https://dagster.io/ logo
#ask-ai
Title
# ask-ai
m

Michelle Ragsac

02/21/2024, 12:53 AM
@Scout I have an
@op
that fans out multiple
DynamicOutput()
to run in separate
@op
functions. The second
@op
produces multiple
DynamicOutput()
. How do I consolidate the outputs from the second
@op
?
s

Scout

02/21/2024, 12:53 AM
To consolidate the outputs from an
@op
that produces multiple
DynamicOutput
instances, you can use the
collect
method. The
collect
method creates a fan-in dependency over all the dynamic copies of an op, allowing the dependent op to receive a list containing all the values from the dynamic outputs. Here's a step-by-step explanation based on the documentation: 1. Define your first
@op
that fans out multiple
DynamicOutput
instances. Each
DynamicOutput
should have a unique
mapping_key
. 2. Define your second
@op
that also produces multiple
DynamicOutput
instances, again with unique
mapping_keys
. 3. To consolidate the outputs from the second
@op
, you would use the
collect
method on the dynamic output from the second
@op
. This will gather all the dynamic outputs into a list that can be passed to another
@op
. Here is a conceptual example based on the documentation:
Copy code
python @op(out=DynamicOut()) def first_op():     # ... logic to produce multiple DynamicOutput instances     yield DynamicOutput(value, mapping_key=str(key))  @op(out=DynamicOut()) def second_op(input_value):     # ... logic to produce multiple DynamicOutput instances based on the input_value     yield DynamicOutput(transformed_value, mapping_key=str(new_key))  @op def consolidate_op(collected_values):     # ... logic to process the list of collected values from the dynamic outputs  @job def my_job():     dynamic_results = first_op().map(second_op)     consolidated_result = dynamic_results.collect()     consolidate_op(consolidated_result)
In this example,
first_op
fans out multiple dynamic outputs, which are then individually processed by
second_op
in parallel. The
collect
method is called on the dynamic output from
second_op
to gather all the results into a list, which is then passed to
consolidate_op
for further processing. Please note that the actual implementation will depend on your specific use case and the structure of your data and operations. The provided example is a high-level conceptual guide based on the documentation.
m

Michelle Ragsac

02/21/2024, 12:58 AM
@Scout How do I write a
collect
method to collect
DynamicOutput()
from another
@op
to process the outputs together downstream in an
@op
?
s

Scout

02/21/2024, 12:58 AM
To collect
DynamicOutput
instances from an
@op
and process them together downstream in another
@op
, you don't need to write a
collect
method yourself. Instead, you use the
collect
method provided by Dagster's API to create a fan-in dependency. The
collect
method is called on the dynamic output from an
@op
, and it gathers all the dynamic outputs into a list that can be passed to another
@op
. Here's how you can use the
collect
method in your Dagster job: 1. Define your
@op
that produces multiple
DynamicOutput
instances. 2. Use the
collect
method on the dynamic output from the
@op
. 3. Define another
@op
that takes the collected list of values and processes them. Here is a conceptual example:
Copy code
python from dagster import job, op, DynamicOut, DynamicOutput  @op(out=DynamicOut()) def produce_dynamic_outputs():     # Replace with your logic to produce dynamic outputs     for i in range(5):         yield DynamicOutput(value=i, mapping_key=str(i))  @op def process_collected_outputs(collected_values):     # This op will receive a list of all values from the dynamic outputs     # Replace with your logic to process the collected values     total = sum(collected_values)     return total  @job def my_job():     dynamic_outputs = produce_dynamic_outputs()     collected = dynamic_outputs.collect()     process_collected_outputs(collected)
In this example,
produce_dynamic_outputs
yields multiple
DynamicOutput
instances. The
collect
method is used to gather these dynamic outputs into a list. The
process_collected_outputs
op then takes this list as input and processes it (in this case, by summing the values). When you run
my_job
, Dagster will automatically handle the collection of dynamic outputs and pass them as a list to the
process_collected_outputs
op.
2 Views