OPS Regression Failure: C3317113891-POCLOUD Troubleshooting

by Alex Johnson 60 views

Encountering a regression failure in your system can be a daunting experience. When that failure involves a specific collection like C3317113891-POCLOUD within an OPS (Operations) context, it’s crucial to have a systematic approach to identify and resolve the issue. This article delves into the intricacies of debugging such failures, offering a comprehensive guide to help you navigate the complexities and restore your system to optimal functionality. We will break down the error messages, discuss potential causes, and propose step-by-step solutions to address this specific regression failure.

Understanding the Error

To effectively tackle this regression failure, let's dissect the error messages and diagnostic information provided. The core issue revolves around the inability to find granules for the OPS collection C3317113891-POCLOUD. This failure manifested in two test types: spatial and temporal, both pointing to the same root cause.

The error message from the test logs states:

[gw0] linux -- Python 3.10.19 /home/runner/.cache/pypoetry/virtualenvs/l2ss-py-autotest-iYz8Sff2-py3.10/bin/python
verify_collection.py:125: in granule_json
 pytest.fail(f"No granules found for OPS collection {collection_concept_id}. CMR search used was {cmr_url}")
E Failed: No granules found for OPS collection C3317113891-POCLOUD. CMR search used was https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=C3317113891-POCLOUD&sort_key=-start_date&page_size=1

This message indicates that the test verify_collection.py failed specifically at line 125, within the granule_json function. The failure is triggered because no granules were found for the collection when querying the CMR (Common Metadata Repository). The CMR search URL used in the test is also provided, which allows for manual verification of the search results.

The summary further clarifies that the test failed because the CMR search query returned zero granules despite using the correct parameters (collection_concept_id, sort_key, and page_size). This suggests that the issue is not with the search query itself but rather with the availability or indexing of granule data within CMR.

Understanding this fundamental problem is the first step towards implementing effective solutions. We need to investigate why granules are not being found for this specific collection.

Identifying Potential Causes

Several factors could contribute to the absence of granules for the C3317113891-POCLOUD collection. Let's explore some of the most common causes:

  1. Incorrect or Inactive Collection Concept ID: The collection concept ID, C3317113891-POCLOUD, might be incorrect or the collection itself might be inactive. This is a fundamental check to ensure that the identifier used in the search is valid and corresponds to an active collection within the system. A simple typo or a deactivated collection could lead to the observed failure.
  2. Granules Not Yet Published in CMR: Even if the collection is active, the granules associated with it might not have been published in the CMR yet. This could be due to processing delays, data ingestion issues, or other pipeline-related bottlenecks. It's crucial to verify that the expected granules have been successfully ingested and indexed within the CMR.
  3. Staging or Test Environment Issues: The collection might reside in a staging or test environment where the data is intentionally limited or not fully populated. If the test environment does not mirror the production environment accurately, such discrepancies can lead to false negatives during testing.
  4. Data Indexing Problems in CMR: Issues within the CMR indexing process can prevent granules from being discoverable, even if they exist in the system. Indexing delays or failures can occur due to various reasons, such as system overload, software bugs, or data corruption. Checking the CMR API status and logs can provide insights into potential indexing issues.
  5. Network Connectivity Issues: Although less common, network connectivity problems between the test environment and the CMR API endpoint (cmr.earthdata.nasa.gov) could prevent the search query from executing correctly. Verifying network connectivity is a basic but essential troubleshooting step.

By systematically considering these potential causes, you can narrow down the source of the problem and focus your efforts on the most relevant areas.

Step-by-Step Solutions

Based on the potential causes identified, let's outline a series of steps to address the regression failure. These solutions are designed to be methodical, starting with the simplest checks and progressing to more complex investigations.

  1. Verify the Collection Concept ID:

    • Action: Double-check that the collection_concept_id (C3317113891-POCLOUD) is correct and active. This can be done by querying the CMR directly through its API or user interface. Ensure there are no typos or discrepancies in the ID.
    • How: Use the CMR search API or a CMR client tool to look up the collection using the concept ID. Confirm that the collection exists and is in an active state.

  2. Check for Published Granules:

    • Action: Verify if the collection has published granules in the CMR system. Use the CMR search API with the collection_concept_id to search for granules. If no granules are returned, it indicates a potential issue with data ingestion or indexing.
    • How: Execute a CMR granule search query using the provided URL (https://cmr.earthdata.nasa.gov/search/granules.umm_json?collection_concept_id=C3317113891-POCLOUD&sort_key=-start_date&page_size=1) or a similar query via the CMR API. Analyze the results to confirm the presence or absence of granules.

  3. Confirm Environment Context:

    • Action: Ascertain whether the collection is in a staging/test environment. If so, check if the test data is expected to be present in that environment. Sometimes, test environments have limited datasets or are not fully synchronized with production data.
    • How: Review the test environment configurations and documentation. Consult with the team responsible for managing the test environment to understand its data availability and synchronization policies.

  4. Investigate CMR Indexing:

    • Action: Determine if there are any CMR API status issues or potential indexing delays. CMR might be experiencing temporary problems that prevent newly ingested data from being indexed promptly.
    • How: Check the CMR status page or contact CMR support for information on any ongoing issues. Review CMR logs for error messages related to indexing or data ingestion.

  5. Test with a Known Collection:

    • Action: Consider using a different collection with known granules for testing. This can help isolate whether the issue is specific to the C3317113891-POCLOUD collection or a more general problem with the test setup or CMR interaction.
    • How: Modify the test configuration to use a different collection_concept_id that is known to have granules. Run the test and observe if it passes successfully.

  6. Implement Detailed Logging:

    • Action: Add logging to capture the actual CMR response for debugging. This provides valuable insights into the data returned by CMR and helps pinpoint discrepancies or unexpected results.
    • How: Modify the verify_collection.py script to log the full JSON response from the CMR API. Analyze the logs to identify any error messages or unexpected data structures.

  7. Verify Network Connectivity:

    • Action: Ensure that the test environment has network connectivity to cmr.earthdata.nasa.gov. Network issues can prevent the test from reaching the CMR API.
    • How: Use standard network diagnostic tools (e.g., ping, traceroute) to verify connectivity to cmr.earthdata.nasa.gov. Check firewall settings and proxy configurations to ensure they are not blocking access.

By systematically following these steps, you can effectively troubleshoot the regression failure and identify the root cause. Each step provides a targeted approach to address specific potential issues, allowing for a comprehensive investigation.

Addressing the Root Cause

Once you've identified the root cause of the regression failure, the next step is to implement a solution. The specific solution will depend on the nature of the problem, but here are some common scenarios and their corresponding remedies:

  • Incorrect Collection Concept ID: If the concept ID was incorrect, update the test configuration or script with the correct ID. Ensure that the ID is consistently used across all relevant systems and documentation.
  • Granules Not Published: If the granules were not yet published in CMR, coordinate with the data providers or ingestion team to ensure the data is properly processed and indexed. Monitor the data ingestion pipeline for any delays or errors.
  • Staging Environment Issues: If the test environment lacked the necessary data, work with the environment administrators to synchronize the test environment with production data or create a dedicated test dataset for the collection.
  • CMR Indexing Problems: If CMR indexing was the issue, contact CMR support or the system administrators to resolve the indexing problem. Monitor CMR status and logs for any recurring issues.
  • Network Connectivity Issues: If network connectivity was the problem, address the network configuration issues (e.g., firewall rules, proxy settings) to restore connectivity to CMR.

In addition to addressing the immediate failure, consider implementing preventative measures to avoid similar issues in the future. This might include:

  • Automated Data Validation: Implement automated checks to validate the integrity and completeness of data before it is ingested into CMR.
  • Regular Test Environment Synchronization: Establish a process for regularly synchronizing test environments with production data to ensure test accuracy.
  • Comprehensive Monitoring: Implement comprehensive monitoring of CMR status and data ingestion pipelines to detect and address issues proactively.

By addressing the root cause and implementing preventative measures, you can enhance the reliability and stability of your system.

Conclusion

Regression failures can be challenging, but a systematic approach to troubleshooting can significantly streamline the resolution process. In the case of the C3317113891-POCLOUD OPS collection failure, understanding the error messages, identifying potential causes, and following a step-by-step solution methodology are crucial. By verifying the collection ID, checking for published granules, considering environment context, investigating CMR indexing, testing with known collections, implementing detailed logging, and verifying network connectivity, you can effectively diagnose and resolve the issue.

Remember, addressing the root cause and implementing preventative measures are key to ensuring long-term system stability. By embracing a proactive approach to testing and monitoring, you can minimize the impact of future failures and maintain a robust and reliable system.

For more information about CMR and data management, visit the NASA Earthdata website.