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Lightdash Preview

Developer previews are temporary Lightdash projects where you can safely experiment with your metrics, dimensions and charts without affecting your production project.

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

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.

Problems with credentials?

When you create developer previews, Lightdash will use the same warehouse connection settings you have in your profiles.yml file for your current dbt project. This can be a problem if you're using a local database that your laptop can reach but your Lightdash instance cannot.

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.

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.

info

If you already have a GitHub action for Lightdash, then you can use the same Lightdash secrets you created for your other action.

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.

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.

BigQuery
BigQuery OAuth:

Step 1: create a secret called GOOGLE_APPLICATION_CREDENTIALS

Add the service account credentials (the JSON file) that you want to use for your GitHub action. It should look something like this:

{
  "type": "service_account",
  "project_id": "jaffle_shop",
  "private_key_id": "12345",
  "private_key": "-----BEGIN PRIVATE KEY----- ... -----END PRIVATE KEY-----\n",
  "client_email": "jaffle_shop@jaffle_shop.iam.gserviceaccount.com",
  "client_id": "12345",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/jaffle_shop"
}

Step 2: create another secret called DBT_PROFILES

Copy-paste this template into the secret and fill out the details

[my-bigquery-db]: # this is the name of your project
  target: dev
  outputs:
    dev:
      type: bigquery
      method: oauth
      keyfile: keyfile.json # no need to change this! We'll automatically use the keyfile you created in the last step.
      project: [GCP project id]
      dataset: [the name of your dbt dataset]

More info in dbt's profiles docs: https://docs.getdbt.com/reference/warehouse-profiles/bigquery-profile#service-account-file

Postgres
Postgres profile configuration:
company-name:
  target: dev
  outputs:
    dev:
      type: postgres
      host: [hostname]
      user: [username]
      password: [password]
      port: [port]
      dbname: [database name]
      schema: [dbt schema]
      threads: [1 or more]
      keepalives_idle: 0
      connect_timeout: 10
      retries: 1

More info in dbt's profiles docs: https://docs.getdbt.com/reference/warehouse-profiles/postgres-profile#profile-configuration

Redshift
Redshift password-based authentication:
company-name:
  target: dev
  outputs:
    dev:
      type: redshift
      host: [hostname.region.redshift.amazonaws.com]
      user: [username]
      password: [password]
      port: 5439
      dbname: analytics
      schema: analytics
      threads: 4
      keepalives_idle: 240
      connect_timeout: 10
      ra3_node: true # enables cross-database sources

More info in dbt's profiles docs: https://docs.getdbt.com/reference/warehouse-profiles/redshift-profile#password-based-authentication

Snowflake
User / Password authentication:
my-snowflake-db:
  target: dev
  outputs:
    dev:
      type: snowflake
      account: [account id]

      # User/password auth
      user: [username]
      password: [password]

      role: [user role]
      database: [database name]
      warehouse: [warehouse name]
      schema: [dbt schema]
      threads: [1 or more]
      client_session_keep_alive: False
      query_tag: [anything]

More info in dbt's profiles docs: https://docs.getdbt.com/reference/warehouse-profiles/snowflake-profile#user--password-authentication

DataBricks
Set up a DataBricks target:
your_profile_name:
  target: dev
  outputs:
    dev:
      type: databricks
      catalog:
        [
          optional catalog name,
          if you are using Unity Catalog,
          only available in dbt-databricks>=1.1.1,
        ]
      schema: [schema name]
      host: [yourorg.databrickshost.com]
      http_path: [/sql/your/http/path]
      token: [dapiXXXXXXXXXXXXXXXXXXXXXXX] # Personal Access Token (PAT)
      threads: [1 or more]

More info in dbt's profiles docs: https://docs.getdbt.com/reference/warehouse-profiles/bigquery-profile#service-account-json

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

Github actions page

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.

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

Github actions stop preview

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.

info

If you use dbt cloud IDE to create commits and pull requests you need a few extra steps. We need to add a step in the GitHub action to fetch the user that created 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

When copying the following templates, you should replace ${{ github.actor }} with ${{steps.get_pr_creator.outputs.result}}.

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 }}

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 }}

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.