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# ask-ai
s

Sandeep Mullangi

03/05/2024, 11:22 AM
@Scout How do i create job which accepts my dbt model name dynamically and i want to trigger this job on CLI
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dagster job backfill -p do_stuff_partitioned --from 2021-04-01 --to 2021-05-01
s

Scout

03/05/2024, 11:22 AM
To create a job that accepts your dbt model name dynamically and trigger it from the CLI, you can follow these steps: 1. Define your dbt models as assets: First, you need to define your dbt models as assets in Dagster. You can use the
load_assets_from_dbt_project
function to load your dbt models as assets. This allows you to load all your dbt models at once and select them when you’re building your jobs. 2. Create a job that runs a subset of your dbt graph: Once you have your dbt models loaded as assets, you can create a job that runs a subset of your dbt graph. Previously, you would have to invoke
load_assets_from_dbt_project
multiple times with a different
select
parameter if you wanted to create a job that did anything other than run your entire dbt project. Now, you can select the dbt models when you’re building your jobs. 3. Trigger the job from the CLI: To trigger the job from the CLI, you can use the
dagster job backfill
command. This command allows you to backfill a subset of partitions for a given job. You can specify a range of partitions using the
--from
and
--to
arguments. Here is an example of how you might trigger a backfill for a job that includes dbt models:
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bash dagster job backfill -p my_dbt_job --from 2021-04-01 --to 2021-05-01
In this example,
my_dbt_job
would be the name of the job that includes the dbt models you want to backfill. The
--from
and
--to
arguments specify the range of partitions you want to backfill. Please note that the exact implementation details, such as how to dynamically select dbt models when building your job, will depend on your specific use case and the structure of your dbt project. The documentation provides guidance on how to work with dbt models and assets, but for more detailed instructions tailored to your project, you may need to refer to the Dagster and dbt integration documentation or seek further assistance from the Dagster community.
s

Sandeep Mullangi

03/05/2024, 11:30 AM
what would be sample code of my_dbt_job @Scout
s

Scout

03/05/2024, 11:30 AM
To provide you with a sample code for
my_dbt_job
, we need to assume that you have already set up your dbt project and have a manifest file generated by dbt. The manifest file contains metadata about your dbt project and is used by Dagster to create assets. Here's a simplified example of how you might define
my_dbt_job
in Dagster, which includes dbt models as assets:
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python from dagster import job from dagster_dbt import dbt_cli_resource, load_assets_from_dbt_project  # Assuming you have a dbt project with a generated manifest.json file DBT_PROJECT_DIR = "path/to/your/dbt/project" DBT_PROFILES_DIR = "path/to/your/dbt/profiles"  # This may not be needed if using default location  # Load dbt models as assets dbt_assets = load_assets_from_dbt_project(DBT_PROJECT_DIR, DBT_PROFILES_DIR)  # Define a job that includes all dbt models as assets @job(resource_defs={"dbt": dbt_cli_resource}) def my_dbt_job():     return dbt_assets  # If you want to run a specific model or a subset of models, you can use the `select` parameter # when loading assets. For example, to run a model named 'my_model': dbt_assets_for_my_model = load_assets_from_dbt_project(     DBT_PROJECT_DIR,     DBT_PROFILES_DIR,     select=["my_model"] )  # Define a job for a specific dbt model @job(resource_defs={"dbt": dbt_cli_resource}) def my_specific_dbt_model_job():     return dbt_assets_for_my_model
In this example,
my_dbt_job
is a job that includes all dbt models as assets, and
my_specific_dbt_model_job
is a job that includes only the
my_model
dbt model as an asset. The
select
parameter is used to specify which models to include. To run
my_dbt_job
from the CLI, you would use the following command:
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bash dagster job execute -f path_to_your_python_file.py -j my_dbt_job
And to backfill the job, you would use:
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bash dagster job backfill -j my_dbt_job --from 2021-04-01 --to 2021-05-01
Please replace
path_to_your_python_file.py
with the actual path to the Python file where you defined
my_dbt_job
. Keep in mind that this is a simplified example. Depending on your dbt project configuration and how you want to run your dbt models, you might need to customize the job definition and the dbt CLI resource. Always refer to the latest Dagster documentation for the most accurate and up-to-date information on integrating dbt with Dagster.
s

Sandeep Mullangi

03/05/2024, 11:40 AM
why do we need to create job for backfill? on UI, we right click on asset and click on materialize. so when triggering backfill from UI does not require job? @Scout
s

Scout

03/05/2024, 11:40 AM
In Dagster, backfills can be initiated for both jobs and assets, and the process can differ depending on the context. When you right-click on an asset in the UI and select "Materialize," you are initiating a materialization for that specific asset. This action does not require a job because you are directly interacting with the asset itself. The UI provides a way to materialize assets without needing to define a job explicitly for that purpose. However, when you want to perform a backfill operation from the command line interface (CLI), you typically do so in the context of a job. A job in Dagster is a collection of executable steps, and it can include one or more assets. By defining a job, you create a structured way to execute these steps, which can be particularly useful when you want to run multiple assets together or apply specific configurations. The reason you might define a job for a backfill operation is to have more control over the execution context, such as specifying resources, configurations, and dependencies between assets. A job can also be useful when you want to backfill a set of assets that share the same partitioning scheme or when you want to apply a consistent set of configurations across multiple materializations. In summary, the UI allows for direct materialization of individual assets without the need for a job, while the CLI requires a job to perform backfill operations because it provides a structured way to execute and manage the materialization of assets, especially when dealing with complex workflows or multiple assets. The documentation provides guidance on how to perform backfills for both partitioned assets and jobs, and it's important to choose the method that best fits your use case.
s

Sandeep Mullangi

03/05/2024, 1:14 PM
@Scout How to find if model is present in manifest.
s

Scout

03/05/2024, 1:14 PM
To determine if a specific dbt model is present in the dbt
manifest.json
file, you would typically load the manifest file and check for the existence of the model by its unique identifier. The
manifest.json
file is generated by dbt and contains metadata about all the models, seeds, snapshots, tests, and more in your dbt project. Here's a step-by-step guide on how to check if a model is present in the `manifest.json`: 1. Locate the
manifest.json
file
: This file is usually found in the
target/
directory of your dbt project after you've run dbt commands like
dbt run
or
dbt compile
. 2. Load the
manifest.json
file
: Read the file into your Python environment using the
json
module. 3. Search for the model: Look for the model's unique identifier within the
nodes
key of the loaded manifest data. Here's an example of how you might do this in Python:
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python import json from pathlib import Path  # Path to your dbt project's target directory target_dir = Path("path/to/your/dbt/project/target")  # Load the manifest.json file manifest_path = target_dir / "manifest.json" with open(manifest_path, "r") as f:     manifest_data = json.load(f)  # Function to check if a model is in the manifest def is_model_in_manifest(model_name, manifest):     # dbt generates unique IDs for models in the form of 'model.<package_name>.<model_name>'     # Adjust the pattern if your dbt project uses a different naming convention     model_id = f"model.{model_name}"     return any(model_id in node.get('unique_id') for node in manifest.get('nodes', {}).values())  # Example usage model_name_to_check = "my_model_name" if is_model_in_manifest(model_name_to_check, manifest_data):     print(f"Model '{model_name_to_check}' is present in the manifest.") else:     print(f"Model '{model_name_to_check}' is not found in the manifest.")
Replace
"path/to/your/dbt/project/target"
with the actual path to your dbt project's
target
directory and
"my_model_name"
with the name of the model you're checking for. This script checks if the specified model is present in the
manifest.json
by looking for its unique identifier within the
nodes
dictionary. If the model is found, it prints a confirmation message; otherwise, it prints a message indicating that the model is not found.
s

Sandeep Mullangi

03/05/2024, 1:20 PM
@Scout If i want to trigger job like following `
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dagster job backfill -p trips_update_job
What code should i have in my job trips_update_job? This is for dbt.
s

Scout

03/05/2024, 1:20 PM
To trigger a backfill for a dbt job in Dagster, you need to have a job defined that represents your dbt project or a subset of your dbt models. The job should be configured to use the
dbt_cli_resource
to run dbt commands and should include the dbt models as assets. Here's an example of how you might define a job for a dbt project:
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python from dagster import job from dagster_dbt import dbt_cli_resource, load_assets_from_dbt_project  # Define the path to your dbt project and profiles directories dbt_project_dir = "/path/to/your/dbt/project" dbt_profiles_dir = "/path/to/your/dbt/profiles"  # Load dbt models as assets dbt_assets = load_assets_from_dbt_project(project_dir=dbt_project_dir, profiles_dir=dbt_profiles_dir)  # Define a job that includes all dbt models as assets @job(resource_defs={"dbt": dbt_cli_resource.configured({"project_dir": dbt_project_dir, "profiles_dir": dbt_profiles_dir})}) def trips_update_job():     # If you want to run specific models, you can use the `select` parameter with `load_assets_from_dbt_project`     # For example: select=["+my_model"]     return dbt_assets
In this example,
trips_update_job
is a job that includes all dbt models as assets. The
dbt_cli_resource
is configured with the paths to the dbt project and profiles directories. The
load_assets_from_dbt_project
function loads the dbt models as assets, which can then be materialized by the job. To trigger a backfill for this job from the CLI, you would use the command you provided:
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bash dagster job backfill -p trips_update_job
This command will initiate a backfill for the
trips_update_job
. If your dbt models are partitioned, you can specify the partitions to backfill using the
--from
and
--to
arguments, or you can use the
--partitions
argument to list specific partitions. Please ensure that the paths to your dbt project and profiles directories are correctly specified in the job definition. Also, make sure that your dbt project has been compiled and that the
manifest.json
file is present in the
target
directory, as Dagster uses this file to load the dbt assets.
s

Sandeep Mullangi

03/05/2024, 2:17 PM
@Scout I have job on UI with following code. How do i accept parameter to selection from launchpad? my_asset_job = define_asset_job( name="backfill_asset_job", selection=["calendar"] )
s

Scout

03/05/2024, 2:17 PM
In Dagster, the
define_asset_job
function creates a job that materializes a selection of assets. If you want to accept parameters from the launchpad to dynamically select assets when triggering the job, you would typically use the job's configuration to pass in the selection criteria. However, as of my knowledge cutoff in early 2023, there isn't a built-in way to dynamically alter the
selection
parameter of
define_asset_job
directly from the launchpad. The
selection
parameter is usually static and determined when you define the job in code. If you want to dynamically select assets based on user input from the launchpad, you would need to implement a custom mechanism to handle this. One approach could be to define multiple jobs with different selections and trigger the appropriate job based on the user's choice. Another approach could be to use a sensor or schedule that reads from an external source (like a database or a configuration file) to determine which assets to materialize and then triggers the job with the appropriate selection. Here's an example of how you might define a sensor that reads from an external configuration to determine the selection:
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python
from dagster import sensor, RunRequest, repository
from dagster_dbt import dbt_cli_resource

# This function would read from an external source to determine the selection
def get_dynamic_selection():
    # Placeholder: fetch the selection from an external source
    # For example, read from a database, a file, or an environment variable
    return ["+my_dynamic_model"]

@sensor(job=my_asset_job)
def dynamic_selection_sensor():
    selection = get_dynamic_selection()
    run_config = {"ops": {"my_asset_job": {"config": {"selection": selection}}}}
    yield RunRequest(run_key=None, run_config=run_config)

@repository
def my_repo():
    return [my_asset_job, dynamic_selection_sensor]
In this example,
dynamic_selection_sensor
is a sensor that reads the selection from an external source and triggers
my_asset_job
with the dynamic selection. The
run_config
is constructed with the dynamic selection and passed to the
RunRequest
. Please note that this is a conceptual example and would require further development to integrate with your specific external configuration source. Additionally, the
selection
parameter in the
run_config
is not a standard way to configure the selection of assets for a job, and this example is meant to illustrate the concept rather than provide a working solution. For a working solution, you would need to customize the job execution based on the dynamic parameters, which might involve using custom resources or hooks to modify the behavior of the job at runtime. Always refer to the latest Dagster documentation for the most accurate and up-to-date
information on dynamic job configuration and execution.