.

Your Linux system should have at least:

4 CPUs
16 GB of memory

Set up an object storage bucket

Tempo uses object storage as the backend for its trace storage. It also uses object storage for storing various data related to the state of the system.

Tempo supports using the local filesystem as the backend for trace storage as well. This is not recommended for production deployments. This guide focuses on setup with an object storage.

This example uses Amazon S3 on the AWS us-east-1 region as your object store. If you plan on using a different region or object storage service, update the storage fields in the configuration file below. Currently, the supported object storage backends are AWS S3, other S3-compliant object stores like Minio,Azure, and Google Cloud Storage.

After you have provisioned an object storage backend, create the bucket grafana-traces-data. The buckets will be referenced in the configuration file of this guide. You may need to alter the bucket name to be globally unique.

Consider adding a prefix for your organization to the bucket, for example, myorg-grafana-traces-data, and then replacing the names in the rest of these instructions with those bucket names.

Install Tempo

For a linux-amd64 installation, run the following commands via the command line interface on your Linux machine. You need administrator privileges to do this by running as the root user or via sudo as a user with permissions to do so.

Download the Tempo binary, verify checksums (listed in SHA256SUMS), and add network capabilities to the binary. Be sure to download the correct package installation for your OS and architecture. The following example downloads and installs Tempo 2.9.0 for the AMD64 (x86_64) processor architecture on a Linux distribution supporting deb packages:

curl -Lo tempo_2.9.0_linux_amd64.deb https://github.com/grafana/tempo/releases/download/v2.9.0/tempo_2.9.0_linux_amd64.deb
echo c1bc0fc8af5dcbe1b4806bdfad6d120e5a5df376d6782d22d0c3190be14fff05 \
  tempo_2.9.0_linux_amd64.deb | sha256sum -c
sudo dpkg -i tempo_2.9.0_linux_amd64.deb

Create a Tempo configuration file

Copy the following YAML configuration to a file called tempo.yaml.

Paste in your S3 credentials for admin_client and the storage backend. If you wish to give your cluster a unique name, add a cluster property with the appropriate name.

Refer to the Tempo configuration documentation for explanations of the available options.

In the following configuration, Tempo options are altered to only listen to the OTLP gRPC and HTTP protocols. By default, Tempo listens for all compatible protocols. By default, the OpenTelemetry Collector receiver binds to localhost instead of 0.0.0.0. This example binds to all ports, bear in mind that this can be a security risk if your Tempo instance is exposed to the public internet.

server:
  http_listen_port: 3200

distributor:
  receivers:
    otlp:
      protocols:
        grpc:
          endpoint: "0.0.0.0:4317"
        http:
          endpoint: "0.0.0.0:4318"

ingester:
  max_block_duration: 5m

compactor:
  compaction:
    block_retention: 48h               # configure total trace retention here

metrics_generator:
  registry:
    external_labels:
      source: tempo
      cluster: linux-microservices
  storage:
    path: /tmp/tempo/generator/wal
    remote_write:
      - url: http://prometheus:9090/api/v1/write
        send_exemplars: true

storage:
  trace:
    backend: s3                        # backend configuration to use
    s3:
      endpoint: s3.us-east-1.amazonaws.com
      bucket: grafana-traces-data
      forcepathstyle: true
      enable_dual_stack: false
      # set to false if endpoint is https
      insecure: true
      access_key: # TODO - Add S3 access key
      secret_key: # TODO - Add S3 secret key
    wal:
      path: /var/tmp/tempo/wal         # where to store the wal locally
    local:
      path: /var/tmp/tempo/blocks

overrides:
  metrics_generator_processors: [service-graphs, span-metrics] # enables metrics generator

Note
The above configuration is compatible with the downloaded release of Tempo. Future release may impact the configuration content. In the configuration shown above, the metrics-generator is enabled to generate Prometheus metrics data from incoming trace spans. This is sent to a Prometheus remote-write compatible metrics store at http://prometheus:9090/api/v1/write in the metrics_generator configuration block. Make sure you change the relevant url parameter to your own Prometheus compatible storage instance, or disable the metrics-generator by removing the metrics_generators_processors if you do not wish to generate span metrics.

Move the configuration file to the proper directory

Copy the tempo.yaml to /etc/tempo/config.yml:

cp tempo.yaml /etc/tempo/config.yml

Restart the tempo service

Use systemctl to restart the service (depending on how you installed Tempo, this may be different):

systemctl start tempo.service

You can replace restart with stop to stop the service, and start to start the service again after it’s stopped, if required.

Verify your cluster is working

To verify that Tempo is working, run the following command:

systemctl is-active tempo

You should see the status active returned. If you don’t, check that the configuration file is correct, and then restart the service. You can also use journalctl -u tempo to view the logs for Tempo to determine if there are any obvious reasons for failure to start.

Verify that your storage bucket has received data by signing in to your storage provider and determining that a file has been written to storage. It should be called tempo_cluster_seed.json.

Test your installation

Once Tempo is running, you can use the K6 with Traces Docker example to verify that trace data is sent to Tempo. This procedure sets up a sample data source in Grafana to read from Tempo.

Backend storage configuration

The Tempo examples running with docker-compose all include a version of Tempo and a storage backend like S3 and GCS. Because Tempo is installed with a backend storage configured, you need to change the docker-compose.yaml file to remove Tempo and instead point trace storage to the installed version. These steps are included in this section.

Network configuration

Docker compose uses an internal networking bridge to connect all of the defined services. Because the Tempo instance is running as a service on the local machine host, you need the resolvable IP address of the local machine so the docker containers can use the Tempo service. You can find the host IP address of your Linux machine using a command such as ip addr show.

Steps

Clone the Tempo repository:

git clone https://github.com/grafana/tempo.git

Go into the examples directory:
```
cd tempo/example/docker-compose/local
```
Edit the file docker-compose.yaml, and remove the tempo service and all its properties, so that the first service defined is k6-tracing. The start of your docker-compose.yaml should look like this:
```
version: "3"
services:

k6-tracing:
```
Edit the k6-tracing service, and change the value of ENDPOINT to the local IP address of the machine running Tempo and docker compose, eg. 10.128.0.104:4317. This is the OTLP gRPC port:
```
environment:
  - ENDPOINT=10.128.0.104:4317
```
This ensures that the traces sent from the example application go to the locally running Tempo service on the Linux machine.
Edit the k6-tracing service and remove the dependency on Tempo by deleting the following lines:
```
depends_on:
tempo
```
Save the docker-compose.yaml file and exit your editor.
Edit the default Grafana data source for Tempo that is included in the examples. Edit the file located at tempo/example/shared/grafana-datasources.yaml, and change the url field of the Tempo data source to point to the local IP address of the machine running the Tempo service instead (eg. url: http://10.128.0.104:3200). The Tempo data source section should resemble this:
```
- name: Tempo
  type: tempo
  access: proxy
  orgId: 1
  url: http://10.128.0.104:3200
```
Save the file and exit your editor.
Edit the Prometheus configuration file so it uses the Tempo service as a scrape target. Change the target to the local Linux host IP address. Edit the tempo/example/shared/prometheus.yaml file, and alter the tempo job to replace tempo:3200 with the Linux machine host IP address.
YAML
```
  - job_name: 'tempo'
	static_configs:
  	- targets: [ '10.128.0.104:3200' ]
```
Save the file and exit your editor.**
Start the three services that are defined in the docker-compose file:
Bash
```
docker compose up -d
```

Verify that the services are running using docker compose ps. You should see something like:

NAME             	IMAGE                                   	COMMAND              	SERVICE         	CREATED         	STATUS          	PORTS
local-grafana-1  	grafana/grafana:9.3.2                   	"/run.sh"            	grafana         	2 minutes ago   	Up 3 seconds    	0.0.0.0:3000->3000/tcp, :::3000->3000/tcp
local-k6-tracing-1   ghcr.io/grafana/xk6-client-tracing:v0.0.9   "/k6-tracing run /ex…"   k6-tracing      	2 minutes ago   	Up 2 seconds
local-prometheus-1   prom/prometheus:latest                  	"/bin/prometheus --c…"   prometheus      	2 minutes ago   	Up 2 seconds    	0.0.0.0:9090->9090/tcp, :::9090->9090/tcp

Grafana is running on port 3000, Prometheus is running on port 9090. Both should be bound to the host machine.

As part of the docker compose manifest, Grafana is now running on your Linux machine, reachable on port 3000. Point your web browser to the Linux machine on port 3000. You might need to port forward the local port if you’re doing this remotely, for example, via SSH forwarding.
Once logged in, navigate to the Explore page, select the Tempo data source and select the Search tab. Select Run query to list the recent traces stored in Tempo. Select one to view the trace diagram:
Alter the Tempo configuration to point to the instance of Prometheus running in docker compose. To do so, edit the configuration at /etc/tempo/config.yaml and change the storage block under the metrics_generator section so that the remote write URL is http://localhost:9090. The configuration section should look like this:
YAML
```
 storage:
     path: /var/tempo/generator/wal
     remote_write:
        - url: http://localhost:9090/api/v1/write
        send_exemplars: true
```
Save the file and exit the editor.
Finally, restart the Tempo service by running:
```
sudo systemctl restart tempo
```
A couple of minutes after Tempo has successfully restarted, select the Service graph tab for the Tempo data source in the Explore page. Select Run query to view a service graph, generated by Tempo’s metrics-generator.

Deploy Tempo using Docker Compose

Thu, 09 Apr 2026 16:01:48 +0000

Deploy Tempo using Docker Compose

The example directory in the Tempo repository provides a docker-compose file that you can use to run Tempo locally with different configurations.

The easiest example to start with is Local Storage. This example runs Tempo as a single binary together with the synthetic-load-generator, to generate traces, and Grafana, to query Tempo. Data is stored locally on disk.

The following examples showcase specific features or integrations:

Grafana Alloy provides a simple example using the Grafana Alloy as a tracing pipeline.
OpenTelemetry Collector is a basic example using the OpenTelemetry Collector as a tracing pipeline.
OpenTelemetry Collector Multitenant uses the OpenTelemetry Collector in an advanced multi-tenant configuration.

The Local storage example uses the local backend, suitable for local testing and development.

Deploy Tempo locally on Grafana Labs

Deploy on Linux

Deploy on Linux

Before you begin

System requirements

Set up an object storage bucket

Install Tempo

Create a Tempo configuration file

Move the configuration file to the proper directory

Restart the tempo service

Verify your cluster is working

Test your installation

Backend storage configuration

Network configuration

Steps

Deploy Tempo using Docker Compose

Deploy Tempo using Docker Compose