Yes, you can understand the relationship between assets and ops within a job by looking at the job's definition and its execution plan. In Dagster, a job is a logical, ordered grouping of assets or ops, and the core of a job is a "graph" of "ops." When you run a job, it will materialize one or more assets, and these assets can be the result of executing ops. The documentation mentions that when viewing the asset graph in the job overview page, each asset node should be annotated with the op that produced it, unless the op name and the asset name are identical. Similarly, when viewing the op graph view, each op that produces assets should be annotated with the name of the first asset it produces, and if there are more than one asset, it can append a "+ X assets" to it. Users can click on the op to view a full list of the assets produced by that op in the right sidebar. Therefore, by examining the job overview page and the execution plan, you should be able to identify all assets associated with their corresponding ops within a job. If you need more detailed information on how assets relate to ops and graphs, the documentation recommends consulting the relevant sections for a deeper understanding.

In Dagster, an I/O manager is responsible for handling the inputs and outputs of assets or ops. The I/O manager's

handle_output

method is called to handle the output of an op, and the

load_input

method is called to load an input for an op. However, the I/O manager's methods are typically invoked by the Dagster framework during the execution of a job, rather than being called directly within user-defined ops. To achieve the behavior you're describing—where a single op triggers the loading of inputs and yields asset materializations for multiple assets—you would typically define a job that includes the necessary ops to load and materialize each asset. Each op would be responsible for a specific piece of the computation and would use the I/O manager implicitly to handle its inputs and outputs. If you want to programmatically control the materialization process within a single op, you could potentially create a custom I/O manager that delegates to other I/O managers or performs multiple storage operations within its

handle_output

method. However, this is not the standard usage pattern in Dagster, and it would require careful design to ensure that it aligns with the rest of your Dagster system and execution model. The documentation does not provide a direct example of triggering I/O manager operations from within a single op to yield multiple asset materializations. The typical pattern is to define separate ops for each asset's computation and let Dagster manage the I/O operations according to the job's execution plan. If you have a specific use case or pattern in mind that requires this kind of behavior, it might be worth exploring custom I/O manager implementations or discussing the use case with the Dagster community to find the best approach within the framework's capabilities.

To execute a job in process with multiple partition keys at once, you can use the

execute_in_process

method on the job object. However, as of my knowledge cutoff date, Dagster does not support directly passing multiple partition keys to

execute_in_process

for a single execution. The

execute_in_process

method is typically used for testing and local development, and it is designed to execute a job with a single partition key or without any partitioning. If you want to execute a job for multiple partition keys in a single run, you would typically use backfills or schedules that are designed to handle multiple partitions. For example, you can define a schedule that triggers a backfill over a range of partition keys. Here's an example of how you might define a schedule that triggers a job execution for a range of partition keys:

python from dagster import schedule  @schedule(     cron_schedule="0 0 * * *", # This is a daily schedule     job=my_job,     execution_timezone="UTC" ) def my_daily_schedule(context):     date = context.scheduled_execution_time.strftime("%Y-%m-%d")     # Define the range of partition keys you want to execute     partition_keys = [f"{date}-part1", f"{date}-part2", f"{date}-part3"]     run_config = {}     for partition_key in partition_keys:         run_config["solids"] = {"my_solid": {"config": {"partition_key": partition_key}}}         # Execute the job for each partition key         result = my_job.execute_in_process(run_config=run_config)         # Handle the result as needed

In this example,

my_job

is the job you want to execute, and

my_solid

is a solid within that job that is configured to handle a specific partition key. The schedule is set to run daily, and for each execution, it generates a list of partition keys for that day and executes the job for each partition key using

execute_in_process

. Please note that this is a simplified example and may not directly apply to your use case. The actual implementation would depend on the specifics of your job and partitioning scheme. If you need to execute a job for multiple partition keys within a single run, you might need to implement custom logic within your job to handle multiple partitions at once. This could involve using dynamic outputs or custom partitioning logic to process multiple partitions within the same job execution. For more detailed guidance on your specific use case, you may want to consult the latest Dagster documentation or seek advice from the Dagster community, as there may have been updates or new features introduced after my knowledge cutoff date that could provide additional capabilities for executing jobs with multiple partition keys.