Using environment variables and secrets#

Environment variables, which are key-value pairs configured outside your source code, allow you to dynamically modify application behavior depending on environment.

Using environment variables, you can define various configuration options for your Dagster application and securely set up secrets. For example, instead of hard-coding database credentials - which is bad practice and cumbersome for development - you can use environment variables to supply user details. This allows you to parameterize your pipeline without modifying code or insecurely storing sensitive data.

Declaring environment variables#

How environment variables are declared depends on how your Dagster project is deployed.

Dagster Cloud#

Environment variables can be set a variety of ways in Dagster Cloud. Refer to the Dagster Cloud environment variables guide for more info.

Dagster Open Source#

Setting environment variables in Dagster Open Source depends on where Dagster is deployed. Refer to the deployment guide for your platform for more info:

Accessing environment variables#

In this section, we'll demonstrate how to access environment variables once they've been declared. There are two ways to do this:

In Python code#

To access environment variables in your Dagster code, you can use os.getenv:

import os

database_name = os.getenv("DATABASE_NAME")

This approach also works for accessing built-in environment variables for Dagster Cloud:

import os

deployment_name = os.getenv("DAGSTER_CLOUD_DEPLOYMENT_NAME")

Refer to the Dagster Cloud Branch Deployments example for a real-world example.

From Dagster configuration#

Configurable Dagster objects - such as ops, assets, resources, I/O managers, and so on - can accept configuration from environment variables.

To access an environment variable as part of a Dagster configuration, use the following syntax:

"PARAMETER_NAME": {"env": "ENVIRONMENT_VARIABLE_NAME"}

For example:

"access_token": {"env": "GITHUB_ACCESS_TOKEN"}

Refer to the Handling secrets section and Per-environment configuration example for examples.

Handling secrets#

Using environment variables to provide secrets ensures sensitive info won't be visible in your code or the launchpad in the UI. In Dagster, best practice for handling secrets uses configuration and resources.

A resource is typically used to connect to an external service or system, such as a database. Resources can be configured separately from the rest of your app, allowing you to define it once and reuse it as needed.

Let's take a look at an example from the Dagster Crash Course, which creates a GitHub resource and supplies it to assets in a Dagster repository. Let's start by looking at the resource:

## resources.py

from dagster import StringSource, resource
from github import Github

@resource(config_schema={"access_token": StringSource})
def github_api(init_context):
    return Github(init_context.resource_config["access_token"])

Let's review what's happening here:

  • This code creates a GitHub resource named github_api
  • Using ConfigSchema, we've indicated that resource can accept a single config parameter, access_token
  • Using StringSource, we've indicated that the access_token config parameter can either be:
    • An environment variable, or
    • Provided directly in the configuration

As storing secrets in configuration is bad practice, we'll opt for using an environment variable. In this code, we're configuring the resource and supplying it to assets in the repository:

## repository.py

from dagster import (
    load_assets_from_package_module,
    repository,
    with_resources,
)
from my_dagster_project import assets
from my_dagster_project.resources import github_api

@repository
def my_dagster_project():
    return [
        with_resources(
            load_assets_from_package_module(assets),
            {"github_api": github_api.configured({"access_token": {"env": "GITHUB_ACCESS_TOKEN"}})},
        ),
    ]

Let's review what's happening here:

  • Using with_resources adds the github_api resource to the assets in the repository. In assets, we'll use the github_api resource key to reference the resource.
  • Using the configured method on the github_api resource, we can pass configuration info to the resource. In this example, we're telling Dagster to load the access_token from the GITHUB_ACCESS_TOKEN environment variable.

Parameterizing pipeline behavior#

Using environment variables, you define how your code should execute at runtime.

Example 1: Per-environment configuration#

In this example, we'll demonstrate how to use different I/O manager configurations for local and production environments using configuration (specifically the configured API) and resources.

This example is adapted from the Transitioning data pipelines from development to production guide:

# repository.py


@repository
def repo():
    resource_defs = {
        "local": {
            "snowflake_io_manager": snowflake_io_manager.configured(
                {
                    "account": "abc1234.us-east-1",
                    "user": {"env": "DEV_SNOWFLAKE_USER"},
                    "password": {"env": "DEV_SNOWFLAKE_PASSWORD"},
                    "database": "LOCAL",
                    "schema": {"env": "DEV_SNOWFLAKE_SCHEMA"},
                }
            ),
        },
        "production": {
            "snowflake_io_manager": snowflake_io_manager.configured(
                {
                    "account": "abc1234.us-east-1",
                    "user": "system@company.com",
                    "password": {"env": "SYSTEM_SNOWFLAKE_PASSWORD"},
                    "database": "PRODUCTION",
                    "schema": "HACKER_NEWS",
                }
            ),
        },
    }
    deployment_name = os.getenv("DAGSTER_DEPLOYMENT", "local")

    return [
        *with_resources(
            [items, comments, stories], resource_defs=resource_defs[deployment_name]
        )
    ]

Let's review what's happening here:

  • This code defines a Dagster repository named repo
  • Using resource_defs, we've created a list of resource definitions, named after our local and production environments, that are available to the repository. In this example, we're using a Snowflake I/O manager.
  • For both local and production, we used the configured method on snowflake_io_manager to provide environment-specific run configuration. Note the differences in configuration between local and production, specifically where environment variables were used.
  • Following the list of resources, we define the deployment_name variable, which determines the current executing environment. This variable defaults to local, ensuring that DAGSTER_DEPLOYMENT=PRODUCTION must be set to use the production configuration.
  • Lastly, we use with_resources to:
    • Provide the resources to the assets in the repository
    • Parameterize usage of the resources based on the value of DAGSTER_DEPLOYMENT

Example 2: Dagster Cloud Branch Deployments#

This section is applicable only to Dagster Cloud.

Adapted from the Testing against production with Dagster Cloud Branch Deployments guide, this example demonstrates how to determine the current deployment type at runtime - Branch Deployment or full deployment - without using resources or configuration.

Let's look at a function that determines the current deployment using the DAGSTER_CLOUD_IS_BRANCH_DEPLOYMENT environment variable:

def get_current_env():
  is_branch_depl = os.getenv("DAGSTER_CLOUD_IS_BRANCH_DEPLOYMENT") == "1"
  assert is_branch_depl != None  # env var must be set
  return "branch" if is_branch_depl else "prod"

This function checks the value of DAGSTER_CLOUD_IS_BRANCH_DEPLOYMENT and, if equal to 1, returns a variable with the value of branch. This indicates that the current deployment is a Branch Deployment. Otherwise, the deployment is a full deployment and is_branch_depl will be returned with a value of prod.

Using this info, we can write code that executes differently when in a Branch Deployment or a full deployment.