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#ask-ai
Title
# ask-ai
t

Tara Larrue

02/13/2024, 1:58 AM
How do I define an IO manager to append scraped data to an existing postgres database table?
s

Scout

02/13/2024, 1:58 AM
To define an IO manager that appends scraped data to an existing PostgreSQL database table, you would typically create a custom IO manager that inherits from Dagster's
IOManager
base class. This custom IO manager would implement the
handle_output
method to append data to the database table and the
load_input
method to load data for downstream assets. However, the documentation does not provide a specific code example for a custom IO manager that appends data to a PostgreSQL table. Instead, it provides general guidance on how to work with IO managers and databases. For instance, it mentions that IO managers define the relationship between how data is saved as a table in the database and what in-memory data format to represent the data as. Dagster includes out-of-the-box IO managers for common databases, and when a DataFrame is returned in an asset’s definition, it is translated into a database-compatible format, and the IO manager runs a SQL query to write the data into the database. Here are the general steps you would follow to create a custom IO manager for appending data to a PostgreSQL table: 1. Define a custom IO manager class that inherits from
IOManager
. 2. Implement the
handle_output
method to append the data to the PostgreSQL table. You would use a library like
psycopg2
or
sqlalchemy
to connect to the PostgreSQL database and execute an
INSERT
or
COPY
command to append the data. 3. Implement the
load_input
method if you need to load data from the database for downstream assets. 4. Configure your custom IO manager in your Dagster repository so that it can be used by your assets. Here's a conceptual example based on the documentation's guidance:
Copy code
python
from dagster import IOManager, io_manager
import pandas as pd
import sqlalchemy

class PostgresAppendIOManager(IOManager):
    def handle_output(self, context, obj):
        # obj is expected to be a pandas DataFrame
        if not isinstance(obj, pd.DataFrame):
            raise ValueError("Output data must be a pandas DataFrame")

        # Use SQLAlchemy to connect to your PostgreSQL database
        engine = sqlalchemy.create_engine('<postgresql://user:password@host:port/dbname>')

        # Append data to the table
        obj.to_sql('your_table_name', engine, if_exists='append', index=False)

    def load_input(self, context):
        # Implement this method if you need to load data from the database
        pass

@io_manager
def postgres_append_io_manager():
    return PostgresAppendIOManager()

# Then, in your repository definition, you would include this IO manager:
from dagster import repository

@repository
def my_repository():
    return [postgres_append_io_manager, ...]  # Include your assets and pipelines
Please note that this is a conceptual example a
nd not a direct code snippet from the documentation. You would need to adapt this example to fit your specific use case, including handling database connections, credentials, and any specific logic related to your scraped data and database schema.
t

Tara Larrue

02/13/2024, 2:14 AM
@Scout can I parameterize an IOManager by a table name?
s

Scout

02/13/2024, 2:14 AM
Yes, you can parameterize an IOManager by a table name. This can be done by leveraging the metadata system in Dagster, which allows you to pass additional information to the IOManager through the
OutputDefinition
of an op or through the definition of an asset. When defining an op or asset, you can include metadata that specifies the table name. The IOManager can then access this metadata when storing or retrieving data, via the
OutputContext
. Here's a conceptual example based on the documentation's guidance:
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python from dagster import IOManager, OutputDefinition, Out, op, io_manager  class MyIOManager(IOManager):     def handle_output(self, context, obj):         table_name = context.metadata.get("table_name")         # Use table_name to parameterize the output handling logic         # ...      def load_input(self, context):         table_name = context.upstream_output.metadata.get("table_name")         # Use table_name to parameterize the input loading logic         # ...  @op(out=Out(metadata={"table_name": "my_table"})) def my_op(context):     # Perform some computation     result = ...     return result  @io_manager def my_io_manager():     return MyIOManager()  # In your repository definition, include this IO manager: from dagster import repository  @repository def my_repository():     return [my_io_manager, ...]  # Include your assets and pipelines
In this example, the
my_op
op includes metadata specifying the table name "my_table". The
MyIOManager
class then retrieves this table name from the
OutputContext
in the
handle_output
method and from the
InputContext
in the
load_input
method. This allows the IOManager to be parameterized by the table name for each specific output. Remember to adapt this example to your specific requirements, including the actual logic for interacting with the database and handling the data.
t

Tara Larrue

02/13/2024, 3:13 AM
@Scout how do I trigger an asset job based on the materialization of an upstream asset?
s

Scout

02/13/2024, 3:14 AM
To trigger an asset job based on the materialization of an upstream asset, you can use an asset sensor. Asset sensors in Dagster allow you to define logic that listens for materialization events of specific assets and triggers jobs in response. Here's a high-level overview of how you can set this up: 1. Define the assets with their dependencies, ensuring that the upstream and downstream relationships are correctly specified. 2. Create an asset job that includes the assets you want to materialize. 3. Define an asset sensor that monitors the upstream asset's materialization events and triggers the asset job when those events occur. Here's a conceptual example:
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python from dagster import asset, repository, AssetKey, asset_sensor, RunRequest  # Define your upstream asset @asset def upstream_asset():     # Your logic to produce the upstream asset     pass  # Define your downstream asset, which depends on the upstream asset @asset def downstream_asset(upstream_asset):     # Your logic to produce the downstream asset     pass  # Define an asset job that includes the downstream asset # Dagster will automatically include the upstream assets as well downstream_asset_job = define_asset_job(name="downstream_asset_job", selection=[downstream_asset])  # Define an asset sensor that triggers the downstream_asset_job when upstream_asset is materialized @asset_sensor(asset_key=AssetKey("upstream_asset"), job=downstream_asset_job) def upstream_asset_sensor(context):     upstream_events = context.latest_materialization_records()     if upstream_events:         # Optionally, you can check for specific conditions before triggering the job         return RunRequest(run_key=None)  # Returning a RunRequest triggers the job  # Include the assets and the sensor in your repository @repository def my_repository():     return [upstream_asset, downstream_asset, downstream_asset_job, upstream_asset_sensor]
In this example, the
upstream_asset_sensor
function is decorated with
@asset_sensor
, which specifies the
asset_key
of the upstream asset to monitor. When the upstream asset is materialized, the sensor function is invoked, and if the conditions are met (in this case, simply checking if there are any materialization events), a
RunRequest
is returned, which triggers the
downstream_asset_job
. Please adapt this example to fit your specific use case, including the actual logic for your assets and any additional conditions you might want to check before triggering the job.
t

Tara Larrue

02/13/2024, 3:22 AM
@Scout how would I modify if my upstream and downstream assets are both partitioned by the same partitions, and I only want to trigger partitions downstream if the matching partitions are materialized upstream?
s

Scout

02/13/2024, 3:22 AM
If your upstream and downstream assets are both partitioned by the same partitions, and you want to trigger downstream partitions only if the matching partitions are materialized upstream, you can use a
multi_asset_sensor
to monitor the materialization of the upstream partitions and trigger the downstream job for the corresponding partitions. Here's a conceptual example of how you might set this up:
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python from dagster import multi_asset_sensor, AssetKey, RunRequest, define_asset_job  # Define your partitioned assets and jobs as before  # Define a multi_asset_sensor that monitors the upstream asset's partitions @multi_asset_sensor(     monitored_assets=[AssetKey("upstream_asset")],     job=downstream_asset_job, ) def upstream_partition_sensor(context):     run_requests = []          # Get the latest materialization records for the monitored assets     for partition, materializations_by_asset in context.latest_materialization_records_by_partition_and_asset().items():         # Check if the upstream asset has been materialized for this partition         if context.has_materialization(AssetKey("upstream_asset"), partition):             # Trigger the downstream job for the matching partition             run_requests.append(RunRequest(run_key=partition, partition_key=partition))          return run_requests  # Include the assets, jobs, and sensor in your repository @repository def my_repository():     return [upstream_asset, downstream_asset, downstream_asset_job, upstream_partition_sensor]
In this example, the
upstream_partition_sensor
function is decorated with
@multi_asset_sensor
, which specifies the
monitored_assets
as a list containing the
AssetKey
of the upstream asset. The sensor monitors the materialization of these assets and, upon detecting a new materialization, checks if the corresponding partition of the upstream asset has been materialized. If it has, the sensor creates a
RunRequest
for the downstream job with the matching
partition_key
. Please note that this is a conceptual example, and you will need to adapt it to fit your specific use case, including the actual logic for your partitioned assets and the conditions under which you want to trigger the downstream job. Additionally, ensure that your assets are correctly defined with partitioning and that the job is set up to handle partitioned runs.
t

Tara Larrue

02/13/2024, 3:29 AM
@Scout I'm using dagster 1.6.3 and getting the error: AttributeError: 'MultiAssetSensorEvaluationContext' object has no attribute 'has_materialization'
s

Scout

02/13/2024, 3:29 AM
As of my knowledge cutoff date in early 2023, the
MultiAssetSensorEvaluationContext
object does not have a method called
has_materialization
. This means that the example provided in the previous message, which uses
context.has_materialization
, would not work as expected with Dagster version 1.6.3 or any other version available at that time. To trigger downstream partitions based on the materialization of matching upstream partitions, you would typically use the
latest_materialization_records_by_partition
method available on the
MultiAssetSensorEvaluationContext
object to check for the latest materializations of the upstream asset for each partition. Here's a revised conceptual example that uses `latest_materialization_records_by_partition`:
Copy code
python from dagster import multi_asset_sensor, AssetKey, RunRequest, define_asset_job  # Define your partitioned assets and jobs as before  # Define a multi_asset_sensor that monitors the upstream asset's partitions @multi_asset_sensor(     monitored_assets=[AssetKey("upstream_asset")],     job=downstream_asset_job, ) def upstream_partition_sensor(context):     run_requests = []          # Get the latest materialization records for the monitored assets     latest_materializations = context.latest_materialization_records_by_partition()          # Iterate over the partitions and check for materializations     for partition, materialization_record in latest_materializations.items():         if materialization_record:             # If there is a materialization record for the partition, trigger the downstream job             run_requests.append(RunRequest(run_key=partition, partition_key=partition))          return run_requests  # Include the assets, jobs, and sensor in your repository @repository def my_repository():     return [upstream_asset, downstream_asset, downstream_asset_job, upstream_partition_sensor]
In this revised example, the
upstream_partition_sensor
function uses
latest_materialization_records_by_partition
to get the latest materialization records for each partition. It then checks if there is a materialization record for each partition and, if so, creates a
RunRequest
for the downstream job with the matching
partition_key
. Please adapt this example to fit your specific use case and ensure that your assets are correctly defined with partitioning and that the job is set up to handle partitioned runs. If you encounter any issues or if the API has changed in a newer version of Dagster, please refer to the latest documentation or release notes for the most up-to-date guidance.
t

Tara Larrue

02/13/2024, 2:37 PM
@Scout how do I define a graph asset that is partitioned?
s

Scout

02/13/2024, 2:37 PM
In Dagster, you can define a graph asset that is partitioned by using the
@graph_asset
decorator and specifying a
partitions_def
argument. The
partitions_def
argument takes a
PartitionsDefinition
object, which defines how the asset is partitioned. Here's an example of how you might define a partitioned graph asset:
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python from dagster import graph, op, graph_asset, DailyPartitionsDefinition  # Define your ops @op def my_op():     # Your op logic here     pass  # Define your graph @graph def my_graph():     my_op()  # Define your graph asset with partitions @graph_asset(     partitions_def=DailyPartitionsDefinition(start_date="2023-01-01") ) def my_partitioned_graph_asset():     my_graph()
In this example,
DailyPartitionsDefinition
is used to define daily partitions starting from January 1, 2023. Each partition represents a day, and when you materialize the asset, you can specify which partition (i.e., which day) you want to materialize. Please note that the
@graph_asset
decorator and the concept of graph assets may not be available in all versions of Dagster. If you are using an older version of Dagster that does not support graph assets, you would typically define partitioned assets using the
@asset
decorator and specify the
partitions_def
argument there. Always refer to the latest Dagster documentation for the version you are using to ensure that you are using the correct APIs and decorators for defining partitioned assets. The APIs and features in Dagster can evolve over time, and the documentation will provide the most accurate and up-to-date information.
t

Tara Larrue

02/13/2024, 2:42 PM
@Scout I've defined a graph asset like this, but I'm getting an error with my
context
parameter, what could be wrong here?
Copy code
@graph_asset(partitions_def=input_data_sources_partitions_def)
def input_data_sources(context, config: ScrapeConfig) -> pd.DataFrame:
s

Scout

02/13/2024, 2:42 PM
The error you're encountering with the
context
parameter in your
graph_asset
definition is likely due to the fact that
graph_asset
does not accept a
context
parameter directly in its function signature. In Dagster, the
context
parameter is typically used within ops to access execution context information, but
graph_asset
is a higher-level abstraction that composes ops together and does not interact with the context in the same way. To fix the error, you should remove the
context
parameter from the
graph_asset
function signature. If you need to access context within the ops that make up the graph, you should include the
context
parameter in the individual op definitions instead. Here's a revised version of your code without the
context
parameter in the `graph_asset`:
Copy code
python from dagster import graph_asset, op import pandas as pd from some_module import ScrapeConfig, input_data_sources_partitions_def  # Define your op with the context parameter @op def scrape_op(context, config: ScrapeConfig) -> pd.DataFrame:     # Your scraping logic here, using context as needed     pass  # Define your graph asset without the context parameter @graph_asset(partitions_def=input_data_sources_partitions_def) def input_data_sources(config: ScrapeConfig) -> pd.DataFrame:     return scrape_op(config)
In this revised example, the
scrape_op
op is defined with a
context
parameter, which allows you to access the execution context within the op. The
input_data_sources
graph asset then composes this op without including a
context
parameter in its own function signature. Remember to adjust the logic within your op to use the
context
parameter as needed, and ensure that any resources or configurations required by the op are provided when the graph asset is executed.
t

Tara Larrue

02/13/2024, 2:44 PM
@Scout what happens if an asset conditionally returns
None
? Will that partition simply not be materialized?
s

Scout

02/13/2024, 2:44 PM
In Dagster, if an asset function conditionally returns
None
, it means that the asset will not produce a materialization for that particular run. This behavior is used to implement conditional materialization, where you may decide not to materialize an asset based on certain conditions, such as the absence of new data or a decision to skip computation under specific circumstances. When an asset function returns
None
, the following occurs: 1. No asset materialization event will be created for that run. 2. The I/O manager associated with the asset will not be invoked to handle output. 3. Downstream assets that depend on this asset will not be materialized, unless they are configured to handle the absence of an upstream materialization. 4. Asset sensors monitoring the asset for materialization events will not trigger. This behavior allows for more efficient pipeline runs by avoiding unnecessary computations and storage operations when the conditions for materializing an asset are not met. Here's a conceptual example of an asset that conditionally materializes:
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python from dagster import asset  @asset def conditional_asset():     data = fetch_data()     if should_materialize(data):         return data     else:         return None  # No materialization will occur
In this example,
fetch_data
is a hypothetical function that retrieves data, and
should_materialize
is a hypothetical function that determines whether the data should lead to a materialization. If
should_materialize
returns
False
, the asset function returns
None
, and no materialization occurs. It's important to note that the behavior of conditional materialization may vary depending on the version of Dagster you are using and the specific configuration of your assets and pipelines. Always refer to the latest Dagster documentation for the version you are using to ensure that you understand the current behavior and capabilities related to conditional materialization.
t

Tara Larrue

02/13/2024, 2:57 PM
@Scout will this work for dagster 1.6.3? How do I specify a type for the asset such that this doesn't result in a run failure, but simply does not materialize?
s

Scout

02/13/2024, 2:57 PM
As of Dagster version 1.6.3, you can use the
@asset
decorator with the
output_required
parameter set to
False
to indicate that the asset does not always need to materialize an output. If your asset function does not yield an
Output
object, then no materialization event will be created, and the run will not fail due to the absence of an output. Here's an example of how you can define such an asset:
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python from dagster import asset, Output, Nothing  @asset(output_required=False) def conditional_asset():     data = fetch_data()     if should_materialize(data):         yield Output(data)     else:         yield Output(value=None, output_name="result", metadata={}, dagster_type=Nothing)
In this example,
fetch_data
and
should_materialize
are hypothetical functions where
fetch_data
retrieves data and
should_materialize
determines whether the data should lead to a materialization. If
should_materialize
returns
False
, the asset function yields an
Output
with a value of
None
and a
dagster_type
of
Nothing
. This indicates that there is no materialization for this run, and it will not result in a run failure. The
Nothing
type is a special Dagster type that represents the absence of a value. It is useful in situations where an op or asset might not produce an output. Please note that the behavior of conditional materialization and the use of the
Nothing
type should be consistent with Dagster version 1.6.3, but it is always a good practice to refer to the specific version's documentation to confirm that there have been no changes in behavior or API.
t

Tara Larrue

02/13/2024, 3:15 PM
@Scout how do I intentionally raise an error and fail an asset job using dagster 1.6.3?
s

Scout

02/13/2024, 3:15 PM
In Dagster version 1.6.3, you can intentionally raise an error and fail an asset job by simply raising a Python exception within the asset function. When an exception is raised, Dagster will recognize it as a failure, and the asset job will fail accordingly. Here's an example of how you can define an asset that intentionally fails:
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python from dagster import asset  @asset def my_failing_asset():     # Your logic here     # ...      # Intentionally raise an error to fail the asset job     raise Exception("Intentional failure for demonstration purposes.")
In this example, the
my_failing_asset
function raises a generic
Exception
with a message indicating that the failure is intentional. When this asset is executed as part of an asset job, the job will fail, and the error message will be captured in the run logs. If you want to provide more structured metadata along with the failure, you can use Dagster's
Failure
exception, which allows you to include additional metadata that will be rendered in the UI:
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python from dagster import asset, Failure  @asset def my_failing_asset_with_metadata():     # Your logic here     # ...      # Intentionally raise a Failure to fail the asset job with metadata     raise Failure(         description="Intentional failure with metadata",         metadata={             "reason": "Demonstration of a failing asset with metadata",             # Include other metadata as needed         },     )
In this example, the
my_failing_asset_with_metadata
function raises a
Failure
exception with a description and metadata. The metadata can include any relevant information that you want to be displayed in the UI, making it easier to understand the context of the failure. Remember that raising an exception will stop the execution of the current asset, and depending on how your assets and dependencies are structured, it may also prevent downstream assets from being materialized. If you want to handle failures more gracefully or perform cleanup actions, you should include appropriate try-except blocks within your asset functions.
a

Alex Smith

03/05/2024, 3:37 PM
Is there no way I can retrospectively add a dependency between two assets? @Scout
s

Scout

03/05/2024, 3:37 PM
Once assets have been defined and materialized, you cannot retrospectively add dependencies between them directly. Dependencies in Dagster are defined at development time, and they dictate the execution order of assets as well as how data flows between them. These dependencies are used to construct the asset graph, which is then used to orchestrate the execution of asset jobs. However, if you need to establish a dependency between two existing assets after they have been created, you would typically update the asset definitions in your code to include the new dependency. This means modifying the downstream asset to specify that it depends on the upstream asset. Once you've updated the code, any new materializations of the downstream asset will respect this new dependency. Here's an example of how you might add a dependency between two assets:
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python from dagster import asset  # Assume this is your existing upstream asset @asset def upstream_asset():     # Logic for upstream asset     pass  # This is your existing downstream asset, which you want to update to depend on upstream_asset @asset def downstream_asset(upstream_asset):     # Logic for downstream asset that now depends on upstream_asset     pass
In this example, the
downstream_asset
function is updated to take
upstream_asset
as an argument, establishing a dependency. When you redeploy your code with these changes, Dagster will recognize the new dependency and execute the assets accordingly. If you're dealing with external assets or assets materialized by other systems, you might use Dagster's asset observation capabilities to reflect dependencies that aren't directly managed by Dagster. This would involve defining external asset specifications and injecting events into the event log to represent these dependencies. Remember that changing asset dependencies can have implications for how your asset jobs are executed and may affect downstream consumers of the assets. It's important to carefully manage these changes to ensure that your data pipelines remain consistent and reliable.
a

Alex Smith

03/05/2024, 3:39 PM
@Scout The issue is I don't have access the the downstream dbt asset definition
s

Scout

03/05/2024, 3:39 PM
If you don't have access to modify the downstream dbt asset definition directly, you won't be able to add a dependency in the traditional way by changing the asset's code. In Dagster, dependencies are defined declaratively in the asset definitions, and if you can't modify those definitions, you can't change the dependencies. However, there are a couple of approaches you might consider to work around this limitation: 1. Asset Sensors: You could create an asset sensor that triggers a job or an op when the upstream asset is materialized. This doesn't create a formal dependency in the asset graph, but it allows you to orchestrate behavior based on the materialization of assets. 2. Intermediate Assets: If possible, create an intermediate asset that depends on the upstream asset and is, in turn, used by the downstream dbt asset. This would require that you have some control over how the dbt asset is invoked or can influence its inputs. 3. Job Orchestration: If you have control over the job that materializes the dbt asset, you might be able to orchestrate the job to run after the upstream asset is materialized, ensuring that the dbt asset is updated accordingly. 4. Manual Intervention: If the dbt asset is materialized through a job that you can trigger manually, you might choose to do so only after confirming that the upstream asset has been materialized. 5. Communication with the Asset Owners: If the dbt asset is owned by another team or individual, you might need to communicate with them to discuss the need for the dependency and have them update the asset definition. 6. Version Control and Deployment: If the dbt asset definitions are stored in a version control system to which you have access, you could potentially propose a change to the asset definitions through the appropriate channels (e.g., pull requests). It's important to note that these approaches are workarounds and may not be ideal. They could introduce complexity or operational overhead, and they may not be as reliable as having a formal dependency defined in the asset graph. If possible, it's always best to work with the owners of the dbt asset to update the definitions to reflect the correct dependencies.
s

Sergio Villegas

03/25/2024, 4:36 PM
@Scout can I materialize policy that executes even if the same partition was materelized?
s

Scout

03/25/2024, 4:36 PM
Yes, you can configure an auto-materialization policy in Dagster that allows an asset to be materialized even if the same partition has already been materialized. This can be useful in scenarios where you want to recompute an asset on a regular basis, regardless of whether it has changed. The documentation describes the use of
AutoMaterializePolicy
with rules such as
materialize_on_cron
to specify that an asset should be materialized based on a cron schedule. Here's an example of how you might configure such a policy:
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python from dagster import asset, AutoMaterializePolicy, AutoMaterializeRule  # Define a policy that materializes an asset based on a cron schedule materialize_on_cron_policy = AutoMaterializePolicy.eager().with_rules(     # Try to materialize this asset if it hasn't been materialized since the last cron tick     AutoMaterializeRule.materialize_on_cron("0 9 * * *", timezone="US/Central"), )  # Apply the policy to an asset @asset(auto_materialize_policy=materialize_on_cron_policy) def my_asset():     # Asset computation logic     ...
In this example, the
materialize_on_cron_policy
is defined to materialize
my_asset
every day at 9:00 AM Central Time, even if the partition for that day has already been materialized. Please note that the behavior of auto-materialization policies and their configuration options may vary between Dagster versions. Always refer to the documentation for the specific version of Dagster you are using to ensure that you are using the correct syntax and features.
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