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Title
# ask-ai
r

Rudy Jariwala

03/06/2024, 12:56 AM
given a reference to an asset key in python code. how can I find out which partitions are missing and only mark those as successful?
s

Scout

03/06/2024, 12:57 AM
To determine which partitions of an asset are missing and then mark only those as successful, you would typically follow these steps: 1. Identify Missing Partitions: You need to check which partitions of the asset have not been materialized. This can be done by comparing the expected partitions (based on your partitioning scheme) with the list of partitions that have already been materialized. 2. Mark Missing Partitions as Successful: Once you have identified the missing partitions, you can create asset materialization events for those partitions to mark them as successful. The documentation provides a method called
all_partitions_materialized
which can be used to check if a provided list of partitions have been materialized for a particular asset. However, this method checks all materializations for the asset and returns a boolean indicating whether all selected partitions have been materialized. It does not directly provide a list of missing partitions. To mark partitions as materialized, you can use the
DagsterInstance.report_runless_asset_event
method, which allows you to report asset materialization events without a corresponding pipeline run. This method can be used to mark the identified missing partitions as successful. Here is a general approach based on the documentation:
Copy code
python
from dagster import DagsterInstance, AssetKey, AssetMaterialization, EventRecordsFilter, DagsterEventType

# Get a reference to the Dagster instance
instance = DagsterInstance.get()

# Define your asset key
asset_key = AssetKey("your_asset_key")

# Define the expected list of partition keys based on your partitioning scheme
expected_partition_keys = ["partition1", "partition2", "partition3", ...]

# Check which partitions have been materialized
materialized_partitions = set()
records = instance.get_event_records(
    EventRecordsFilter(
        asset_key=asset_key,
        event_type=DagsterEventType.ASSET_MATERIALIZATION,
    )
)
for record in records:
    if record.dagster_event and record.dagster_event.asset_key == asset_key:
        materialized_partitions.add(record.dagster_event.partition)

# Identify missing partitions
missing_partitions = set(expected_partition_keys) - materialized_partitions

# Mark missing partitions as successful
for partition_key in missing_partitions:
    asset_materialization = AssetMaterialization(
        asset_key=asset_key,
        partition=partition_key,
        # Additional metadata can be provided if needed
    )
    instance.report_runless_asset_event(asset_materialization)
Please note that the above code is a general approach and may need to be adapted to your specific use case. The actual implementation details, such as how you determine the expected partitions and how you interact with the Dagster
instance, may vary depending on your environment and the version of Dagster you are using.
r

Rudy Jariwala

03/06/2024, 1:04 AM
how can I use
allPartitionsMaterialized()
?
@Scout how can I use
allPartitionsMaterialized()
?
s

Scout

03/06/2024, 1:04 AM
The
all_partitions_materialized
method is a utility method that can be used within the context of a multi-asset sensor to check if a provided list of partitions have been materialized for a particular asset. This method returns a boolean indicating whether all selected partitions have been materialized. Here's a step-by-step guide on how you might use `all_partitions_materialized`: 1. Define a Multi-Asset Sensor: Create a sensor that monitors the asset in question. Within the sensor, you will use the
all_partitions_materialized
method. 2. Use
all_partitions_materialized
: Call this method with the asset key and the list of partitions you want to check. If you do not provide a list of partitions, it will check all partitions for the asset. 3. Take Action Based on the Result: If the method returns
False
, it means that not all partitions have been materialized, and you can take appropriate action, such as triggering a pipeline run to materialize the missing partitions. Here is an example of how you might define a sensor that uses `all_partitions_materialized`:
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python from dagster import sensor, AssetKey, RunRequest from dagster.core.definitions.sensor_definition import MultiAssetSensorEvaluationContext  @sensor(job=my_job) def my_asset_sensor(context: MultiAssetSensorEvaluationContext):     asset_key = AssetKey(["my_asset"])     partitions_to_check = ["partition1", "partition2", "partition3"]  # Define your partitions      # Check if all partitions have been materialized     if not context.all_partitions_materialized(asset_key, partitions_to_check):         # If not all partitions are materialized, you can trigger a pipeline run         # or take other remedial action here         yield RunRequest(run_key="run_for_missing_partitions", run_config={})
In this example,
my_job
is the job that you want to trigger if there are missing partitions. The
partitions_to_check
list should contain the partition keys that you expect to be materialized. If
all_partitions_materialized
returns
False
, a
RunRequest
is yielded to trigger a run of
my_job
. Please note that the actual implementation details 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 accurate and up-to-date information.
r

Rudy Jariwala

03/06/2024, 1:06 AM
@Scout how can I use
allPartitionsMaterialized()
if I only have a
DagsterInstance
?
s

Scout

03/06/2024, 1:06 AM
The
all_partitions_materialized
method is specifically designed to be used within the context of a multi-asset sensor, and it is not directly available on the
DagsterInstance
object. The method is part of the
MultiAssetSensorEvaluationContext
, which is provided to sensors when they are evaluated by the Dagster scheduler or daemon. If you only have a
DagsterInstance
and want to check if all partitions of an asset have been materialized, you would need to manually replicate the logic that
all_partitions_materialized
performs. This involves querying the event log for materialization events for each partition and determining if all expected partitions have corresponding materialization events. Here's an example of how you might do this using the `DagsterInstance`:
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python from dagster import DagsterInstance, AssetKey, EventRecordsFilter, DagsterEventType  # Get a reference to the Dagster instance instance = DagsterInstance.get()  # Define your asset key asset_key = AssetKey("your_asset_key")  # Define the expected list of partition keys based on your partitioning scheme expected_partition_keys = ["partition1", "partition2", "partition3", ...]  # Check which partitions have been materialized materialized_partitions = set() records = instance.get_event_records(     EventRecordsFilter(         asset_key=asset_key,         event_type=DagsterEventType.ASSET_MATERIALIZATION,     ) ) for record in records:     if record.dagster_event and record.dagster_event.asset_key == asset_key:         materialized_partitions.add(record.dagster_event.partition)  # Determine if all partitions have been materialized all_partitions_materialized = all(     partition_key in materialized_partitions for partition_key in expected_partition_keys )  print(f"All partitions materialized: {all_partitions_materialized}")
In this example,
expected_partition_keys
should be a list of all the partition keys that you expect to be materialized. The code queries the event log for materialization events related to the specified asset key and then checks if each expected partition key is in the set of materialized partitions. Please note that this is a simplified example and may need to be adapted to your specific use case. Additionally, the performance of this approach may vary depending on the number of partitions and materialization events, so it's best suited for scenarios with a manageable number of partitions. For more complex scenarios, consider using the sensor-based approach as intended by the Dagster API design.
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