I have an

@op

with

Out()

that does

return a, b

how can I change it so that it is a generator instead

yield a, b

In Dagster, when you have an

@op

that needs to yield multiple outputs, you should yield

Output

objects separately, each with a specified

output_name

. The

output_name

corresponds to the names you define in the

out

parameter of the

@op

decorator. Here's how you can modify your

@op

to use a generator that yields multiple outputs:

python from dagster import Output, op, Out  @op(out={"output_a": Out(), "output_b": Out()}) def my_op():     # Compute or retrieve values for a and b     a = compute_a()     b = compute_b()          yield Output(a, output_name="output_a")     yield Output(b, output_name="output_b")

In this example,

compute_a()

and

compute_b()

are placeholder functions that represent the computation or retrieval of the values for

a

and

b

. The

Output

objects are yielded with their respective

output_name

that matches the keys defined in the

out

parameter. Remember that when you yield outputs from an

@op

, you cannot use type annotations for the return type as you would with a regular Python function that returns a tuple. Instead, you specify the output types and names using the

out

parameter of the

@op

decorator, and then yield

Output

objects accordingly.

@Scout It looks like the Dagster

@op

generator yields all the values at once instead of each time its called. How do you recommend this be passed to another

@op

with

In()

?

In Dagster, when you have an

@op

that yields multiple outputs, you can pass these outputs to another

@op

by connecting them in a graph or job. Each output from the yielding

@op

can be individually passed to downstream ops by referencing the specific output names. Here's an example of how you can pass the yielded outputs to another `@op`:

python from dagster import op, Out, Output, In, job  # Define the op that yields multiple outputs @op(out={"output_a": Out(), "output_b": Out()}) def my_yielding_op():     a = compute_a()  # Replace with actual computation     b = compute_b()  # Replace with actual computation     yield Output(a, output_name="output_a")     yield Output(b, output_name="output_b")  # Define the op that takes multiple inputs @op def my_receiving_op(input_a, input_b):     # Do something with input_a and input_b     process_inputs(input_a, input_b)  # Define a job that connects the ops @job def my_job():     output_a, output_b = my_yielding_op()     my_receiving_op(input_a=output_a, input_b=output_b)

In this example,

my_yielding_op

yields two outputs,

output_a

and

output_b

. In the

my_job

definition, these outputs are captured as

output_a

and

output_b

and then passed as arguments to

my_receiving_op

using the parameter names

input_a

and

input_b

. If you want to pass the outputs to an

@op

that explicitly expects inputs with

In()

, you can define the receiving

@op

like this:

python # Define the op that takes multiple inputs with explicit In() annotations @op(ins={"input_a": In(), "input_b": In()}) def my_receiving_op_explicit(input_a, input_b):     # Do something with input_a and input_b     process_inputs(input_a, input_b)

The

In()

annotations are not strictly necessary if you're just passing values, but they can be used to provide additional metadata or configuration for the inputs. Remember to replace

compute_a

,

compute_b

, and

process_inputs

with your actual computation and processing logic. The

my_job

function connects the ops together, ensuring that the outputs from

my_yielding_op

are passed to

my_receiving_op

.

@Scout So if I have a generator

foo()

I only want it to return data when I call it.

@op(out={"foo_first": foo_first}, out={"foo_second": foo_second)
def foo():
    for foo_first, foo_second in some_data:
        yield Output(foo_first, output_name="foo_first")
        yield Output(foo_second, output_name="foo_second")

@op(ins={"foo_first": In(), "foo_second": In()):
def bar(foo_first, foo_second):
    do_stuff(foo_first, foo_second)

@graph
def try_this():
    foo = foo()
    bar(foo)
    baz(foo)

Is something like this possible? If not, how should I go about it? Is this possible?

In Dagster, when you define an

@op

that yields multiple outputs, you should yield each output separately within a single iteration of the loop, not in separate yields, because each yield statement is considered as a separate event in the execution of the op. Additionally, the syntax you've used for defining multiple outputs in the

@op

decorator is incorrect. Here's how you can correctly define an

@op

that yields multiple outputs:

python from dagster import op, Out, Output  @op(out={"foo_first": Out(), "foo_second": Out()}) def foo():     for foo_first, foo_second in some_data:         yield Output(foo_first, output_name="foo_first")         yield Output(foo_second, output_name="foo_second")

However, the above code will not work as intended because it yields

foo_first

and

foo_second

in separate iterations, which is not the correct way to yield multiple outputs in Dagster. Instead, you should yield all outputs for a single iteration together, like this:

python from dagster import op, Out, Output  @op(out={"foo_first": Out(), "foo_second": Out()}) def foo():     for foo_first, foo_second in some_data:         yield Output(foo_first, output_name="foo_first")         yield Output(foo_second, output_name="foo_second")

Now, to connect this op to downstream ops in a graph, you would do the following:

python from dagster import graph  @op def bar(foo_first, foo_second):     do_stuff(foo_first, foo_second)  @op def baz(foo_first, foo_second):     do_other_stuff(foo_first, foo_second)  @graph def try_this():     foo_results = foo()     bar(foo_first=foo_results.foo_first, foo_second=foo_results.foo_second)     baz(foo_first=foo_results.foo_first, foo_second=foo_results.foo_second)

In this graph,

foo_results

is an object that contains both outputs from the

foo

op. You can then pass these outputs to

bar

and

baz

by specifying the output names (

foo_first

and

foo_second

). Please note that the

do_stuff

and

do_other_stuff

functions should be replaced with your actual logic, and

some_data

should be replaced with the actual data you're iterating over.

@Scout So if I have a generator

foo()

I only want it to return data when I call it.

@op(out={"foo_first": foo_first}, out={"foo_second": foo_second)
def foo():
    for foo_first, foo_second in some_data:
        yield Output(foo_first, output_name="foo_first")
        yield Output(foo_second, output_name="foo_second")

@op(ins={"foo_first": In(), "foo_second": In()):
def bar(foo_first, foo_second):
    do_stuff(foo_first, foo_second)

@graph
def try_this():
    bar(foo())
    baz(foo())

Is something like this possible? If not, how should I go about it? Is this possible?

In Dagster, the way you've written the

@op

decorator with multiple

out

parameters is not correct. You should define a single

out

parameter with a dictionary specifying each output. Additionally, the

@graph

definition is not correctly connecting the outputs of

foo

to the inputs of

bar

and

baz

. Here's how you can correct these issues: First, define the

foo

op with multiple outputs correctly:

python from dagster import op, Out, Output  @op(out={"foo_first": Out(), "foo_second": Out()}) def foo():     for foo_first_value, foo_second_value in some_data:         yield Output(foo_first_value, output_name="foo_first")         yield Output(foo_second_value, output_name="foo_second")

Then, define the

bar

and

baz

ops that take inputs:

python @op def bar(foo_first, foo_second):     do_stuff(foo_first, foo_second)  @op def baz(foo_first, foo_second):     do_other_stuff(foo_first, foo_second)

Finally, define the graph that connects the outputs of

foo

to the inputs of

bar

and

baz

. However, you cannot call

foo()

directly in the

bar

and

baz

invocations because

foo()

is an op that yields multiple outputs. Instead, you should capture the outputs of

foo

and pass them to

bar

and `baz`:

python from dagster import graph  @graph def try_this():     foo_results = foo()     bar(foo_first=foo_results.foo_first, foo_second=foo_results.foo_second)     baz(foo_first=foo_results.foo_first, foo_second=foo_results.foo_second)

In this graph,

foo_results

is a structure that contains both outputs from the

foo

op, and you can access each output by its name (

foo_first

and

foo_second

). These named outputs are then passed as inputs to

bar

and

baz

. Please note that

some_data

should be replaced with the actual data you're iterating over, and

do_stuff

and

do_other_stuff

should be replaced with your actual logic for

bar

and

baz

, respectively. If you want

foo

to act as a generator that only computes values when called, you would typically structure your code as shown above. Each time

foo

is invoked within a Dagster job or graph, it will execute and yield its outputs, which can then be passed to other ops. Dagster handles the execution flow, ensuring that

foo

is called and its outputs are passed to downstream ops as needed.

@Scout I'm getting the error: foo() returned foo_first multiple times

The error message you're encountering suggests that the

foo

op is yielding outputs with the same output name multiple times, which is not allowed in Dagster. Each output name must be unique within the context of a single op execution. Here's how you can modify the

foo

op to yield multiple outputs correctly:

python from dagster import op, Out, Output  @op(out={"foo_first": Out(), "foo_second": Out()}) def foo():     # Assuming some_data is an iterable of tuples (foo_first_value, foo_second_value)     for foo_first_value, foo_second_value in some_data:         yield Output(foo_first_value, output_name="foo_first")         yield Output(foo_second_value, output_name="foo_second")

However, the above code will yield the

foo_first

and

foo_second

outputs in separate iterations, which is not the correct way to yield multiple outputs in Dagster. Instead, you should yield all outputs for a single iteration together, like this:

python from dagster import op, Out, Output  @op(out={"foo_first": Out(), "foo_second": Out()}) def foo():     # Assuming some_data is an iterable of tuples (foo_first_value, foo_second_value)     for foo_first_value, foo_second_value in some_data:         yield Output(foo_first_value, output_name="foo_first")         yield Output(foo_second_value, output_name="foo_second")         # Break after yielding both outputs to ensure we don't yield the same output names again         break

In this corrected version,

foo

yields both

foo_first

and

foo_second

once per iteration and then breaks out of the loop to prevent yielding outputs with the same names again. If you intend for

foo

to yield multiple pairs of outputs (one pair per iteration), you should consider restructuring your graph or job to handle this correctly. Dagster expects each output name to be yielded exactly once per op execution. If you need further assistance, please provide more context or clarify your use case, and I can offer more specific guidance.

@Scout I'm still getting the error: Compute for op "foo()" returned "foo_first" multiple times.

The error you're encountering indicates that the

foo

op is yielding the same output name more than once during its execution, which is not allowed. In Dagster, each output name must be yielded exactly once. To fix this, you should ensure that each output name is yielded only once per execution of the op. Here's an example of how you can do this:

python
from dagster import op, Out, Output

@op(out={"foo_first": Out(), "foo_second": Out()})
def foo():
    # Assuming some_data is an iterable of tuples (foo_first_value, foo_second_value)
    for foo_first_value, foo_second_value in some_data:
        # Yield both outputs at once, then break to ensure we don't yield the same output names again
        yield Output(foo_first_value, output_name="foo_first")
        yield Output(foo_second_value, output_name="foo_second")
        break  # Important: break after yielding both outputs to prevent yielding the same output names again

However, if

some_data

contains multiple pairs of data and you want to process each pair in a separate execution of

foo

, you should not use a loop inside the

foo

op. Instead, you should structure your graph or job to call

foo

multiple times, once for each pair of data. If you want to process each pair of data from

some_data

and pass them to other ops like

bar

and

baz

, you might need to restructure your graph to iterate over

some_data

outside of the

foo

op. Here's an example of how you might do this:

python
from dagster import graph, op, Out, Output

# Define the op that processes a single pair of data
@op(out={"foo_first": Out(), "foo_second": Out()})
def foo(data_pair):
    foo_first_value, foo_second_value = data_pair
    yield Output(foo_first_value, output_name="foo_first")
    yield Output(foo_second_value, output_name="foo_second")

# Define the ops that consume the outputs of foo
@op
def bar(foo_first, foo_second):
    # Process the outputs
    pass

@op
def baz(foo_first, foo_second):
    # Process the outputs
    pass

# Define a graph that processes each pair of data
@graph
def process_data_pairs():
    for data_pair in some_data:
        foo_results = foo(data_pair)
        bar(foo_results.foo_first, foo_results.foo_second)
        baz(foo_results.foo_first, foo_results.foo_second)

# Now you can execute the graph for each pair of data
process_data_pairs()

In this example,

some_data

should be a list or iterable of tuples, where each tuple represents a pair of data to be processed. The

process_data_pairs

graph calls

foo

for each pair, then passes the results to

bar

and

baz

. Please note that this example assumes that

some_data

is available at graph construction time. If

some_data

is not available until runtime, you would need to handle this differently, potentially by us

@Scout show me this example with DynamicOut and DynamicOutput

To handle a scenario where you have an iterable of data that you want to process dynamically at runtime, you can use

DynamicOut

and

DynamicOutput

in Dagster. This allows you to dynamically generate downstream ops for each piece of data. Here's an example of how you can use

DynamicOut

and

DynamicOutput

to process each pair of data from `some_data`:

python from dagster import op, DynamicOut, DynamicOutput, graph, job  # Define the op that dynamically yields multiple pairs of data @op(out=DynamicOut()) def foo():     # Assuming some_data is an iterable of tuples (foo_first_value, foo_second_value)     for idx, (foo_first_value, foo_second_value) in enumerate(some_data):         yield DynamicOutput((foo_first_value, foo_second_value), mapping_key=str(idx))  # Define the ops that consume the outputs of foo @op def bar(data_pair):     foo_first, foo_second = data_pair     # Process the outputs     pass  @op def baz(data_pair):     foo_first, foo_second = data_pair     # Process the outputs     pass  # Define a job that processes each pair of data dynamically @job def process_data_pairs_job():     dynamic_results = foo()     # Map each dynamic output to the bar and baz ops     dynamic_results.map(bar)     dynamic_results.map(baz)  # Now you can execute the job process_data_pairs_job()

In this example,

foo

is an op that yields a

DynamicOutput

for each pair of data in

some_data

. Each

DynamicOutput

contains the data pair and a unique

mapping_key

. The

dynamic_results

object represents the dynamic output from

foo

. The

dynamic_results.map(bar)

and

dynamic_results.map(baz)

lines map each dynamic output to the

bar

and

baz

ops, respectively. This means that for each pair of data yielded by

foo

, a separate instance of

bar

and

baz

will be executed. Please note that

some_data

should be replaced with the actual data you're iterating over. This example assumes that

some_data

is available at runtime and that

bar

and

baz

are designed to process a tuple containing a pair of data. The

DynamicOut

and

DynamicOutput

constructs are particularly useful when the exact number of data pairs to process is not known until runtime, allowing for dynamic parallel execution of downstream ops.

@Scout Let's start again from scratch. I like the Dynamic Out and Output stuff. Say I have a list of key, value dictionary pairs. How would I go about setting up a graph that calls ops with Dynamic Out and Output? I don't think I need to use the keyword yield.

Certainly! If you have a list of key-value dictionary pairs and you want to process each item dynamically, you can set up a Dagster graph that uses

DynamicOut

and

DynamicOutput

without using the

yield

keyword. Instead, you can return a list of

DynamicOutput

objects from your op. Here's an example of how you might set up such a graph:

python from dagster import op, DynamicOut, DynamicOutput, graph, job  # Assume you have a list of dictionary pairs like this: list_of_dict_pairs = [     {"key": "key1", "value": "value1"},     {"key": "key2", "value": "value2"},     # ... more pairs ]  # Define the op that processes the list of dictionary pairs and returns DynamicOutputs @op(out=DynamicOut()) def process_dict_pairs():     outputs = []     for idx, dict_pair in enumerate(list_of_dict_pairs):         # Create a DynamicOutput for each dictionary pair         outputs.append(DynamicOutput(value=dict_pair, mapping_key=str(idx)))     return outputs  # Define the op that consumes each dictionary pair @op def process_pair(dict_pair):     # Process the dictionary pair     # For example, you might want to do something with the key and value     key = dict_pair["key"]     value = dict_pair["value"]     # ... your processing logic here  # Define a graph that sets up the dynamic processing @graph def process_pairs_graph():     dynamic_pairs = process_dict_pairs()     # Map each dynamic output to the process_pair op     dynamic_pairs.map(process_pair)  # Define a job that encapsulates the graph @job def process_pairs_job():     process_pairs_graph()  # Now you can execute the job process_pairs_job()

In this example,

process_dict_pairs

is an op that takes no inputs and processes a predefined list of dictionary pairs. For each dictionary pair, it creates a

DynamicOutput

object with a unique

mapping_key

. The op then returns a list of these

DynamicOutput

objects. The

process_pair

op is defined to take a single dictionary pair as input and contains the logic for processing that pair. The

process_pairs_graph

graph sets up the dynamic processing by mapping each

DynamicOutput

from

process_dict_pairs

to the

process_pair

op. Finally,

process_pairs_job

is a job that encapsulates the graph, and you can execute this job to run the dynamic processing. This setup allows Dagster to dynamically generate separate instances of

process_pair

for each item in

list_of_dict_pairs

at runtime.

@Scout now can u do it again with the keyword yield?

Certainly! If you prefer to use the

yield

keyword to emit

DynamicOutput

objects from an op, you can do so by yielding each

DynamicOutput

within a loop. Here's how you can set up a Dagster graph that processes each key-value dictionary pair using `yield`:

python from dagster import op, DynamicOut, DynamicOutput, graph, job  # Assume you have a list of dictionary pairs like this: list_of_dict_pairs = [     {"key": "key1", "value": "value1"},     {"key": "key2", "value": "value2"},     # ... more pairs ]  # Define the op that processes the list of dictionary pairs and yields DynamicOutputs @op(out=DynamicOut()) def process_dict_pairs():     for idx, dict_pair in enumerate(list_of_dict_pairs):         # Yield a DynamicOutput for each dictionary pair         yield DynamicOutput(value=dict_pair, mapping_key=str(idx))  # Define the op that consumes each dictionary pair @op def process_pair(dict_pair):     # Process the dictionary pair     # For example, you might want to do something with the key and value     key = dict_pair["key"]     value = dict_pair["value"]     # ... your processing logic here  # Define a graph that sets up the dynamic processing @graph def process_pairs_graph():     dynamic_pairs = process_dict_pairs()     # Map each dynamic output to the process_pair op     dynamic_pairs.map(process_pair)  # Define a job that encapsulates the graph @job def process_pairs_job():     process_pairs_graph()  # Now you can execute the job process_pairs_job()

In this example, the

process_dict_pairs

op uses a loop to iterate over

list_of_dict_pairs

, and for each dictionary pair, it yields a

DynamicOutput

object with a unique

mapping_key

. The rest of the graph and job definitions remain the same as in the previous example. By using

yield

, you instruct Dagster to emit each

DynamicOutput

as soon as it's generated within the loop. This can be particularly useful if you have a large number of items to process or if the processing of each item is independent and can be parallelized. The

process_pair

op is defined to take a single dictionary pair as input and contains the logic for processing that pair. The

process_pairs_graph

graph sets up the dynamic processing by mapping each

DynamicOutput

from

process_dict_pairs

to the

process_pair

op. Finally,

process_pairs_job

is a job that encapsulates the graph, and you can execute this job to run the dynamic processing.

@Scout So will

dynamic_pairs.map(process_pair)

be called numerous times for each unique pair in the list?

Yes, when you use

dynamic_pairs.map(process_pair)

in the graph, Dagster will create a separate instance of the

process_pair

op for each

DynamicOutput

emitted by the

process_dict_pairs

op. Each instance of

process_pair

will be called with a unique dictionary pair from the list. Here's what happens step by step: 1. The

process_dict_pairs

op iterates over

list_of_dict_pairs

and yields a

DynamicOutput

for each dictionary pair, each with a unique

mapping_key

. 2. The

dynamic_pairs

object in the graph represents the collection of dynamic outputs from

process_dict_pairs

. 3. When

dynamic_pairs.map(process_pair)

is called, Dagster maps the

process_pair

op over each item in

dynamic_pairs

. This means that for each

DynamicOutput

yielded by

process_dict_pairs

, a new instance of

process_pair

will be invoked with the value contained in that

DynamicOutput

. 4. Each invocation of

process_pair

is independent and can potentially be executed in parallel, depending on the executor configuration and the resources available. 5. As a result,

process_pair

will be called as many times as there are items in

list_of_dict_pairs

, with each call receiving a different dictionary pair as input. This dynamic mapping allows for parallel processing of the items in the list, which can be beneficial for performance, especially when dealing with a large number of items or when the processing of each item is computationally expensive.

@Scout I'm getting a "required missing config at ops" error for the DynamicOutput

The "required missing config at ops" error typically indicates that Dagster expects some configuration for an op within the job, but it hasn't been provided. This can happen if an op has a

config_schema

defined and the necessary configuration is not supplied when the job is executed. When using

DynamicOutput

and

DynamicOut

, the error should not be related to the dynamic nature of the outputs themselves, but rather to the configuration of the ops that are being dynamically mapped. Here's a general approach to troubleshoot and resolve this issue: 1. Check Config Schema: Ensure that all ops that require configuration have a

config_schema

defined and that the schema is correct. 2. Provide Required Config: When executing the job, make sure to provide the required configuration for all ops. This can be done in a YAML file or directly in the code when using

execute_in_process

or similar methods. 3. Review Job Execution: If you're executing the job from the command line or the Dagster UI, ensure that the configuration is being passed correctly. For command-line execution, you would typically use the

--config

flag followed by the path to your YAML configuration file. 4. Check for Typos: A common issue is typos in the configuration keys or in the op names. Make sure that the keys in your configuration match the

config_schema

and that the op names match those used in your job definition. 5. Validate Configuration: You can use Dagster's tools to validate your configuration before running the job. This can help catch errors in the configuration structure or values. If you continue to face issues, please provide more details about your job definition, the ops that require configuration, and how you are providing the configuration when executing the job. This information will help diagnose the problem more accurately.

@Scout For the DynamicOutput example you showed did any of them need a config?

In the DynamicOutput example I provided earlier, there was no explicit configuration required for the ops. The ops were defined without a

config_schema

, which means they do not expect any run-time configuration to be provided. Here's a recap of the example with the

DynamicOut

and `DynamicOutput`:

python from dagster import op, DynamicOut, DynamicOutput, graph, job  # Assume you have a list of dictionary pairs like this: list_of_dict_pairs = [     {"key": "key1", "value": "value1"},     {"key": "key2", "value": "value2"},     # ... more pairs ]  # Define the op that processes the list of dictionary pairs and yields DynamicOutputs @op(out=DynamicOut()) def process_dict_pairs():     for idx, dict_pair in enumerate(list_of_dict_pairs):         # Yield a DynamicOutput for each dictionary pair         yield DynamicOutput(value=dict_pair, mapping_key=str(idx))  # Define the op that consumes each dictionary pair @op def process_pair(dict_pair):     # Process the dictionary pair     # For example, you might want to do something with the key and value     key = dict_pair["key"]     value = dict_pair["value"]     # ... your processing logic here  # Define a graph that sets up the dynamic processing @graph def process_pairs_graph():     dynamic_pairs = process_dict_pairs()     # Map each dynamic output to the process_pair op     dynamic_pairs.map(process_pair)  # Define a job that encapsulates the graph @job def process_pairs_job():     process_pairs_graph()  # Now you can execute the job process_pairs_job()

In this example, neither

process_dict_pairs

nor

process_pair

requires configuration. If you encounter a "required missing config at ops" error, it suggests that there might be another op within your job or graph that does require configuration, or there might be a misunderstanding about how the configuration is being provided. If you have ops that do require configuration, you would need to provide that configuration when you execute the job. The documentation provides guidance on how to supply run-time configuration for jobs, which can be done via YAML files, environment variables, or directly in code when using methods like

execute_in_process

. If you need to add configuration to the ops in the example, you would define a

config_schema

for each op that requires it and then ensure that the necessary configuration is provided at run time. If you're still having trouble, please share more details about your specific use case, and I can provide more targeted assistance.