OPS Regression Failure: C2491735309-POCLOUD Analysis

This article delves into the regression failure observed for OPS C2491735309-POCLOUD AVHRR_SST_METOP_A-OSISAF-L2P-v1.0, providing a comprehensive analysis of the error, its causes, and suggested solutions. This issue was identified within the podaac/l2ss-py-autotest framework, specifically during automated testing procedures. The failure, which occurred on 2025-12-01 12:56:59 UTC, highlights critical challenges in accessing and verifying collection variables from the NASA Common Metadata Repository (CMR).

Understanding the Regression Failure

The regression failure in question pertains to the AVHRR_SST_METOP_A-OSISAF-L2P-v1.0 dataset, identified by the Concept ID C2491735309-POCLOUD. This dataset is crucial for sea surface temperature (SST) monitoring, utilizing data from the Advanced Very High-Resolution Radiometer (AVHRR) onboard the METOP-A satellite. The failure was detected in two distinct test types: temporal and spatial, indicating a broad issue rather than one confined to a specific dimension of data verification.

Temporal Test Failure

The temporal test failure occurred during the execution of verify_collection.py, a script designed to validate collection variables. The error message points to a requests.exceptions.HTTPError, specifically a 500 Server Error originating from the CMR API. This error arose when attempting to retrieve variable metadata using a GET request to the https://cmr.earthdata.nasa.gov/search/variables.umm_json endpoint. The request included a large number of Concept IDs (over 40) and set page_size=0. The core issue is that the CMR API, under these conditions, failed to process the request, resulting in a server-side error.

This temporal test failure indicates a problem with the CMR API's ability to handle large requests for variable metadata. The error message suggests that the server was overwhelmed when trying to process a request with numerous Concept IDs while also being instructed to return zero results per page (page_size=0). This scenario likely exposed a limitation in the API's query processing or resource allocation mechanisms. The verify_collection.py script, while designed to ensure data integrity, inadvertently triggered this failure by constructing a request that exceeded the API's capacity. This highlights the importance of implementing robust error handling and optimization strategies when interacting with external APIs, especially those with known limitations or potential for overload.

Spatial Test Failure

The spatial test failure mirrors the temporal test failure in its root cause. It also presents a 500 Server Error from the CMR API, triggered during the execution of verify_collection.py. The error message indicates the same problematic GET request to the CMR API endpoint, attempting to retrieve variable metadata with multiple Concept IDs and page_size=0. In this instance, the request included 17 Concept IDs. The spatial test, which focuses on geographic aspects of the dataset, encountered the same limitation as the temporal test, highlighting that the issue is not specific to the temporal characteristics of the data but rather a general problem with how the API request is being handled.

The significance of the spatial test failure is that it reinforces the generality of the issue. It's not just a matter of temporal data processing; the API is failing under the load of multiple Concept IDs regardless of the specific test being performed. This broader impact necessitates a solution that addresses the underlying API interaction strategy rather than a localized fix specific to one type of test. The fact that the failure occurs with a smaller set of Concept IDs (17 in the spatial test compared to 40+ in the temporal test) further suggests that the problem is not solely a matter of the number of IDs but also potentially related to the complexity of the associated metadata or the API's handling of zero-page-size requests. Understanding this nuance is crucial for devising an effective and scalable solution.

Suggested Solutions and Mitigation Strategies

Given the nature of the errors, several solutions have been proposed to mitigate these regression failures and ensure the stability of the l2ss-py-autotest framework. These solutions primarily focus on optimizing the interaction with the CMR API and implementing robust error handling mechanisms.

Batching Concept IDs

One of the primary recommendations is to batch the Concept IDs into smaller chunks. Instead of sending a single request with a large number of IDs, the script can divide the IDs into smaller groups and send multiple requests. This approach reduces the load on the CMR API, making it less likely to trigger a 500 Server Error. Batching is a common strategy for handling large datasets or API requests, as it allows for more efficient processing and reduces the risk of overwhelming the server. By breaking down the request into manageable parts, the API can process each part more easily, and the overall process becomes more resilient to errors. This approach aligns with best practices for API interaction, especially when dealing with services that may have rate limits or performance constraints.

Adjusting Page Size

Another suggested solution is to change page_size=0 to a more reasonable value, such as page_size=100. Setting page_size to zero instructs the API to return no results, which may lead to unexpected behavior or errors. By requesting a reasonable number of results per page, the API can handle the request more efficiently. A page size of 100 is often a good starting point, as it balances the need to retrieve a sufficient amount of data with the desire to avoid overwhelming the server. Adjusting the page size is a straightforward way to optimize API interactions, and it can often resolve issues related to server errors or timeouts. This adjustment is particularly relevant in this case, as the combination of a large number of Concept IDs and a zero page size seems to be a key factor in triggering the 500 Server Error.

Utilizing POST Requests

The suggestion to use POST instead of GET for sending the request is another critical optimization. GET requests have limitations on the amount of data that can be included in the URL, whereas POST requests allow for sending data in the request body, which has a much higher capacity. When dealing with a large number of Concept IDs, switching to POST requests can prevent the URL from becoming too long, which can cause errors or be rejected by the server. POST requests are also generally more secure for sending sensitive data, as the data is not exposed in the URL. In the context of the CMR API, using POST requests can provide a more robust and reliable way to send complex queries with numerous parameters, reducing the likelihood of encountering URL-length limitations or other request-related errors. This change can significantly improve the stability and performance of the verify_collection.py script.

Implementing Retry Logic

The proposed solution to add retry logic is essential for handling transient errors and ensuring the resilience of the system. Retry logic involves automatically retrying a failed request after a certain delay. This is particularly useful for dealing with intermittent issues such as network problems or temporary server unavailability. By implementing retry logic, the system can recover from these transient errors without manual intervention. The suggestion to use exponential backoff is a refinement of this strategy. Exponential backoff means that the delay between retries increases exponentially, giving the server more time to recover and preventing the system from overwhelming the server with repeated requests. For example, the first retry might occur after 1 second, the second after 2 seconds, the third after 4 seconds, and so on. This approach is a best practice for handling API errors, as it balances the need to retry the request with the need to avoid exacerbating the problem. Implementing retry logic with exponential backoff can significantly improve the reliability of the l2ss-py-autotest framework.

Wrapping in Try-Catch Blocks

Wrapping the API calls in try-catch blocks is a fundamental aspect of robust error handling. A try-catch block allows the code to attempt an operation that might fail and then gracefully handle the error if it occurs. In this context, it means that if the CMR API request fails and raises an HTTPError, the code can catch the error and take appropriate action, such as logging the error, retrying the request, or alerting an administrator. Without try-catch blocks, an unhandled HTTPError would cause the script to crash, potentially disrupting the entire testing process. By wrapping the API calls in try-catch blocks, the system becomes more resilient to errors and can continue to operate even when the CMR API encounters issues. This is a critical step in ensuring the stability and reliability of the l2ss-py-autotest framework, as it prevents failures from propagating and allows for more controlled error management.

Checking CMR API Status

Finally, the suggestion to check the CMR API status is a proactive measure that can help prevent failures before they occur. By monitoring the status of the CMR API, the system can detect potential issues such as downtime or performance degradation. If the API is experiencing problems, the system can take preventative action, such as delaying tests or switching to a backup API. Checking the API status can be done programmatically, using the API's status endpoint or by monitoring relevant metrics. This proactive approach allows for more informed decision-making and can prevent the system from attempting to use the API when it is known to be unavailable. Integrating API status checks into the l2ss-py-autotest framework can significantly improve its reliability and reduce the likelihood of encountering errors due to API issues.

Conclusion

The regression failure for OPS C2491735309-POCLOUD highlights the importance of robust API interaction strategies and error handling mechanisms. The 500 Server Errors from the CMR API, triggered by large requests and specific parameter settings, underscore the need for optimization and resilience in the l2ss-py-autotest framework. By implementing the suggested solutions—batching Concept IDs, adjusting page size, using POST requests, adding retry logic with exponential backoff, wrapping API calls in try-catch blocks, and checking the CMR API status—the system can become more reliable and efficient. These measures not only address the immediate issue but also contribute to the overall robustness of the data verification process.

For more information on API error handling and best practices, consider exploring resources like the NASA Earthdata website.