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<@U0667DNC02Y> I'm triggering multiple API async r...
# ask-ai
s
@Scout I'm triggering multiple API async requests at the same time, and I need to wait with a sensor for all of them to be finished (a file is created on Google cloud storage bucket for each call) before proceeding to the next step.
s
To handle a scenario where you need to wait for multiple asynchronous API requests to complete before proceeding with the next step in Dagster, you can use a sensor to monitor the Google Cloud Storage bucket for the presence of the expected files. Once all files are detected, the sensor can yield a 
RunRequest
to trigger the next step of your pipeline. Here's a general approach based on the documentation: 1. Define a Sensor: Use the 
@sensor
decorator to define a sensor that will monitor the Google Cloud Storage bucket. The sensor function can be scheduled to run at a regular interval. 2. Check for Files: Within the sensor function, check the Google Cloud Storage bucket for the presence of the files created by the API requests. You might need to use the Google Cloud Storage client library to list the files in the bucket. 3. Determine Completion: Implement logic to determine if all expected files are present. This could involve checking for specific filenames, file counts, or other markers that indicate all API requests have completed successfully. 4. Yield RunRequest: Once you have confirmed that all files are present, yield a 
RunRequest
from the sensor function to trigger the next step in your pipeline. 5. Use Cursors for Optimization: If the number of files is large or if the sensor needs to be highly performant, consider using cursors to track the state across evaluations. This way, the sensor can pick up from where it left off in the previous evaluation, rather than re-scanning the entire bucket each time. Here is a somewhat contrived example of how you might implement a sensor that uses a cursor to track state across evaluations:
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python
from dagster import sensor, SensorEvaluationContext, RunRequest

@sensor(job=my_job)
def my_sensor(context: SensorEvaluationContext):
    last_cursor = context.cursor
    new_cursor = None
    file_paths = list_files_from_gcs_bucket()  # Replace with actual GCS listing logic

    # Logic to determine if all expected files are present
    all_files_present = check_all_files_present(file_paths, last_cursor)

    if all_files_present:
        new_cursor = get_new_cursor(file_paths)  # Logic to update the cursor
        context.update_cursor(new_cursor)
        yield RunRequest(run_key="unique_run_key")

    # If not all files are present, you can update the cursor to the latest state
    # and the sensor will check again in the next evaluation
    if new_cursor and new_cursor != last_cursor:
        context.update_cursor(new_cursor)
Please note that this is a high-level example and you will need to implement the functions 
list_files_from_gcs_bucket
, 
check_all_files_present
, and 
get_new_cursor
to fit your specific use case. Additionally, you will need to handle authentica
tion with Google Cloud Storage and any other operational considerations. Remember to test your sensor thoroughly to ensure it behaves as expected, especially in edge cases where files may be added or removed unexpectedly.
s
@Scout what's the simplest way to pass the list of files to be generated from the operation triggering the api calls to the sensor waiting for the files?
s
The simplest way to pass the list of expected files from an operation that triggers API calls to a sensor waiting for the files is to use Dagster's event log and tags. When the operation that triggers the API calls completes, you can emit an event with metadata that includes the list of expected files. Then, the sensor can read this metadata from the event log to know which files to wait for. Here's a step-by-step approach: 1. Emit Event with Metadata: In the operation that triggers the API calls, emit an event with metadata that includes the list of expected files. You can use 
context.log_event
or 
yield AssetMaterialization
with metadata entries. 2. Tag the Run: Optionally, you can also tag the run with relevant information that the sensor can use to filter runs and retrieve the correct metadata. 3. Sensor Reads Event Log: In the sensor, use the 
instance.get_event_records
method to read the event log and filter for the specific run or metadata entries that contain the list of expected files. 4. Sensor Logic: The sensor can then use this list to check the presence of files in the Google Cloud Storage bucket. Here's an example of how you might implement this: ```python from dagster import op, Out, Output, AssetMaterialization, MetadataEntry, sensor, SensorEvaluationContext, RunRequest # Operation that triggers API calls and emits an event with metadata @op(out=Out()) def trigger_api_calls(context): # Logic to trigger API calls and generate the list of expected files expected_files = ["file1.txt", "file2.txt", "file3.txt"] # Emit an event with metadata yield AssetMaterialization( asset_key="api_triggered_files", metadata_entries=[MetadataEntry.json({"expected_files": expected_files}, label="Expected Files")] ) yield Output(expected_files) # Sensor that waits for files @sensor(job=my_job) def my_sensor(context: SensorEvaluationContext): # Logic to retrieve the list of expected files from the event log event_records = context.instance.get_event_records( event_type=DagsterEventType.ASSET_MATERIALIZATION, asset_key="api_triggered_files", ) if not event_records: yield SkipReason("No expected files metadata found.") return # Assuming the latest event record contains the latest list of expected files expected_files = event_records[-1].event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries[0].entry_data # Check for the presence of expected files in GCS files_in_gcs = list_files_from_gcs_bucket() # Replace with actual GCS listing logic if all(file in files_in_gcs for file in expected_files): yield RunRequest(run_key="unique_run_key") else: yield SkipReason("Waiting for all e
xpected files to be present in GCS.") ``` In this example, 
trigger_api_calls
is an operation that triggers the API calls and emits an 
AssetMaterialization
event with metadata containing the list of expected files. The 
my_sensor
sensor then reads the event log to retrieve this list and checks for the presence of these files in the Google Cloud Storage bucket. Please adapt the code to fit your specific use case, including the logic for triggering API calls, listing files from GCS, and any other operational considerations.
s
@Scout why 
trigger_api_calls
needs to yield Metadata and Output? Why do we filter the context with 
event_type=DagsterEventType.ASSET_MATERIALIZATION
.
s
The 
trigger_api_calls
operation in the example provided yields both 
Metadata
and 
Output
for illustrative purposes, to demonstrate how you can emit metadata about an event and also pass data between operations within a Dagster job. However, in the context of your question, where you only need to pass a list of expected files to a sensor, yielding an 
Output
may not be necessary if you do not need to pass that data to subsequent operations within a job. The reason for yielding 
AssetMaterialization
with metadata is to record an event in the event log that includes structured metadata about the external side effect of the operation (in this case, the list of expected files). This metadata can then be retrieved by a sensor to determine which files to wait for. We filter the context in the sensor with 
event_type=DagsterEventType.ASSET_MATERIALIZATION
because we are specifically interested in events that correspond to asset materializations. Asset materialization events are used to indicate that an asset has been created or updated as a result of an operation. By filtering for this event type, the sensor can find the specific event that contains the metadata with the list of expected files. If you are not using asset materialization events to pass the list of expected files, you would not need to filter for 
DagsterEventType.ASSET_MATERIALIZATION
. Instead, you could use a different mechanism to pass this information to the sensor, such as custom event types or run tags. In summary, the 
trigger_api_calls
operation should emit an event that records the list of expected files in a way that can be retrieved by the sensor. The sensor then uses this information to determine when all the expected files have been created in the Google Cloud Storage bucket before proceeding. The exact mechanism for emitting and retrieving this information can vary depending on your specific use case and the capabilities of Dagster.
s
@Scout I need to execute a query against BigQuery to create an asset on disk (a CSV file). Then I need to detect that the given file has been created, split it in chunks of 1000 lines, and call the external API once per chunk.
s
To execute a query against BigQuery, create an asset on disk, and then process that asset by splitting it into chunks and calling an external API, you can follow these steps in Dagster: 1. Create a BigQuery Asset: Write an operation that executes a BigQuery query and writes the results to a CSV file on disk. You can use the 
bigquery
client library provided by Google to execute the query and export the results. 2. Split the CSV into Chunks: Write another operation that reads the CSV file, splits it into chunks of 1000 lines, and yields each chunk as an output. 3. Call the External API: Write an operation that takes a chunk of lines as input and makes an API call for each chunk. 4. Define a Job: Compose these operations into a Dagster job that defines the execution order. Here's a high-level example of what this might look like in Dagster:
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python
from dagster import job, op, Out, Output
from google.cloud import bigquery
import pandas as pd
import requests

# Replace with your Google Cloud credentials and project details
client = bigquery.Client()

@op
def export_bigquery_to_csv(context):
    query = "SELECT * FROM `your-project.your_dataset.your_table`"
    df = client.query(query).to_dataframe()
    csv_file_path = "/path/to/your/output.csv"
    df.to_csv(csv_file_path, index=False)
    <http://context.log.info|context.log.info>(f"CSV file created at {csv_file_path}")
    return csv_file_path

@op(out=Out(is_required=False))
def split_csv_into_chunks(context, csv_file_path):
    chunk_size = 1000  # Define the size of each chunk
    for chunk in pd.read_csv(csv_file_path, chunksize=chunk_size):
        yield Output(chunk)

@op
def call_external_api(context, chunk):
    api_url = "<https://your-api.com/endpoint>"
    # Convert the DataFrame chunk to a suitable format for your API call
    # For example, you might convert it to JSON
    data = chunk.to_json(orient='records')
    response = <http://requests.post|requests.post>(api_url, json=data)
    if response.status_code == 200:
        <http://context.log.info|context.log.info>("API call successful.")
    else:
        context.log.error(f"API call failed with status code: {response.status_code}")

@job
def process_bigquery_results():
    csv_file_path = export_bigquery_to_csv()
    chunks = split_csv_into_chunks(csv_file_path)
    chunks.map(call_external_api)
In this example: - 
export_bigquery_to_csv
executes a BigQuery query and writes the results to a CSV file. - 
split_csv_into_chunks
reads the CSV file and splits it into chunks of 1000 lines, yielding each chunk as an output. - 
call_external_api
takes each chunk and makes an API call. - 
process_bigquery_results
is the job that orchestrates the execution of these operations. Please note that you will need to handle authentication with Google Cloud and the external API, as well as a
ny error handling and retry logic for the API calls. Additionally, you may need to adjust the chunking and API call logic to match the specific requirements of your external API. This example assumes that the CSV file is not too large to be processed in memory. If the CSV file is very large, you may need to implement a more sophisticated chunking mechanism that does not rely on loading the entire file into memory at once.
s
@Scout I need a sensor, as this file can be written from an op in Dagster, or just left there by an external process
s
To handle both cases where a CSV file could be written by an operation within Dagster or by an external process, you can use a sensor to monitor the directory where the file is expected to be created. The sensor will check for the presence of the CSV file and, once detected, can trigger a job that processes the file. Here's a general approach to creating such a sensor: 1. Define a Sensor: Use the 
@sensor
decorator to define a sensor that will monitor the directory for the CSV file. 2. Check for the CSV File: Within the sensor function, check for the presence of the CSV file. If the file exists, the sensor should yield a 
RunRequest
to trigger the job that processes the file. 3. Process the File: The job triggered by the sensor should include operations to read the CSV file, split it into chunks, and call the external API for each chunk. Here's an example of how you might implement the sensor:
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python import os from dagster import sensor, RunRequest, SensorEvaluationContext  # Replace with the path to the directory where the CSV file is expected WATCH_DIRECTORY = '/path/to/watch/directory' EXPECTED_FILENAME = 'output.csv'  @sensor(job=process_bigquery_results) def csv_file_sensor(context: SensorEvaluationContext):     csv_file_path = os.path.join(WATCH_DIRECTORY, EXPECTED_FILENAME)     if os.path.isfile(csv_file_path):         # If the file exists, yield a RunRequest to trigger the job         yield RunRequest(             run_key=csv_file_path,             run_config={                 'ops': {                     'split_csv_into_chunks': {                         'inputs': {'csv_file_path': csv_file_path}                     }                 }             }         )
In this example, 
process_bigquery_results
is the job that processes the CSV file. The 
csv_file_sensor
sensor checks for the presence of the CSV file in the specified directory. If the file is found, it yields a 
RunRequest
with a run key derived from the file path to ensure that each file is processed only once. The 
run_config
specifies the path to the CSV file as an input to the 
split_csv_into_chunks
operation. Please note that this sensor will trigger the job as soon as it detects the file. If the file is large and takes time to be fully written, you may need to implement additional logic to ensure that the file is not processed until it is complete. This could involve checking file modification times, using file locks, or having a separate process signal completion. Remember to test your sensor thoroughly to ensure it behaves as expected, especially in edge cases where files may be added or removed unexpectedly.
s
@Scout why are we defining to job to be executed 
process_bigquery_results
but also passing a run config with the op to execute, 
split_csv_into_chunks
. Wouldn't the op be automatically executed as in in the job code?
s
You are correct that if 
process_bigquery_results
is a job that already includes the 
split_csv_into_chunks
operation, then it is not necessary to specify the operation in the 
run_config
when yielding a 
RunRequest
. The job itself defines the execution graph, and all operations within that job will be executed according to their dependencies when the job is triggered. The 
run_config
in the 
RunRequest
is used to provide runtime configuration for the job, such as resources, inputs, and other settings that might vary per run. If 
split_csv_into_chunks
does not require any specific runtime configuration and the job 
process_bigquery_results
is set up to execute it as part of its execution plan, then you can omit the 
run_config
from the 
RunRequest
. Here's a simplified version of the sensor that assumes 
process_bigquery_results
already includes the 
split_csv_into_chunks
operation:
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python import os from dagster import sensor, RunRequest, SensorEvaluationContext  # Replace with the path to the directory where the CSV file is expected WATCH_DIRECTORY = '/path/to/watch/directory' EXPECTED_FILENAME = 'output.csv'  @sensor(job=process_bigquery_results) def csv_file_sensor(context: SensorEvaluationContext):     csv_file_path = os.path.join(WATCH_DIRECTORY, EXPECTED_FILENAME)     if os.path.isfile(csv_file_path):         # If the file exists, yield a RunRequest to trigger the job         yield RunRequest(             run_key=csv_file_path             # No need to specify run_config if the job has no special runtime configuration         )
In this version, the sensor simply checks for the presence of the CSV file and yields a 
RunRequest
without specifying a 
run_config
. The job 
process_bigquery_results
will be triggered with its predefined execution plan, which includes the 
split_csv_into_chunks
operation. If you need to pass specific configurations or inputs to the job, you can include them in the 
run_config
of the 
RunRequest
. Otherwise, if the job can run with its default configuration and all operations are defined within it, there's no need to specify the 
run_config
.
s
@Scout how does the job access to the csv_file_path?
s
In Dagster, to make data such as 
csv_file_path
available to a job, you typically pass it as a configuration to the job's resources or to the inputs of the operations within the job. When a sensor triggers a job, it can provide a 
run_config
that includes this information. Here's how you can modify the sensor to pass 
csv_file_path
to the job:
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python import os from dagster import sensor, RunRequest, SensorEvaluationContext  # Replace with the path to the directory where the CSV file is expected WATCH_DIRECTORY = '/path/to/watch/directory' EXPECTED_FILENAME = 'output.csv'  @sensor(job=process_bigquery_results) def csv_file_sensor(context: SensorEvaluationContext):     csv_file_path = os.path.join(WATCH_DIRECTORY, EXPECTED_FILENAME)     if os.path.isfile(csv_file_path):         # If the file exists, yield a RunRequest to trigger the job         yield RunRequest(             run_key=csv_file_path,             run_config={                 'ops': {                     'name_of_op_that_needs_csv_file_path': {                         'inputs': {                             'input_name_for_csv_file_path': csv_file_path                         }                     }                 }             }         )
In this example, 
name_of_op_that_needs_csv_file_path
should be replaced with the actual name of the operation within your job that requires the 
csv_file_path
as an input. Similarly, 
input_name_for_csv_file_path
should be replaced with the actual input name defined in that operation. Here's an example of what the operation might look like:
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python from dagster import op  @op def my_op_with_csv_input(context, input_name_for_csv_file_path):     <http://context.log.info|context.log.info>(f"CSV file path is: {input_name_for_csv_file_path}")     # Your logic here
And the job definition would include this operation:
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python from dagster import job  @job def process_bigquery_results():     my_op_with_csv_input()
When the sensor triggers the job, the 
run_config
provided in the 
RunRequest
will be used to pass the 
csv_file_path
to the operation as an input. This way, the operation within the job can access the file path and perform the necessary processing.
s
@Scout why is the op the one receiving the config, and not the job?
s
In Dagster, configuration can be provided at different levels of the system, including at the instance, resource, job, and operation (op) levels. The granularity of the configuration depends on where you need to apply specific settings or parameters. When you provide configuration to an op, it's typically because that op requires specific parameters to execute its logic. This allows each op to be self-contained with its own configuration schema, making it reusable and testable in different contexts. Ops can have config schemas that define the structure and type of configuration they expect. On the other hand, providing configuration at the job level is more about setting up the execution environment for the entire job, which may include configuring resources that are shared across ops, setting up execution parameters, or defining how outputs are handled. In the context of a sensor triggering a job based on the presence of a file, you might choose to pass the file path directly to the op if the following conditions are met: 1. The file path is specific to the logic within that op and not relevant to the broader job context. 2. The op is designed to accept a file path as an input or configuration parameter. 3. You want to maintain the flexibility to reuse the op in different jobs with different file paths. If the file path is relevant to multiple ops within the job or pertains to the job's overall execution context, you might instead pass it as part of the job's configuration. This would typically be done by configuring a resource that is accessible to all ops within the job. In summary, whether you pass the 
csv_file_path
to the op or the job depends on the scope at which the configuration is relevant and how you've structured your Dagster assets to handle configuration. If the file path is only relevant to a single op, it makes sense to pass it directly to that op. If it's relevant to the entire job or multiple ops, you might consider passing it at the job level or via a resource.
s
@Scout give me an example with the job receiving the file name, and passing it to the op
s
Certainly! To pass the file name from the job to an op, you can define the job to accept configuration for the file name and then pass that configuration down to the op. Here's an example of how you might set this up: First, define an op that accepts a file path as an input argument:
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python from dagster import op  @op(config_schema={"file_path": str}) def process_file(context):     file_path = context.op_config["file_path"]     <http://context.log.info|context.log.info>(f"Processing file at path: {file_path}")     # Your file processing logic here
Next, define a job that includes this op and provides the file path to it via configuration:
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python from dagster import job  @job def my_file_processing_job():     process_file()
When you want to execute this job, you can provide the configuration for the file path. If you're triggering this job from a sensor, you would include the file path in the 
run_config
of the `RunRequest`:
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python from dagster import sensor, RunRequest, SensorEvaluationContext import os  WATCH_DIRECTORY = '/path/to/watch/directory' EXPECTED_FILENAME = 'output.csv'  @sensor(job=my_file_processing_job) def csv_file_sensor(context: SensorEvaluationContext):     csv_file_path = os.path.join(WATCH_DIRECTORY, EXPECTED_FILENAME)     if os.path.isfile(csv_file_path):         yield RunRequest(             run_key=csv_file_path,             run_config={                 "ops": {                     "process_file": {                         "config": {                             "file_path": csv_file_path                         }                     }                 }             }         )
In this example, the 
csv_file_sensor
sensor checks for the presence of the CSV file and, if found, triggers the 
my_file_processing_job
job with the 
run_config
that includes the file path. The 
process_file
op within the job then receives this file path as part of its configuration and can use it to process the file. This approach allows you to dynamically pass the file path to the op based on the sensor's detection of the file, while keeping the job definition static. The job doesn't need to know the file path ahead of time; it receives the path at runtime through the sensor's 
RunRequest
.
s
@Scout so the job - not the sensor - cannot pass it directly to the op? The job is only a container of ops?
s
Yes, that's correct. In Dagster, a job is essentially a container that defines the execution graph of ops, specifying how they are connected and in what order they should run. The job itself does not pass data directly to ops. Instead, data is passed to ops either through their inputs (if the data is the output of another op) or through configuration. When you want to provide runtime-specific information to an op, such as a file path, you typically do so by including that information in the 
run_config
when you launch the job. The 
run_config
can specify configurations for resources, ops, loggers, and more. Each op can have its own 
config_schema
that defines what configuration it accepts. The sensor's role is to monitor for certain conditions and trigger a job run when those conditions are met. As part of triggering the job, the sensor can provide a 
run_config
that includes the necessary configuration for the ops within the job. This is how you can dynamically provide a file path or other parameters to an op based on external conditions detected by a sensor. Here's a quick recap of the roles: - Ops: Perform computation and can receive inputs from other ops or configuration. - Jobs: Define the execution graph of ops and how they are connected. - Sensors: Monitor for external conditions and trigger job runs with specific 
run_config
when those conditions are met. So, while the job organizes ops and defines their execution order, it does not directly pass data to them. Data is passed to ops through the mechanisms described above.
s
@Scout I need the op the generates the CSV file to be independent, and I need to able to trigger in from the Dagster UI interface. The query for BigQuery is
Copy code
select
            asin,
            amazon_domain,
            type,
            sort_by,
            global_reviews,
            review_stars,
        from base.stg_rf_reviews_initial_parameters
        order by 1,2,3,4,5,6
s
To create an independent op that generates a CSV file from a BigQuery query and to be able to trigger it from the Dagster UI, you can define an op that encapsulates the logic for executing the query and writing the results to a CSV file. This op can then be included in a job, which you can trigger from the Dagster UI. Here's an example of how you might define such an op:
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python from dagster import op, Out, Output from google.cloud import bigquery import pandas as pd  # Replace with your Google Cloud credentials and project details client = bigquery.Client()  @op(out=Out()) def export_bigquery_to_csv(context):     query = """         select             asin,             amazon_domain,             type,             sort_by,             global_reviews,             review_stars,         from base.stg_rf_reviews_initial_parameters         order by 1,2,3,4,5,6     """     df = client.query(query).to_dataframe()     csv_file_path = "/path/to/your/output.csv"  # Replace with your desired file path     df.to_csv(csv_file_path, index=False)     <http://context.log.info|context.log.info>(f"CSV file created at {csv_file_path}")     return csv_file_path
Next, define a job that includes this op:
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python from dagster import job  @job def bigquery_export_job():     export_bigquery_to_csv()
Now, you can trigger 
bigquery_export_job
from the Dagster UI. When you run the job, it will execute the 
export_bigquery_to_csv
op, which runs the BigQuery query and writes the results to a CSV file. Please ensure that you have the necessary permissions and that the Google Cloud credentials are correctly set up for the Dagster instance to access BigQuery. You may also need to handle authentication with Google Cloud explicitly if your environment requires it. Once you have this job defined, you can go to the Dagster UI, find the job, and trigger a run manually. The UI provides an interface to start runs, monitor their progress, and review logs and outputs.
s
@Scout why a job and not an asset? Why the op has 
out=Out()
? Why the op has a 
return
?
s
A job in Dagster is a collection of ops that defines an executable workflow. You would use a job instead of an asset when your primary goal is to execute a series of computations or tasks, rather than to define and track the lineage of a data asset. In the context of your requirement, where you want to execute a BigQuery query and write the results to a CSV file, a job is appropriate because it represents a discrete unit of work that can be executed on demand. The 
out=Out()
in the op definition is used to declare that the op has an output. This is part of Dagster's type system, which allows you to specify the types of inputs and outputs for ops. In this case, the op is expected to produce an output, which is the path to the CSV file that it creates. By declaring an output, you can pass the result of this op to other ops within a job if needed. The 
return
statement in the op is used to return the output value from the op. When you return a value from an op with an output definition, Dagster knows to pass this value to downstream ops or to make it available for other uses, such as in hooks or resources. In this example, the op returns the 
csv_file_path
, which is the path to the CSV file that was written. This could be useful if you have subsequent ops that need to know the location of the CSV file to perform further processing. If you don't need to pass the file path to other ops and you're not interested in capturing the output for any other reason, you could omit the 
out=Out()
and the 
return
statement. The op would still write the CSV file, but Dagster wouldn't track the file path as an output of the op. In summary, you would use a job when you want to execute a task, and you would define outputs in an op when you want to pass data to other ops or track the results of the op's execution. If you don't need to track the output, you can omit the output definition and the 
return
statement.
s
@Scout once I call the API multiple times, I need a sensor that waits for a specific list of files generated by these api calls. How do I share this list from the op to the sensor?
s
To share a list of files generated by API calls from an op to a sensor, you can use Dagster's event log to record metadata during the op's execution. Then, the sensor can read this metadata from the event log to retrieve the list of files. Here's how you can implement this: 1. Record Metadata in the Op: When your op makes the API calls and expects files to be generated, emit an event with metadata that includes the list of expected files. You can use 
yield AssetMaterialization
with metadata entries for this purpose.
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python from dagster import op, AssetMaterialization, MetadataEntry  @op def my_api_calling_op(context):     # Logic to make API calls and generate the list of expected files     expected_files = ["file1.txt", "file2.txt", "file3.txt"]     # Emit an event with metadata     for file in expected_files:         yield AssetMaterialization(             asset_key="expected_file",             metadata_entries=[MetadataEntry.text(text=file, label="Expected File")]         )     # Continue with the rest of your logic
2. Create a Sensor to Read the Event Log: Define a sensor that reads the event log for the 
AssetMaterialization
events and retrieves the list of expected files.
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python from dagster import sensor, SensorEvaluationContext, RunRequest  @sensor(job=my_job) def my_file_sensor(context: SensorEvaluationContext):     records = context.instance.get_event_records(         event_type=DagsterEventType.ASSET_MATERIALIZATION,         asset_key="expected_file",     )     if not records:         <http://context.log.info|context.log.info>("No expected files metadata found.")         return      # Assuming the latest record contains the latest list of expected files     expected_files = [         entry.entry_data.text         for record in records         for entry in record.event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries         if entry.label == "Expected File"     ]      # Check for the presence of expected files in your storage     # and yield RunRequest if all files are present     # ...
3. Trigger the Job with the Sensor: When the sensor detects that all the expected files are present, it can yield a 
RunRequest
to trigger the next step in your pipeline. Please note that this example assumes that each expected file is recorded as a separate 
AssetMaterialization
event. Depending on your use case, you might want to record all expected files in a single event or use a different approach to structure the metadata. This approach allows you to pass dynamic information from an op to a sensor without requiring them to be directly connected. The sensor can independently check the event log and act based on the metadata recorded by the op.
s
@Scout I think it would be better to pass all the files together.
s
Certainly, passing all the expected file names together in a single metadata entry is a cleaner approach. You can modify the op to emit a single 
AssetMaterialization
event with a metadata entry that contains the list of all expected file names. Here's how you can do it: 1. Record Metadata in the Op: Emit an event with metadata that includes the list of expected files as a JSON list.
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python from dagster import op, AssetMaterialization, MetadataEntry  @op def my_api_calling_op(context):     # Logic to make API calls and generate the list of expected files     expected_files = ["file1.txt", "file2.txt", "file3.txt"]     # Emit an event with metadata     yield AssetMaterialization(         asset_key="expected_files",         metadata_entries=[MetadataEntry.json({"expected_files": expected_files}, label="Expected Files")]     )     # Continue with the rest of your logic
2. Create a Sensor to Read the Event Log: Define a sensor that reads the event log for the 
AssetMaterialization
event and retrieves the list of expected files from the metadata.
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python from dagster import sensor, SensorEvaluationContext, RunRequest, DagsterEventType  @sensor(job=my_job) def my_file_sensor(context: SensorEvaluationContext):     records = context.instance.get_event_records(         event_type=DagsterEventType.ASSET_MATERIALIZATION,         asset_key="expected_files",     )     if not records:         <http://context.log.info|context.log.info>("No expected files metadata found.")         return      # Assuming the latest record contains the latest list of expected files     latest_record = records[-1]     expected_files_metadata = latest_record.event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries      # Find the metadata entry with the label "Expected Files"     for metadata_entry in expected_files_metadata:         if metadata_entry.label == "Expected Files":             expected_files = metadata_entry.entry_data.json             break     else:         <http://context.log.info|context.log.info>("Expected files metadata entry not found.")         return      # Check for the presence of expected files in your storage     # and yield RunRequest if all files are present     # ...
3. Trigger the Job with the Sensor: When the sensor detects that all the expected files are present, it can yield a 
RunRequest
to trigger the next step in your pipeline. By emitting a single 
AssetMaterialization
event with a JSON list of expected files, you can easily pass this list from the op to the sensor. The sensor then reads this list from the event log and uses it to check for the presence of the files. This approach ensures that all expected file names are kept together and can be retrieved as a single unit.
s
@Scout is this not breaking Law of Demeter 
expected_files_metadata = latest_record.event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries
? Is really this the most elegant way to obtain the metadata?
s
The Law of Demeter is a guideline that suggests that a given object should avoid reaching into the internals of other objects. The line of code you've highlighted does indeed traverse multiple levels of object properties, which could be seen as a violation of this principle. However, in the context of a framework like Dagster, it's sometimes necessary to navigate through the provided API to retrieve the information you need. The documentation indicates that within the body of an op, a stream of structured events can be yielded or logged, and these events will be processed by Dagster and recorded in the event log. To access this information later, such as in a sensor, you may need to traverse the event log structure to get to the specific data you're interested in. While the line of code does access multiple properties, it is using the official API provided by Dagster to access event log records. This is the prescribed way to retrieve metadata about events that have occurred during pipeline execution. If you're looking for a more encapsulated way to access this data, you could potentially wrap this logic in a helper function or method that abstracts away the traversal of the event log structure. This would make your sensor code cleaner and more maintainable. Here's an example of how you might do that:
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python from dagster import sensor, SensorEvaluationContext, RunRequest, DagsterEventType  def get_expected_files_from_event_log(context: SensorEvaluationContext):     records = context.instance.get_event_records(         event_type=DagsterEventType.ASSET_MATERIALIZATION,         asset_key="expected_files",     )     if not records:         return None      latest_record = records[-1]     for metadata_entry in latest_record.event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries:         if metadata_entry.label == "Expected Files":             return metadata_entry.entry_data.json     return None  @sensor(job=my_job) def my_file_sensor(context: SensorEvaluationContext):     expected_files = get_expected_files_from_event_log(context)     if expected_files is None:         <http://context.log.info|context.log.info>("No expected files metadata found.")         return      # Check for the presence of expected files in your storage     # and yield RunRequest if all files are present     # ...
By using a helper function like 
get_expected_files_from_event_log
, you can keep your sensor code clean and adhere more closely to the Law of Demeter. This function encapsulates the logic for traversing the event log and can be reused wherever you need to access the expected files metadata.
s
@Scout I have the sensor that does the following @sensor(job=process_bigquery_results) def csv_file_sensor(context: SensorEvaluationContext): csv_files = glob.glob(os.path.join(WATCH_DIRECTORY, "*.csv")) for csv_file_path in csv_files: # If the file exists, yield a RunRequest to trigger the job yield RunRequest( run_key=csv_file_path # No need to specify run_config if the job has no special runtime configuration ) How do I acccess to the run_key in the process_bigquery_results job?
s
In Dagster, the 
run_key
provided in a 
RunRequest
is used to uniquely identify a run triggered by a sensor. It is not directly passed as an input or configuration to the job or its ops. Instead, the 
run_key
is used by Dagster to deduplicate runs and ensure that the same run is not triggered multiple times for the same sensor evaluation. If you want to access the file path (which you are using as the 
run_key
) within the 
process_bigquery_results
job, you should pass it as part of the 
run_config
when yielding the 
RunRequest
. Here's how you can modify the sensor to include the file path in the `run_config`:
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python import glob import os from dagster import sensor, RunRequest, SensorEvaluationContext  WATCH_DIRECTORY = '/path/to/watch/directory'  @sensor(job=process_bigquery_results) def csv_file_sensor(context: SensorEvaluationContext):     csv_files = glob.glob(os.path.join(WATCH_DIRECTORY, "*.csv"))     for csv_file_path in csv_files:         # If the file exists, yield a RunRequest to trigger the job         yield RunRequest(             run_key=csv_file_path,             run_config={                 "ops": {                     "name_of_op_that_needs_csv_file_path": {                         "config": {                             "csv_file_path": csv_file_path                         }                     }                 }             }         )
In this example, 
name_of_op_that_needs_csv_file_path
should be replaced with the actual name of the op within your 
process_bigquery_results
job that requires the CSV file path as a configuration parameter. Next, you'll need to ensure that the op within your job is configured to accept the 
csv_file_path
as a configuration parameter. Here's an example of what the op might look like:
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python from dagster import op, Field, String  @op(config_schema={"csv_file_path": Field(String)}) def my_op_with_csv_input(context):     csv_file_path = context.op_config["csv_file_path"]     <http://context.log.info|context.log.info>(f"CSV file path is: {csv_file_path}")     # Your logic here
And the job definition would include this op:
Copy code
python from dagster import job  @job def process_bigquery_results():     my_op_with_csv_input()
When the sensor triggers the job, the 
run_config
provided in the 
RunRequest
will be used to pass the CSV file path to the op as a configuration parameter. This way, the op within the job can access the file path and perform the necessary processing.
s
@Scout dagster._core.errors.DagsterInvariantViolationError: No repository and more than one job found in "review_scraper". If you load a file or module directly it must have only one job in scope. Found jobs defined in variables or decorated functions: ['process_bigquery_results', 'process_rainforestapi_results']. /usr/local/lib/python3.11/site-packages/dagster/_core/workspace/context.py619 UserWarning: Error loading repository location review_scraperdagster. core.errors.DagsterInvariantViolationError No repository and more than one job found in "review_scraper". If you load a file or module directly it must have only one job in scope. Found jobs defined in variables or decorated functions: ['process_bigquery_results', 'process_rainforestapi_results'].
s
The error message you're encountering indicates that Dagster has found more than one job in the "review_scraper" module or file, and you haven't explicitly defined a repository to contain these jobs. When you load a file or module directly in Dagster, it expects to find only one job unless you define a repository that groups multiple jobs together. To resolve this error, you should define a repository that includes all the jobs you want to be available in the "review_scraper" module. Here's an example of how to define a repository:
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python from dagster import repository from .jobs import process_bigquery_results, process_rainforestapi_results  @repository def my_repository():     """     The repository definition for this Dagster project.     """     return [process_bigquery_results, process_rainforestapi_results]
In this example, 
process_bigquery_results
and 
process_rainforestapi_results
are the jobs you've defined elsewhere in your module (you'll need to adjust the import paths according to your project structure). The 
@repository
decorator is used to create a repository that includes both jobs. Once you've defined a repository, Dagster will be able to load it and recognize all the jobs contained within it. Make sure that the repository is accessible from the module or file you're loading in your workspace configuration, and Dagster should be able to load the repository without the 
DagsterInvariantViolationError
.
s
@Scout what's the difference between a Repository or a Definition? Both seem to be a container of jobs, sensors, assets...
s
In Dagster, a Repository is a legacy concept that was used to group together jobs, sensors, assets, and other definitions. It served as a container for these components and was the primary way to organize and load them into tools like the Dagster CLI or Dagit UI. Definitions, on the other hand, is a newer API introduced to simplify the organization of Dagster components and to replace the Repository and Workspace concepts. Definitions is designed to be a more straightforward and robust way to define a collection of jobs, assets, and other Dagster objects within a single code location. With Definitions, there can only be one Definitions object per code location, which helps to avoid confusion and streamline the structure of Dagster projects. The key differences between Repository and Definitions are: 1. Multiplicity: With the older Repository API, you could have multiple repositories within a single code location. With Definitions, there is only one Definitions object per code location. 2. Simplicity: Definitions provides a simpler, more intuitive API with typed, named arguments, which improves discovery, documentation, and robustness. 3. Resources: Definitions takes a top-level resources argument, which is automatically applied to assets, rather than requiring the use of 
with_resources
. Additionally, the resources dictionary in Definitions can accept raw objects, not just resource definitions. 4. Migration: For users who want to migrate from the older Repository API to the new Definitions API, there are migration paths available that allow for incremental changes. This includes the ability to convert a Definitions object to a repository using helper functions, allowing users to maintain their existing deployment setup during the transition. Overall, Definitions is considered the best practice for organizing Dagster components in new projects, while Repository is maintained for backward compatibility and for users who are in the process of migrating to the new API.
s
@Scout I have two jobs and one sensor, job_1 and job_2, and sensor_1. sensor_1 is waiting for an async output triggered by job_1 (an api call that creates a file). Is there any way to have a reference between them? If I open the dagster UI, they're completely independent jobs.
s
In Dagster, sensors are typically used to monitor external systems and trigger jobs based on changes in those systems. If you have a sensor (
sensor_1
) that is waiting for an asynchronous output triggered by 
job_1
(such as an API call that creates a file), and you want to establish a reference between 
job_1
, 
job_2
, and 
sensor_1
, you can use the following approaches: 1. Run Tags: When 
job_1
triggers the API call, it can add specific tags to its run. These tags can include identifiers that the sensor can look for. When 
sensor_1
detects the file and triggers 
job_2
, it can copy these tags from the triggering run to the new run, creating a traceable link between them. 2. Asset Materialization: If the file created by the API call represents an asset, 
job_1
can emit an 
AssetMaterialization
event. The sensor can then be configured to trigger 
job_2
based on the presence of this asset. This creates a lineage between the jobs through the asset. 3. Custom Event Logs: 
job_1
can emit custom events to the Dagster event log with metadata about the API call. 
sensor_1
can read these events and use the information to trigger 
job_2
, effectively linking the jobs through the event log. 4. Shared Storage or Database: 
job_1
can write metadata about the API call and the expected file to a shared storage system or database. 
sensor_1
can poll this storage to check for updates and trigger 
job_2
accordingly. In the Dagster UI, jobs and sensors are displayed independently because they are separate entities within the system. However, by using the methods above, you can create an implicit reference between them based on the metadata, tags, or assets they share. This won't necessarily change how they appear in the UI, but it will allow you to trace the relationship between the jobs and the sensor through the metadata and logs. It's important to note that while these methods can create a reference between the jobs and the sensor, Dagster's UI may not visually represent these relationships directly. The linkage is more about the logical and data dependencies that you can infer from the metadata and event logs.
s
@Scout my interest is to create some kind of visual lineage. How would I do it with asset materialization?
s
To create a visual lineage in Dagster using asset materialization, you would define assets that represent the outputs of your jobs and then use the asset observation and materialization events to track the lineage of these assets. When an asset is materialized, Dagster records this event, and you can view the lineage of assets in the Dagster UI. Here's a step-by-step guide on how to do it: 1. Define Assets: Define assets in your Dagster system that correspond to the outputs of 
job_1
and 
job_2
. These assets should represent the data artifacts that are produced by the jobs, such as files or database entries. 2. Emit AssetMaterialization Events: In 
job_1
, when the API call is made and the file is created, emit an 
AssetMaterialization
event. This event should include metadata about the file and mark it as a materialized asset.
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python from dagster import op, AssetMaterialization  @op def job_1_op(context):     # ... logic to trigger API call and create a file ...     file_path = "path/to/generated/file.csv"     yield AssetMaterialization(         asset_key="generated_file",         description="File generated by API call",         metadata={"file_path": file_path}     )     # ... rest of the logic ...
3. Use Sensors to Monitor Assets: Create a sensor (
sensor_1
) that monitors for the materialization of the asset created by 
job_1
. When the sensor detects the materialization, it triggers 
job_2
.
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python from dagster import sensor, RunRequest  @sensor(job=job_2) def sensor_1(context):     # Logic to check for the materialization of the asset from job_1     # If the asset is materialized, trigger job_2     if check_asset_materialized("generated_file"):         yield RunRequest(run_key="unique_run_key_for_job_2")
4. Visualize Lineage in the UI: Once you have assets defined and materialized, you can view the lineage of these assets in the Dagster UI. The UI provides a visual representation of the relationships between assets, including their upstream and downstream dependencies. By following these steps, you create a visual lineage in the Dagster UI that shows how assets are related to each other and how data flows through your system. The lineage graph will update as new materializations occur, providing a dynamic view of your data pipeline. Please note that the function 
check_asset_materialized
in the sensor example is a placeholder for whatever logic you would use to check for the materialization of the asset. You might query the Dagster event log or use other mechanisms to determine if the asset has been materialized.
s
@Scout Let's say I trigger
Copy code
yield AssetMaterialization(
        asset_key="generated_file",
        description="File generated by API call",
        metadata={"file_path": "data/file.csv" }
    )
How would I do the
Copy code
check_asset_materialized("generated_file")
s
In Dagster, there isn't a built-in function called 
check_asset_materialized
that you can call directly to check if an asset has been materialized. Instead, you would typically write a sensor that polls an external system or checks some condition to determine if the asset should be considered materialized. However, if you want to check for an 
AssetMaterialization
event within the Dagster event log, you would need to query the event log for the specific asset key. Here's an example of how you might write a sensor to do this:
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python from dagster import sensor, SensorEvaluationContext, RunRequest from dagster.core.storage.event_log import EventRecordsFilter from dagster.core.definitions.events import DagsterEventType  @sensor(job=job_2) def sensor_1(context: SensorEvaluationContext):     # Define the asset key we're interested in     asset_key = "generated_file"          # Query the event log for AssetMaterialization events for this asset key     records = context.instance.get_event_records(         EventRecordsFilter(             event_type=DagsterEventType.ASSET_MATERIALIZATION,             asset_key=asset_key,         ),         limit=1,     )          # If we find a record, it means the asset has been materialized     if records:         yield RunRequest(run_key="unique_run_key_for_job_2")
In this example, 
context.instance.get_event_records
is used to query the event log for 
AssetMaterialization
events related to the asset key 
generated_file
. If such an event is found, it indicates that the asset has been materialized, and the sensor yields a 
RunRequest
to trigger 
job_2
. Please note that this example assumes that the sensor has access to the Dagster instance (
context.instance
) and that the asset key is unique enough to identify the materialization event you're interested in. If there are multiple materialization events for the same asset key, you may need to add additional logic to determine which event corresponds to the materialization you want to trigger on. Keep in mind that this sensor will trigger 
job_2
every time it runs and detects that the asset has been materialized at least once. If you want to ensure that 
job_2
is triggered only once per materialization, you'll need to implement additional logic to track which materializations have already been processed. This could involve using run tags, custom metadata, or external tracking mechanisms.
s
@Scout In my example, the op of job_1 is calling an external API, that creates a file in an async way, so the file is not ready when I trigger the event. How can we have a lineage between job_1 and sensor_1?
s
To establish a lineage between 
job_1
and 
sensor_1
when 
job_1
triggers an external API call that creates a file asynchronously, you can use the following approach: 1. Tag the Run in 
job_1
: When 
job_1
triggers the API call, include a unique identifier as a tag in the run. This tag could be a unique ID, timestamp, or any other identifier that can be associated with the expected file.
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python from dagster import op, job, RunRequest, AssetMaterialization  @op def trigger_api_call(context):     # Logic to trigger the external API call     unique_id = "unique_identifier_for_the_run"  # Generate or obtain a unique ID     context.add_run_tag("unique_id", unique_id)     # Optionally, yield an AssetMaterialization for lineage tracking     yield AssetMaterialization(asset_key="async_api_call", metadata={"unique_id": unique_id})  @job def job_1():     trigger_api_call()
2. Poll for the File in 
sensor_1
: The sensor will poll for the file's presence. Once the file is detected, the sensor can use the unique identifier to link the file back to the original run of 
job_1
.
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python from dagster import sensor, SensorEvaluationContext, RunRequest import os  WATCH_DIRECTORY = '/path/to/watch/directory'  @sensor(job=job_2) def sensor_1(context: SensorEvaluationContext):     # Logic to check for the presence of the file     for filename in os.listdir(WATCH_DIRECTORY):         if filename.endswith(".csv"):             file_path = os.path.join(WATCH_DIRECTORY, filename)             # Logic to extract the unique identifier from the file or its metadata             unique_id = extract_unique_id(file_path)             if unique_id:                 # Use the unique identifier as the run_key to establish lineage                 yield RunRequest(run_key=unique_id)
3. Visualize Lineage in the UI: In the Dagster UI, you can view the lineage of assets and runs. The 
AssetMaterialization
event emitted by 
job_1
will show up in the asset catalog, and the runs triggered by 
sensor_1
will have the unique identifier as a tag, allowing you to trace the lineage from the run of 
job_1
to the file creation and subsequent triggering of 
job_2
. Please note that the function 
extract_unique_id
is a placeholder for your logic to associate the detected file with the unique identifier from 
job_1
. This could involve naming conventions, metadata within the file, or a separate tracking system. By using run tags and asset materialization events, you can create a visual lineage in the Dagster UI that connects 
job_1
, the asynchronous file creation, and 
sensor_1
. This approach allows you to trace the flow of data and the relationship between different components of your pipeline.
s
@Scout I still don't see how with this code will Dagster be able to understand they're both connected. And why does the sensor need 
context: SensorEvaluationContext
that it's never used?
s
You're correct that the code provided does not explicitly connect 
job_1
and 
sensor_1
in a way that Dagster can automatically understand and visualize in the UI. The connection between the job and the sensor is logical rather than explicit, based on the unique identifier used as a tag and the file creation event. Dagster's current UI does not automatically visualize the lineage between jobs and sensors based on such indirect connections. To clarify the use of 
context: SensorEvaluationContext
in the sensor, it's a parameter that provides context for the sensor evaluation, including access to the Dagster instance, which can be used to read run logs, access resources, and more. In the example provided, 
context
is indeed not used, but it's part of the sensor function signature, and it's there to allow you to use the context if needed. For visual lineage in the UI, Dagster currently supports direct asset lineage visualization, where assets are connected based on their input and output relationships. However, for the scenario you described, where an external process creates a file that a sensor then detects, the lineage is not directly visualizable in the UI because it involves external systems and asynchronous events that are not captured as part of the asset's input/output graph. To improve the traceability of such operations in the UI, you could: 1. Use Asset Sensors: If the file created by the API call can be considered an asset, you could create an asset sensor that triggers a job when a new asset materialization event is detected. This would allow you to see the asset in the asset catalog and track runs associated with it. 2. Custom UI Enhancements: For a more explicit lineage visualization, you might need to look into custom UI enhancements or integrations that can track and display these indirect relationships. This could involve additional metadata tracking and custom visualization tools. 3. Documentation and Naming Conventions: Document the relationship between jobs and sensors and use consistent naming conventions for runs, files, and assets to make it easier to manually trace the lineage. As of my knowledge cutoff in early 2023, there is an open feature request on Dagster's GitHub repository discussing the idea of displaying sensors in the asset lineage view to connect assets and jobs, which indicates that this is a recognized area for potential improvement in the platform. For now, the lineage between asynchronous operations and sensors is something you may need to track manually or through custom tooling. The 
SensorEvaluationContext
is there to provide you with the necessary hooks into the Dagster system should you need them for more complex sensor logic.
s
@Scout give me an example using asset sensors?
s
To use asset sensors for lineage tracking in Dagster, you would define an asset sensor that triggers a job when a new 
AssetMaterialization
event is detected for a particular asset. Here's an example of how you can set this up: First, define an asset within 
job_1
that represents the file created by the API call. When the file is created, emit an 
AssetMaterialization
event:
Copy code
python from dagster import op, AssetMaterialization, AssetKey  @op def job_1_op(context):     # Logic to trigger the external API call and create a file     # ...      # Once the file is created, emit an AssetMaterialization event     yield AssetMaterialization(         asset_key=AssetKey("generated_file"),         description="File generated by API call",         metadata={"file_path": "data/file.csv"}     )
Next, define an asset sensor that monitors for materializations of the 
generated_file
asset and triggers 
job_2
when a new materialization is detected:
Copy code
python from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  @asset_sensor(asset_key=AssetKey("generated_file"), job=job_2) def asset_file_sensor(context: SensorEvaluationContext):     # The sensor is triggered by a materialization of the "generated_file" asset     yield RunRequest(         run_key=context.cursor,  # Use the cursor as a unique identifier for the run         run_config={             # Include any necessary run configuration for job_2         }     )
In this example, 
job_2
is the job that you want to trigger when the 
generated_file
asset is materialized. The 
asset_file_sensor
is decorated with 
@asset_sensor
, specifying the asset key to monitor and the job to trigger. When 
job_1_op
emits an 
AssetMaterialization
event, the 
asset_file_sensor
will detect this new materialization and yield a 
RunRequest
to trigger 
job_2
. The 
run_key
is set to 
context.cursor
, which is a unique identifier that Dagster updates after each sensor evaluation, ensuring that each run is unique. In the Dagster UI, you will be able to see the lineage between the 
generated_file
asset and any downstream computations or jobs triggered by the asset sensor. This provides a visual representation of how data flows through your system based on asset materializations. Please note that the actual logic for triggering the API call and detecting the file creation is not shown here and would need to be implemented according to your specific use case. Additionally, the 
run_config
in the 
RunRequest
should be configured with any necessary settings for 
job_2
.
s
@Scout but job_1_op is calling an API that generates a file in async way. The sensor needs to way for the file to be materialized
s
In the case where 
job_1_op
triggers an API call that will asynchronously generate a file, you would use an asset sensor to wait for the file to be materialized. The asset sensor periodically checks for new materialization events and triggers downstream jobs when such events are detected. Here's a conceptual example of how you might set up an asset sensor to wait for a file to be materialized: 1. Define an Asset in 
job_1
: This asset represents the expected file. When 
job_1
runs, it will trigger the API call and expect the file to be created at some point in the future.
Copy code
python
from dagster import op, AssetMaterialization, AssetKey

@op
def job_1_op(context):
    # Logic to trigger the external API call
    # ...

    # Emit an AssetMaterialization event to indicate that the file is expected
    yield AssetMaterialization(
        asset_key=AssetKey("expected_file"),
        description="Expected file to be generated by an external API"
    )
2. Create an Asset Sensor: This sensor will monitor for the actual materialization of the 
expected_file
asset, which occurs when the file is finally created by the external API.
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python
from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey

@asset_sensor(asset_key=AssetKey("expected_file"), job=job_2)
def asset_file_sensor(context: SensorEvaluationContext):
    # The sensor is triggered by a materialization of the "expected_file" asset
    # Here you would include logic to check if the file has actually been created
    # by the external API and is now present in the expected location

    # If the file is detected, yield a RunRequest to trigger job_2
    if file_is_present("path/to/expected/file.csv"):
        yield RunRequest(
            run_key=context.cursor,  # Use the cursor as a unique identifier for the run
            run_config={
                # Include any necessary run configuration for job_2
            }
        )
In this example, 
file_is_present
would be a function you define that checks whether the expected file has been created by the external API. The asset sensor runs periodically, and each time it runs, it will check for the presence of the file. When the file is detected, the sensor yields a 
RunRequest
to trigger 
job_2
. The 
run_key
is set to 
context.cursor
, which Dagster uses to ensure that each run is unique. This setup allows you to create a lineage between 
job_1
, which expects a file to be created, and 
job_2
, which is triggered by the actual creation of the file. The asset sensor serves as the bridge between the asynchronous file creation and the continuation of your data pipeline. Please note that the actual implementation of 
file_is_present
and the logic to check for the file's presence will depend on your s
pecific infrastructure and how the external API indicates that the file has been created.
s
@Scout why do we need a cursor? I really don't understand the logic of it? And what if I need to wait for multiple files to be generated before job_2 is executed?
s
The use of a cursor in a sensor is for state management across sensor evaluations. It helps the sensor remember what it has already processed, so it doesn't re-trigger runs for the same events. In the context of an asset sensor, the cursor is automatically managed by Dagster and is updated to the timestamp of the last materialization event that the sensor has processed. However, if you're waiting for multiple files to be generated before executing 
job_2
, and these files are all part of a single asset, you wouldn't necessarily need to manage the cursor yourself. Instead, you would rely on the asset sensor to trigger 
job_2
once all the expected files have been materialized. Here's a conceptual example of how you might set up an asset sensor to wait for multiple files: 1. Define an Asset in 
job_1
: This asset represents the expected files. When 
job_1
runs, it will trigger the API calls and expect the files to be created at some point in the future.
Copy code
python
from dagster import op, AssetMaterialization, AssetKey

@op
def job_1_op(context):
    # Logic to trigger the external API calls
    # ...

    # Emit an AssetMaterialization event to indicate that the files are expected
    yield AssetMaterialization(
        asset_key=AssetKey("expected_files"),
        description="Expected files to be generated by external API calls"
    )
2. Create an Asset Sensor: This sensor will monitor for the actual materialization of the 
expected_files
asset, which occurs when all the files are finally created by the external API.
Copy code
python
from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey

@asset_sensor(asset_key=AssetKey("expected_files"), job=job_2)
def asset_files_sensor(context: SensorEvaluationContext):
    # The sensor is triggered by a materialization of the "expected_files" asset
    # Here you would include logic to check if all the expected files have been created
    # by the external API and are now present in the expected location

    # If all files are detected, yield a RunRequest to trigger job_2
    if all_files_are_present(["path/to/expected/file1.csv", "path/to/expected/file2.csv"]):
        yield RunRequest(
            run_key=context.cursor,  # Use the cursor as a unique identifier for the run
            run_config={
                # Include any necessary run configuration for job_2
            }
        )
In this example, 
all_files_are_present
would be a function you define that checks whether all the expected files have been created by the external API. The asset sensor runs periodically, and each time it runs, it will check for the presence of all the files. When all the files are detected, the sensor yields a 
RunRequest
to trigger 
job_2
. The 
run_key
is set to `context.curso
r`, which Dagster uses to ensure that each run is unique. This setup allows you to create a lineage between 
job_1
, which expects multiple files to be created, and 
job_2
, which is triggered by the actual creation of all the files. The asset sensor serves as the bridge between the asynchronous file creation and the continuation of your data pipeline. Please note that the actual implementation of 
all_files_are_present
and the logic to check for the files' presence will depend on your specific infrastructure and how the external API indicates that the files have been created.
s
@Scout how is dagster able to create the lineage between both job_1 and sensor_1?
s
In Dagster, lineage between jobs and sensors is not automatically inferred based on the execution of jobs and the triggering of sensors. Instead, lineage is typically established through the materialization of assets and the relationships between them. When a job materializes an asset and a sensor triggers another job based on the materialization of that asset, the lineage is implicitly created through these asset dependencies. Here's how you can establish lineage between 
job_1
and 
sensor_1
using asset materialization: 1. Materialize an Asset in 
job_1
: When 
job_1
triggers the external API call, it should materialize an asset representing the expected outcome, such as the creation of a file.
Copy code
python
from dagster import op, AssetMaterialization, AssetKey

@op
def job_1_op(context):
    # Logic to trigger the external API call
    # ...

    # Emit an AssetMaterialization event to indicate that the file is expected
    yield AssetMaterialization(
        asset_key=AssetKey("expected_file"),
        description="Expected file to be generated by an external API"
    )
2. Create an Asset Sensor (
sensor_1
): Define a sensor that monitors for the materialization of the asset. When the asset is materialized (i.e., the file is created), the sensor will trigger 
job_2
.
Copy code
python
from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey

@asset_sensor(asset_key=AssetKey("expected_file"), job=job_2)
def sensor_1(context: SensorEvaluationContext):
    # The sensor is triggered by a materialization of the "expected_file" asset
    # Here you would include logic to check if the file has actually been created
    # by the external API and is now present in the expected location

    # If the file is detected, yield a RunRequest to trigger job_2
    if file_is_present("path/to/expected/file.csv"):
        yield RunRequest(
            run_key=context.cursor,  # Use the cursor as a unique identifier for the run
            run_config={
                # Include any necessary run configuration for job_2
            }
        )
In the Dagster UI, you can view the asset catalog, which shows all materialized assets and their lineage. When 
job_1
materializes the "expected_file" asset, it will appear in the asset catalog. Then, when 
sensor_1
detects the materialization and triggers 
job_2
, you can see the lineage from 
job_1
to 
job_2
through the asset materialization events. It's important to note that while Dagster tracks asset materializations and allows you to view the asset catalog, the lineage between jobs and sensors based on asset materializations is not explicitly visualized in the UI. However, there is an open feature request on Dagster's GitHub repository discussing the idea of displaying sensors in t
he asset lineage view to connect assets and jobs, which indicates that this is a recognized area for potential improvement in the platform.
s
@Scout raise DagsterInvalidSubsetError( dagster._core.errors.DagsterInvalidSubsetError: AssetKey(s) {AssetKey(['generate_initial_asin_list_asset'])} were selected, but no AssetsDefinition objects supply these keys. Make sure all keys are spelled correctly, and all AssetsDefinitions are correctly added to the 
Definitions
.
s
The error message you're encountering, 
DagsterInvalidSubsetError
, indicates that there is a mismatch between the asset keys you've specified and the asset definitions available in your Dagster 
Definitions
. This error typically occurs when you attempt to select or reference an asset that has not been defined or is not correctly registered within the 
Definitions
object. Here are the steps you should take to resolve this issue: 1. Check Asset Key Spelling: Ensure that the asset key 
'generate_initial_asin_list_asset'
is spelled correctly and matches the key used in the asset definition. 2. Verify Asset Definitions: Make sure that the asset corresponding to the key 
'generate_initial_asin_list_asset'
is defined using the 
@asset
decorator or equivalent method and that it is included in the 
Definitions
. 3. Include Assets in Definitions: When creating your 
Definitions
object, ensure that all asset definitions are included. If you have multiple asset definitions across different modules, you need to collect them and pass them to the 
Definitions
. Here's an example of how you might define an asset and include it in the `Definitions`:
Copy code
python from dagster import asset, Definitions  @asset def generate_initial_asin_list_asset():     # Your asset logic here     ...  # Include the asset in the Definitions defs = Definitions(assets=[generate_initial_asin_list_asset])
If your assets are spread across multiple files or modules, you need to import them and include them in the 
Definitions
object:
Copy code
python from dagster import Definitions from some_module import generate_initial_asin_list_asset from another_module import another_asset  # Include all assets in the Definitions defs = Definitions(assets=[generate_initial_asin_list_asset, another_asset])
Make sure that the 
Definitions
object is correctly referenced in your repository or workspace configuration so that Dagster can recognize and load all the asset definitions. If you've checked all of these and the error persists, it could be helpful to review the stack trace for additional context or to look for any discrepancies in how you're selecting assets for execution, especially if you're using subsets or groups. If necessary, consult the documentation or seek support from the Dagster community for further assistance.
s
@Scout I have the following code. Why there's no lineage between generate_initial_asin_list_asset, process_asin_list_sensor and process_asin_list @asset( description="Generate initial CSV file with all ASINs and regions to be scraped", ) def generate_initial_asin_list_asset(context): query = """ select asin, amazon_domain, type, sort_by, global_reviews, review_stars, from base.stg_rf_reviews_initial_parameters order by 1,2,3,4,5,6 limit 100 """ df = client.query(query).to_dataframe() csv_file_path = os.path.join( WATCH_DIRECTORY, f"all_asins_{datetime.now().strftime('%Y%m%d%H%M%S')}.csv" ) df.to_csv(csv_file_path, index=False) context.log.info(f"CSV file created at {csv_file_path}") # Emit an AssetMaterialization event to indicate that the file is expected yield AssetMaterialization( asset_key=AssetKey("generate_initial_asin_list"), description="CSV file with all ASINs" ) generate_initial_asin_list = define_asset_job( "generate_initial_asin_list_job", selection="generate_initial_asin_list_asset" ) @op(config_schema={"csv_file_path": Field(String)}) def process_asin_list_op(context): csv_file_path = context.op_config["csv_file_path"] context.log.info(f"CSV file path is: {csv_file_path}") file_ids = rainforestapi.process_with_rainforestapi( api_key=os.getenv("RAINFOREST_API_KEY"), type="review", csv_file_path=csv_file_path, ) yield AssetMaterialization( asset_key="expected_files", metadata_entries=[MetadataEntry.text(text=file_ids, label="Expected files")], ) # the job is a container of ops, but it does not receive parameters. # the sensor needs to reference the job that it will trigger # if we want to pass parameters to the ops, it has to be done through # a run_config in the sensor @job def process_asin_list(): process_asin_list_op() @asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list) def process_asin_list_sensor(context: SensorEvaluationContext): csv_files = glob.glob(os.path.join(WATCH_DIRECTORY, "*.csv")) for csv_file_path in csv_files: # If the file exists, yield a RunRequest to trigger the job yield RunRequest( run_key=context.cursor, run_config={ "ops": { "process_asin_list_op": { "config": {"csv_file_path": csv_file_path} } } }, )
s
In the code you've provided, there are a few key components that are intended to work together: 1. `generate_initial_asin_list_asset`: An asset that represents the generation of a CSV file. 2. `process_asin_list_op`: An op that processes the CSV file. 3. `process_asin_list`: A job that contains the 
process_asin_list_op
. 4. `process_asin_list_sensor`: A sensor that triggers the 
process_asin_list
job when a new CSV file is detected. The lineage between these components in Dagster is established through the materialization of assets and the triggering of jobs by sensors based on those materializations. However, there are a few reasons why the lineage might not be visible or behaving as expected: - Asset Materialization Events: The 
generate_initial_asin_list_asset
asset yields an 
AssetMaterialization
event with an 
asset_key
of 
"generate_initial_asin_list"
. However, the 
process_asin_list_sensor
is looking for materializations of an asset with the same key. If the CSV file generation is not being detected as a materialization of that asset, the sensor will not trigger the job. - Asset Keys: The 
process_asin_list_op
yields an 
AssetMaterialization
with an 
asset_key
of 
"expected_files"
. This asset key does not seem to be connected to the 
generate_initial_asin_list_asset
asset or its materialization event. For lineage to be established, there should be a clear relationship between the assets. - Sensor Triggering Logic: The 
process_asin_list_sensor
is designed to trigger the 
process_asin_list
job when a CSV file is found. However, it does not check whether the CSV file is a result of the 
generate_initial_asin_list_asset
asset's execution. The sensor should ideally check for some marker or metadata that connects the detected CSV file to the specific run of 
generate_initial_asin_list_asset
. To improve the lineage tracking, consider the following adjustments: - Ensure that the 
AssetMaterialization
events emitted by your assets use consistent and meaningful asset keys that reflect the actual assets being materialized. - Use metadata entries in your 
AssetMaterialization
events to provide additional context that can be used by sensors to establish connections between assets. - Modify the sensor logic to check for specific markers or metadata that tie the presence of a CSV file to the execution of 
generate_initial_asin_list_asset
. By making these adjustments, you should be able to establish a clearer lineage between your assets and jobs in the Dagster UI. Remember that lineage in Dagster is primarily visualized through the asset graph, which shows the relationships between assets based on their definitions and materializations.
s
@Scout so 
@asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list)
is not actually indicating the sensor to look for materializations of 
generate_initial_asin_list_asset
? How would I do it?
s
The 
@asset_sensor
decorator is indeed used to create a sensor that triggers a job when a materialization event for a specific asset is detected. The 
asset_key
parameter in the 
@asset_sensor
decorator specifies which asset's materialization events the sensor should monitor. In your code, the 
@asset_sensor
is set up to monitor materializations of the asset with the key 
"generate_initial_asin_list"
. When a materialization event for this asset is detected, the sensor should trigger the specified job, which in your case is 
process_asin_list
. Here's the relevant part of your code:
Copy code
python
@asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list)
def process_asin_list_sensor(context: SensorEvaluationContext):
    csv_files = glob.glob(os.path.join(WATCH_DIRECTORY, "*.csv"))
    for csv_file_path in csv_files:
        # If the file exists, yield a RunRequest to trigger the job
        yield RunRequest(
            run_key=context.cursor,
            run_config={
                "ops": {
                    "process_asin_list_op": {
                        "config": {"csv_file_path": csv_file_path}
                    }
                }
            },
        )
This sensor is designed to trigger the 
process_asin_list
job whenever a new CSV file is detected in the 
WATCH_DIRECTORY
. However, the sensor does not currently use the materialization event information to filter which files to process. It simply looks for any CSV files in the directory. To ensure that the sensor only triggers the job when the 
generate_initial_asin_list_asset
asset is materialized, you should modify the sensor to check the event log for the latest materialization event of that asset. Here's an example of how you might do that:
Copy code
python
from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey

@asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list)
def process_asin_list_sensor(context: SensorEvaluationContext, materialization_event):
    # Extract the file path from the materialization event metadata
    csv_file_path = materialization_event.dagster_event.event_specific_data.materialization.metadata_entries[0].entry_data.text

    # Yield a RunRequest to trigger the job with the specific CSV file path
    yield RunRequest(
        run_key=context.cursor,
        run_config={
            "ops": {
                "process_asin_list_op": {
                    "config": {"csv_file_path": csv_file_path}
                }
            }
        },
    )
In this modified sensor, the 
materialization_event
parameter provides access to the 
AssetMaterialization
event that triggered the sensor. The sensor then extracts the file path from the event's metadata and uses it to confi
gure the 
process_asin_list_op
within the 
process_asin_list
job. This approach ensures that the sensor only triggers the job in response to the materialization of the 
generate_initial_asin_list_asset
asset, thereby establishing a clear lineage between the asset's materialization and the subsequent job execution. The lineage should then be visible in the Dagster UI, showing the connection between the asset and the job triggered by its materialization.
s
@Scout the asset is called generate_initial_asin_list_asset. How should the asset key in the configuration of the asset_sensor be called? Also, do I need to yield an AssetMaterialization event or it's automatic? In case I yield it, do I need to put the file name somewhere for the sensor to be able to pick it up with 
csv_file_path = materialization_event.dagster_event.event_specific_data.materialization.metadata_entries[0].entry_data.text
?
s
The asset key in the configuration of the 
@asset_sensor
should match the key of the asset you want to monitor. In your case, since the asset is defined as 
generate_initial_asin_list_asset
, you should use an 
AssetKey
that matches the name you've given to this asset when you yield the 
AssetMaterialization
event. Regarding whether you need to yield an 
AssetMaterialization
event, yes, you do need to yield it explicitly in your op if you want to create an asset materialization event that the sensor can pick up. This event is not automatic; it's a way to manually signal that an asset has been created or updated. When you yield an 
AssetMaterialization
event, you should include metadata that contains the file name or path if you want the sensor to pick it up later. Here's an example of how you might yield an 
AssetMaterialization
event with the file path in the metadata:
Copy code
python
from dagster import op, AssetMaterialization, MetadataEntry

@op
def generate_initial_asin_list_asset(context):
    # ... your logic to generate the CSV file ...
    csv_file_path = "path/to/generated/file.csv"
    # Emit an AssetMaterialization event with the file path in the metadata
    yield AssetMaterialization(
        asset_key=AssetKey("generate_initial_asin_list"),
        metadata_entries=[MetadataEntry.path(csv_file_path)]
    )
In this example, 
MetadataEntry.path(csv_file_path)
is used to attach the file path to the 
AssetMaterialization
event. This metadata entry will then be available to the sensor. Now, when you define your 
@asset_sensor
, you can access this metadata in the sensor function to get the file path:
Copy code
python
from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey

@asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list)
def process_asin_list_sensor(context: SensorEvaluationContext, materialization_event):
    # Extract the file path from the materialization event metadata
    csv_file_path = materialization_event.dagster_event.event_specific_data.materialization.metadata_entries[0].entry_data.path

    # Yield a RunRequest to trigger the job with the specific CSV file path
    yield RunRequest(
        run_key=context.cursor,
        run_config={
            "ops": {
                "process_asin_list_op": {
                    "config": {"csv_file_path": csv_file_path}
                }
            }
        },
    )
In this sensor function, 
materialization_event.dagster_event.event_specific_data.materialization.metadata_entries[0].entry_data.path
is used to retrieve the file path from the metadata of the 
AssetMaterialization
event. This path is then passed to the 
process_asin_list_op
via the 
run_config
in the 
RunRequest
. By following this approach, you ens
ure that the 
@asset_sensor
is correctly configured to monitor the materialization of your asset and that the 
AssetMaterialization
event contains the necessary metadata for the sensor to trigger the downstream job with the appropriate file path.
s
@Scout dagster._core.errors.DagsterExecutionStepExecutionError: Error occurred while executing op "generate_initial_asin_list_asset":: AttributeError: type object 'MetadataEntry' has no attribute 'path'
s
The error message you're encountering indicates that there is no attribute 
path
on the 
MetadataEntry
type. This is correct; the 
MetadataEntry
class does not have a 
path
method. Instead, you should use the appropriate method to create a metadata entry for the file path. To include the file path in the metadata of an 
AssetMaterialization
, you can use the 
MetadataEntry.text
or 
MetadataEntry.fspath
method, depending on how you want to represent the file path. The 
fspath
method is specifically designed for file paths and will ensure that the Dagster UI displays the path correctly. Here's how you can modify your op to yield an 
AssetMaterialization
event with the correct metadata entry for the file path:
Copy code
python from dagster import op, AssetMaterialization, MetadataEntry, AssetKey  @op def generate_initial_asin_list_asset(context):     # ... your logic to generate the CSV file ...     csv_file_path = "path/to/generated/file.csv"     # Emit an AssetMaterialization event with the file path in the metadata     yield AssetMaterialization(         asset_key=AssetKey("generate_initial_asin_list"),         metadata_entries=[MetadataEntry.fspath(csv_file_path)]     )
In this example, 
MetadataEntry.fspath(csv_file_path)
is used to attach the file path to the 
AssetMaterialization
event. When defining your 
@asset_sensor
, you can access this metadata in the sensor function to get the file path:
Copy code
python from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  @asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list) def process_asin_list_sensor(context: SensorEvaluationContext, materialization_event):     # Extract the file path from the materialization event metadata     for metadata_entry in materialization_event.dagster_event.event_specific_data.materialization.metadata_entries:         if metadata_entry.label == "path":             csv_file_path = metadata_entry.entry_data.path      # Yield a RunRequest to trigger the job with the specific CSV file path     yield RunRequest(         run_key=context.cursor,         run_config={             "ops": {                 "process_asin_list_op": {                     "config": {"csv_file_path": csv_file_path}                 }             }         },     )
In this sensor function, we iterate over the metadata entries to find the one with the label 
"path"
and then retrieve the file path from it. This path is then passed to the 
process_asin_list_op
via the 
run_config
in the 
RunRequest
. By using 
MetadataEntry.fspath
, you ensure that the file path is correctly included in the 
AssetMaterialization
event and can be accessed by the sensor to trigger the downstream job.
s
@Scout we write the path with fspath, but later recover it searching for a metadata_entry with label "path"?
s
I apologize for the confusion. When you use 
MetadataEntry.fspath
to include the file path in the metadata of an 
AssetMaterialization
, you do not need to assign a label called "path" to it. The 
fspath
method will automatically handle the file path as a special kind of metadata entry that is recognized by Dagster. When you want to retrieve the file path from the 
AssetMaterialization
event in the sensor, you should look for the metadata entry that contains the file path. The 
fspath
method creates a 
PathMetadataEntry
, and you can access the path directly from the 
entry_data
attribute of the metadata entry. Here's how you can yield an 
AssetMaterialization
with the file path using `MetadataEntry.fspath`:
Copy code
python from dagster import op, AssetMaterialization, MetadataEntry, AssetKey  @op def generate_initial_asin_list_asset(context):     # ... your logic to generate the CSV file ...     csv_file_path = "path/to/generated/file.csv"     # Emit an AssetMaterialization event with the file path in the metadata     yield AssetMaterialization(         asset_key=AssetKey("generate_initial_asin_list"),         metadata_entries=[MetadataEntry.fspath(csv_file_path)]     )
And here's how you can access the file path in the sensor:
Copy code
python from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  @asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list) def process_asin_list_sensor(context: SensorEvaluationContext, materialization_event):     # Extract the file path from the materialization event metadata     csv_file_path = None     for metadata_entry in materialization_event.dagster_event.event_specific_data.materialization.metadata_entries:         if isinstance(metadata_entry.entry_data, PathMetadataEntryData):             csv_file_path = metadata_entry.entry_data.path             break      if csv_file_path:         # Yield a RunRequest to trigger the job with the specific CSV file path         yield RunRequest(             run_key=context.cursor,             run_config={                 "ops": {                     "process_asin_list_op": {                         "config": {"csv_file_path": csv_file_path}                     }                 }             },         )
In this sensor function, we iterate over the metadata entries and check if the 
entry_data
is an instance of 
PathMetadataEntryData
, which is the type of data that 
MetadataEntry.fspath
would produce. Once we find it, we retrieve the path. This approach ensures that the file path is correctly included in the 
AssetMaterialization
event and can be accessed by the sensor to trigger the downstream job with the appropriate file path.
s
@Scout AttributeError: type object 'MetadataEntry' has no attribute 'fspath'
s
I apologize for the confusion earlier. Based on the documentation, to include a file path in the metadata of an 
AssetMaterialization
, you should use 
MetadataValue.path
which is the correct static constructor for a metadata value wrapping a path as 
PathMetadataValue
. Here's how you can yield an 
AssetMaterialization
with the file path using `MetadataValue.path`:
Copy code
python from dagster import op, AssetMaterialization, MetadataValue, AssetKey  @op def generate_initial_asin_list_asset(context):     # ... your logic to generate the CSV file ...     csv_file_path = "path/to/generated/file.csv"     # Emit an AssetMaterialization event with the file path in the metadata     yield AssetMaterialization(         asset_key=AssetKey("generate_initial_asin_list"),         metadata={"file_path": MetadataValue.path(csv_file_path)}     )
When you want to retrieve the file path from the 
AssetMaterialization
event in the sensor, you should look for the metadata entry that contains the file path. Here's an example of how you can access the file path in the sensor:
Copy code
python from dagster import asset_sensor, RunRequest, SensorEvaluationContext, AssetKey  @asset_sensor(asset_key=AssetKey("generate_initial_asin_list"), job=process_asin_list) def process_asin_list_sensor(context: SensorEvaluationContext, materialization_event):     # Extract the file path from the materialization event metadata     csv_file_path = materialization_event.dagster_event.event_specific_data.materialization.metadata["file_path"].path      # Yield a RunRequest to trigger the job with the specific CSV file path     yield RunRequest(         run_key=context.cursor,         run_config={             "ops": {                 "process_asin_list_op": {                     "config": {"csv_file_path": csv_file_path}                 }             }         },     )
In this sensor function, we access the file path directly from the metadata dictionary of the 
AssetMaterialization
event using the key 
"file_path"
. This key corresponds to the one used when emitting the 
AssetMaterialization
event. By using 
MetadataValue.path
, you ensure that the file path is correctly included in the 
AssetMaterialization
event and can be accessed by the sensor to trigger the downstream job with the appropriate file path.
s
@Scout
Copy code
2024-02-11 19:34:53 +0000 - dagster - ERROR - __ASSET_JOB - f89aa64b-07c6-49eb-afca-bb1bbe879df9 - 135746 - generate_initial_asin_list_asset - STEP_FAILURE - Execution of step "generate_initial_asin_list_asset" failed.

dagster._core.errors.DagsterStepOutputNotFoundError: Core compute for op "generate_initial_asin_list_asset" did not return an output for non-optional output "result"

Stack Trace:
  File "/usr/local/lib/python3.11/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step
    for step_event in check.generator(step_events):
  File "/usr/local/lib/python3.11/site-packages/dagster/_core/execution/plan/execute_step.py", line 519, in core_dagster_event_sequence_for_step
    for user_event in _step_output_error_checked_user_event_sequence(
  File "/usr/local/lib/python3.11/site-packages/dagster/_core/execution/plan/execute_step.py", line 314, in _step_output_error_checked_user_event_sequence
    raise DagsterStepOutputNotFoundError(
s
The error message 
DagsterStepOutputNotFoundError
indicates that the op 
generate_initial_asin_list_asset
did not return an output for a non-optional output named 
result
. In Dagster, when an op is expected to produce an output, you must explicitly yield an 
Output
object for each output defined in the op's signature. If your op is intended to produce an output, you should ensure that it yields an 
Output
object with the corresponding output name. If the op does not have any outputs or if the output is optional and no value is produced, you should adjust the op's signature accordingly. Here's an example of how you might modify the op to yield an `Output`:
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python from dagster import op, Output, AssetMaterialization, AssetKey  @op def generate_initial_asin_list_asset(context):     # ... your logic to generate the CSV file ...     csv_file_path = "path/to/generated/file.csv"     # Emit an AssetMaterialization event with the file path in the metadata     yield AssetMaterialization(         asset_key=AssetKey("generate_initial_asin_list"),         metadata={"file_path": MetadataValue.path(csv_file_path)}     )     # Yield an Output object if the op is expected to produce an output     yield Output(csv_file_path, output_name="result")
In this example, the op yields an 
Output
object with the value 
csv_file_path
and the output name 
result
, which should match the output definition in the op's signature. If the op is not supposed to produce an output named 
result
, you should remove the output from the op's signature or mark it as optional if it's not always produced. Here's an example of how to define an op with no outputs:
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python from dagster import op  @op def generate_initial_asin_list_asset(context):     # ... your logic to generate the CSV file ...     csv_file_path = "path/to/generated/file.csv"     # Emit an AssetMaterialization event with the file path in the metadata     yield AssetMaterialization(         asset_key=AssetKey("generate_initial_asin_list"),         metadata={"file_path": MetadataValue.path(csv_file_path)}     )     # No Output is yielded because this op does not produce an output
Make sure that the op's definition and usage are consistent with the expectations for outputs in your job or graph. If you continue to encounter issues, review the op's implementation to ensure that all expected outputs are properly yielded, and check the job or graph definition to ensure that the op's outputs are correctly specified.
s
@Scout with the latest documentation, how should I indicate that the asset has no result?
s
According to the latest documentation, if you have an asset that may not always need to materialize a result, you can use the 
output_required
parameter in the 
@asset
decorator. If 
output_required
is set to 
False
, and your function does not yield an 
Output
object, then no asset materialization event will be created, and downstream assets will not be materialized. Here's an example of how to define an asset that may not always materialize a result:
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python from dagster import asset, Output  @asset(output_required=False) def my_optional_asset():     # Your asset computation logic here     condition = determine_whether_to_materialize()  # Your condition logic     if condition:         # Materialize the asset only if the condition is met         yield Output(your_data)     # If the condition is not met, do not yield an Output
In this example, 
determine_whether_to_materialize
is a placeholder for whatever logic you use to decide whether the asset should be materialized. If the condition is met, you yield an 
Output
object with the data. If the condition is not met, you do not yield an 
Output
, and no materialization event is created for this asset. By using 
output_required=False
, you indicate to Dagster that it's acceptable for this asset not to produce an output during some runs, and this will not be treated as an error. This allows you to have conditional materialization logic within your asset definitions.
s
@Scout why in this example from the Dagster documentation the assent_sensor receives and asset_event and not materialization_event
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@asset_sensor(asset_key=AssetKey("my_table"), job=my_job)
def my_asset_sensor(context: SensorEvaluationContext, asset_event: EventLogEntry):
    assert asset_event.dagster_event and asset_event.dagster_event.asset_key
    yield RunRequest(
        run_key=context.cursor,
        run_config=RunConfig(
            ops={
                "read_materialization": ReadMaterializationConfig(
                    asset_key=list(asset_event.dagster_event.asset_key.path)
                )
            }
        ),
    )
s
In the example from the Dagster documentation, the 
asset_sensor
is defined to receive an 
asset_event
parameter, which is an instance of 
EventLogEntry
. This parameter represents the event log entry for an 
AssetMaterialization
event associated with the specified asset key. The term 
asset_event
is used here as a variable name to represent the event log entry that contains information about the materialization of an asset. It's a naming choice made by the author of the example to indicate the purpose of the parameter within the context of the sensor function. The name 
materialization_event
could also be used; it's simply a matter of naming preference and does not affect the functionality of the sensor. The 
asset_event
parameter provides access to the 
DagsterEvent
object, which includes the 
asset_key
and other details about the materialization event. The sensor function can then use this information to create a 
RunRequest
to trigger a job, and it can pass relevant information from the event to the job's run configuration. Here's a breakdown of the parameter: - `context`: The 
SensorEvaluationContext
provides context for the sensor evaluation, including access to the Dagster instance. - `asset_event`: The 
EventLogEntry
object that represents the materialization event for the asset being monitored. The 
asset_event.dagster_event
attribute contains the 
DagsterEvent
object, which includes the 
asset_key
and other event-specific data. The 
assert
statement in the function is a safety check to ensure that the 
asset_event
parameter indeed contains a 
DagsterEvent
with an 
asset_key
, which is necessary for the sensor to function correctly. The 
run_key
for the 
RunRequest
is set to 
context.cursor
, which is a unique identifier that Dagster updates after each sensor evaluation to help with deduplication of runs. The 
run_config
is constructed to pass the necessary configuration to the job that will be triggered by the sensor.
s
@Scout I'm calling methods from another module no dagster aware. How can I can context.log from there?
s
If you're calling methods from another module that is not aware of Dagster and you want to use Dagster's logging within those methods, you can use the 
get_dagster_logger
function to obtain a logger that is integrated with Dagster's logging system. This allows you to log messages that will be captured by Dagster and associated with the current Dagster context, such as within a pipeline run. Here's how you can use 
get_dagster_logger
in a non-Dagster-aware module:
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python from dagster import get_dagster_logger  def my_non_dagster_function():     log = get_dagster_logger()     <http://log.info|log.info>("This message will be captured by Dagster's logging system.")
When you call 
my_non_dagster_function
from within a Dagster op, any log messages produced using the logger obtained from 
get_dagster_logger
will be handled as if they were logged using the 
context.log
within an op. This means they will appear in the Dagster event log and be associated with the current pipeline run. Here's an example of how you might call this function from within a Dagster op:
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python from dagster import op from my_non_dagster_module import my_non_dagster_function  @op def my_dagster_op(context):     <http://context.log.info|context.log.info>("Calling a non-Dagster-aware function with Dagster logging.")     my_non_dagster_function()
By using 
get_dagster_logger
, you can ensure that log messages from non-Dagster-aware code are captured and managed by Dagster's logging system, providing a consistent logging experience across your entire pipeline.
s
@Scout can I have an op that triggers other ops?
s
In Dagster, an op itself does not directly trigger other ops. Instead, ops are defined as individual units of computation that can be composed into a graph or a job, where the dependencies between ops are explicitly declared. The execution order of ops is determined by these dependencies, which are based on the inputs and outputs of the ops. If you want one op to "trigger" another, you would set up a dependency in a graph or job definition. Here's a simple example:
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python from dagster import op, graph  @op def first_op():     # Your logic here     return "result from first_op"  @op def second_op(input_value):     # This op is "triggered" by first_op because it depends on its output     pass  @graph def my_graph():     second_op(first_op())
In this example, 
second_op
will execute after 
first_op
because it depends on the output of 
first_op
. When you execute the 
my_graph
graph, Dagster will automatically handle the execution order based on these dependencies. If you want to create more complex execution logic, such as conditionally executing ops based on runtime values or external conditions, you can use Dagster's control flow mechanisms like hooks or dynamic outputs to influence the execution plan at runtime. For example, you can use dynamic outputs to conditionally branch execution:
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python from dagster import op, DynamicOut, DynamicOutput, graph  @op(out=DynamicOut()) def branching_op():     # Your logic here     yield DynamicOutput(value="branch_1", mapping_key="branch_1")     # You can conditionally yield other DynamicOutputs based on your logic  @op def downstream_op(branch):     # This op will be executed for each DynamicOutput from branching_op     pass  @graph def my_conditional_graph():     branches = branching_op()     branches.map(downstream_op)
In this example, 
branching_op
yields dynamic outputs, and 
downstream_op
will be executed for each of those outputs. This allows you to create more dynamic execution patterns within your graphs. Remember that in Dagster, the execution of ops is always defined declaratively through graphs or jobs, and the actual triggering of ops is managed by the Dagster runtime based on these definitions.
s
@Scout I have a job with a loop that triggers an op multiple times. Each execution returns the name of a file that will be created asynchronously by an API call done by the job, and I have a sensor that should wait for these files to be created. How do I pass the list of files to the sensor?
s
In Dagster, to pass a list of files that will be created asynchronously by an API call from a job to a sensor, you can use Dagster's event log to record metadata during the job's execution. The sensor can then read this metadata from the event log to retrieve the list of expected files. Here's a step-by-step guide on how to set this up: 1. Record Metadata in the Job: When your job triggers the API calls and expects files to be generated, emit an event with metadata that includes the list of expected files. You can use 
yield AssetMaterialization
with metadata entries for this purpose within the op that is being looped over.
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python from dagster import op, AssetMaterialization, MetadataEntry, AssetKey  @op def my_op(context):     # Logic to trigger API call and generate the expected file name     expected_file_name = "expected_file_name_generated_by_api.csv"     # Emit an event with metadata     yield AssetMaterialization(         asset_key=AssetKey("expected_files"),         metadata_entries=[MetadataEntry.json({"expected_file": expected_file_name})]     )     return expected_file_name  # In your job, you would loop over the op and collect the file names
2. Create a Sensor to Read the Event Log: Define a sensor that reads the event log for the 
AssetMaterialization
events and retrieves the list of expected files from the metadata.
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python from dagster import sensor, SensorEvaluationContext, RunRequest, DagsterEventType  @sensor(job=my_job) def my_file_sensor(context: SensorEvaluationContext):     records = context.instance.get_event_records(         event_type=DagsterEventType.ASSET_MATERIALIZATION,         asset_key=AssetKey("expected_files"),     )     if not records:         <http://context.log.info|context.log.info>("No expected files metadata found.")         return      expected_files = [         record.dagster_event.event_specific_data.materialization.metadata_entries[0].entry_data.json["expected_file"]         for record in records     ]      # Check for the presence of expected files in your storage     # and yield RunRequest if all files are present     # ...
3. Trigger the Job with the Sensor: When the sensor detects that all the expected files are present, it can yield a 
RunRequest
to trigger the next step in your pipeline. Please note that this example assumes that each 
AssetMaterialization
event contains metadata for a single expected file. If you want to emit a single event with a list of all expected files, you would adjust the metadata entry accordingly. This approach allows you to pass dynamic information from a job to a sensor without requiring them to be directly connected. The sensor can independently check the event log and act based on the metadata recorded by the job.
s
@Scout please show an example of the job storing the info in the 
asset_key=AssetKey("expected_files")
s
To store information about expected files in an 
AssetMaterialization
event with a specific asset key, you would emit this event within the body of an op that is part of your job. Here's an example of how you might set this up: First, define an op that triggers the API calls and expects files to be generated. This op will yield an 
AssetMaterialization
event with metadata entries that include the list of expected files:
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python from dagster import op, AssetMaterialization, MetadataEntry, AssetKey  @op def trigger_api_calls_and_expect_files(context):     # Logic to trigger multiple API calls and generate the list of expected files     expected_files = ["file1.csv", "file2.csv", "file3.csv"]          # Emit an AssetMaterialization event with metadata that includes the list of expected files     yield AssetMaterialization(         asset_key=AssetKey("expected_files"),         metadata_entries=[MetadataEntry.json({"expected_files": expected_files})]     )
Next, define a job that includes this op. The job will execute the op, which in turn will emit the 
AssetMaterialization
event:
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python from dagster import job  @job def my_job():     trigger_api_calls_and_expect_files()
When 
my_job
is executed, the 
trigger_api_calls_and_expect_files
op will run and yield an 
AssetMaterialization
event. This event will be recorded in Dagster's event log with the asset key 
"expected_files"
. The metadata for this event will include a JSON object with the key 
"expected_files"
that contains the list of file names that are expected to be created by the API calls. The sensor can then monitor for materialization events with this asset key and use the metadata to retrieve the list of expected files, as shown in the previous example. Please note that the actual implementation of the API call logic and the generation of expected file names will depend on your specific use case. The 
expected_files
list should be dynamically generated based on the results of your API calls.
s
@Scout you're triggering the files from a single op, but I'd like something like (pseudo-code)
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@op
def my_op(call):
   return api_call(call)

@job 
def my_job():
   files = []
   for call in calls:
      files.append(my_op(call))
   
    yield AssetMaterialization (files)
s
In Dagster, you cannot yield events like 
AssetMaterialization
directly from within a job. Instead, you yield events from within ops. To achieve the behavior you're describing, you would need to collect the results from 
my_op
within a downstream op that aggregates the results and yields the 
AssetMaterialization
event. Here's an example of how you might structure your code to do this:
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python from dagster import op, job, Out, Output, AssetMaterialization, AssetKey  @op(out=Out()) def my_op(context, call):     # Replace this with the actual logic to make the API call and return the expected file name     file_name = api_call(call)     return file_name  @op def aggregate_files(context, file_names):     # Yield an AssetMaterialization event with metadata that includes the list of expected files     yield AssetMaterialization(         asset_key=AssetKey("expected_files"),         metadata={"file_names": file_names}     )  @job def my_job():     files = []     for call in calls:         files.append(my_op(call))     aggregate_files(files)
In this setup, 
my_op
is an op that makes the API call and returns the expected file name. The 
my_job
job collects these file names into a list and then passes them to the 
aggregate_files
op. The 
aggregate_files
op then yields an 
AssetMaterialization
event that includes the list of file names in its metadata. This way, the 
AssetMaterialization
event is associated with the execution of the 
aggregate_files
op, and the sensor can monitor for materialization events with the asset key 
"expected_files"
to trigger downstream processing based on the list of files. Please note that the actual implementation of 
api_call
and how you collect and pass the file names will depend on your specific use case. The 
calls
variable should be a list of calls that you want to make, and you should replace the placeholder logic with the actual API call logic.
s
@Scout yield AssetMaterialization( asset_key="rainforest_files", metadata_entries=[MetadataEntry.text(text=file_ids, label="Expected files")], ) and a sensor that does expected_files = [ entry.entry_data.text for record in records for entry in record.event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries if entry.label == "Expected files" ] Why do I need to filter by entry.lable? Just in case we have more than one materialization called "rainforest_files"?
s
The filtering by 
entry.label
in the sensor code is a way to ensure that you are retrieving the correct metadata entry from potentially multiple metadata entries associated with a single 
AssetMaterialization
event. Each 
AssetMaterialization
can have multiple metadata entries, each with its own label and data. By filtering on the label, you are specifying exactly which piece of metadata you want to extract. In the example you provided:
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python yield AssetMaterialization(     asset_key="rainforest_files",     metadata_entries=[MetadataEntry.text(text=file_ids, label="Expected files")], )
You are yielding an 
AssetMaterialization
event with a single metadata entry that has the label 
"Expected files"
. If this is the only metadata entry you ever attach to an 
AssetMaterialization
for the asset key 
"rainforest_files"
, and you are sure that no other metadata with different labels will be attached, then you might not need to filter by label when retrieving the metadata. However, it's a good practice to filter by label because: 1. It makes your code more robust to changes. If someone else (or you in the future) adds another metadata entry to the 
AssetMaterialization
event for 
"rainforest_files"
, your sensor code will still correctly retrieve only the expected files metadata. 2. It makes the intent of your code clearer. Anyone reading the sensor code can immediately understand that it is specifically looking for metadata related to the expected files. 3. It prevents potential bugs that could occur if additional metadata entries are added to the 
AssetMaterialization
event. Here's an example of how the sensor might look with the label filtering:
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python expected_files = [     entry.entry_data.text     for record in records     for entry in record.event_log_entry.dagster_event.event_specific_data.materialization.metadata_entries     if entry.label == "Expected files" ]
In this sensor code, you're iterating over all the metadata entries of each 
AssetMaterialization
event and selecting only the entries that have the label 
"Expected files"
. This ensures that 
expected_files
will contain only the data from the metadata entries that are relevant to your sensor's logic.
s
@Scout could this be an asset? @op(config_schema={"csv_file_path": Field(String)}) def process_asin_list_op(context): csv_file_path = context.op_config["csv_file_path"] context.log.info(f"CSV file path is: {csv_file_path}") file_ids = rainforestapi.process_with_rainforestapi( api_key=os.getenv("RAINFOREST_API_KEY"), type="review", csv_file_path=csv_file_path, ) yield AssetMaterialization( asset_key="rainforest_files", metadata_entries=[MetadataEntry.text(text=file_ids, label="files_ids")], )
s
Yes, the code you've provided could be adapted to define an asset in Dagster. In Dagster, assets are a way to define computations that produce data artifacts, and they are typically represented by functions decorated with 
@asset
. These functions can yield 
AssetMaterialization
events to indicate that a data artifact has been created or updated. To convert your op into an asset, you would use the 
@asset
decorator instead of 
@op
. However, assets in Dagster do not use 
config_schema
for configuration. Instead, they receive their inputs from upstream assets or resources. If you need to pass configuration to an asset, you would typically do so via resources or by making the asset depend on other assets that provide the necessary information. Here's an example of how you might define an asset based on your op:
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python from dagster import asset, AssetMaterialization, MetadataEntry import os  @asset def process_asin_list_asset(context):     # Assuming csv_file_path is provided by an upstream asset or resource     csv_file_path = context.resources.csv_file_path     <http://context.log.info|context.log.info>(f"CSV file path is: {csv_file_path}")          file_ids = rainforestapi.process_with_rainforestapi(         api_key=os.getenv("RAINFOREST_API_KEY"),         type="review",         csv_file_path=csv_file_path,     )      # Yield an AssetMaterialization event with metadata that includes the file IDs     yield AssetMaterialization(         asset_key="rainforest_files",         metadata_entries=[MetadataEntry.text(text=file_ids, label="files_ids")],     )
In this example, 
process_asin_list_asset
is an asset that processes a list of ASINs using the Rainforest API. The 
csv_file_path
is assumed to be provided by a resource or an upstream asset, which you would need to define separately. The asset yields an 
AssetMaterialization
event to indicate that the processing has been completed and includes metadata about the file IDs. Please note that you would need to set up the necessary resources or upstream assets to provide the 
csv_file_path
to this asset, and you would need to adapt the 
rainforestapi.process_with_rainforestapi
function call to match your actual API processing logic.
s
@Scout assuming this is a s process_asin_list_op? @asset_sensor( asset_key=AssetKey("process_asin_list_op"), job=process_rainforestapi_results, default_status=DefaultSensorStatus.RUNNING ) def rainforest_results_sensor(context: SensorEvaluationContext, materialization_event: EventLogEntry): how should I access the file_ids?
s
There was an error processing your request. Please try again.
s
@Scout assuming this is a s process_asin_list_op? @asset_sensor( asset_key=AssetKey("process_asin_list_op"), job=process_rainforestapi_results, default_status=DefaultSensorStatus.RUNNING ) def rainforest_results_sensor(context: SensorEvaluationContext, materialization_event: EventLogEntry): how should I access the file_ids?
s
There was an error processing your request. Please try again.
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