@Scout I generate some dynamic partitions in my job and run these partitions through a series of ops before they are collected and ran through a final op. If most of the partitions complete successfully, and a couple fail but re-run successfully, how does the collect function handle the partitions?

The documentation provides information on how dynamic outputs and the

.collect()

function work within Dagster. When you use dynamic outputs in a job, you can map over these outputs to apply operations to each element, and then use

.collect()

to gather the results into a list or perform further operations on the aggregated results. However, the documentation does not explicitly describe the behavior of the

.collect()

function in the scenario where some dynamic partitions fail initially but are then re-run successfully. Generally, the

.collect()

function is designed to wait for all dynamic outputs to be produced before proceeding. If some partitions fail and are retried, the

.collect()

function should, in principle, wait for all the retried partitions to complete successfully before collecting all the results. If you encounter a situation where the behavior of

.collect()

is not clear or does not align with your expectations, it would be advisable to review the job logs to understand how

.collect()

handled the dynamic outputs in your specific case. Additionally, you can check for any relevant configuration or settings that might influence the behavior of dynamic outputs and their collection in the context of retries. For more detailed or specific behaviors, especially regarding retries and the handling of failures, you may need to refer to the latest Dagster documentation or seek guidance from the Dagster community or support channels, as the behavior could be influenced by the version of Dagster you are using or specific configurations in your job.

@Scout if my job fails during a dynamically partitioned section, can I restart all the partitions from the same op at once, instead of having to restart before the partitioning?

Based on the documentation, if you have a job with dynamically partitioned sections and some partitions fail, you can use backfills to re-execute the failed partitions. Backfills in Dagster allow you to target specific partitions for re-execution without having to restart the entire job from the beginning. When you launch a backfill, you can select the failed or missing partitions and re-execute them. Dagster will then launch runs for each of those partitions. This approach is beneficial because it avoids the need to re-run successful partitions and allows you to focus on addressing the failures. The documentation also mentions that Dagster has improved the partitions and backfills experience in Dagit, making it faster and more intuitive. The partitions page displays a status bar that lets you quickly view the status across all your partitions for each job, making it easier to identify and select the failed partitions for backfill. Additionally, Dagster supports the concept of re-execution policies, which can be set to allow for the re-execution of multiple failed job run instances from the Runs page. This feature enables you to re-execute failed runs in bulk, which can be more efficient than re-executing them individually. To summarize, if a dynamically partitioned section of your job fails, you can use the backfill feature to re-execute all the failed partitions at once from the point of failure, without having to restart the job from an earlier op or from the beginning. Always ensure that you are using the latest version of Dagster and refer to the most recent documentation for the most accurate and up-to-date information on backfills and re-execution policies.

@Scout I get this error from the op that runs after the partitions have been collected - I am using the gcs IO manager No previously stored outputs found for source StepOutputHandle(step_key='main_process_4_i[Partition_1_0]', output_name='result', mapping_key=None). This is either because you are using an IO Manager that does not depend on run ID, or because all the previous runs have skipped the output in conditional execution.

The error message you're encountering indicates that Dagster is unable to find the expected output from a previous step when trying to execute a downstream op. This can happen for a few reasons, as the error message suggests: 1. IO Manager Independence from Run ID: If you're using an IO manager that does not rely on the run ID to retrieve outputs (such as the GCS IO manager), it's possible that the output was not stored correctly or that the location where the output is expected to be is incorrect. 2. Skipped Output in Conditional Execution: If the output was conditionally produced and the condition resulted in the output being skipped in previous runs, then the downstream op would not find the expected output. To troubleshoot this issue, consider the following steps: - Check Output Storage: Ensure that the GCS IO manager is correctly configured to store and retrieve outputs. Verify that the GCS bucket and object naming conventions match what Dagster expects. You may need to check the GCS bucket to see if the outputs were indeed stored and if the paths match. - Review Job Logic: Look at the logic of your job to see if there's a conditional branch that might result in the output being skipped. If the output is optional, ensure that downstream ops are designed to handle cases where the output might not be present. - Examine Retry Behavior: If the partitions failed and were retried, ensure that the retry logic correctly stores the outputs upon successful completion of the retries. - Check for Configuration Issues: Make sure that the GCS IO manager is correctly set up in your resource configuration, and that any necessary authentication and access permissions are correctly configured. - Review Partition Handling: If you're using dynamic partitioning, ensure that the partitions are being handled correctly and that the outputs for each partition are being stored as expected. - Consult Documentation and Community: Review the Dagster documentation for the GCS IO manager and dynamic partitioning to ensure that you're using these features as intended. If the issue persists, consider reaching out to the Dagster community or support channels for further assistance. By following these steps, you should be able to identify the cause of the error and take appropriate action to resolve it. If the issue is not clear after these checks, you may need to provide more details about your job's configuration and the context in which the error occurs for further diagnosis.

My job is only partly partitioned, is that why I have no option to backfill failed partitions in the UI? @Scout

In Dagster, backfills are typically used with partitioned jobs or assets, where each partition corresponds to a discrete subset of the data or computation. If your job is only partly partitioned, it may affect how you can use the backfill functionality in the UI. According to the documentation, the backfill UI is designed to work with partitioned jobs and assets, allowing you to select specific partitions to re-execute. If only part of your job is partitioned, and the failed steps are within the non-partitioned sections, the backfill UI may not provide the option to backfill those specific failed partitions because it expects the entire job to be partitioned. Here are a few considerations: 1. Partitioned Assets: If the failed steps are associated with partitioned assets, you should be able to use the backfill UI to re-execute the failed partitions. 2. Non-Partitioned Steps: If the failed steps are not associated with partitions, you may not see the option to backfill them in the UI. Instead, you might need to re-execute the entire job or manually re-execute the failed steps. 3. Hybrid Jobs: For jobs that have both partitioned and non-partitioned components, the backfill UI may only be applicable to the partitioned parts. You may need to handle the non-partitioned parts separately. 4. Backfill Policies: The documentation mentions that backfill policies are not respected when launching job backfills for assets that have backfill policies other than the default one. This could also affect the availability of backfill options in the UI. If you're not seeing the option to backfill failed partitions in the UI, you may want to review the job's configuration to ensure that the partitioned sections are correctly defined and that the backfill functionality is applicable to those sections. Additionally, you can check if there are any known issues or limitations with the backfill UI that might be relevant to your situation. If you're still unsure why the backfill option is not available for your partly partitioned job, consider reaching out to the Dagster community or support channels for further assistance. They may be able to provide more specific guidance based on the details of your job's configuration and the behavior you're observing.

@Scout if I was to split the job into three seperate jobs, before partitioning, partitioned, and after partition. How would I define the partitioned job when I currently generate dynamic outputs using the DynamicOutput class like this: yield DynamicOutput(value={ 'partition': partition, 'data_partitions': data_partitions, 'group_num': i, }, mapping_key=partition_key) i+=1 I'd also need to ensure the job after the partition doesn't run until all partitions are completed

To define a partitioned job in Dagster when you're generating dynamic outputs, you'll need to set up your job to handle dynamic partitions. Here's a step-by-step guide based on the documentation: 1. Define Dynamic Outputs: Continue to use

DynamicOutput

to yield dynamic outputs from your op. Each

DynamicOutput

should have a unique

mapping_key

that corresponds to a partition. 2. Map Dynamic Outputs: Use the

.map

method on the dynamic output to apply an op to each dynamic output. This will create a new op instance for each dynamic output, effectively creating a dynamic partition. 3. Collect Dynamic Outputs: Use the

.collect()

method to gather all the dynamic outputs. This creates a fan-in dependency, ensuring that the downstream ops will only run after all dynamic partitions have been processed. 4. Define Downstream Ops: Define the ops that should run after all dynamic partitions are completed. These ops will depend on the collected output from the previous step. 5. Define Jobs: Split your workflow into three separate jobs: - Before Partitioning Job: This job includes the ops that run before the dynamic partitioning starts. - Partitioned Job: This job handles the dynamic partitioning. It starts with the op that generates the dynamic outputs and includes the mapping and collecting of these outputs. - After Partition Job: This job includes the ops that should run after the partitioned job has completed. To ensure that this job doesn't run until all partitions are completed, you can set up dependencies or use Dagster's sensor or schedule system to trigger this job only when the partitioned job has finished successfully. Here's an example of how you might define the partitioned job:

python
from dagster import job, op, DynamicOut, DynamicOutput

@op(out=DynamicOut())
def generate_partitions():
    # Your logic to generate partitions
    i = 0
    for partition_key, partition_data in your_partition_logic():
        yield DynamicOutput(value={
            'partition': partition_data['partition'],
            'data_partitions': partition_data['data_partitions'],
            'group_num': i,
        }, mapping_key=partition_key)
        i += 1

@op
def process_partition(partition_data):
    # Your logic to process each partition
    pass

@job
def partitioned_job():
    partitions = generate_partitions()
    # Map over each dynamic output to process partitions
    processed_partitions = partitions.map(process_partition)
    # Collect results to ensure all partitions are processed before moving on
    collected_results = processed_partitions.collect()

For the job after the partition, you can use a sensor to monitor the completion of the partitioned job. The sensor can check for the success of all p

@Scout this is how my job is currently set up and I can't backfil partitions because it's not an entirely partitioned job

If your job is not entirely partitioned and you're using dynamic outputs, you may not be able to use the backfill functionality in the same way as you would with a fully partitioned job. Backfills in Dagster are typically used with partition sets, where each partition corresponds to a discrete subset of the data or computation, and the job is defined with a partitions definition. Given that your job is only partly partitioned, you might not see the option to backfill failed partitions in the UI because the backfill functionality is designed to work with partitioned jobs or assets. To address this, you have a few options: 1. Fully Partition Your Job: If possible, adjust your job so that it is fully partitioned. This would allow you to use the backfill functionality as intended. 2. Manual Retry: For the dynamic parts of your job that are not partitioned, you may need to manually retry the failed steps or outputs. 3. Programmatic Retry: Implement a custom retry mechanism within your job logic that captures failures and retries the necessary steps. 4. Split the Job: As you suggested, splitting the job into separate jobs for the before-partitioning, partitioned, and after-partition sections could be a solution. You would then define the partitioned section as a fully partitioned job, which would allow you to use the backfill functionality for that section. The other sections would be handled as separate jobs, potentially with their own retry mechanisms or dependencies. When splitting the job, you would define the partitioned job with a partition set and use the partition keys to manage the outputs and downstream dependencies. The downstream job would only be triggered once all partitions from the partitioned job have been successfully processed. You can use Dagster's sensors or schedules to orchestrate this behavior, ensuring that the downstream job waits for the completion of the partitioned job. Please note that the exact implementation details and the best approach for your use case may depend on the specifics of your pipeline and the version of Dagster you are using. Always refer to the latest documentation for the most accurate and up-to-date guidance, and consider reaching out to the Dagster community or support channels if you need further assistance with your particular setup. In job backfill UI, make it clear that b... button Respect partition mappings in asset job ... button Partitioned job backfills do not respect... button Backfilling a job doesn't do it in topol... button Thank you for your feedback!