@Scout how can i process a dynamic out through a graph one at a time?

To process dynamic outputs through a graph one at a time, you can control the concurrency of the operations within the graph. The documentation provides guidance on how to execute dynamic outputs in serial order instead of in parallel by configuring the executor to enforce concurrency limits. Here's a step-by-step approach based on the documentation: 1. Define your dynamic output operation using the

DynamicOut

type. This operation will yield

DynamicOutput

objects, each representing a piece of data to be processed serially. 2. Configure the executor in your job to limit concurrency. You can do this by setting the

max_concurrent

parameter to

1

in the executor config, which ensures that only one operation runs at a time. 3. Map the dynamic output to a graph or a series of operations that will process each piece of data. Since the concurrency is limited, these operations will be executed one after another, in serial order. Here is an example of how you might define the job and configure the executor to enforce serial execution:

python
from dagster import job, op, DynamicOut, DynamicOutput, graph

@op(out=DynamicOut())
def generate_dynamic_outputs():
    # Your logic to yield dynamic outputs
    for i in range(10):
        yield DynamicOutput(value=i, mapping_key=str(i))

@graph
def process_graph(input_data):
    # Your graph logic here

@job(executor_def=in_process_executor.configured({"max_concurrent": 1}))
def my_serial_job():
    dynamic_results = generate_dynamic_outputs()
    dynamic_results.map(process_graph)

In this example,

generate_dynamic_outputs

is an operation that yields dynamic outputs. The

process_graph

is a graph that processes each piece of data. The

my_serial_job

job uses the

in_process_executor

with the

max_concurrent

parameter set to

1

to ensure that the graph processes each dynamic output one at a time. Please note that the actual syntax and configuration may vary depending on the version of Dagster you are using and the specific details of your use case. Always refer to the latest Dagster documentation for the most accurate and up-to-date information. Using Dynamic Graphs and Op Priority to ... button How do I do dynamic fan out with Dagster... button How to control breadth-first vs depth-fi... button Dynamic Graphs | Dagster button Thank you for your feedback!

i want to process the dynamic outputs one at a time - not collect them all, then fanout. is there a way to do this?

Yes, you can process dynamic outputs one at a time without collecting them all first. To achieve this, you can use the

map

method on the dynamic output to apply a function to each element as it is yielded. This will ensure that each dynamic output is processed individually and serially. Here's an example based on the documentation:

python from dagster import job, op, DynamicOut, DynamicOutput  @op(out=DynamicOut()) def generate_dynamic_outputs():     # Your logic to yield dynamic outputs     for i in range(10):         yield DynamicOutput(value=i, mapping_key=str(i))  @op def process_individual_output(input_data):     # Your logic to process each individual output     pass  @job def my_serial_job():     dynamic_results = generate_dynamic_outputs()     dynamic_results.map(process_individual_output)

In this example,

generate_dynamic_outputs

yields dynamic outputs one by one. The

process_individual_output

operation is mapped to each dynamic output, which means it will be invoked for each output separately. The

map

method ensures that each dynamic output is processed as it is yielded, without waiting to collect all outputs first. By using the

map

method, you are effectively fanning out the processing of each dynamic output, but since the

in_process_executor

processes operations serially by default, they will be processed one at a time. If you need to enforce this behavior explicitly, you can configure the executor with a concurrency limit as mentioned in the previous message. Remember to check the documentation for the version of Dagster you are using to ensure that the syntax and behavior are consistent with the current implementation.