Lightdash Preview

Preview projects will copy all spaces/charts/dashboards into your new preview project, so you can test the content and also run validation. This is only copied on preview creation, you can’t sync the content afterwards.

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Run lightdash preview from inside your project

# This will create a preview and will wait until you press a key to delete the preview project
lightdash preview

or

# This will create a preview and exit, you will have to run lightdash stop-preview to delete it
lightdash start-preview

Then cmd + click to open the preview link from your terminal. Once you’re in Lightdash go to Explore —> Tables, then click on the model(s) you just updated to see your changes and play around with them.

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Set up developer previews on your pull requests

If you’ve connected Lightdash to GitHub, you can setup a github action and get Lightdash to create new dynamic preview projects automatically when a new pull request is created, and it will automatically delete the preview project when the pull request is closed or merged.

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Step 1: add the credentials to Github secrets

If you haven’t already set up a GitHub action for Lightdash, you’ll need to add some secrets to GitHub. If you already have a GitHub action for Lightdash, then you can use the same Lightdash secrets you created for your other action.

We are going to add some secrets and config to GitHub actions, but you don’t want those to be public, so the best way to do this is to add them as secrets on Github.

Go to your repo, click on Settings , on the left sidebar, click on Secrets under Security. Now click on the New repository secret

We need to add the following secrets:

LIGHTDASH_API_KEY

Create a new personal access token, by going to Settings > Personal Access Tokens. This is the token you’ll put in for LIGHTDASH_API_KEY.

LIGHTDASH_PROJECT

The UUID for your project. For example, if your URL looks like https://eu1.lightdash.cloud/projects/3538ab33-dc90-aabb-bc00-e50bba3a5f69/tables, then 3538ab33-dc90-45f0-aabb-e50bba3a5f69 is your LIGHTDASH_PROJECT

LIGHTDASH_URL

This is either https://eu1.lightdash.cloud or https://app.lightdash.cloud for Lightdash Cloud users (check the URL to your Lightdash project). If you self-host, this should be your own custom domain.

DBT_PROFILES

Some tips for this bit:

• You might be able to copy a bunch of the information from your local profiles.yml file. You can see what’s in there by typing cat ~/.dbt/profiles.yml in your terminal.

• If you have a separate prod and dev profile, you probably want to use the information from your prod profile for your GitHub action.

• If you want to have different connection settings depending on the user that opened the pull request (dev profiles), then check out this guide.

Find your data warehouse from the list below to get a profiles.yml file template. Fill out this template, and this is your DBT_PROFILES secret.

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Step 2: Create start-preview.yml and close-preview.yml workflows in Github

Go to your repo, click on Actions menu, and click on Configure

Now copy this start-preview.yml file from our cli-actions repo

And save by clicking on Start commit

Do the same with this close-preview.yml file.

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You’re done!

Everytime you create a new pull request , a new preview project with your branch name will be created on your organization. Everytime you make a change to that branch, the preview environment will get updated. Once you close or merge your pull request, the preview project will get deleted.

You can see the log on Github actions page

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How to use the developer credentials in your preview project

When developing in dbt, you typically have a different set of credentials and dataset/schema than when you are running in production. Here are two options on how to set them up based on the developer that opened the Pull Request.

- uses: actions/github-script@v6
  id: get_pr_creator
  with:
    script: |
      return (
        await github.rest.repos.listPullRequestsAssociatedWithCommit({
          commit_sha: context.sha,
          owner: context.repo.owner,
          repo: context.repo.repo,
        })
      ).data[0].user.login;
    result-encoding: string

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Using profile targets

Update your DBT_PROFILES to have 1 target per developer. The target name should be their GitHub username.

jaffle_shop:
  target: prod
  outputs:
    prod:
      type: bigquery
      method: oauth
      keyfile: keyfile.json
      project: jaffle-shop
      dataset: prod
    katie: # example developer 1, should be GitHub username
      type: bigquery
      method: oauth
      keyfile: keyfile.json
      project: jaffle-shop
      dataset: dbt_katie
    jose: # example developer 2
      type: bigquery
      method: oauth
      keyfile: keyfile.json
      project: jaffle-shop
      dataset: dbt_jose

Then, update your GitHub action to use the username as the --target flag for the lightdash start-preview command.

run: lightdash start-preview --project-dir "$PROJECT_DIR" --profiles-dir . --name ${GITHUB_REF##*/} --target ${{ github.actor }}

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Using github environments

Setup a GitHub environment for each developer where the secrets are specifically for them. The environment name should be their GitHub username. Then, update your GitHub action to use the username as the environment.

jobs:
  preview:
    runs-on: ubuntu-latest
    environment: ${{ github.actor }}

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How to use the dbt cloud schema in your preview project

If you are using a continuous integration job in dbt cloud, you can use the schema that is created by dbt cloud (dbt_cloud_pr_<job_id>_<pr_id>) for your preview project.

First we need to add an environment variable to your profile.yml file that will be used by dbt to connect to the correct schema.

schema: "{{ env_var('DBT_SCHEMA') }}"

If you are using BigQuery, it should be dataset instead of schema.

Then we need to add a step in the GitHub action to fetch the pull request id.

- uses: actions/github-script@v6
  id: pr_id
  with:
    script: |
      if (context.issue.number) {
        // Return issue number if present
        return context.issue.number;
      } else {
        // Otherwise return issue number from commit
        return (
          await github.rest.repos.listPullRequestsAssociatedWithCommit({
            commit_sha: context.sha,
            owner: context.repo.owner,
            repo: context.repo.repo,
          })
        ).data[0].number;
      }
    result-encoding: string

After that we need to add a new env variable to the step “Lightdash CLI start preview” which is the schema that dbt cloud will use.

Note that in this example we assume the job id is 1234. You will need to replace this with the actual job id.

env:
  # ... keep existing env variables
  DBT_SCHEMA: 'dbt_cloud_pr_1234_${{steps.pr_id.outputs.result}}'

Now dbt will use the correct schema when running in the preview environment.