Deployments in Tinybird

Changing state in data infrastructure can be complex. Each state transition must ensure data integrity and consistency.

Tinybird deployments simplify this process by providing robust mechanisms for managing state changes, allowing you to validate and push updates seamlessly while minimizing the risk of data conflicts or loss.

What is a deployment?

Deployments are versions of your project resources and data running on local or cloud infrastructure.

Types of deployments

There are two types of deployments:

  • Staging deployments: Deployments you can use to validate your changes. You access them using the --staging flag.
  • Live deployments: Deployments that make your changes available to your users.

Deployment status

Deployments have the following statuses:

  • In progress: The deployment is in progress. Use --wait to wait for it to finish.
  • Live: The deployment is active and has been promoted from staging.
  • Staging: The deployment is active in staging. Use --staging to access it.
  • Failed: The deployment failed. Try tb deploy --check to debug the issue.
  • Deleted: The deployment was deleted as a result of creating new deployments.

Deployment methods

The following deployment methods are available:

If your project was initialized with tb init --dev-mode local or tb init --dev-mode branch, tb deploy without explicit environment flags deploys to your cloud main workspace.

Staging deployments

You can write data to, and read data from, a staging deployment before promoting it to live. This is useful when you've made schema changes that might be incompatible with the current live deployment, like adding new columns.

Automatic data source changes that Tinybird applies with ALTER, such as adding a column or changing a data source TTL, are applied only when the deployment is promoted to live. Those changes aren't available in staging deployments.

Use staging deployments to validate a deployment before it goes live. For longer-lived development environments that shouldn't block the deployment process, use Cloud Branches instead.

Writing to staging deployments

You can use the Events API to write directly to staging deployments through the __tb__min_deployment parameter, which indicates the target deployment ID. For example:

curl \
    -H "Authorization: Bearer <import_token>" \
    -d '{"date": "2020-04-05 00:05:38", "city": "Chicago", "new_column": "value"}' \
    'https://<your_host>/v0/events?name=events_test&__tb__min_deployment=5'

In the example, if the ID of your current live deployment is 4 and you're creating deployment with an ID of 5, the data will be ingested into the staging deployment 5 only. This allows you to:

  1. Make schema changes in a staging deployment.
  2. Ingest data compatible with the new schema.
  3. Validate the changes work as expected.
  4. Promote the deployment to live when ready.

Without the parameter, data would be rejected if it doesn't match the schema of the current live deployment.

If you discard the staging deployment instead of promoting it, data written only to that staging deployment won't be available in the live deployment.

To get the deployment ID, run tb deployment ls.

Reading from staging deployments

You can query data from a staging deployment using pipe endpoints. To access a staging endpoint, add the __tb__deployment parameter to your API request:

curl \
    -H "Authorization: Bearer <query_token>" \
    'https://<your_host>/v0/pipes/my_endpoint?__tb__deployment=5'

This allows you to:

  1. Test your endpoints with the new schema changes.
  2. Validate query results before promoting to live.
  3. Ensure your application works correctly with the updated data structure.

To get the deployment ID, run tb deployment ls.

Continuous operation

Once the deployment is promoted to live, you can continue using the same API calls. In the previous example, calls using the __tb__min_deployment=5 or __tb__deployment=5 parameters will keep working without interruption. The parameters ensure compatibility both before and after promotion.

For more details on the Events API parameters, see the Events API Reference.

On-demand compute for deployment populates

When a deployment needs to populate Data Sources, such as when creating Materialized Views or evolving schemas, Tinybird can run the populate on dedicated on-demand compute instead of your main workspace infrastructure. This keeps the populate isolated from production query and ingestion workloads.

Tinybird automatically uses on-demand compute for large deployment populates. For pricing, regional rates, and example calculations, see On-demand CPUs.

Next steps

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