@Scout How would you use dagster to create a pipeline where I need to query an external API once for each rows of a table I have in a database.

To create a pipeline in Dagster where you need to query an external API once for each row of a table in a database, you would typically follow these steps: 1. Define Resources: Define a resource for the database connection and another for the API client. Resources in Dagster are a way to manage connections to external services like databases and APIs. They allow for flexibility in configuration and create a single source to monitor connections in the Dagster UI. 2. Create Solids: Create solids (the functional units of computation in Dagster) for querying the database and for making the API call. You would have one solid to fetch the rows from the database and another solid that takes each row as input and performs the API call. 3. Pipeline Construction: Construct a pipeline that stitches these solids together. The solid that fetches rows from the database would pass each row to the solid that makes the API call. 4. Configuration: Configure the resources and solids using the

run_config

to specify the necessary parameters such as database credentials, API keys, or any other required configuration. 5. Execution: Execute the pipeline, ensuring that the solid that makes the API call is invoked for each row fetched by the database query solid. Here's a high-level example of what the code might look like, based on the concepts from the documentation:

python
from dagster import pipeline, solid, execute_pipeline, resource

# Define a resource for the database connection
@resource
def db_connection(context):
    # Code to establish database connection
    pass

# Define a resource for the API client
@resource
def api_client(context):
    # Code to create an API client
    pass

# Define a solid to query the database
@solid(required_resource_keys={'db_connection'})
def query_database(context):
    # Use the db_connection resource to fetch rows from the database
    rows = context.resources.db_connection.fetch_rows()
    return rows

# Define a solid to call the API for each row
@solid(required_resource_keys={'api_client'})
def call_api_for_row(context, row):
    # Use the api_client resource to make the API call for the given row
    response = context.resources.api_client.call_api(row)
    return response

# Construct the pipeline
@pipeline(mode_defs=[ModeDefinition(resource_defs={'db_connection': db_connection, 'api_client': api_client})])
def my_pipeline():
    rows = query_database()
    responses = call_api_for_row.map(rows)

# Execute the pipeline
if __name__ == "__main__":
    result = execute_pipeline(my_pipeline, run_config={
        'resources': {
            'db_connection': {'config': {'connection_string': '...'}},
            'api_client': {'config': {'api_key': '...'}}
        }
    })

Please note that this is

@Scout How would you use dagster to create a pipeline where I need to query an external API once for each rows of a table I have in a database using Dagster 1.6

As of my knowledge cutoff in early 2023, Dagster 1.6 had not been released, and I do not have information on features or changes that may be introduced in that version. However, I can guide you on how to create a pipeline using the version of Dagster available at my last update, which should be largely applicable to future versions unless there are significant changes to the core API. To create a pipeline in Dagster where you need to query an external API once for each row of a table in a database, you would follow these general steps: 1. Define Resources: Create resources for the database connection and the API client. Resources in Dagster are reusable components that manage external connections and state. 2. Create Solids: Implement solids for querying the database and for making the API call. One solid would retrieve the rows from the database, and another would take each row as input and perform the API call. 3. Pipeline Construction: Assemble these solids into a pipeline, ensuring that the solid fetching rows from the database passes each row to the solid making the API call. 4. Configuration: Configure the pipeline with the necessary parameters for the database and API client using the

run_config

. 5. Execution: Run the pipeline, which will execute the solid that makes the API call for each row fetched from the database. Here's a conceptual example based on the Dagster API: ```python from dagster import pipeline, solid, execute_pipeline, resource, ModeDefinition # Define a resource for the database connection @resource def db_connection(context): # Code to establish database connection pass # Define a resource for the API client @resource def api_client(context): # Code to create an API client pass # Define a solid to query the database @solid(required_resource_keys={'db_connection'}) def query_database(context): # Use the db_connection resource to fetch rows from the database rows = context.resources.db_connection.fetch_rows() return rows # Define a solid to call the API for each row @solid(required_resource_keys={'api_client'}) def call_api_for_row(context, row): # Use the api_client resource to make the API call for the given row response = context.resources.api_client.call_api(row) return response # Construct the pipeline @pipeline(mode_defs=[ModeDefinition(resource_defs={'db_connection': db_connection, 'api_client': api_client})]) def my_pipeline(): rows = query_database() for row in rows: call_api_for_row(row) # Execute the pipeline if name == "__main__": result = execute_pipeline(my_pipeline, run_config={ 'resources': { 'db_connection': {'config': {'connection_string': '...'}}, 'api_client': {'config': {'api_

@Scout Did you get nerf from yesterday? You are talking about solid that have been removed from Dagster.

I apologize for the confusion. You are correct that the term "solid" has been replaced with "op" in more recent versions of Dagster, as part of the effort to make the framework's terminology more intuitive and approachable. To create a pipeline in Dagster where you need to query an external API once for each row of a table in a database, you would use "ops" instead of "solids". Here's how you might structure your Dagster job using "ops": 1. Define Resources: Create resources for the database connection and the API client. 2. Create Ops: Implement ops for querying the database and for making the API call. One op would retrieve the rows from the database, and another would take each row as input and perform the API call. 3. Job Construction: Assemble these ops into a job. 4. Configuration: Configure the job with the necessary parameters for the database and API client using the

run_config

. 5. Execution: Run the job, which will execute the op that makes the API call for each row fetched from the database. Here's a conceptual example using the updated terminology:

python
from dagster import job, op, resource

# Define a resource for the database connection
@resource
def db_connection(context):
    # Code to establish database connection
    pass

# Define a resource for the API client
@resource
def api_client(context):
    # Code to create an API client
    pass

# Define an op to query the database
@op(required_resource_keys={'db_connection'})
def query_database(context):
    # Use the db_connection resource to fetch rows from the database
    rows = context.resources.db_connection.fetch_rows()
    return rows

# Define an op to call the API for each row
@op(required_resource_keys={'api_client'})
def call_api_for_row(context, row):
    # Use the api_client resource to make the API call for the given row
    response = context.resources.api_client.call_api(row)
    return response

# Construct the job
@job(resource_defs={'db_connection': db_connection, 'api_client': api_client})
def my_job():
    rows = query_database()
    for row in rows:
        call_api_for_row(row)

# Execute the job
if __name__ == "__main__":
    result = my_job.execute_in_process(run_config={
        'resources': {
            'db_connection': {'config': {'connection_string': '...'}},
            'api_client': {'config': {'api_key': '...'}}
        }
    })

Please note that this is a high-level example and does not include the actual implementation details for database querying or API calling, which would be specific to your database and API. You would need to fill in the logic for

db_connection.fetch_rows()

and

api_client.call_api(row)

based on your actual database and API. Remember to check the latest Dagster documentation for any updat

@Scout how would you store the responses from the Op? Consider that I want to store each Op result that succeed. If 1 call to the API fails, only this response wouldn’t be stored.

To store the responses from each successful API call in a Dagster job, you can use an IOManager. An IOManager in Dagster is responsible for handling the inputs and outputs of ops, allowing you to customize how and where data is stored. Here's how you can implement this: 1. Define an IOManager: Create a custom IOManager that will handle the storage of successful API call responses. This IOManager can write to a file, a database, cloud storage, or any other data sink you choose. 2. Attach the IOManager: Attach the IOManager to the output of the op that makes the API calls. 3. Handle Failures: In the op that makes the API calls, handle any exceptions that may occur and ensure that only successful responses are passed to the output. Here's an example of how you might implement this:

python
from dagster import IOManager, io_manager, op, Out, Output, job, resource

class MyIOManager(IOManager):
    def handle_output(self, context, obj):
        # Implement logic to store the object (the API response)
        # For example, write to a file or a database
        pass

    def load_input(self, context):
        # Implement logic to load inputs if necessary
        pass

@io_manager
def my_io_manager(_):
    return MyIOManager()

# Define a resource for the API client
@resource
def api_client(context):
    # Code to create an API client
    pass

# Define an op to call the API for each row and store the result
@op(required_resource_keys={'api_client'}, out=Out(io_manager_key="my_io_manager"))
def call_api_for_row(context, row):
    try:
        # Use the api_client resource to make the API call for the given row
        response = context.resources.api_client.call_api(row)
        # Return the response as an output to be handled by the IOManager
        return Output(response)
    except Exception as e:
        context.log.error(f"API call for row {row} failed with exception: {e}")
        # Do not return an output for failed API calls

# Construct the job with the custom IOManager attached to the op's output
@job(resource_defs={'api_client': api_client, 'my_io_manager': my_io_manager})
def my_job():
    # Assume `query_database` is an op that retrieves rows from a database
    rows = query_database()
    for row in rows:
        call_api_for_row(row)

# Execute the job
if __name__ == "__main__":
    result = my_job.execute_in_process(run_config={
        'resources': {
            'api_client': {'config': {'api_key': '...'}}
        }
    })

In this example,

MyIOManager

is a custom IOManager that you would need to implement with the logic for storing the API responses. The

call_api_for_row

op is designed to handle exceptions and only produce an output if the API call is successful. The successful responses are then passed to the

@Scout Is there a way I could use the last version of your code and group all ops into a graph asset and get the same result?

Yes, you can group all ops into a graph and create a graph-backed asset in Dagster. This allows you to encapsulate the logic of your ops within a graph and then create an asset from that graph. The asset will represent the output of the graph, and you can use the same IOManager to handle the storage of successful API call responses. Here's how you can modify the previous example to use a graph-backed asset: 1. Define the Graph: Create a graph that includes the logic of querying the database and making the API calls. 2. Create a Graph-Backed Asset: Use the graph to define a graph-backed asset. This asset will be the result of the successful API calls. 3. Attach the IOManager: Attach the IOManager to the asset to handle the storage of the successful responses. Here's an example of how you might implement this:

python
from dagster import IOManager, io_manager, op, Out, Output, graph, AssetMaterialization, asset
from dagster.core.definitions.decorators.graph import graph_asset

class MyIOManager(IOManager):
    def handle_output(self, context, obj):
        # Implement logic to store the object (the API response)
        # For example, write to a file or a database
        pass

    def load_input(self, context):
        # Implement logic to load inputs if necessary
        pass

@io_manager
def my_io_manager(_):
    return MyIOManager()

# Define a resource for the API client
@resource
def api_client(context):
    # Code to create an API client
    pass

# Define an op to call the API for each row
@op(required_resource_keys={'api_client'}, out=Out(io_manager_key="my_io_manager"))
def call_api_for_row(context, row):
    try:
        # Use the api_client resource to make the API call for the given row
        response = context.resources.api_client.call_api(row)
        # Return the response as an output to be handled by the IOManager
        return Output(response)
    except Exception as e:
        context.log.error(f"API call for row {row} failed with exception: {e}")
        # Do not return an output for failed API calls

# Define a graph that encapsulates the logic
@graph
def my_graph(rows):
    for row in rows:
        call_api_for_row(row)

# Create a graph-backed asset
@graph_asset
def my_asset():
    # Assume `query_database` is an op that retrieves rows from a database
    rows = query_database()
    my_graph(rows)

# Execute the job
if __name__ == "__main__":
    result = my_asset.execute_in_process(run_config={
        'resources': {
            'api_client': {'config': {'api_key': '...'}}
        }
    })

In this example,

my_graph

is a graph that includes the logic for making API calls for each row. The

my_asset

function is decorated with

@graph_asset

, which creates a graph-backed asset from the

my_graph

grap

@Scout in that example what would happen if any of the call_api_for_row would fail?

In the provided example, if any of the

call_api_for_row

ops fail due to an exception, the following would happen: 1. The exception is caught by the

try-except

block within the

call_api_for_row

op. 2. An error message is logged using

context.log.error

, indicating that the API call for the specific row failed. 3. The op does not yield an

Output

object for that particular row because the

return Output(response)

line is inside the

try

block and will be skipped in case of an exception. 4. The IOManager's

handle_output

method will not be called for that failed op, as there is no output to handle. 5. The pipeline will continue executing the remaining ops for other rows. The failure of one op does not stop the execution of subsequent ops unless you have configured the pipeline to behave differently upon encountering an error. The result is that only successful API call responses are stored by the IOManager, and any failed calls are logged and skipped without causing the entire job to fail. This behavior allows the job to be resilient to individual failures, which is often desirable when dealing with external API calls that may occasionally fail due to transient issues. Remember that this behavior is based on the specific error handling logic you implement within your ops. If you want to change how failures are handled (for example, to retry failed calls or to halt the entire job), you would need to adjust the error handling code accordingly.

@Scout Could I do the same thing but instead of logging an error each of the rows would be in their how run, and if one fail the run would fail?

Yes, you can achieve this by structuring your Dagster job to execute each API call in its own run. This way, if one API call fails, it will cause that specific run to fail without affecting the other runs. Each row would be processed in isolation, and the success or failure of one run would not impact the others. To implement this, you would typically use Dagster's dynamic orchestration capabilities, where you can dynamically create runs for each row. Here's a high-level approach to how you might set this up: 1. Create an Op for Row Processing: Define an op that takes a row as input and performs the API call. If the API call fails, the op will fail, and thus the run will fail. 2. Use a Sensor or Schedule: Implement a sensor or schedule that triggers a new run for each row in the database. The sensor or schedule would query the database, and for each row, it would launch a new run of the job that processes that row. 3. Run Isolation: Since each row is processed in its own run, failures are isolated to individual runs. You can monitor the success or failure of each run separately. Here's a conceptual example using a sensor to trigger runs:

python
from dagster import sensor, RunRequest, job, op, repository

# Define an op that performs the API call for a given row
@op
def process_row(row):
    # Code to perform the API call
    # If the API call fails, the op will fail, and the run will fail
    pass

# Define a job that processes a single row
@job
def process_single_row():
    process_row()

# Define a sensor that triggers a run for each row in the database
@sensor(job=process_single_row)
def row_processing_sensor(context):
    # Code to query the database and get rows that need processing
    rows_to_process = get_rows_to_process()

    for row in rows_to_process:
        # Create a run request for each row
        yield RunRequest(
            run_key=f"row-{row.id}",  # Unique identifier for the run
            run_config={
                "ops": {
                    "process_row": {
                        "inputs": {
                            "row": row,
                        }
                    }
                }
            },
        )

# Define a repository to hold the job and sensor
@repository
def my_repository():
    return [process_single_row, row_processing_sensor]

def get_rows_to_process():
    # Placeholder function to fetch rows from the database
    # Replace with actual database query logic
    return [{"id": 1, "data": "row1"}, {"id": 2, "data": "row2"}]

In this example,

row_processing_sensor

is a sensor that checks for rows that need to be processed. For each row, it creates a

RunRequest

that triggers a new run of the

process_single_row

job with the specific row data as input. Please note t

@Scout Everyday at noon, at I want to look at the content of one of my table in a database, an execute call to an external api for each of the rows returned. I want to store the result in another table. Since there might be a lot of rows, and that this task might take a while I need to store the successful results even if not all the call to the external api succeed. I also want to be alerted if any of the rows would fails. Also I need to be able to retry all the failed jobs manually.

To achieve this in Dagster, you can set up a scheduled job that queries your database table every day at noon, makes an API call for each row, stores successful results in another table, and alerts you if any of the calls fail. Additionally, you can configure retries for failed jobs. Here's a step-by-step approach to setting this up: 1. Define Resources: Create resources for the database connection and the API client. 2. Create Ops: Implement ops for querying the database, making the API call, storing successful results, and handling alerts for failures. 3. Define a Schedule: Set up a schedule to trigger the job every day at noon. 4. Configure Retries: Configure the job to allow for manual retries of failed runs. 5. Execution: The scheduled job will execute daily, and you can monitor and retry failed runs as needed. Here's a conceptual example of how you might implement this in Dagster: ```python from dagster import job, op, schedule, repository, RetryPolicy from dagster.core.definitions.schedule import ScheduleEvaluationContext from dagster.core.execution.context.compute import OpExecutionContext from dagster.core.execution.context.system import JobExecutionContext # Define resources (not shown here) for database connection and API client # ... # Define an op to query the database @op(required_resource_keys={'db_connection'}) def query_database(context: OpExecutionContext): # Use the db_connection resource to fetch rows from the database rows = context.resources.db_connection.fetch_rows() return rows # Define an op to call the API for each row @op(required_resource_keys={'api_client'}, retry_policy=RetryPolicy(max_retries=3)) def call_api_and_store(context: OpExecutionContext, row): try: # Use the api_client resource to make the API call for the given row response = context.resources.api_client.call_api(row) # Store the successful result in another table context.resources.db_connection.store_result(row, response) return response except Exception as e: # Alerting logic (e.g., send a message to Slack or email) context.resources.alerting.send_alert(f"API call failed for row {row}: {e}") # Reraise the exception to trigger a retry or mark the op as failed raise # Define a job that orchestrates the ops @job(resource_defs={'db_connection': db_connection, 'api_client': api_client, 'alerting': alerting}) def daily_api_calls_job(): rows = query_database() for row in rows: call_api_and_store(row) # Define a schedule for the job @schedule(cron_schedule="0 12 * * *", job=daily_api_calls_job, execution_timezone="UTC") def daily_noon_schedule(context: ScheduleEvaluationContext): # You can add additional logic here if neede