Jinja Variables
Work with Variables in Jinja to parameterize your SQL and YML files
SDF supports several variable types and scopes with Jinja. These allow you to parameterize your SQL and YML files, making them more reusable and easier to maintain.
In this guide, we’ll cover the following variable types:
Local Variables
Local variables are defined in the same file where they are used, and they are scoped to the file they are defined in and not accessible in other files.
Let’s look at an example, imagine we have a model that calculates a variety of metrics starting from a specific date, then aggregates them into one table. We can define a local variable to store the start date, and use it in our SQL query. Click between the two files below to see the pre and post processed SQL.
SDF Variables specified via the command line, or in the workspace.sdf.yml
file, can also be accessed in the same way as local variables. They are namespaced globally, and can be accessed in any file in the workspace.
Local variables are prioritized over global variables if they share the same name.
Since local variables are scoped to the file they are defined in, you can define a variable with the same name in another file without any conflicts. Furthermore, you can reference the variable by name alone without any prefix or namespace.
Namespaced Variables
Namespaced variables are defined in a separate file and are accessible in across the entire workspace. This allows you to define variables once and use them across multiple files. These variables
are namespaced with the workspace name, and therefore accessed using <workspace-name>.<variable-name>
.
Let’s look at an example, imagine we have several models that all use the same list of countries. We can define a namespaced variable to store the list of countries, and use it in our SQL queries.
For the sake of the this example, let’s assume we have a workspace named transformations
. We can define a namespaced variable in a file named countries.jinja
as follows:
Macros and variables are both namespaced to the workspace name so that they can be used inside other workspaces. This enables workspaces to act as SDF packages that can be shared and reused across multiple projects.
Environment Variables
Environment variables can be accessed via the built in jinja macro env_var(VAR)
. This function is accessible in both SQL, and YML files.
Give it a try in SQL:
Navigate to Your Workspace
Navigate to your SDF workspace. To create a new one, see our getting started section here.
Turn on the macro preprocessor
SDF will only use the macro-processor when instructed. In your workspace.sdf.yml
, in the workspace, block,
add the preprocessor configuration.
Give it a Try!
Now you have access to environment variables. Try if for yourself. In your terminal, run an ad-hoc query against SDF:
You’ll see as output:
Give it a try in YML:
Navigate to Your Workspace
Navigate to your SDF workspace. To create a new one, see our getting started section here.
Turn on the macro preprocessor
SDF will only use the macro-processor when instructed. In your workspace.sdf.yml
, in the workspace, block,
add the preprocessor configuration.
Let’s also add a default catalog name:
Give it a Try!
First, run sdf compile
with the existing configuration. Notice that the catalog name of all
assets should be my_catalog
as we set it up in the previous step.
Now, let’s introduce an environment variable.
Change the default catalog to point to an environment variable. In this example, we will use the local username:
Now, when we run sdf compile
, the catalog name of all aassets will be {your_username}_catalog
, when {your_username}
is
the username you use for your local machine.
You can run printenv
in your terminal to view all available encironment variables on your machine.
Create a New Environment variable
It is possible to create new environment variables and reference them using SDF.
Let’s create a new variable named DEFAULT_CATALOG
. For the sake of this example,
we can set it to be my_default_catalog
.
In your terminal, run:
Now, we need to update the env_var
reference we set in the previous step:
Run sdf compile
again. This time, the catalog name of all aassets will match the new environment variable we set up -
my_default_catalog
.
Environment Variables as Credentials
A common use case for environment variables is to inject credentials into .sdf.yml
files so that they are not stored in plain text.
Here’s an example of using these to hydrate Snowflake credentials in your project. Let’s start by creating a file creds.sdf.yml
and placing it next to your workspace.sdf.yml
file.
Next, add the following content to the file:
Next, add an includes path to your workspace.sdf.yml
like so:
Now, compiling or running your SDF workspace will require those environment variables to be set. This protects your credentials from being committed to git while still enabling the flexibility of using them in your project.