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Title
# ask-ai
c

Corbett Carrel

02/09/2024, 12:33 AM
@Scout how do I schedule a job to backfill a partitioned asset every 30 days and backfill the 30 missing partitions
s

Scout

02/09/2024, 12:33 AM
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

Corbett Carrel

02/09/2024, 12:36 AM
@Scout How do I find missing partitions in a partitioned asset
s

Scout

02/09/2024, 12:36 AM
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

Corbett Carrel

02/09/2024, 12:50 AM
@Scout how do I figure out the last time an asset was materialized?
s

Scout

02/09/2024, 12:50 AM
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

Corbett Carrel

02/12/2024, 3:29 PM
@Scout how do I create a run request for a DBT asset?
s

Scout

02/12/2024, 3:29 PM
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

Corbett Carrel

02/13/2024, 6:33 PM
@Scout how do I set a run request to run a single dbt model or asset?
s

Scout

02/13/2024, 6:33 PM
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

Corbett Carrel

02/13/2024, 7:26 PM
@Scout create a run_config to run a single dbt asset or model
s

Scout

02/13/2024, 7:26 PM
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

Corbett Carrel

02/13/2024, 8:25 PM
@Scout how do I figure out what partitions have been materialized in a partitioned asset
s

Scout

02/13/2024, 8:25 PM
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

Corbett Carrel

02/13/2024, 8:46 PM
@Scout write a sensor that looks for a dbt asset to be materialized
s

Scout

02/13/2024, 8:46 PM
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

Corbett Carrel

02/14/2024, 2:16 PM
@Scout how can I create an asset sensor that triggers a DBT model to run
s

Scout

02/14/2024, 2:16 PM
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

Corbett Carrel

02/14/2024, 2:32 PM
@Scout how do I create a sensor that looks at a partitioned asset and only triggers after the last partition is materialized
s

Scout

02/14/2024, 2:32 PM
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

Corbett Carrel

02/14/2024, 3:09 PM
@Scout can I add a list of values to the context to use in another asset?
s

Scout

02/14/2024, 3:09 PM
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

Corbett Carrel

02/14/2024, 3:10 PM
@Scout how do I pass a list of values between sensors
s

Scout

02/14/2024, 3:10 PM
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:
Copy code
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

Corbett Carrel

02/23/2024, 4:03 PM
@Scout how do I set DagsterInstance.ephemeral() for local testing
s

Scout

02/23/2024, 4:03 PM
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

Corbett Carrel

02/27/2024, 2:54 PM
@Scout how do I build a job for dbt assets
s

Scout

02/27/2024, 2:54 PM
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:
Copy code
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

Corbett Carrel

02/28/2024, 2:09 AM
@Scout how do I create a sensor that fills in the most recent missing partition
s

Scout

02/28/2024, 2:09 AM
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:
Copy code
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

Corbett Carrel

02/28/2024, 2:11 AM
@Scout t can I use graph operators in a run config in a sensor
s

Scout

02/28/2024, 2:12 AM
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:
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": {                 "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

Corbett Carrel

02/28/2024, 2:15 AM
@Scout can I use graph operators in a sensor to trigger multiple dbt assets
s

Scout

02/28/2024, 2:15 AM
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:
Copy code
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

Corbett Carrel

03/06/2024, 11:40 PM
@Scout Tell me more about the snowflake resource
s

Scout

03/06/2024, 11:40 PM
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:
Copy code
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

Corbett Carrel

04/01/2024, 4:53 PM
@Scout how can i access tmp storage in an asset
s

Scout

04/01/2024, 4:53 PM
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:
Copy code
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

Corbett Carrel

04/02/2024, 3:51 PM
@Scout how can I pass a dbt asset to a python asset for use in the python asset
s

Scout

04/02/2024, 3:51 PM
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.
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