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<@U0667DNC02Y> how do I schedule a job to backfill...
# ask-ai
c
@Scout how do I schedule a job to backfill a partitioned asset every 30 days and backfill the 30 missing partitions
s
To schedule a job that backfills a partitioned asset every 30 days and targets the 30 missing partitions, you would need to create a custom schedule that calculates the partitions to backfill and then triggers runs for those partitions. Here's a step-by-step guide based on the documentation: 1. Define your partitioned asset: Ensure that your asset is partitioned in a way that aligns with your backfill strategy. For example, if you have daily partitions, you would have a 
DailyPartitionsDefinition
. 2. Create a custom schedule: You will need to write a custom schedule function that determines which partitions are missing and should be backfilled. This function will be invoked according to the schedule you set (every 30 days in your case). 3. Calculate the partitions to backfill: Inside your custom schedule function, calculate the date range that covers the last 30 partitions. You can use datetime operations to find the start and end dates for the partitions you want to backfill. 4. Create RunRequests for the missing partitions: For each of the missing partitions within the calculated date range, create a 
RunRequest
object. This object should specify the partition key that corresponds to the partition you want to backfill. 5. Set the schedule's cron expression: Define the cron expression for your schedule to trigger every 30 days. For example, if you want the schedule to run on the first day of every month, your cron expression would be 
"0 0 1 * *"
. 6. Register the schedule: Add the schedule to your repository so that Dagster is aware of it and can manage its execution. Here is a conceptual example of what the custom schedule function might look like:
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python
from datetime import datetime, timedelta
from dagster import schedule, RunRequest

@schedule(cron_schedule="0 0 1 * *", job=my_partitioned_job)
def my_monthly_backfill_schedule(context):
    today = datetime.today()
    thirty_days_ago = today - timedelta(days=30)
    
    # Assuming you have a function to get the list of missing partition keys
    # for the last 30 days
    missing_partition_keys = get_missing_partition_keys(thirty_days_ago, today)
    
    run_requests = []
    for partition_key in missing_partition_keys:
        run_requests.append(RunRequest(run_key=partition_key, run_config={}))
    
    return run_requests
Please note that the function 
get_missing_partition_keys
is hypothetical and would need to be implemented based on your specific logic to determine which partitions are missing and need to be backfilled. Remember to replace 
my_partitioned_job
with the actual job that you want to schedule for backfilling. The cron expression 
"0 0 1 * *"
is just an example; you should adjust it to match your exact scheduling needs. Keep in m
ind that the above example is a conceptual guide. You will need to adapt the code to fit your specific use case and ensure that it aligns with the capabilities and APIs provided by Dagster. Schedules for Partitioned assets button Thank you for your feedback!
c
@Scout How do I find missing partitions in a partitioned asset
s
To find missing partitions in a partitioned asset, you can use the Dagster instance to query the materialization history of the asset and determine which partitions have not been materialized. The documentation provides a utility method 
all_partitions_materialized
that 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 does not directly provide a list of missing partitions. To find the missing partitions, you would typically need to: 1. Define the range of partitions you want to check. 2. Use the Dagster instance to get the materialization events for the asset. 3. Compare the expected partitions with the materialized ones to identify which are missing. Here's a conceptual example of how you might write a function to find missing partitions:
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python
from dagster import DagsterInstance, AssetKey

def find_missing_partitions(asset_key, partition_keys):
    instance = DagsterInstance.get()
    materialized_partitions = set(
        event.partition
        for event in instance.get_event_records(
            event_type=DagsterEventType.ASSET_MATERIALIZATION,
            asset_key=asset_key,
        )
        if event.is_dagster_event and event.dagster_event.partition
    )
    missing_partitions = [
        partition_key for partition_key in partition_keys
        if partition_key not in materialized_partitions
    ]
    return missing_partitions

# Example usage:
# Define your asset key and the range of partition keys you want to check
my_asset_key = AssetKey(["my_asset"])
my_partition_keys = ["2023-01-01", "2023-01-02", "2023-01-03"]  # etc.

# Find the missing partitions
missing_partitions = find_missing_partitions(my_asset_key, my_partition_keys)
print("Missing partitions:", missing_partitions)
In this example, 
my_asset_key
should be replaced with the actual 
AssetKey
of your partitioned asset, and 
my_partition_keys
should be a list of partition keys that you expect to be materialized. Please note that this is a simplified example. Depending on your specific use case and the partitioning scheme of your asset, you may need to adjust the logic to correctly identify the range of expected partitions and to handle any nuances related to your asset's partitioning. [UI] Partitioned Asset materialization m... button Thank you for your feedback!
c
@Scout how do I figure out the last time an asset was materialized?
s
To figure out the last time an asset was materialized, you can query the Dagster instance for the latest materialization event of the asset. The documentation provides a method for this purpose, which you can use within your Dagster code. Here's an example of how you might write a function to get the timestamp of the last materialization event for a specific asset:
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python from dagster import AssetKey, DagsterInstance  def get_last_materialization_time(asset_key):     instance = DagsterInstance.get()     latest_materialization = instance.get_latest_materialization_event(asset_key=asset_key)          if latest_materialization is not None:         return latest_materialization.timestamp     else:         return None  # Example usage: # Define your asset key my_asset_key = AssetKey(["my_asset"])  # Get the last materialization time last_materialization_time = get_last_materialization_time(my_asset_key) if last_materialization_time:     print(f"The asset was last materialized at: {last_materialization_time}") else:     print("The asset has not been materialized yet.")
In this example, 
my_asset_key
should be replaced with the actual 
AssetKey
of your asset. The function 
get_last_materialization_time
will return the timestamp of the last materialization event if it exists, or 
None
if the asset has never been materialized. Please ensure that you have the appropriate context to access the Dagster instance, as this code assumes you are running within a Dagster environment where 
DagsterInstance.get()
can be called to retrieve the current instance.
To determine the last time an asset was materialized, you can use the 
get_latest_materialization_event
method provided by the Dagster instance. This method allows you to query for the most recent materialization event of a specific asset. Here's an example of how you can use this method:
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python from dagster import AssetKey, DagsterInstance  # Get the Dagster instance instance = DagsterInstance.get()  # Define the asset key for which you want to find the last materialization time asset_key = AssetKey("my_asset")  # Get the latest materialization event for the asset latest_materialization = instance.get_latest_materialization_event(asset_key=asset_key)  # Extract the timestamp from the latest materialization event if latest_materialization:     last_materialization_time = latest_materialization.timestamp     print(f"The asset was last materialized at: {last_materialization_time}") else:     print("The asset has not been materialized yet.")
In this code snippet, replace 
"my_asset"
with the name of your actual asset. The 
get_latest_materialization_event
method will return the latest materialization event for the given 
AssetKey
, from which you can extract the timestamp to find out when the asset was last materialized. If there is no materialization event for the asset, it means the asset has not been materialized yet.
c
@Scout how do I create a run request for a DBT asset?
s
Creating a run request for a DBT asset in Dagster involves defining a job that includes the DBT asset, and then creating a schedule or sensor that generates run requests for that job. Here's a high-level overview of how you can do this: 1. Define your DBT asset: Use the 
@asset
decorator or the 
@dbt_assets
decorator if you are working with DBT models to define your DBT asset within Dagster. 2. Create a job: Define a job that includes the DBT asset. This job will be responsible for running the DBT commands that materialize your asset. 3. Create a schedule or sensor: Define a schedule or sensor that will create run requests for the job. A schedule runs at a specific time interval, while a sensor triggers runs based on certain conditions. Here's a conceptual example of how you might set this up:
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python from dagster import job, schedule, RunRequest, repository from dagster_dbt import dbt_cli_resource, dbt_run_op  # Define a job that includes the DBT asset @job(resource_defs={"dbt": dbt_cli_resource}) def my_dbt_job():     dbt_run_op()  # Define a schedule that creates run requests for the DBT job @schedule(cron_schedule="0 0 * * *", job=my_dbt_job, execution_timezone="UTC") def my_dbt_schedule(context):     # You can customize the run config or tags as needed     run_config = {"resources": {"dbt": {"config": {"project_dir": "path/to/dbt/project"}}}}     return RunRequest(run_key=context.scheduled_execution_time.strftime("%Y-%m-%d-%H-%M"), run_config=run_config)  # Collect jobs and schedules in a repository @repository def my_repository():     return [my_dbt_job, my_dbt_schedule]
In this example, 
dbt_run_op
is an operation provided by the 
dagster_dbt
library that runs the 
dbt run
command. The 
dbt_cli_resource
is a resource that configures how the DBT CLI is invoked. The 
my_dbt_schedule
schedule is set to run every day at midnight UTC, and it creates a 
RunRequest
with a specific 
run_key
and 
run_config
. Please adjust the 
run_config
and the 
cron_schedule
according to your specific DBT project configuration and scheduling needs. Also, ensure that the 
project_dir
points to the correct location of your DBT project. Remember that this is a high-level example, and you will need to adapt the code to fit your specific use case, including the correct setup for your DBT assets and resources.
c
@Scout how do I set a run request to run a single dbt model or asset?
s
To run a single dbt model or asset, you can use the 
dbt run
command with the 
--select
flag to specify the particular model you want to run. In Dagster, you can create a job that includes an operation to execute this dbt command, and then create a run request for that job with the appropriate configuration. Here's a conceptual example of how you might set up a Dagster job to run a single dbt model:
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python from dagster import job, op, repository from dagster_dbt import dbt_cli_resource  # Define an operation that runs a single dbt model @op(required_resource_keys={"dbt"}) def run_single_dbt_model(context):     # Replace 'my_model' with the name of your dbt model     context.resources.dbt.run(["run", "--select", "my_model"])  # Define a job that includes the operation @job(resource_defs={"dbt": dbt_cli_resource}) def my_single_dbt_model_job():     run_single_dbt_model()  # Collect jobs in a repository @repository def my_repository():     return [my_single_dbt_model_job]
In this example, the 
run_single_dbt_model
operation uses the 
dbt_cli_resource
to run the 
dbt run
command with the 
--select
flag to target a specific model named 
my_model
. You should replace 
'my_model'
with the actual name of the dbt model you want to run. To create a run request for this job, you would typically use a schedule or sensor. However, if you want to create a run request manually, you can do so through the Dagster UI or the GraphQL API. Here's an example of how you might create a run request manually through the Dagster UI: 1. Navigate to the Dagster UI. 2. Go to the "Jobs" section and find 
my_single_dbt_model_job
. 3. Click on the "Launchpad" tab for the job. 4. Configure any necessary run configuration, if needed. 5. Click the "Launch Run" button to execute the job. If you need to create a run request programmatically, you can use the GraphQL API provided by Dagster to trigger a run for the job with the desired configuration. Please note that the above example assumes you have set up the 
dbt_cli_resource
with the appropriate configuration for your dbt project. Adjust the resource configuration to match your dbt project's directory and profiles as needed.
c
@Scout create a run_config to run a single dbt asset or model
s
To create a 
run_config
that specifies running a single dbt model or asset, you can use the 
dbt_cli_resource
in Dagster and provide the necessary configuration to the 
dbt run
command. The 
run_config
should include the 
dbt run
command with the 
--select
flag followed by the name of the model or asset you want to run. Here's an example of how you might define a 
run_config
for a job that runs a single dbt model:
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python from dagster import job, op, repository from dagster_dbt import dbt_cli_resource  # Define an operation that runs a single dbt model @op(required_resource_keys={"dbt"}) def run_single_dbt_model(context):     # The model name is provided through the run config     model_name = context.op_config["model_name"]     context.resources.dbt.run(["run", "--select", model_name])  # Define a job that includes the operation @job(resource_defs={"dbt": dbt_cli_resource}) def my_single_dbt_model_job():     run_single_dbt_model()  # Define the run_config for the job run_config = {     "ops": {         "run_single_dbt_model": {             "config": {                 "model_name": "my_model"  # Replace with your dbt model name             }         }     },     "resources": {         "dbt": {             "config": {                 "project_dir": "path/to/your/dbt/project",  # Replace with the path to your dbt project                 # Include other necessary dbt configuration here             }         }     } }  # Collect jobs in a repository @repository def my_repository():     return [my_single_dbt_model_job]
In this 
run_config
, the 
ops
section specifies the configuration for the 
run_single_dbt_model
operation, which includes the name of the dbt model to run. The 
resources
section includes the configuration for the 
dbt_cli_resource
, such as the 
project_dir
which should be set to the path of your dbt project. When you launch a run for the 
my_single_dbt_model_job
job, you would provide this 
run_config
to specify that you want to run the 
my_model
dbt model. You can launch the run through the Dagster UI or programmatically via the Dagster API. Please ensure that you replace 
"my_model"
with the actual name of your dbt model and 
"path/to/your/dbt/project"
with the correct path to your dbt project directory. Additionally, you may need to include other dbt configuration options specific to your setup.
c
@Scout how do I figure out what partitions have been materialized in a partitioned asset
s
To determine which partitions have been materialized for a partitioned asset in Dagster, you can use the DagsterInstance to query the materialization events for the asset. The documentation provides a method called 
get_event_records
that can be used to fetch materialization events, which you can filter by the asset key and event type. Here's a conceptual example of how you might write a function to find out which partitions have been materialized for a given asset:
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python from dagster import DagsterInstance, EventRecordsFilter, DagsterEventType, AssetKey  def get_materialized_partitions(asset_key):     instance = DagsterInstance.get()     materialization_events = instance.get_event_records(         EventRecordsFilter(             event_type=DagsterEventType.ASSET_MATERIALIZATION,             asset_key=asset_key,         )     )          # Extract the partition keys from the materialization events     materialized_partitions = {         event.dagster_event.partition for event in materialization_events         if event.is_dagster_event and event.dagster_event.partition     }          return materialized_partitions  # Example usage: # Define your asset key my_asset_key = AssetKey(["my_asset"])  # Get the set of materialized partitions materialized_partitions = get_materialized_partitions(my_asset_key) print("Materialized partitions:", materialized_partitions)
In this example, 
my_asset_key
should be replaced with the actual 
AssetKey
of your partitioned asset. The function 
get_materialized_partitions
will return a set of partition keys that have been materialized for the given asset. Please ensure that you have the appropriate context to access the Dagster instance, as this code assumes you are running within a Dagster environment where 
DagsterInstance.get()
can be called to retrieve the current instance.
c
@Scout write a sensor that looks for a dbt asset to be materialized
s
To write a sensor that triggers when a dbt asset is materialized, you can use the 
@asset_sensor
decorator provided by Dagster. This sensor will monitor materialization events for a specific asset key and can be configured to trigger a job or some other action when a materialization occurs. Here's an example of how you might define an asset sensor for a dbt asset:
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python from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  # Define the job that should be triggered by the asset sensor my_downstream_job = ...  @asset_sensor(asset_key=AssetKey("my_dbt_asset"), job=my_downstream_job) def my_dbt_asset_sensor(context: SensorEvaluationContext):     asset_event = context.latest_materialization     if asset_event:         # Optionally, you can use the materialization timestamp or other metadata         # from the asset_event to customize the run request or run config         timestamp = asset_event.timestamp          # Create a RunRequest to trigger the job         yield RunRequest(             run_key=f"my_dbt_asset_{timestamp}",             run_config={                 # Include any necessary run configuration here             },         )
In this example, replace 
"my_dbt_asset"
with the actual name of your dbt asset. The 
my_downstream_job
should be the job that you want to trigger when the dbt asset is materialized. The sensor function 
my_dbt_asset_sensor
checks for the latest materialization event of the asset and yields a 
RunRequest
to trigger the specified job. The 
run_key
is constructed using the materialization timestamp to ensure uniqueness, but you can customize it as needed. The 
run_config
can also be customized to provide any necessary configuration for the job being triggered. Please note that you will need to replace 
my_downstream_job
with the actual job you want to run when the dbt asset is materialized. Also, ensure that the 
run_config
is properly set up for your specific use case. Once you have defined the sensor, you need to include it in your repository definition so that Dagster can execute it:
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python from dagster import repository  @repository def my_repository():     return [my_downstream_job, my_dbt_asset_sensor]
This sensor will now monitor for materializations of the specified dbt asset and trigger the downstream job accordingly.
c
@Scout how can I create an asset sensor that triggers a DBT model to run
s
To create an asset sensor that triggers a dbt model to run, you can define a sensor using the 
@asset_sensor
decorator in Dagster. This sensor will monitor for materialization events of a particular asset and can trigger a dbt job that includes the specific dbt model you want to run. Here's a conceptual example based on the documentation:
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python from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  # Define the job that should be triggered by the asset sensor # This job should include the dbt model you want to run my_dbt_job = ...  @asset_sensor(asset_key=AssetKey("my_asset"), job=my_dbt_job) def my_dbt_model_sensor(context: SensorEvaluationContext):     asset_event = context.latest_materialization     if asset_event:         # Optionally, you can use the materialization timestamp or other metadata         # from the asset_event to customize the run request or run config         timestamp = asset_event.timestamp          # Create a RunRequest to trigger the dbt job         yield RunRequest(             run_key=f"my_dbt_model_{timestamp}",             run_config={                 # Specify the dbt model to run using the `models` parameter                 # in your dbt run config                 "ops": {                     "dbt_run": {                         "config": {                             "models": ["my_specific_dbt_model"]                         }                     }                 }             },         )
In this example, replace 
"my_asset"
with the actual name of the asset whose materialization should trigger the dbt model run. The 
my_dbt_job
should be the job that includes the dbt operation to run the dbt model. The 
my_dbt_model_sensor
function checks for the latest materialization event of the asset and yields a 
RunRequest
to trigger the dbt job with a specific run configuration. The 
run_config
is set up to include the 
models
parameter, which specifies the dbt model you want to run. Replace 
"my_specific_dbt_model"
with the actual name of your dbt model. Please note that you will need to replace 
my_dbt_job
with the actual job you want to run when the asset is materialized. Also, ensure that the 
run_config
is properly set up for your specific use case, including the correct dbt CLI configuration. Once you have defined the sensor, you need to include it in your repository definition so that Dagster can execute it:
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python from dagster import repository  @repository def my_repository():     return [my_dbt_job, my_dbt_model_sensor]
This sensor will now monitor for materializations of the specified asset and trigger the dbt job to run the specific dbt model accordingly.
c
@Scout how do I create a sensor that looks at a partitioned asset and only triggers after the last partition is materialized
s
To create a sensor that looks at a partitioned asset and only triggers after the last partition is materialized, you can use the 
@multi_asset_sensor
decorator provided by Dagster. This sensor can monitor multiple assets or partitions and trigger a job only when certain conditions are met, such as all partitions being materialized. Here's a conceptual example based on the documentation:
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python from dagster import multi_asset_sensor, RunRequest, AssetKey, MultiAssetSensorEvaluationContext  # Define the job that should be triggered by the asset sensor # This job should be the one you want to run after the last partition is materialized my_job = ...  @multi_asset_sensor(monitored_assets=[AssetKey("my_partitioned_asset")], job=my_job) def my_partitioned_asset_sensor(context: MultiAssetSensorEvaluationContext):     # Get the latest materialization records for each partition     materialization_records = context.latest_materialization_records_by_partition(AssetKey("my_partitioned_asset"))      # Check if all expected partitions have been materialized     expected_partitions = [...]  # List of expected partition keys     if all(partition in materialization_records for partition in expected_partitions):         # All expected partitions have been materialized, trigger the job         yield RunRequest(             run_key="run_after_all_partitions_materialized",             run_config={                 # Include any necessary run configuration here             },         )
In this example, replace 
"my_partitioned_asset"
with the actual name of your partitioned asset. The 
expected_partitions
should be a list of partition keys that you expect to be materialized before triggering the job. The 
my_partitioned_asset_sensor
function checks if all expected partitions have materialization records, and if so, it yields a 
RunRequest
to trigger the job. Please note that you will need to replace 
my_job
with the actual job you want to run when all partitions are materialized. Also, ensure that the 
expected_partitions
list accurately reflects the partitions you are monitoring. Once you have defined the sensor, you need to include it in your repository definition so that Dagster can execute it:
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python from dagster import repository  @repository def my_repository():     return [my_job, my_partitioned_asset_sensor]
This sensor will now monitor for materializations of the specified partitioned asset and trigger the job only after the last partition has been materialized.
c
@Scout can I add a list of values to the context to use in another asset?
s
In Dagster, you can pass values between assets by defining dependencies between them. When one asset depends on another, the output of the upstream asset is passed as an input to the downstream asset. This is done by defining the dependencies in the 
@asset
decorator using the 
ins
parameter. Here's an example of how you might define assets with dependencies:
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python from dagster import asset, AssetIn  # Define an upstream asset that produces a list of values @asset def upstream_asset():     return [1, 2, 3]  # Define a downstream asset that takes the list of values from the upstream asset @asset(ins={"upstream_values": AssetIn()}) def downstream_asset(upstream_values):     # Use the list of values from the upstream asset     return sum(upstream_values)
In this example, 
downstream_asset
depends on 
upstream_asset
, and the list of values produced by 
upstream_asset
is passed to 
downstream_asset
as the 
upstream_values
input. If you want to add additional context or metadata to be used by another asset, you can use the 
AssetExecutionContext
within the asset function to access the context and pass metadata or other information to downstream assets. Here's an example of using the context within an asset:
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python from dagster import asset, AssetExecutionContext  @asset def asset_with_context(context: AssetExecutionContext):     # You can access the context to log information, access resources, etc.     <http://context.log.info|context.log.info>("Producing a list of values.")     # You can also add metadata or other context-related information here     return [1, 2, 3]  @asset def another_asset(values):     # Use the values from the upstream asset     return sum(values)
In this example, 
asset_with_context
uses the 
AssetExecutionContext
to log information, and it could also use the context to add metadata or access resources. The values produced by 
asset_with_context
are then passed to 
another_asset
. Please note that while you can pass data directly between assets, the context itself is not passed between assets. Instead, the context is used within each asset to access execution-related information and capabilities. If you need to pass additional metadata or configuration, you should include it in the data produced by the assets or use resources to share common configuration or state.
c
@Scout how do I pass a list of values between sensors
s
In Dagster, sensors are used to monitor external systems and trigger runs based on changes in those systems. Sensors do not directly pass data between each other; instead, they create 
RunRequest
objects that can include run configuration data to parameterize the runs they trigger. If you want to pass a list of values from a sensor to a job or asset, you can include that list in the 
run_config
of the 
RunRequest
. The job or asset can then access this configuration when it executes. Here's an example of how you might create a sensor that includes a list of values in the `run_config`:
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python from dagster import sensor, RunRequest, repository, job  @job def my_job():     # Job definition that will use the list of values from the sensor     pass  @sensor(job=my_job) def my_sensor(context):     # Define the list of values you want to pass to the job     list_of_values = [1, 2, 3, 4, 5]      # Create a RunRequest with a run_config that includes the list of values     run_request = RunRequest(         run_key=context.cursor,         run_config={             "ops": {                 "my_op": {  # Replace "my_op" with the actual op name in your job                     "config": {                         "list_of_values": list_of_values                     }                 }             }         }     )     yield run_request  @repository def my_repository():     return [my_job, my_sensor]
In this example, 
my_sensor
creates a 
RunRequest
that includes a 
run_config
with a list of values under the 
ops
configuration for an operation named 
my_op
. When the sensor triggers a run of 
my_job
, the job will receive this configuration and can access the list of values. Please note that you need to replace 
"my_op"
with the name of the operation in your job that should receive the list of values. The operation should be defined to accept configuration that includes the 
list_of_values
field. Keep in mind that sensors are not designed to pass data between each other but to trigger runs based on external events. The pattern shown here is for passing configuration data from a sensor to a job at the time the run is triggered.
In Dagster, sensors are used to monitor external systems and trigger runs based on changes in those systems. Sensors do not directly pass data between each other; instead, they yield 
RunRequest
objects that can trigger pipeline runs with a specific configuration. If you want to pass a list of values from one sensor to another, you would typically do this by triggering a run with a specific configuration that includes the values you want to pass. These values can be included in the 
run_config
of the 
RunRequest
and then accessed within the pipeline run. Here's a conceptual example of how you might create a sensor that triggers a run with a list of values:
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python from dagster import sensor, RunRequest  @sensor(job=my_job) def my_sensor():     # Define the list of values you want to pass to the pipeline run     values_to_pass = [1, 2, 3]      # Create a RunRequest with a run_config that includes the list of values     yield RunRequest(         run_key="unique_run_key",  # Unique identifier for this run request         run_config={             "solids": {                 "my_solid": {                     "config": {                         "my_values": values_to_pass                     }                 }             }         }     )
In this example, 
my_job
is the job that will be triggered by the sensor, and 
my_solid
is a solid within that job that expects a configuration field called 
my_values
. The 
values_to_pass
list is included in the 
run_config
under the appropriate solid's config. When the sensor triggers a run of 
my_job
, the 
my_solid
solid within that job will receive the 
values_to_pass
list as part of its configuration and can use it during execution. Please note that this example is conceptual, and you will need to adapt the code to fit your specific use case, including the correct job and solid names, as well as the structure of your 
run_config
. Sensors themselves do not have a mechanism for directly passing data to each other; they are designed to observe external conditions and trigger runs accordingly.
c
@Scout how do I set DagsterInstance.ephemeral() for local testing
s
To set up a 
DagsterInstance.ephemeral()
for local testing, you can use the 
DagsterInstance.ephemeral()
method to create an in-memory instance of Dagster that is suitable for ephemeral execution. This is particularly useful in test contexts where you don't want to persist any data or configuration to disk. Here's an example of how you might use 
DagsterInstance.ephemeral()
in a test:
Copy code
python from dagster import DagsterInstance  def test_my_thing():     # Create an ephemeral Dagster instance     with DagsterInstance.ephemeral() as instance:         # Your test code here, using the ephemeral instance         ...
When using 
DagsterInstance.ephemeral()
, the instance is created for the duration of the context manager block and is automatically cleaned up when the block exits. This means that any runs, events, or other data associated with the instance are only stored in memory and are not persisted after the test completes. This approach is helpful when writing unit tests for Dagster code, as it allows you to test your pipelines, solids, and assets without affecting your actual Dagster instance or requiring any external resources like a database. Remember to replace 
...
with your actual test code that interacts with the ephemeral Dagster instance.
c
@Scout how do I build a job for dbt assets
s
To build a job for dbt assets in Dagster, you can use the 
define_asset_job
function provided by Dagster. This function allows you to define a job that materializes a set of assets, which can include dbt assets loaded into Dagster. Here's a step-by-step guide on how to build a job for dbt assets: 1. Load dbt assets into Dagster: Use the 
load_assets_from_dbt_project
or 
load_assets_from_dbt_manifest
function provided by the 
dagster-dbt
library to load your dbt models as Dagster assets. 2. Define a job for dbt assets: Use the 
define_asset_job
function to create a job that materializes the dbt assets. You can specify which assets to include in the job using 
AssetSelection
. 3. Register the job in your repository: Include the job in your repository definition so that Dagster can execute it. Here's an example of how you might do this:
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python from dagster import define_asset_job, repository from dagster_dbt import load_assets_from_dbt_project, dbt_cli_resource  # Load dbt assets into Dagster dbt_assets = load_assets_from_dbt_project(     project_dir='path/to/your/dbt/project',     profiles_dir='path/to/your/dbt/profiles', )  # Define a job that materializes all dbt assets dbt_assets_job = define_asset_job(     name='dbt_assets_job',     selection=dbt_assets,     resource_defs={'dbt': dbt_cli_resource.configured({'project_dir': 'path/to/your/dbt/project'})}, )  # Collect jobs in a repository @repository def my_repository():     return [dbt_assets_job]
In this example, replace 
'path/to/your/dbt/project'
and 
'path/to/your/dbt/profiles'
with the actual paths to your dbt project and profiles directories. The 
dbt_assets_job
is configured to materialize all dbt assets loaded into Dagster. Please note that the 
dbt_cli_resource
is configured with the path to your dbt project directory. You may need to configure additional settings for the 
dbt_cli_resource
based on your dbt project's requirements. This job can now be executed through the Dagster UI, CLI, or scheduled to run at specific intervals using Dagster's scheduling system.
c
@Scout how do I create a sensor that fills in the most recent missing partition
s
Creating a sensor that fills in the most recent missing partition involves writing a sensor that checks for the latest partition that should have been materialized and then triggers a run if that partition is missing. Here's a conceptual guide on how to create such a sensor: 1. Define your partitioned asset: Ensure that your asset is partitioned according to your desired partitioning scheme. 2. Write a sensor function: Create a sensor function that checks for the most recent partition that should be materialized and whether it has been materialized. 3. Create a 
RunRequest
: If the most recent partition is missing, the sensor should yield a 
RunRequest
to trigger a pipeline run that will materialize that partition. Here's an example of how you might implement this:
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python
from dagster import sensor, RunRequest, DagsterInstance, AssetKey
from datetime import datetime, timedelta

# Define the partitioned asset key
partitioned_asset_key = AssetKey("my_partitioned_asset")

# Define the sensor
@sensor(job=my_job)
def my_partition_sensor(context):
    instance = DagsterInstance.get()
    # Define the function to calculate the most recent partition
    def get_most_recent_partition():
        # This is an example and should be replaced with your actual logic
        # to calculate the most recent partition key
        today = datetime.today()
        return today.strftime("%Y-%m-%d")

    # Get the most recent partition key
    most_recent_partition = get_most_recent_partition()

    # Check if the most recent partition has been materialized
    materializations = instance.get_event_records(
        EventRecordsFilter(
            event_type=DagsterEventType.ASSET_MATERIALIZATION,
            asset_key=partitioned_asset_key,
        )
    )
    materialized_partitions = {
        event.dagster_event.partition for event in materializations
        if event.is_dagster_event and event.dagster_event.partition
    }

    # If the most recent partition is not materialized, yield a RunRequest
    if most_recent_partition not in materialized_partitions:
        yield RunRequest(
            run_key=f"fill_missing_partition_{most_recent_partition}",
            run_config={
                # Include any necessary run configuration here
                # For example, you might need to specify the partition to materialize
                "solids": {
                    "my_solid": {
                        "config": {
                            "partition_key": most_recent_partition
                        }
                    }
                }
            },
        )
In this example, 
my_job
is the job that will be triggered by the sensor to materialize the missing partition. The 
get_most_recent_partition
function is a placeholder an
d should be replaced with your actual logic to determine the most recent partition key based on your partitioning scheme. The 
run_config
in the 
RunRequest
should be configured to include any necessary configuration for the job to materialize the specific partition. The 
run_key
is constructed using the most recent partition key to ensure uniqueness. Please note that this example is conceptual, and you will need to adapt the code to fit your specific use case, including the correct job, asset key, and partition calculation logic.
c
@Scout t can I use graph operators in a run config in a sensor
s
As of my knowledge cutoff date in early 2023, the term "grap operated" does not appear to be a recognized concept or feature within the context of Dagster or its documentation. It's possible that there might be a typo or misunderstanding in the term "grap operated." If you are referring to specifying a graph operation in a 
run_config
within a sensor, in Dagster, you can indeed specify the configuration for a job's ops (or solids) when creating a 
RunRequest
in a sensor. A job in Dagster can be composed of ops or a graph of ops, and you can configure these ops in the 
run_config
to pass specific parameters or settings that they need to execute. Here's a conceptual example of how you might specify op configuration in a 
run_config
within a sensor:
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python from dagster import sensor, RunRequest  @sensor(job=my_job) def my_sensor(context):     # Define the configuration for the ops within the job     op_config = {         "ops": {             "my_op": {                 "config": {                     "param1": "value1",                     "param2": "value2",                 }             }         }     }      # Create a RunRequest with the specified op configuration     yield RunRequest(         run_key="unique_run_key",         run_config=op_config,     )
In this example, 
my_job
is the job that will be triggered by the sensor. The 
my_op
is an op within that job, and we are specifying its configuration in the 
run_config
dictionary under the 
ops
key. The 
param1
and 
param2
are hypothetical configuration parameters for 
my_op
. Please replace 
my_job
, 
my_op
, 
param1
, and 
param2
with the actual job, op, and configuration parameters relevant to your use case. If "grap operated" refers to something else or if you have additional context or a different question, please provide more details so I can assist you further.
In Dagster, you can specify configurations for graph operations (ops) within a 
run_config
when creating a 
RunRequest
in a sensor. This allows you to configure the behavior of the ops that will be executed when the sensor triggers a pipeline run. Here's an example of how you might include op configurations in a 
run_config
within a sensor:
Copy code
python from dagster import sensor, RunRequest  @sensor(job=my_job) def my_sensor(context):     # Define the configuration for the ops within the job     op_config = {         "ops": {             "my_op": {                 "inputs": {                     "input_name": {"value": "some_value"}                 },                 "config": {                     "param1": "value1",                     "param2": "value2",                 }             }         }     }      # Create a RunRequest with the specified op configuration     yield RunRequest(         run_key="unique_run_key",         run_config=op_config,     )
In this example, 
my_job
is the job that will be triggered by the sensor. The 
my_op
is an op within that job, and we are specifying its configuration in the 
run_config
dictionary under the 
ops
key. The 
input_name
is a hypothetical input to 
my_op
, and 
param1
and 
param2
are hypothetical configuration parameters for 
my_op
. Please replace 
my_job
, 
my_op
, 
input_name
, 
param1
, and 
param2
with the actual job, op, input, and configuration parameters relevant to your use case. When the sensor triggers a run of 
my_job
, the 
my_op
op within that job will receive the specified inputs and configuration parameters as part of its execution context. Keep in mind that the structure of the 
run_config
should match the expectations of the job and its ops. The configuration keys and values should correspond to the defined inputs and configuration schema of the ops in your job.
c
@Scout can I use graph operators in a sensor to trigger multiple dbt assets
s
In Dagster, sensors are used to monitor external conditions and trigger runs based on those conditions. If you want to trigger multiple asset computations from a sensor, you would typically do so by triggering a job that includes the asset computations you want to run. While you cannot directly use graph operators within a sensor, you can configure the 
run_config
in the 
RunRequest
yielded by a sensor to specify which assets or operations should be executed when the job is triggered. This is done by specifying the relevant op configurations or asset keys within the 
run_config
. Here's a conceptual example of how you might trigger multiple assets from a sensor:
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python from dagster import sensor, RunRequest  @sensor(job=my_assets_job) def my_sensor(context):     # Define the configuration for the job that includes the assets     run_config = {         "solids": {             "my_solid": {                 "config": {                     # Configuration specific to the solid that computes the asset                 }             }         },         "assets": {             "my_asset_1": {                 # Configuration specific to my_asset_1             },             "my_asset_2": {                 # Configuration specific to my_asset_2             },             # Add additional assets as needed         }     }      # Create a RunRequest with the specified run configuration     yield RunRequest(         run_key="unique_run_key",         run_config=run_config,     )
In this example, 
my_assets_job
is the job that includes the asset computations you want to trigger. The 
run_config
specifies the configuration for the solids or assets that are part of the job. When the sensor triggers a run of 
my_assets_job
, the specified assets will be computed according to the provided configuration. Please replace 
my_assets_job
, 
my_solid
, 
my_asset_1
, 
my_asset_2
, and the configuration details with the actual job, solids, assets, and configuration relevant to your use case. Remember that the 
run_config
should match the structure expected by your job and its assets. The configuration keys and values should correspond to the defined configuration schema of the assets and their associated computations.
In Dagster, you can use a sensor to trigger a job that runs multiple dbt assets. However, sensors themselves do not directly invoke graph operators or dbt assets. Instead, they create 
RunRequest
objects that specify the job to run and the 
run_config
to use, which can include configurations for dbt assets. To trigger multiple dbt assets from a sensor, you would typically have a job that includes the dbt assets you want to run. The sensor would then yield a 
RunRequest
for that job, and you can use the 
run_config
to specify which dbt models to run using the 
models
parameter in the dbt CLI configuration. Here's a conceptual example of how you might set up a sensor to trigger a job that runs multiple dbt assets:
Copy code
python from dagster import sensor, RunRequest  # Define the job that includes multiple dbt assets my_dbt_job = ...  @sensor(job=my_dbt_job) def my_dbt_sensor(context):     # Define the configuration for the dbt CLI to run specific models     run_config = {         "resources": {             "dbt": {                 "config": {                     "models": ["+my_first_dbt_model", "+my_second_dbt_model"]                 }             }         }     }      # Create a RunRequest with the specified dbt CLI configuration     yield RunRequest(         run_key="unique_run_key_for_dbt_assets",         run_config=run_config,     )
In this example, 
my_dbt_job
is the job that includes the dbt assets you want to run. The 
run_config
specifies the dbt CLI configuration to run the models 
my_first_dbt_model
and 
my_second_dbt_model
, including their dependencies (as indicated by the 
+
prefix). Please replace 
my_dbt_job
, 
my_first_dbt_model
, and 
my_second_dbt_model
with the actual job and model names relevant to your dbt project. When the sensor triggers a run of 
my_dbt_job
, the specified dbt models will be run according to the configuration provided in the 
run_config
. Keep in mind that the structure of the 
run_config
should match the expectations of the dbt CLI and your dbt project configuration. The 
models
parameter should list the dbt models you want to run, and you can use dbt's selection syntax to specify models and their dependencies.
In Dagster, you cannot directly use graph operators within a sensor. However, you can trigger a job from a sensor that includes multiple dbt assets. To do this, you would define a job that runs the dbt assets you want to materialize, and then use a sensor to trigger that job. Here's a conceptual example of how you might set up a sensor to trigger a job that materializes multiple dbt assets:
Copy code
python from dagster import sensor, RunRequest, repository from dagster_dbt import dbt_cli_resource  # Define a job that runs multiple dbt assets @job(resource_defs={"dbt": dbt_cli_resource}) def my_dbt_assets_job():     # Your dbt asset operations go here     ...  # Define a sensor that triggers the dbt assets job @sensor(job=my_dbt_assets_job) def my_dbt_assets_sensor(context):     # Logic to determine when to trigger the job     # For example, you might check for certain conditions or events     if should_trigger_dbt_assets():         yield RunRequest(             run_key="unique_run_key_for_this_trigger",             run_config={                 # You can specify run config for the dbt job here if needed             },         )  # Include the job and sensor in your repository @repository def my_repository():     return [my_dbt_assets_job, my_dbt_assets_sensor]
In this example, 
my_dbt_assets_job
is a job that includes the dbt asset operations you want to run. The 
my_dbt_assets_sensor
sensor checks for certain conditions using a hypothetical 
should_trigger_dbt_assets
function, and when those conditions are met, it yields a 
RunRequest
to trigger 
my_dbt_assets_job
. Please replace the placeholder code with the actual logic for your dbt assets and the conditions under which you want to trigger them. The 
run_config
in the 
RunRequest
can be used to pass any necessary configuration to the dbt job when it runs. Keep in mind that the 
run_key
should be unique for each trigger to avoid collisions with other runs. The 
run_config
should match the structure expected by your dbt job and its assets. The configuration keys and values should correspond to the defined configuration schema of the dbt assets and their associated computations.
c
@Scout Tell me more about the snowflake resource
s
The Snowflake resource in Dagster is a predefined resource that facilitates interactions with a Snowflake database. It provides a convenient way to execute queries and perform database operations within your Dagster jobs and assets. The Snowflake resource is part of the 
dagster-snowflake
library, which is an integration between Dagster and Snowflake. The Snowflake resource abstracts away the details of connecting to Snowflake, allowing you to focus on writing your data processing logic. It manages the lifecycle of the connection to the Snowflake database, ensuring that connections are properly opened and closed. Here's an example of how you might use the Snowflake resource in a Dagster asset:
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python
from dagster import asset
from dagster_snowflake import SnowflakeResource

@asset(required_resource_keys={"snowflake"})
def my_snowflake_asset(context):
    # Use the Snowflake resource to execute a query
    with context.resources.snowflake.get_connection() as conn:
        result = conn.cursor().execute("SELECT * FROM my_table").fetchall()
        # Do something with the result
        return result
In this example, the 
my_snowflake_asset
asset requires the Snowflake resource, which is specified by the 
required_resource_keys
parameter. Inside the asset function, the 
get_connection
method is used to obtain a connection to the Snowflake database, and a query is executed using that connection. To configure the Snowflake resource, you would typically provide the necessary connection parameters, such as account, user, password, database, schema, and warehouse. These parameters can be set directly or through environment variables for better security and flexibility. Here's an example of how you might configure the Snowflake resource in your repository definition:
Copy code
python
from dagster import repository
from dagster_snowflake import SnowflakeResource

@repository
def my_repository():
    return {
        "resources": {
            "snowflake": SnowflakeResource.configured({
                "account": "my_snowflake_account",
                "user": "my_snowflake_user",
                "password": "my_snowflake_password",
                "database": "my_snowflake_database",
                "schema": "my_snowflake_schema",
                "warehouse": "my_snowflake_warehouse",
            })
        },
        "assets": [my_snowflake_asset],
    }
In this repository definition, the Snowflake resource is configured with the necessary connection parameters. These parameters are then used by the 
my_snowflake_asset
asset when it is executed. The Snowflake resource provides a Pythonic way to interact with Snowflake, and it can be used in conjunction with other Dagster features, such as IO managers and asset materializations, to build rob
ust data pipelines that interact with Snowflake. Dagster Docs button snowflake_asset_io_manager button Add table and storage methods for "asset... button Using Dagster with Snowflake | Dagster D... button Thank you for your feedback!
c
@Scout how can i access tmp storage in an asset
s
In Dagster, if you want to access temporary storage within an asset, you can use Python's built-in 
tempfile
module to create temporary files or directories. These temporary files and directories are useful for storing intermediate data that you don't need to persist beyond the lifetime of the asset computation. Here's an example of how you might use 
tempfile
within an asset:
Copy code
python import tempfile from dagster import asset  @asset def my_asset_with_temp_storage():     # Create a temporary file     with tempfile.NamedTemporaryFile(mode='w+', delete=True) as tmp_file:         # Write some data to the temporary file         tmp_file.write('some temporary data')         tmp_file.flush()          # Read the data back from the temporary file         tmp_file.seek(0)         data = tmp_file.read()          # The temporary file is automatically deleted after exiting the block      # Do something with the data     return data
In this example, 
NamedTemporaryFile
is used to create a temporary file that is automatically deleted when the file is closed (when exiting the 
with
block). The 
delete=True
argument ensures that the file is deleted after use. You can write to and read from the temporary file as needed. If you need a temporary directory instead of a file, you can use 
tempfile.TemporaryDirectory
in a similar way:
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python import tempfile from dagster import asset  @asset def my_asset_with_temp_dir():     # Create a temporary directory     with tempfile.TemporaryDirectory() as tmp_dir:         # Use the temporary directory to store files or other data         tmp_file_path = os.path.join(tmp_dir, 'temp_file.txt')         with open(tmp_file_path, 'w') as tmp_file:             tmp_file.write('some temporary data')          # The temporary directory and all its contents are automatically deleted after exiting the block      # Continue with your asset logic     ...
In this example, 
TemporaryDirectory
is used to create a temporary directory that is automatically cleaned up when the context manager exits. Please note that temporary files and directories created with 
tempfile
are usually stored in the system's default temporary directory (e.g., 
/tmp
on Unix-like systems). The exact location can vary based on the operating system and environment configuration. If you need to know the path to the temporary file or directory, you can use the 
.name
attribute of the object returned by 
NamedTemporaryFile
or the string value provided to the context manager by 
TemporaryDirectory
.
c
@Scout how can I pass a dbt asset to a python asset for use in the python asset
s
To pass a dbt asset to a Python asset for use in the Python asset, you can define an upstream dependency relationship between the dbt asset and the Python asset. In Dagster, you can use the 
AssetIn
and 
AssetKey
classes to specify dependencies between assets. Here's a step-by-step guide on how to set up this relationship: 1. Define your dbt asset: Use the 
@dbt_assets
decorator to load your dbt models as Dagster assets. 2. Define your Python asset: Use the 
@asset
decorator to define your Python asset. Specify the dbt asset as a dependency using the 
ins
parameter. 3. Access the dbt asset in the Python asset: The dbt asset will be passed as an argument to the Python asset function, where you can use it. Here's a conceptual example:
Copy code
python from dagster import asset, AssetIn, AssetKey from dagster_dbt import dbt_assets  # Load dbt models as Dagster assets @dbt_assets(...) def my_dbt_assets():     ...  # Define a Python asset that depends on a specific dbt model @asset(ins={"my_dbt_model": AssetIn(key=AssetKey("my_dbt_model"))}) def my_python_asset(my_dbt_model):     # Use the dbt model data in the Python asset     # Perform your Python logic here, using the dbt model data     ...
In this example, 
my_dbt_assets
is a function decorated with 
@dbt_assets
that loads dbt models as Dagster assets. 
my_python_asset
is a Python asset that depends on the dbt model named 
my_dbt_model
. The 
AssetIn
object specifies that 
my_python_asset
has an input asset with the key 
AssetKey("my_dbt_model")
, which corresponds to the dbt model you want to use. When you execute 
my_python_asset
, Dagster will ensure that 
my_dbt_model
is materialized first, and its output will be passed to 
my_python_asset
as an argument. Please note that you need to replace 
"my_dbt_model"
with the actual name of your dbt model, and you should define the 
@dbt_assets
function according to your dbt project's configuration. Additionally, you may need to configure an IO manager to handle how the output of the dbt asset is stored and passed to the Python asset. The IO manager will be responsible for loading the materialized dbt model data into a format that your Python asset can use, such as a Pandas DataFrame or a CSV file.
c
@Scout can I use a variable set outside an asset in an asset
s
In Dagster, assets are designed to be self-contained and deterministic, meaning that their computations should ideally depend only on their inputs and not on external state. However, there are scenarios where you might want to use external configuration or context within an asset. To achieve this, you can use Dagster's configuration system to pass values to assets at runtime. Here's how you can pass a variable to an asset using Dagster's configuration system: 1. Define the asset with configuration: Use the 
@asset
decorator to define your asset and specify a configuration schema if needed. 2. Access the configuration within the asset: Within the asset function, you can access the configuration values through the context parameter. 3. Provide the configuration when materializing the asset: When you materialize the asset, you can provide the configuration values, which can include the variable you want to use. Here's a conceptual example:
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python from dagster import asset, Field  # Define an asset that expects a configuration value @asset(config_schema={"external_variable": Field(str)}) def my_asset(context):     # Access the external variable from the asset's configuration     external_variable = context.op_config["external_variable"]     # Use the external variable in your asset logic     ...
In this example, 
my_asset
is defined with a configuration schema that expects a field named 
external_variable
. Within the asset function, you can access this configuration value through 
context.op_config
. When you materialize the asset, you can provide the configuration, including the value for `external_variable`:
Copy code
python result = my_asset.materialize(     run_config={         "ops": {             "my_asset": {                 "config": {                     "external_variable": "value_from_outside"                 }             }         }     } )
Replace 
"value_from_outside"
with the actual value you want to pass to the asset. Please note that while you can use external variables in this way, it's important to manage them carefully to maintain the reproducibility and clarity of your asset computations. Using environment variables, resource configurations, or other Dagster mechanisms can help manage external dependencies in a more controlled manner.
c
@Scout why am I getting this Skipping step user_streak_s3 due to skipped dependencies: ['my_dbt_assets.model_dbt_consumer_app_fct_user_streak'].
s
The message "Skipping step user_streak_s3 due to skipped dependencies: ['my_dbt_assets.model_dbt_consumer_app_fct_user_streak']" indicates that Dagster has decided to skip the execution of the 
user_streak_s3
step because one of its dependencies, specifically the 
my_dbt_assets.model_dbt_consumer_app_fct_user_streak
asset, has been skipped. In Dagster, steps or assets can be skipped for several reasons: 1. Conditional Execution: If the execution logic of your pipeline includes conditions that determine whether certain steps should run, and those conditions evaluate to 
False
, the dependent steps will be skipped. 2. Upstream Failures: If an upstream step or asset fails or is skipped, all downstream steps that depend on it will also be skipped to prevent execution with incomplete or incorrect data. 3. Unmet Expectations: If an asset has expectations or checks that are not met, it may result in the asset being skipped, along with any downstream assets that depend on it. 4. Missing Inputs: If an asset or step expects inputs that are not provided or generated by upstream steps, it will be skipped. To resolve this issue, you should investigate why the 
my_dbt_assets.model_dbt_consumer_app_fct_user_streak
asset is being skipped. Here are some steps you can take: - Check Upstream Assets: Ensure that all upstream assets that the 
my_dbt_assets.model_dbt_consumer_app_fct_user_streak
asset depends on have been successfully materialized. - Review Asset Definitions: Look at the definitions of the skipped asset and its dependencies to ensure they are configured correctly and that any conditions or expectations are met. - Examine Logs: Review the logs for the run to find any error messages or warnings that occurred before the skip message. This can provide clues as to why the asset was skipped. - Check Configuration: Verify that the 
run_config
provided for the run includes all necessary configurations for the assets and that any required inputs are available. - Review Code Changes: If the skipping behavior started after a code change, review recent changes to the asset definitions or pipeline code to identify any modifications that could lead to skipped executions. By investigating these areas, you should be able to determine the cause of the skipped asset and take appropriate action to ensure that it and its downstream dependencies are executed as expected. If the issue persists, you may want to reach out to the Dagster community or support channels for further assistance.
c
@Scout sensor that looks at a dbt asset to trigger a python asset
s
To create a sensor that monitors a dbt asset and triggers a Python asset in Dagster, you can use the 
@asset_sensor
decorator. This sensor will monitor the materialization events of the dbt asset and trigger a job that includes the Python asset when the dbt asset is materialized. Here's a step-by-step guide on how to set this up: 1. Define your dbt asset: Use the 
@dbt_assets
decorator to load your dbt models as Dagster assets. 2. Define your Python asset: Use the 
@asset
decorator to define your Python asset. 3. Define a job that includes the Python asset: Create a job that includes the Python asset. 4. Create a sensor that monitors the dbt asset: Use the 
@asset_sensor
decorator to define a sensor that monitors the dbt asset and triggers the job with the Python asset. Here's an example:
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python
from dagster import asset, AssetKey, asset_sensor, RunRequest, SensorEvaluationContext, job
from dagster_dbt import dbt_assets

# Step 1: Define your dbt asset
@dbt_assets(
    manifest_path="path/to/your/dbt/manifest.json",
    select="model_dbt_consumer_app_fct_user_streak"
)
def my_dbt_assets():
    pass

# Step 2: Define your Python asset
@asset
def my_python_asset(context, dbt_output):
    # Use the dbt_output in your Python asset logic
    <http://context.log.info|context.log.info>(f"Received dbt output: {dbt_output}")
    # Perform your Python logic here
    return dbt_output

# Step 3: Define a job that includes the Python asset
@job
def my_python_job():
    my_python_asset()

# Step 4: Create a sensor that monitors the dbt asset
@asset_sensor(asset_key=AssetKey("model_dbt_consumer_app_fct_user_streak"), job=my_python_job)
def my_dbt_asset_sensor(context: SensorEvaluationContext, asset_event):
    # Extract the dbt output from the asset event
    dbt_output = asset_event.dagster_event.step_materialization_data.materialization.metadata_entries
    # Trigger the job with the Python asset
    yield RunRequest(
        run_key=context.cursor,
        run_config={
            "ops": {
                "my_python_asset": {
                    "inputs": {
                        "dbt_output": dbt_output
                    }
                }
            }
        }
    )
In this example: - 
my_dbt_assets
is a function decorated with 
@dbt_assets
that loads the dbt model 
model_dbt_consumer_app_fct_user_streak
as a Dagster asset. - 
my_python_asset
is a Python asset that takes the output of the dbt asset as an input. - 
my_python_job
is a job that includes the 
my_python_asset
. - 
my_dbt_asset_sensor
is a sensor that monitors the materialization of the dbt asset 
model_dbt_consumer_app_fct_user_streak
and triggers 
my_python_job
when the dbt asset is materialized. Make sure to replace 
"path/to/your/dbt/manifest.json"
with the actual path
to your dbt manifest file and adjust the asset names and paths as needed. This setup ensures that whenever the dbt asset 
model_dbt_consumer_app_fct_user_streak
is materialized, the sensor will trigger the job that runs the Python asset 
my_python_asset
, passing the dbt output to it. Sensor to monitor OpExecutionContext for... button How to Trigger a Job with Sensors Based ... button Asset Sensors | Dagster Docs button allow sensors to directly target softwar... button Thank you for your feedback!
c
@Scout how can I get the most recent partition in a partitioned job that has been materialized
s
To get the most recent partition in a partitioned job that has been materialized, you can use the DagsterInstance to query the materialization events for the asset. Specifically, you can use the 
get_latest_materialization_event
method to fetch the latest materialization event for each partition and then determine the most recent one. Here's a step-by-step guide on how to achieve this: 1. Define your partitioned asset: Ensure that your asset is partitioned using a 
PartitionsDefinition
. 2. Query the latest materialization events: Use the DagsterInstance to fetch the latest materialization events for the asset. 3. Determine the most recent partition: Extract the partition keys from the materialization events and determine the most recent one. Here's an example of how you might implement this:
Copy code
python
from dagster import DagsterInstance, AssetKey, DailyPartitionsDefinition, asset
from datetime import datetime

# Step 1: Define your partitioned asset
partitions_def = DailyPartitionsDefinition(start_date="2023-01-01")

@asset(partitions_def=partitions_def)
def my_partitioned_asset():
    # Asset logic here
    pass

# Step 2: Query the latest materialization events
def get_most_recent_materialized_partition(asset_key):
    instance = DagsterInstance.get()
    materialization_events = instance.get_event_records(
        EventRecordsFilter(
            event_type=DagsterEventType.ASSET_MATERIALIZATION,
            asset_key=asset_key,
        )
    )

    # Step 3: Determine the most recent partition
    materialized_partitions = [
        event.dagster_event.partition for event in materialization_events
        if event.is_dagster_event and event.dagster_event.partition
    ]

    if not materialized_partitions:
        return None

    # Assuming partition keys are date strings, sort them to find the most recent one
    most_recent_partition = max(materialized_partitions, key=lambda p: datetime.strptime(p, "%Y-%m-%d"))
    return most_recent_partition

# Example usage
asset_key = AssetKey("my_partitioned_asset")
most_recent_partition = get_most_recent_materialized_partition(asset_key)
print(f"The most recent materialized partition is: {most_recent_partition}")
In this example: - 
my_partitioned_asset
is defined with a daily partitioning scheme starting from January 1, 2023. - The 
get_most_recent_materialized_partition
function queries the latest materialization events for the specified asset key and extracts the partition keys. - The function then determines the most recent partition by sorting the partition keys (assuming they are date strings in the format 
%Y-%m-%d
). Replace 
"my_partitioned_asset"
with the actual name of your partitioned asset and adjust the partition key format if necessary. This approach ensures t
hat you can determine the most recent partition that has been materialized for a partitioned asset in Dagster.
c
@Scout sensor that looks at a dbt asset to trigger a partitioned asset to backfill the latest partition
s
To create a sensor that monitors a dbt asset and triggers a partitioned asset to backfill the latest partition, you can use the 
@asset_sensor
decorator. This sensor will monitor the materialization events of the dbt asset and trigger a job that includes the partitioned asset when the dbt asset is materialized. Here's a step-by-step guide on how to set this up: 1. Define your dbt asset: Use the 
@dbt_assets
decorator to load your dbt models as Dagster assets. 2. Define your partitioned asset: Use the 
@asset
decorator to define your partitioned asset with a partitioning scheme. 3. Define a job that includes the partitioned asset: Create a job that includes the partitioned asset. 4. Create a sensor that monitors the dbt asset: Use the 
@asset_sensor
decorator to define a sensor that monitors the dbt asset and triggers the job with the partitioned asset, specifying the latest partition to backfill. Here's an example:
Copy code
python
from dagster import asset, AssetKey, asset_sensor, RunRequest, SensorEvaluationContext, job, DailyPartitionsDefinition
from dagster_dbt import dbt_assets
from datetime import datetime

# Step 1: Define your dbt asset
@dbt_assets(
    manifest_path="path/to/your/dbt/manifest.json",
    select="model_dbt_consumer_app_fct_user_streak"
)
def my_dbt_assets():
    pass

# Step 2: Define your partitioned asset
partitions_def = DailyPartitionsDefinition(start_date="2023-01-01")

@asset(partitions_def=partitions_def)
def my_partitioned_asset(context, partition_key: str):
    # Use the partition_key in your asset logic
    <http://context.log.info|context.log.info>(f"Processing partition: {partition_key}")
    # Perform your partitioned asset logic here
    ...

# Step 3: Define a job that includes the partitioned asset
@job
def my_partitioned_job():
    my_partitioned_asset()

# Step 4: Create a sensor that monitors the dbt asset
@asset_sensor(asset_key=AssetKey("model_dbt_consumer_app_fct_user_streak"), job=my_partitioned_job)
def my_dbt_asset_sensor(context: SensorEvaluationContext, asset_event):
    # Extract the latest partition key from the dbt asset materialization
    latest_partition_key = datetime.now().strftime("%Y-%m-%d")  # Example: use the current date as the partition key

    # Trigger the job with the partitioned asset, specifying the latest partition to backfill
    yield RunRequest(
        run_key=f"backfill_partition_{latest_partition_key}",
        run_config={
            "ops": {
                "my_partitioned_asset": {
                    "config": {
                        "partition_key": latest_partition_key
                    }
                }
            }
        }
    )
In this example: - 
my_dbt_assets
is a function decorated with 
@dbt_assets
that loads the dbt model `model_dbt_consumer_app_fc
t_user_streak` as a Dagster asset. - 
my_partitioned_asset
is a partitioned asset defined with a daily partitioning scheme starting from January 1, 2023. - 
my_partitioned_job
is a job that includes the 
my_partitioned_asset
. - 
my_dbt_asset_sensor
is a sensor that monitors the materialization of the dbt asset 
model_dbt_consumer_app_fct_user_streak
and triggers 
my_partitioned_job
when the dbt asset is materialized. The sensor specifies the latest partition to backfill using the current date as an example partition key. Make sure to replace 
"path/to/your/dbt/manifest.json"
with the actual path to your dbt manifest file and adjust the asset names, paths, and partition key logic as needed. This setup ensures that whenever the dbt asset 
model_dbt_consumer_app_fct_user_streak
is materialized, the sensor will trigger the job that runs the partitioned asset 
my_partitioned_asset
, specifying the latest partition to backfill. how to get partition info from an asset ... button Asset reconciliation sensor is triggerin... button How do I use a sensor with a partition d... button Best way for an asset_sensor to trigger ... button Thank you for your feedback!
c
@Scout I got this error in an asset sensor how do I fix it 
dagster._core.errors.DagsterInvariantViolationError: Cannot access partition_key for a non-partitioned run
s
The error 
dagster._core.errors.DagsterInvariantViolationError: Cannot access partition_key for a non-partitioned run
indicates that the code is trying to access a 
partition_key
in a context where the run is not partitioned. This typically happens when you attempt to access the 
partition_key
in a run that does not have partitioning configured. To fix this error, you need to ensure that the run configuration includes the necessary partition key when triggering the partitioned asset. Here’s how you can address this issue step-by-step: 1. Ensure the Partitioned Asset is Correctly Defined: Make sure your partitioned asset is defined with a partitioning scheme. 2. Modify the Sensor to Include the Partition Key: When creating a 
RunRequest
in the sensor, ensure that you specify the 
partition_key
in the 
run_config
. 3. Check the Job Configuration: Ensure that the job configuration correctly passes the partition key to the partitioned asset. Here's an example of how you might modify the sensor to correctly pass the partition key:
Copy code
python
from dagster import asset, AssetKey, asset_sensor, RunRequest, SensorEvaluationContext, job, DailyPartitionsDefinition
from dagster_dbt import dbt_assets
from datetime import datetime

# Define your dbt asset
@dbt_assets(
    manifest_path="path/to/your/dbt/manifest.json",
    select="model_dbt_consumer_app_fct_user_streak"
)
def my_dbt_assets():
    pass

# Define your partitioned asset
partitions_def = DailyPartitionsDefinition(start_date="2023-01-01")

@asset(partitions_def=partitions_def)
def my_partitioned_asset(context, partition_key: str):
    # Use the partition_key in your asset logic
    <http://context.log.info|context.log.info>(f"Processing partition: {partition_key}")
    # Perform your partitioned asset logic here
    ...

# Define a job that includes the partitioned asset
@job
def my_partitioned_job():
    my_partitioned_asset()

# Create a sensor that monitors the dbt asset
@asset_sensor(asset_key=AssetKey("model_dbt_consumer_app_fct_user_streak"), job=my_partitioned_job)
def my_dbt_asset_sensor(context: SensorEvaluationContext, asset_event):
    # Determine the latest partition key (this example uses the current date)
    latest_partition_key = datetime.now().strftime("%Y-%m-%d")

    # Trigger the job with the partitioned asset, specifying the latest partition to backfill
    yield RunRequest(
        run_key=f"backfill_partition_{latest_partition_key}",
        run_config={
            "ops": {
                "my_partitioned_asset": {
                    "config": {
                        "partition_key": latest_partition_key
                    }
                }
            }
        },
        partition_key=latest_partition_key  # Ensure the partition_key is specified here
    )
In this examp
le: - The 
my_partitioned_asset
is defined with a daily partitioning scheme. - The 
my_dbt_asset_sensor
sensor monitors the materialization of the dbt asset and triggers 
my_partitioned_job
when the dbt asset is materialized. - The 
RunRequest
includes the 
partition_key
in both the 
run_config
and the 
partition_key
parameter to ensure that the partitioned asset receives the correct partition key. Make sure to replace 
"path/to/your/dbt/manifest.json"
with the actual path to your dbt manifest file and adjust the asset names, paths, and partition key logic as needed. By ensuring that the 
partition_key
is correctly specified in the 
RunRequest
, you can avoid the 
DagsterInvariantViolationError
and ensure that the partitioned asset is correctly triggered with the appropriate partition key.
c
@Scout how do I test an asset sensor
s
Testing an asset sensor in Dagster involves creating a test context and simulating the conditions under which the sensor would normally run. This allows you to verify that the sensor behaves as expected without needing to trigger actual asset materializations. Here's a step-by-step guide on how to test an asset sensor: 1. Create a Test Instance: Use 
DagsterInstance.ephemeral()
to create an in-memory instance for testing. 2. Build the Sensor Context: Use 
build_sensor_context
to create a context for the sensor evaluation. 3. Invoke the Sensor: Call the sensor function with the test context and evaluate the results. Here's an example of how you might write a unit test for an asset sensor: ```python from dagster import DagsterInstance, build_sensor_context, materialize_to_memory, asset, AssetKey, asset_sensor, RunRequest, SensorEvaluationContext, job from dagster_dbt import dbt_assets # Define your dbt asset @dbt_assets( manifest_path="path/to/your/dbt/manifest.json", select="model_dbt_consumer_app_fct_user_streak" ) def my_dbt_assets(): pass # Define your partitioned asset @asset def my_partitioned_asset(context, partition_key: str): context.log.info(f"Processing partition: {partition_key}") return partition_key # Define a job that includes the partitioned asset @job def my_partitioned_job(): my_partitioned_asset() # Create a sensor that monitors the dbt asset @asset_sensor(asset_key=AssetKey("model_dbt_consumer_app_fct_user_streak"), job=my_partitioned_job) def my_dbt_asset_sensor(context: SensorEvaluationContext, asset_event): latest_partition_key = "2023-01-01" # Example partition key for testing yield RunRequest( run_key=f"backfill_partition_{latest_partition_key}", run_config={ "ops": { "my_partitioned_asset": { "config": { "partition_key": latest_partition_key } } } }, partition_key=latest_partition_key ) # Test the asset sensor def test_my_dbt_asset_sensor(): with DagsterInstance.ephemeral() as instance: # Materialize the dbt asset to create a materialization event materialize_to_memory([my_dbt_assets], instance=instance) # Build the sensor context context = build_sensor_context(instance=instance) # Invoke the sensor and collect the run requests run_requests = list(my_dbt_asset_sensor(context)) # Assert that the sensor produced the expected run request assert len(run_requests) == 1 assert run_requests[0].run_key == "backfill_partition_2023-01-01" assert run_requests[0].run_config["ops"]["my_partitioned_asset"]["config"]["partition_key"] == "2023-01-
01" # Run the test test_my_dbt_asset_sensor() ``` In this example: 1. Define the Assets and Sensor: The dbt asset, partitioned asset, and sensor are defined as described in the previous examples. 2. Create a Test Instance: The test uses 
DagsterInstance.ephemeral()
to create an in-memory instance for testing. 3. Materialize the dbt Asset: The 
materialize_to_memory
function is used to create a materialization event for the dbt asset. This simulates the condition that the sensor is monitoring. 4. Build the Sensor Context: The 
build_sensor_context
function creates a context for the sensor evaluation. 5. Invoke the Sensor: The sensor function is called with the test context, and the resulting 
RunRequest
objects are collected. 6. Assert the Results: The test asserts that the sensor produced the expected 
RunRequest
, including the correct 
run_key
and 
partition_key
. Make sure to replace 
"path/to/your/dbt/manifest.json"
with the actual path to your dbt manifest file and adjust the asset names, paths, and partition key logic as needed. This approach allows you to test the behavior of your asset sensor in isolation, ensuring that it correctly triggers the expected runs based on the monitored asset's materialization events.
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