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<@U0667DNC02Y> I have define_asset_job and this jo...
# ask-ai
s
@Scout I have define_asset_job and this job should take config values from launchpad and pass to op. Can you write sample code?
s
Certainly! To define a job that takes configuration values from the launchpad and passes them to an asset, you can use the 
RunConfig
object to specify the configuration when executing the job. Here's a sample code snippet based on the documentation:
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python from dagster import asset, define_asset_job, RunConfig  class MyAssetConfig(Config):     person_name: str  @asset def greeting(config: MyAssetConfig) -> str:     return f"hello {config.person_name}"  # Define the job that materializes the 'greeting' asset greeting_job = define_asset_job(name="greeting_job", selection=["greeting"])  # When you want to execute the job, you can provide the configuration like this: result = greeting_job.execute_in_process(     run_config=RunConfig({"greeting": MyAssetConfig(person_name="Alice")}) )
In this example, 
MyAssetConfig
is a subclass of 
Config
that defines the configuration schema for the 
greeting
asset. The 
greeting
asset takes a configuration parameter that is used to personalize the greeting message. The 
greeting_job
is defined using 
define_asset_job
, which materializes the 
greeting
asset. When executing the job, you can provide the configuration values using the 
RunConfig
object, where the keys correspond to the asset names and the values correspond to the configuration values. Please note that the 
RunConfig
object is used to specify the configuration when executing the job programmatically. If you are using the Dagster UI's Launchpad to execute the job, you would provide the configuration values as YAML using the config editor in the UI. Keep in mind that the exact code may vary depending on your specific use case and the version of Dagster you are using. Always refer to the latest Dagster documentation for the most up-to-date examples and best practices.
s
can define_asset_job call op? @Scout
s
No, 
define_asset_job
cannot directly call an op. In Dagster, 
define_asset_job
is used to create a job from a selection of assets. Assets are the main abstraction in Dagster for defining computations that produce and depend on data artifacts. If you want to include op-like behavior within an asset job, you should encapsulate that logic within an asset. If you have a function that you would like to execute as part of an asset job, but it does not fit the asset model (i.e., it does not produce a data artifact), you can still encapsulate the logic within an asset by making it part of the computation graph that produces an asset. Here's an example of how you might do that:
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python from dagster import asset, define_asset_job  # This function acts like an op but is used within an asset computation def perform_computation():     # Your logic here     print("Performing a computation")  @asset def my_asset():     # You can call a function within your asset definition     perform_computation()     # Then produce the actual data artifact     return "data produced by my_asset"  # Define the job that materializes the 'my_asset' asset my_asset_job = define_asset_job(name="my_asset_job", selection=["my_asset"])
In this example, 
perform_computation
is a function that performs some logic but does not produce an asset itself. It is called within the 
my_asset
asset definition, allowing you to include op-like behavior within an asset computation. If you need to include more complex op-based logic within an asset job, you can define a graph that includes your ops and then create a graph-backed asset. This allows you to combine op-based workflows into an asset-based job. However, this is a more advanced pattern and should be used when necessary. Remember that Dagster's asset-based APIs are preferred over ops for defining data dependencies and computations. Assets should be the primary building blocks for your data pipelines in Dagster.
s
@Scout I want to have 1 job which takes config from launchpad and based on logic should create multiple backfills. This is for 1 dbt asset.
s
Creating a job that takes configuration from the launchpad and creates multiple backfills for a dbt asset involves several steps. You'll need to define a job that materializes your dbt asset, and then use the backfill functionality to create multiple backfills based on the provided configuration. Here's a high-level outline of how you might approach this: 1. Define your dbt asset with the necessary configuration schema. 2. Create a job that materializes this dbt asset. 3. Use the Dagster UI's Launchpad to configure and launch the job. 4. Use the backfill functionality to create multiple backfills based on the provided configuration. Here's an example of how you might define the dbt asset and the job:
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python
from dagster import asset, define_asset_job, DailyPartitionsDefinition, repository

# Define your dbt asset
@asset(partitions_def=DailyPartitionsDefinition(start_date="2021-01-01"))
def my_dbt_asset(context):
    # You can access the configuration provided from the launchpad
    # and use it to determine how to run your dbt command
    dbt_config = context.op_config["dbt_config"]
    # Logic to run dbt with the provided configuration
    # ...

# Define the job that materializes the 'my_dbt_asset' asset
my_dbt_asset_job = define_asset_job(
    name="my_dbt_asset_job",
    selection=["my_dbt_asset"],
    config={
        "ops": {
            "my_dbt_asset": {
                "config": {
                    "dbt_config": {
                        # Default configuration can be provided here
                    }
                }
            }
        }
    }
)

@repository
def my_repository():
    return [my_dbt_asset, my_dbt_asset_job]
In this example, 
my_dbt_asset
is a dbt asset that is partitioned daily. The 
my_dbt_asset_job
is a job that materializes this asset. The job's configuration is structured to accept 
dbt_config
as part of the op's configuration. To create multiple backfills: 1. Navigate to the Dagster UI's Launchpad for 
my_dbt_asset_job
. 2. Provide the necessary configuration for your dbt asset. 3. Launch the job to materialize the asset for a specific partition or set of partitions. 4. To create backfills, go to the "Partitions" tab for your asset in the Dagster UI. 5. Use the "Backfill" functionality to select the partitions you want to backfill and launch the backfill process. The backfill process will create runs for each selected partition, using the configuration provided during the launch. If you need to create backfills programmatically based on logic, you would typically use Dagster's Python API to script the creation of backfills, but this is a more advanced use case and would require additional context to provide a detailed example. Please note that the exact implementation details may vary based on y
our specific dbt setup and the version of Dagster you are using. Always refer to the latest Dagster documentation for the most up-to-date examples and best practices.
s
@Scout I want to have a job which creates 3 backfills(one for jan, one for feb, one for march). Internally it has to create 3 backfills 1. dagster job backfill -p do_stuff_partitioned --from 2024-01-01 --to 2024-01-31 2. dagster job backfill -p do_stuff_partitioned --from 2024-02-01 --to 2024-02-29 3. dagster job backfill -p do_stuff_partitioned --from 2024-03-01 --to 2024-03-31
s
Creating backfills programmatically within a job is not a typical use case for Dagster as of my knowledge cutoff in early 2023. Typically, backfills are created either through the Dagster UI or the command line interface (CLI) as a separate step after defining and launching jobs. However, if you want to automate the process of creating backfills, you would need to use the Dagster CLI commands programmatically, which is generally not recommended and not a built-in feature of Dagster's Python API. Instead, you should create backfills manually using the CLI or the UI. Here's how you would create the backfills manually using the CLI, as per your example:
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bash # Backfill for January dagster job backfill -p do_stuff_partitioned --from 2024-01-01 --to 2024-01-31  # Backfill for February dagster job backfill -p do_stuff_partitioned --from 2024-02-01 --to 2024-02-29  # Backfill for March dagster job backfill -p do_stuff_partitioned --from 2024-03-01 --to 2024-03-31
To create backfills through the UI, you would: 1. Navigate to the "Partitions" tab for your job in the Dagster UI. 2. Click the "Launch backfill" button. 3. Select the date range for the backfill (e.g., 2024-01-01 to 2024-01-31 for January). 4. Submit the backfill. 5. Repeat steps 2-4 for each month. If you have a strong need to automate this process within a job, you might consider creating a custom script that uses the 
subprocess
module in Python to call the Dagster CLI commands. However, this is outside the scope of typical Dagster usage and could lead to unexpected behavior, as it's not a supported feature of the Dagster API. For more information on backfills and how to manage them, please refer to the latest Dagster documentation on backfills. If there have been updates to Dagster after my knowledge cutoff that allow for this functionality, you would find that information in the updated documentation or release notes.
s
cant we trigger any backfill programatically? I dont want to use UI @Scout
s
As of my last update, Dagster does not provide a direct Python API for triggering backfills programmatically within a job. Backfills are typically initiated through the Dagster UI or the command line interface (CLI). However, you can use the Dagster CLI programmatically from a Python script using the 
subprocess
module to run the backfill commands. This is a workaround and not an official Dagster API, so it should be used with caution and understanding of the potential risks, such as error handling and process management. Here's an example of how you might use Python's 
subprocess
module to trigger backfills for the specified date ranges:
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python import subprocess  def run_backfill(partition_set_name, start_date, end_date):     command = [         "dagster",         "job",         "backfill",         "-p",         partition_set_name,         "--from",         start_date,         "--to",         end_date     ]     subprocess.run(command, check=True)  try:     # Backfill for January     run_backfill("do_stuff_partitioned", "2024-01-01", "2024-01-31")     # Backfill for February     run_backfill("do_stuff_partitioned", "2024-02-01", "2024-02-29")     # Backfill for March     run_backfill("do_stuff_partitioned", "2024-03-01", "2024-03-31") except subprocess.CalledProcessError as e:     print(f"An error occurred while running the backfill: {e}")
In this script, 
run_backfill
is a function that constructs the CLI command for backfilling a partition set and executes it using 
subprocess.run
. The 
check=True
argument ensures that a 
CalledProcessError
is raised if the command exits with a non-zero status, which you can catch and handle as needed. Please note that this approach requires that the Dagster CLI is installed and accessible in the environment where the script is running. Additionally, this script should be executed in an environment where it has the necessary permissions and access to the Dagster instance and its underlying datastore. For the most up-to-date and recommended practices, please refer to the latest Dagster documentation on backfills. If Dagster has introduced a new API for programmatically triggering backfills after my last update, that would be the preferred method to use.
s
ok i understand we cannot trigger backfills through job using SDK, but can we trigger backfills with define_asset_job? @Scout
s
As of my last update, triggering backfills directly through 
define_asset_job
or any other part of the Dagster Python API is not supported. The 
define_asset_job
function is used to define a job that materializes a set of assets, and it does not include functionality for initiating backfills. Backfills in Dagster are typically initiated through the Dagster UI or the command line interface (CLI), where you can specify the partitions you want to backfill. The process of backfilling is a separate operational concern from the definition of assets and asset jobs. If you need to perform backfills, you would typically do so by: 1. Using the Dagster UI: - Navigate to the "Partitions" tab for your asset or job. - Use the "Backfill" functionality to select the partitions you want to backfill and launch the backfill process. 2. Using the Dagster CLI: - Use the 
dagster job backfill
command with the appropriate flags to specify the partition set and date range for the backfill. While it is technically possible to use Python's 
subprocess
module to invoke the CLI commands programmatically from a Python script, as mentioned in my previous message, this is not an official feature of the Dagster Python API and should be used with caution. For the most accurate and current information, please refer to the latest Dagster documentation on backfills. If new features or APIs have been introduced that allow for programmatic backfilling, the documentation will provide the relevant details and examples.
s
@Scout I have 3 dagster environments, how do i run dagster CLI commands based on environment?
s
To run Dagster CLI commands in different environments, you can use environment variables to switch between your Dagster instances. Environment variables can be used to specify the Dagster instance you want to interact with by setting the 
DAGSTER_HOME
environment variable to the path of the Dagster instance's home directory for each environment. Here's how you can set the 
DAGSTER_HOME
environment variable and run Dagster CLI commands based on the environment: 1. Set the 
DAGSTER_HOME
environment variable: - On Unix-like systems (Linux, macOS), you can set the environment variable in your shell session before running the CLI command:
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bash      export DAGSTER_HOME=/path/to/dagster_home_env1      dagster instance migrate
- On Windows, you can use the 
set
command in Command Prompt:
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cmd      set DAGSTER_HOME=C:\path\to\dagster_home_env1      dagster instance migrate
- Alternatively, you can set the environment variable inline with the command itself:
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bash      DAGSTER_HOME=/path/to/dagster_home_env1 dagster instance migrate
2. Run the Dagster CLI command: - After setting the 
DAGSTER_HOME
environment variable, you can run any Dagster CLI command, and it will use the configuration for the specified environment. 3. Switch to a different environment: - To switch to a different environment, change the 
DAGSTER_HOME
environment variable to the path of the different environment's home directory and then run your CLI commands.
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bash      export DAGSTER_HOME=/path/to/dagster_home_env2      dagster instance migrate
Remember to replace 
/path/to/dagster_home_env1
and 
/path/to/dagster_home_env2
with the actual paths to your Dagster instance home directories for each environment. If you're using Dagster Cloud, you might also need to configure the 
dagster-cloud
CLI with the appropriate credentials and settings for each environment. You can use the 
dagster-cloud
CLI's configuration file or environment variables to specify the organization, deployment, and user token for each environment. For more detailed instructions and best practices, please refer to the latest Dagster documentation on CLI usage and environment configuration. If there are any new features or recommended approaches introduced after my last update, the documentation will provide the necessary guidance.
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