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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 single binary install. 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 (see 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, and Google Cloud’s GCS.

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.

  1. Download the tempo binary, verify checksums, and add network capabilities to the binary. Be sure to download the correct package installation for your OS and architecture:

    curl -Lo tempo_1.5.0_linux_amd64.deb https://github.com/grafana/tempo/releases/download/v1.5.0/tempo_1.5.0_linux_amd64.deb
    echo 967b06434252766e424eef997162ef89257fdb232c032369ad2e644920337a8c \
      tempo_1.5.0_linux_amd64.deb | sha256sum -c
    dpkg -i tempo_1.5.0_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. The extended instructions for installing the TNS application and Grafana Agent to verify that Tempo is receiving traces, relies on the default Jaeger port being available. If Tempo were also attempting to listen on the same port as the Grafana Agent for Jaeger, then Tempo would not start due a port conflict, hence we disable listening on that port in Tempo for a single Linux node.

server:
  http_listen_port: 3200

distributor:
  receivers:
      otlp:
        protocols:
          http:
          grpc:

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://localhost:9090/api/v1/write
      send_exemplars: true

storage:
  trace:
    backend: s3
    s3:
      endpoint: s3.us-east-1.amazonaws.com
      bucket: grafana-traces-data
      forcepathstyle: true
      #set to true if endpoint is https
      insecure: true
      access_key: # TODO - Add S3 access key
      secret_key: # TODO - Add S3 secret key
    wal:
      path: /tmp/tempo/wal         # where to store the the wal locally
    local:
      path: /tmp/tempo/blocks
overrides:
  metrics_generator_processors: [service-graphs, span-metrics]

Note: In the above configuration, 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). Ensure 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 do not, 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

  1. Clone the Tempo repository:

    git clone https://github.com/grafana/tempo.git
    
  2. Go into the examples directory:

    cd tempo/example/docker-compose/local
    
  3. 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:
    
  4. 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.

  5. 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.

  6. 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.

  7. 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.

      - job_name: 'tempo'
    	static_configs:
      	- targets: [ '10.128.0.104:3200' ]
    

    Save the file and exit your editor.**

  8. Start the three services that are defined in the docker-compose file:

    docker compose up -d
    
  9. 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:v0.0.2   "/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.

  10. 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.

  11. 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:

    Query example

  12. 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:

     storage:
         path: /tmp/tempo/generator/wal
         remote_write:
            - url: http://localhost:9090/api/v1/write
            send_exemplars: true
    
    

    Save the file and exit the editor.

  13. Finally, restart the Tempo service by running:

    sudo systemctl restart tempo
    
  14. 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.

    Service graph sample