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<@ULVA73B9P> Worker is running and the work pool a...
# ask-marvin
c
@Marvin Worker is running and the work pool and deployment are flagged as ready. "Late" jobs in the queue. Worker is not picking up jobs and the flows are not running. definitions: work_pools: docker-work-pool: &docker-work-pool name: "docker-work-pool" job_variables: image: "{{ build-image.image }}" volumes: - "{{ $HOME }}/.aws/root/.awsro" - "{{ $PWD }}/opt/prefect/dataflow orchestrationro" actions: local_code: &local_code - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/dataflow-orchestration aodn_docker_build: &aodn_docker_build - prefect_docker.deployments.steps.build_docker_image: id: build-image requires: prefect-docker>=0.3.0 image_name: prefect_aodn tag: 'dev' dockerfile: Dockerfile_AODN buildargs: PIP_REQUIREMENTS: common-requirements.txt deployments: - name: integration-tests description: Run integration tests entrypoint: projects/integration_tests/run_integration_tests.py:integration_tests work_pool: *docker-work-pool pull: *local_code build: *aodn_docker_build
m
Your message was too long by 34 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
I have a deployment that uses a docker work pool. The worker is running and the work pool and deployment are flagged as ready. There are two "Late" jobs in the queue. Yet the worker is not picking up jobs and the flows are not running. Here is my deployments:
name: integration-tests prefect-version: ~=2.0 definitions: work_pools: docker-work-pool: &docker-work-pool name: "docker-work-pool" job_variables: image: "{{ build-image.image }}" volumes: - "{{ $HOME }}/.aws/root/.awsro" - "{{ $PWD }}/opt/prefect/dataflow orchestrationro" actions: local_code: &local_code - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/dataflow-orchestration aodn_docker_build: &aodn_docker_build - prefect_docker.deployments.steps.build_docker_image: id: build-image requires: prefect-docker>=0.3.0 image_name: prefect_aodn tag: 'dev' dockerfile: Dockerfile_AODN buildargs: PIP_REQUIREMENTS: common-requirements.txt deployments: - name: integration-tests description: Run integration tests entrypoint: projects/integration_tests/run_in
Your message was too long by 23 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
The worker is running and the work pool and deployment are flagged as ready. There are two "Late" jobs in the queue. Yet the worker is not picking up jobs and the flows are not running. Here is my deployments:
name: integration-tests prefect-version: ~=2.0 definitions: work_pools: docker-work-pool: &docker-work-pool name: "docker-work-pool" job_variables: image: "{{ build-image.image }}" volumes: - "{{ $HOME }}/.aws/root/.awsro" - "{{ $PWD }}/opt/prefect/dataflow orchestrationro" actions: local_code: &local_code - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/dataflow-orchestration aodn_docker_build: &aodn_docker_build - prefect_docker.deployments.steps.build_docker_image: id: build-image requires: prefect-docker>=0.3.0 image_name: prefect_aodn tag: 'dev' dockerfile: Dockerfile_AODN buildargs: PIP_REQUIREMENTS: common-requirements.txt deployments: - name: integration-tests description: Run integration tests entrypoint: projects/integration_tests/run_integration_tests.py:integration_tests work_pool:
Your message was too long by 9 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
The worker is running and the work pool and deployment are flagged as ready. There are two "Late" jobs in the queue. Yet the worker is not picking up jobs and the flows are not running. Here is my deployments:
definitions: work_pools: docker-work-pool: &docker-work-pool name: "docker-work-pool" job_variables: image: "{{ build-image.image }}" volumes: - "{{ $HOME }}/.aws/root/.awsro" - "{{ $PWD }}/opt/prefect/dataflow orchestrationro" actions: local_code: &local_code - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/dataflow-orchestration aodn_docker_build: &aodn_docker_build - prefect_docker.deployments.steps.build_docker_image: id: build-image requires: prefect-docker>=0.3.0 image_name: prefect_aodn tag: 'dev' dockerfile: Dockerfile_AODN buildargs: PIP_REQUIREMENTS: common-requirements.txt deployments: - name: integration-tests description: Run integration tests entrypoint: projects/integration_tests/run_integration_tests.py:integration_tests work_pool: *docker-work-pool pull: *local_code
Your message was too long by 3 tokens - please shorten it and try again. For reference, here's your message at the allowed limit:
The worker is running and the work pool and deployment are flagged as ready. There are two "Late" jobs in the queue. Yet the worker is not picking up jobs and the flows are not running.
definitions: work_pools: docker-work-pool: &docker-work-pool name: "docker-work-pool" job_variables: image: "{{ build-image.image }}" volumes: - "{{ $HOME }}/.aws/root/.awsro" - "{{ $PWD }}/opt/prefect/dataflow orchestrationro" actions: local_code: &local_code - prefect.deployments.steps.set_working_directory: directory: /opt/prefect/dataflow-orchestration aodn_docker_build: &aodn_docker_build - prefect_docker.deployments.steps.build_docker_image: id: build-image requires: prefect-docker>=0.3.0 image_name: prefect_aodn tag: 'dev' dockerfile: Dockerfile_AODN buildargs: PIP_REQUIREMENTS: common-requirements.txt deployments: - name: integration-tests description: Run integration tests entrypoint: projects/integration_tests/run_integration_tests.py:integration_tests work_pool: *docker-work-pool pull: *local_code build: *aodn
It seems like you're experiencing an issue where the Prefect worker is running, and the work pool and deployment are flagged as ready, but the jobs are sitting in the queue with a "late" status, and the worker is not picking them up to run the flows. Let's go through the potential causes and solutions for this issue. To assist you more effectively, I need to determine which version of Prefect you are using, as the troubleshooting steps may vary between Prefect 2.x and 3.x. Let's first find out the latest version of Prefect and look for relevant information for both Prefect 2.x and 3.x. I'll search the Prefect documentation and GitHub issues to gather useful insights for your problem. Give me a moment to perform these searches. I gathered some insights that might help you troubleshoot the issue of "late" jobs not being picked up by the worker in Prefect: 1. There can be several reasons why the worker might not be picking up jobs: - Work Queue Readiness: Work queues must have a READY status. They hold this status if they have been polled by a worker in the last 60 seconds. Ensure your workers are actively polling the work queue. - Concurrency Limits: If there are active concurrency limits within your work pools, these limits may prevent certain jobs from being picked up. You might find additional information about configuring work pools in Prefect's work pool concepts. 2. There might be scheduling logic related to priority in queues. The document excerpts mention job priorities within work queues, suggesting that work submission is based on priority until they are empty, in a waterfall fashion. If your work queues have different priorities set, the behavior you are observing could be expected if the jobs are sitting in a lower-priority queue, waiting for the higher-priority queues to empty. 3. A GitHub issue related to tasks being stuck ("Dask task scheduling hangs") was identified, although it seems to be more specific to DaskTaskRunner scenarios. You might find related issues here: Dask task scheduling hangs then eventually workflow fails with PoolTimeout. 4. Prefect 3.0 documentation also touches on the importance of work pool readiness and job variable configurations, which are crucial for the correct functioning of deployments, and similarly mentions work queue readiness and priority: Work Pool Concepts. If none of these solutions address your problem, I'd recommend checking any logs for the specific worker to see if there are any errors or messages that could provide helpful troubleshooting information. If you find your implementation uses Prefect 2.x or 3.x and the above explanations apply differently, please let me know so I can provide more tailored help.
c
@Marvin I'm using prefect 2
m
Great, since you are using Prefect 2.x, let's tailor the troubleshooting specifically for this version. Here are some steps you can follow to address the issue where the worker is not picking up jobs and flows are not running: 1. Work Queue Readiness: - Ensure that your work queue has a 
READY
status. This status indicates that a worker has polled the queue in the last 60 seconds. Check that your worker is running and is connected correctly. 2. Concurrency and Priority: - Verify if there are any concurrency limits set on your work pool. These limits could prevent tasks from being executed if they're reached. - Examine the priorities of your queues. If jobs are in a lower-priority queue, they might be waiting for higher-priority queues to clear. 3. Worker Logs: - Check the logs for the worker process. Sometimes, there are errors or warning messages in the logs that can provide clues about why the worker is not picking up jobs. 4. Flow Run State: - Sometimes flow runs get stuck in states like "Late" due to timing and synchronization issues. To mitigate these, you can try rescheduling or canceling the affected runs manually. Prefect provides API interactions to manage flow runs, which might be worth checking if this persists. 5. Documentation and GitHub Issues: - You might want to review related issues for similar cases in Prefect's GitHub repository or the documentation. Here is a relevant section from the Prefect 2.x documentation on work pools: Work Pool Concepts. If you've tried these steps and are still experiencing issues, or if there is a specific error message you encounter in the logs, feel free to share more details. That way, we can look deeper into the problem.
c
@Marvin where are the worker logs
m
In Prefect 2.x, the location of worker logs can vary depending on how you've configured your system and environment. Here are some common places to check for worker logs: 1. Standard Output/Console: By default, if you start a Prefect worker from the command line, the logs should appear directly in the console or terminal where the worker is running. Make sure to check the terminal window where you executed the 
prefect worker start
command. 2. Log Files: If you have configured your Prefect environment to log outputs to files, the logs would typically be stored in a specified log directory. Check your logging configuration in your environment settings or Prefect configuration files. 3. Cloud or Remote Logging Services: If you're using Prefect Cloud or have set up a remote logging service (like AWS CloudWatch, GCP Stackdriver, etc.), you might find the logs there. Ensure that your worker and Prefect settings are correctly configured to send logs to the intended service. 4. Docker Logs: If you’re running Prefect workers in Docker containers, you can check the Docker logs using the command 
docker logs <container_id>
to see all logs related to the container running the Prefect worker. 5. Prefect UI: Some information about worker status and logs can also be found directly in the Prefect UI, though detailed logs are usually more accessible via the above methods. If you still can't locate the logs, let me know more about your setup or how the worker is deployed, and I might help you further with more specific instructions.
c
@Marvin there are no log messages other than Worker 'DockerWorker 4afcd502-eb5e-4534-8f9b-3fdf70b04bc8' started!
m
If the only log message you see is the one indicating that the worker has started, but there are no further logs or errors, it suggests that the worker may not be receiving tasks from the queue. This could be due to several reasons: 1. Work Pool and Queue Configuration: - Double-check your work pool and work queue setup. Ensure that the work queue you're targeting with your flow deployments matches the worker's configuration. 2. Worker Connection: - Verify that the worker is correctly connected to the Prefect service and can access the work queue. Any network or configuration issues could prevent it from polling the queue. 3. Check for Paused Queue: - Ensure that the queue is not paused. A paused queue will not dispatch tasks to workers. 4. Deployment and Flow Setup: - Ensure that your deployments are correctly configured to send tasks to the appropriate work queue. Reassess your 
prefect.yaml
and other configuration files for accuracy. 5. Version Compatibility: - Make sure all components (Prefect server, agent, worker, etc.) are running compatible versions. 6. Manual Intervention: - Consider manually executing a flow to see if it's properly dispatched to the queue and picked up by the worker. This might help isolate if the issue lies with scheduling or the tasks themselves. 7. Check Prefect UI or API: - Use the Prefect UI to see the state of your queues and deployments. You might be able to spot discrepancies or issues visually that aren't apparent from the command line. 8. Prefect Cloud: - If you're using Prefect Cloud, check the cloud dashboard for any alerts, logs, or settings that might be interfering with task execution. If you've reviewed these areas and still face issues, consider increasing the logging level if possible or contact Prefect support for more targeted assistance. Your problem might require a more detailed analysis of your configuration and environment.
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