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When creating a dagster sensor, when should you yi...
# ask-ai
a
When creating a dagster sensor, when should you yield a run request vs creating a run request and returning.
s
When creating a Dagster sensor, the decision to yield a 
RunRequest
versus creating and returning a 
RunRequest
depends on the behavior you want to achieve within the sensor. 1. Yielding a 
RunRequest
: If your sensor is designed to potentially create multiple runs during a single evaluation, you should use 
yield
. This allows you to iterate over a set of conditions or events and yield a 
RunRequest
for each one that should trigger a run. For example, if you are monitoring a directory for new files and want to trigger a run for each new file, you would yield a 
RunRequest
for each file. 2. Returning a 
RunRequest
: If your sensor is designed to create at most one run during a single evaluation, you can simply return a 
RunRequest
. This is a simpler pattern when there is only one condition or event that triggers a run. It's important to note that you should not mix 
yield
and 
return
statements within the same sensor function. Doing so can lead to unexpected behavior or errors. The documentation mentions that for a given sensor, at most one run is created for each 
RunRequest
with a unique 
run_key
. If a sensor yields a new run request with a previously used 
run_key
, Dagster skips processing the new run request to prevent duplicate runs. Here is an example from the documentation of a sensor that yields a `RunRequest`:
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python @sensor(job=my_job) def my_sensor():     # Logic to determine if a run should be triggered     ...     yield RunRequest(run_key="unique_key", run_config={})
And here is an example of a sensor that returns a `RunRequest`:
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python @sensor(job=my_job) def my_sensor():     # Logic to determine if a run should be triggered     ...     return RunRequest(run_key="unique_key", run_config={})
In both cases, the 
RunRequest
includes a 
run_key
to ensure idempotence. Remember that if you manually start or stop a sensor in the UI, that will override any default status that is set in code. Once your sensor is started, if you're running a Dagster daemon as part of your deployment, the sensor will begin executing immediately without needing to restart the dagster-daemon process.
a
@Scout You said “If your sensor is designed to potentially create multiple runs during a single evaluation, you should use `yield`” however, can’t you do that by creating multiple run requests and returning? Why would you yield as opposed to returning multiple run requests in that case?
s
In Python, a function that uses 
yield
becomes a generator function, which allows it to yield multiple values over time, pausing between each one. This is different from returning multiple values at once, such as returning a list. In the context of Dagster sensors, yielding 
RunRequest
objects allows the sensor to produce run requests one at a time during its evaluation. The key difference is that when you yield 
RunRequest
objects, you are streaming them to the Dagster framework as they are generated. This is particularly useful when the number of 
RunRequest
objects is not known in advance or when they are produced in an iterative process. It allows the sensor evaluation to be more memory-efficient, as it does not need to hold all 
RunRequest
objects in memory at once. On the other hand, returning multiple 
RunRequest
objects at once would typically involve collecting them into a list or another collection type and returning that collection. However, Dagster's sensor API is designed to work with yielding patterns rather than returning collections of 
RunRequest
objects. The documentation does not explicitly state that you can return multiple 
RunRequest
objects from a sensor by collecting them into a list. Instead, it provides examples and guidance on yielding 
RunRequest
objects individually. This is the recommended approach when you want to create multiple runs during a single sensor evaluation. Here is an example of a sensor that yields multiple 
RunRequest
objects:
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python @sensor(job=my_job) def my_sensor():     for event in get_events_to_process():         yield RunRequest(run_key=event.key, run_config=event.config)
In this example, 
get_events_to_process()
is a hypothetical function that returns a sequence of events, each of which should trigger a separate run. The sensor iterates over these events and yields a 
RunRequest
for each one. To summarize, you should use 
yield
to produce 
RunRequest
objects one at a time during sensor evaluation, which aligns with the streaming nature of generator functions and the design of Dagster's sensor API.
a
@Scout How should I test a sensor with a debugger?
s
Testing a sensor with a debugger involves running the sensor's evaluation function in an environment where you can step through the code and inspect variables. Here are the general steps you can follow to test a sensor with a debugger: 1. Set up your development environment: Make sure you have a Python debugger installed and configured in your development environment. Common debuggers include 
pdb
for the command line and the debugging tools built into integrated development environments (IDEs) like PyCharm or Visual Studio Code. 2. Insert breakpoints: Place breakpoints in your sensor code where you want to pause execution and inspect the state of your program. In most IDEs, you can click next to the line number to set a breakpoint. If you're using 
pdb
, you can insert 
import pdb; pdb.set_trace()
at the desired location in your code. 3. Invoke the sensor function directly: You can directly invoke the sensor's evaluation function in a script or test by building a sensor context and passing it to the function. This allows you to simulate the sensor's execution environment. Here's an example of how you might write a script to test a sensor using the 
build_sensor_context
function provided by Dagster:
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python from dagster import build_sensor_context from my_sensor_module import my_sensor  # Set up a context for the sensor context = build_sensor_context()  # Run the sensor with the context run_requests = my_sensor(context)  # If the sensor yields RunRequests, you need to iterate over them for run_request in run_requests:     # This is where you might inspect the run_request or continue to the next one     pass
4. Run the script with the debugger: Execute the script using your debugger. The debugger should pause at each breakpoint, allowing you to inspect variables, step through code, and evaluate expressions. 5. Inspect the output: Verify that the sensor behaves as expected by checking the 
RunRequest
objects it yields and any other side effects of the sensor's execution. Remember that when you test a sensor by directly invoking its function, any side effects (such as database writes or external API calls) contained within the sensor's function may be executed. Therefore, it's important to use a test environment or mock any external dependencies to avoid unintended consequences. If you're using an IDE like Visual Studio Code, you can configure the debugger through a 
launch.json
file and then start the debugger from the IDE's interface. If you're using 
pdb
, you can run your script with 
python -m pdb my_script.py
to start the debugging session.
a
@Scout What if the sensor function is in a sensor factory? Can you give example code?
s
If the sensor function is generated by a sensor factory, you would still test it in a similar way, but you would first need to invoke the factory function to get the sensor function itself. Once you have the sensor function, you can proceed to build the sensor context and pass it to the sensor function as before. Here's an example of how you might test a sensor that is created by a sensor factory:
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python from dagster import build_sensor_context  # Assume we have a sensor factory function that creates a sensor def sensor_factory(config):     # The factory function defines the sensor function inside it     @sensor(job=my_job)     def my_sensor(context):         # Sensor logic here         if some_condition_based_on(config):             yield RunRequest(run_key="example", run_config={})         else:             yield SkipReason("Condition not met")      return my_sensor  # Use the factory function to create the sensor my_config = {"example_key": "example_value"} sensor_to_test = sensor_factory(my_config)  # Set up a context for the sensor context = build_sensor_context()  # Run the sensor with the context run_requests_or_skip_reasons = list(sensor_to_test(context))  # Inspect the output for item in run_requests_or_skip_reasons:     if isinstance(item, RunRequest):         # Inspect the RunRequest         pass     elif isinstance(item, SkipReason):         # Inspect the SkipReason         pass
In this example, 
sensor_factory
is a function that takes some configuration and returns a sensor function. The sensor function is decorated with 
@sensor
, which is typical for Dagster sensors. The sensor function checks some condition based on the provided configuration and yields either a 
RunRequest
or a 
SkipReason
. When testing, you call 
sensor_factory
with the desired configuration to get the sensor function. Then, you build a sensor context using 
build_sensor_context
and pass it to the sensor function. Finally, you iterate over the results, which could be a mix of 
RunRequest
and 
SkipReason
objects, and perform any necessary inspections or assertions. Remember to set breakpoints or use 
pdb
as needed if you want to step through the sensor code in a debugger. Also, ensure that any external dependencies are mocked or that you are using a test environment to prevent unintended side effects.
a
@Scout Can you expand your example to include creating 4 sensors to test with the sensor factory and feeding them all a resource?
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