--- title: "Deploy on Linux | Grafana Tempo documentation" description: "Learn how to deploy Tempo on Linux" --- > For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt). # 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](/docs/grafana/latest/setup-grafana/installation/)) - 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](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html) 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](https://github.com/grafana/tempo/releases/tag/v1.5.0) for your OS and architecture: Bash ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```bash 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](../../configuration/) 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](../set-up-test-app/) 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. YAML ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```yaml metrics_generator_enabled: true search_enabled: true server: http_listen_port: 3200 distributor: receivers: # this configuration will listen on all ports and protocols that tempo is capable of. otlp: protocols: http: grpc: ingester: trace_idle_period: 10s # the length of time after a trace has not received spans to consider it complete and flush it max_block_bytes: 1_000_000 # cut the head block when it hits this size or ... max_block_duration: 5m # this much time passes compactor: compaction: compaction_window: 1h # blocks in this time window will be compacted together max_block_bytes: 100_000_000 # maximum size of compacted blocks block_retention: 1h compacted_block_retention: 10m 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 block: bloom_filter_false_positive: .05 # bloom filter false positive rate. lower values create larger filters but fewer false positives index_downsample_bytes: 1000 # number of bytes per index record encoding: zstd # block encoding/compression. options: none, gzip, lz4-64k, lz4-256k, lz4-1M, lz4, snappy, zstd, s2 wal: path: /tmp/tempo/wal # where to store the the wal locally encoding: snappy # wal encoding/compression. options: none, gzip, lz4-64k, lz4-256k, lz4-1M, lz4, snappy, zstd, s2 local: path: /tmp/tempo/blocks pool: max_workers: 100 # worker pool determines the number of parallel requests to the object store backend queue_depth: 10000 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 replacing `metrics_generator_enabled: true` with `metrics_generator_enabled: false` 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`: Bash ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```bash 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): Bash ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```bash 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: Bash ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```bash 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 01. Clone the Tempo repository: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none git clone https://github.com/grafana/tempo.git ``` 02. Go into the examples directory: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none cd tempo/example/docker-compose/local ``` 03. 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: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none version: "3" services: k6-tracing: ``` 04. 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: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none 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. 05. Edit the `k6-tracing` service and remove the dependency on Tempo by deleting the following lines: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none depends_on: tempo ``` Save the `docker-compose.yaml` file and exit your editor. 06. 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: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none - name: Tempo type: tempo access: proxy orgId: 1 url: http://10.128.0.104:3200 ``` Save the file and exit your editor. 07. 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. ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none - job_name: 'tempo' static_configs: - targets: [ '10.128.0.104:3200' ] ``` Save the file and exit your editor.\** 08. Start the three services that are defined in the docker-compose file: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none docker compose up -d ``` 09. Verify that the services are running using `docker compose ps`. You should see something like: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none NAME IMAGE COMMAND SERVICE CREATED STATUS PORTS local-grafana-1 grafana/grafana:9.3.0 "/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](../assets/setup-linux-run-query.png) 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: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none 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: ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```none 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](../assets/setup-linux-node-graph.png)