I have a sensor that execute an asset job. what i need to change if the assets are partitioned? how to tell which partition to run when the job start? here is my snippet that works in case the assets are not partitioned:

@asset
def my_asset_1():
    some_logic()

def my_asset_2():
    some_logic()
 
job_sample = define_asset_job(name="job_sample", selection=["my_asset_1","my_asset_2"]
                                                             "prepared_intended_confiscated_businesses"])

@sensor(job=job_sample)
def sensor_to_start_job(context):
    env_var_value = os.environ.get("ENV_VAR", None)
    if env_var_value == "trigger_job":
        os.environ['ENV_VAR'] = 'stop'
        yield RunRequest(run_key="unique_run_key",
                         run_config={})

Now if I change my asset to:

@asset(
    required_resource_keys={'custom_year_resource'},
    group_name="group_1",
    partitions_def=set_partitions_files,
)
def ingested_conf_dest(context):
    curr_year = context.resources.custom_year_resource['curr_year']
    <http://context.log.info|context.log.info>(f'Current year >>>>>>>>>>>>>>> {curr_year}')
    some_logic()

set_partitions_files = DynamicPartitionsDefinition(name="partition_according_to_sources")

What I need to specify in the sensor? where explicit the partition to consider? usually I select the partition to Materialize from Dagit UI

When dealing with partitioned assets, you need to specify which partition you want to run when the job starts. This is done by providing a

partition_key

to the

RunRequest

within your sensor. The

partition_key

tells Dagster which partition of the asset to materialize. Here's how you can modify your sensor to work with partitioned assets:

python from dagster import sensor, RunRequest, define_asset_job  # Define your assets and partitions as you have done above  # Define your asset job job_sample = define_asset_job(name="job_sample", selection=["ingested_conf_dest"])  @sensor(job=job_sample) def sensor_to_start_job(context):     env_var_value = os.environ.get("ENV_VAR", None)     if env_var_value == "trigger_job":         os.environ['ENV_VAR'] = 'stop'         # Determine the partition key you want to run         partition_key = "the_partition_key_you_want_to_materialize"         yield RunRequest(             run_key="unique_run_key",             run_config={},  # Include any run configuration if needed             partition_key=partition_key,  # Specify the partition key here         )

In the above example, replace

"the_partition_key_you_want_to_materialize"

with the actual partition key you want to materialize. The

partition_key

should correspond to one of the partitions defined in your

DynamicPartitionsDefinition

. When you trigger the sensor, it will now request a run for the specified partition of the asset. If you need to determine the partition key dynamically based on some logic, you can insert that logic into the sensor function before yielding the

RunRequest

. Remember that the

partition_key

must match one of the partition keys defined by your

DynamicPartitionsDefinition

. If you're using dynamic partitions, you might need to implement additional logic to determine the latest or relevant partition key based on your data sources or other criteria.

@Scout what is RunRequest class in details?

The

RunRequest

class in Dagster is used within sensors to request the launch of a pipeline run. A sensor is a function that yields

RunRequest

objects based on some external state, such as a new file in a directory, a change in a database, or a condition met in an environment variable, as in your example. Here are the key components of a `RunRequest`: - run_key (Optional[str]): A string key that uniquely identifies the run request. This is used to deduplicate run requests, ensuring that the same request doesn't result in multiple runs. If a sensor yields a

RunRequest

with a

run_key

that has already been used, Dagster will not start a new run for that request. - run_config (Optional[Dict]): A dictionary representing the run configuration for the pipeline. This includes configurations for resources, loggers, execution, and any other custom configuration that the pipeline expects. - tags (Optional[Dict[str, str]]): A dictionary of key-value pairs that will be attached to the run as tags. Tags can be used for filtering and organizing runs in the Dagster UI (Dagit). - partition_key (Optional[str]): The key of the partition to request a run for. This is used when dealing with partitioned assets or schedules. It tells Dagster which partition of the asset or schedule to materialize or execute. Here's an example of how a

RunRequest

might look in a sensor:

python from dagster import sensor, RunRequest  @sensor(job=my_job) def my_sensor(context):     # Logic to determine whether to trigger a run     if should_trigger_run():         yield RunRequest(             run_key="unique_run_key",             run_config={                 "solids": {                     "my_solid": {                         "config": {                             "param": "value"                         }                     }                 }             },             tags={                 "key": "value"             },             partition_key="2021-01-01"         )

In this example,

should_trigger_run()

is a placeholder for whatever logic you need to determine if a run should be triggered. The

run_config

is specific to the job being run, and the

tags

are arbitrary metadata. The

partition_key

is used if the job is associated with a partitioned asset or schedule. The

RunRequest

is a powerful tool for programmatically controlling the execution of Dagster jobs, allowing for dynamic and conditional run generation based on external or internal factors.

@Scout Is it possible to define the dependency from output_asset and input_asset_1 without importing and passing the asset, but expliciting the input asset in the attribute "ins" of the asset, like when dealing with multi_assets??? If yes what I need to do? change the input_asset_1 method as well?

@asset(
    description="My asset output",
    group_name="Output_town",
    partitions_def=set_partitions_years,
    required_resource_keys={'partition_year_resource'},
)
def output_asset(context, input_asset_1):
    year = int(context.partition_key)
    <http://context.log.info|context.log.info>(f'Generating Output in year: {year}')
    var_1 = MyOutputClass(year=year, logger=context.log)
    df = var_1.create_town()

In Dagster, you can define dependencies between assets without directly importing and passing the asset functions by using the

ins

parameter within the

@asset

decorator. This is similar to how you would define dependencies for

@multi_asset

. The

ins

parameter allows you to specify dependencies using

AssetIn

objects, which can reference other assets by their keys. Here's an example of how you can define the dependency for

output_asset

on

input_asset_1

using the

ins

parameter:

python from dagster import asset, AssetIn  @asset(     description="My asset output",     group_name="Output_town",     partitions_def=set_partitions_years,     required_resource_keys={'partition_year_resource'},     ins={"input_asset_1": AssetIn(asset_key="input_asset_1_key")} ) def output_asset(context, input_asset_1):     year = int(context.partition_key)     <http://context.log.info|context.log.info>(f'Generating Output in year: {year}')     var_1 = MyOutputClass(year=year, logger=context.log)     df = var_1.create_town()     return df

In this example,

input_asset_1_key

should be replaced with the actual asset key of the

input_asset_1

asset. The asset key is a unique identifier for an asset within a Dagster repository and can be explicitly set when defining an asset or defaults to the asset's name if not set. If

input_asset_1

is not already defined with an explicit asset key, you can add one like this:

python from dagster import asset  @asset(     name="input_asset_1",     asset_key="input_asset_1_key",  # Explicitly setting the asset key     # ... other parameters ... ) def input_asset_1():     # ... logic for input_asset_1 ...     return some_data

By using the

ins

parameter with

AssetIn

, you can define the dependency relationship without having to import

input_asset_1

directly in the file where

output_asset

is defined. This can be particularly useful for managing dependencies in larger projects where assets are defined across multiple files or modules.