How do I define an IO manager to append scraped data to an existing postgres database table?

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:

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

@Scout can I parameterize an IOManager by a table name?

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:

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.

@Scout how do I trigger an asset job based on the materialization of an upstream asset?

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:

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.

@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?

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:

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.

@Scout I'm using dagster 1.6.3 and getting the error: AttributeError: 'MultiAssetSensorEvaluationContext' object has no attribute 'has_materialization'

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`:

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.

@Scout how do I define a graph asset that is partitioned?

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:

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.

@Scout I've defined a graph asset like this, but I'm getting an error with my

context

parameter, what could be wrong here?

@graph_asset(partitions_def=input_data_sources_partitions_def)
def input_data_sources(context, config: ScrapeConfig) -> pd.DataFrame:

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`:

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.

@Scout what happens if an asset conditionally returns

None

? Will that partition simply not be materialized?

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:

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.

@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?

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:

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.

@Scout how do I intentionally raise an error and fail an asset job using dagster 1.6.3?

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:

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:

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.

Is there no way I can retrospectively add a dependency between two assets? @Scout

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:

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.

@Scout The issue is I don't have access the the downstream dbt asset definition

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.

@Scout can I materialize policy that executes even if the same partition was materelized?

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:

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.