Dbt Cloud: Parsing Fusion Logs For Warnings

Introduction

This article discusses the need for supporting the parsing of fusion logs for warnings within the dbt Cloud platform. Currently, the get_job_run_error Admin API tool has been enhanced to include warnings in its output. However, the existing implementation relies on parsing structured output from sources.json and run_results.json for source/run results warnings, and raw log text for log warnings, specifically from dbt Core. With the advent of dbt Fusion, which has fundamentally different and evolving log outputs, a new solution is required to accurately parse warnings from fusion logs.

The current implementation is based off of the log outputs of core. Fusion log outputs are fundamentally different than core and are still evolving. Therefore, dbt Platform jobs that use the fusion engine will not have proper parsing of warnings from the logs when calling get_job_run_error(include_warnings=true) through the MCP. This article delves into the problem, proposes a solution, and outlines the steps needed to address this issue.

The Problem: Inaccurate Warning Parsing with dbt Fusion

The core issue lies in the incompatibility between the log outputs of dbt Core and dbt Fusion. The get_job_run_error Admin API tool, recently extended to include warnings, currently parses warnings from structured outputs (sources.json, run_results.json) and raw log text. However, the raw log parsing is designed for dbt Core. Dbt Fusion's log outputs are structured differently and are still under active development, meaning the current parsing logic will not accurately capture warnings from dbt Fusion jobs. This discrepancy leads to incomplete or incorrect warning information when using get_job_run_error(include_warnings=true) in dbt Cloud Platform jobs that utilize the Fusion engine. The significance of accurate warning parsing cannot be overstated, as warnings often indicate potential issues that, while not causing immediate job failure, can lead to data quality problems or performance bottlenecks down the line. Ignoring these warnings can result in increased technical debt and more complex debugging processes in the future. Therefore, ensuring that warnings from dbt Fusion logs are correctly parsed is crucial for maintaining the reliability and efficiency of dbt Cloud projects as they adopt the new Fusion engine. This also ensures that users receive comprehensive feedback on their dbt runs, enabling them to proactively address potential problems and maintain the integrity of their data pipelines. Furthermore, as dbt Fusion matures and becomes more widely adopted, the need for accurate warning parsing will only increase, making it an essential feature for the long-term success of dbt Cloud.

Proposed Solution: Implement Fusion Log Parsing

The proposed solution involves developing a new mechanism for parsing raw log text specifically tailored to dbt Fusion's log outputs. This solution should mirror the existing functionality for dbt Core, accurately capturing log warnings from Fusion jobs. This new parsing mechanism should be implemented when Fusion reaches General Availability (GA) and its log outputs have stabilized. This ensures that the parsing logic is built on a consistent and reliable foundation, reducing the risk of future compatibility issues. The implementation will involve analyzing the structure of Fusion logs, identifying patterns that indicate warnings, and creating code to extract relevant warning information. The solution should also be designed to be maintainable and adaptable, allowing it to evolve alongside future changes in Fusion's log outputs. This might involve using regular expressions or other pattern-matching techniques to identify warning messages within the logs, and then extracting relevant information such as the warning code, the affected model or source, and any relevant context. Additionally, the solution should be integrated seamlessly into the existing get_job_run_error Admin API tool, ensuring that users can easily access warning information from Fusion jobs without requiring significant changes to their workflows. This integration should also include thorough testing to ensure that the parsing logic is accurate and reliable, and that it does not introduce any performance bottlenecks or other issues. By implementing this solution, dbt Cloud can ensure that users receive comprehensive and accurate warning information from all dbt jobs, regardless of whether they are using dbt Core or dbt Fusion.

Implementation Details

Before the official development begins, local experimentation with fusion logs will be conducted to understand the structure and identify key patterns. This will involve analyzing sample fusion logs to identify the different types of warning messages that can occur, and the specific formatting used for each message. This initial exploration will inform the design of the parsing logic and help to ensure that it is comprehensive and accurate. Once a clear understanding of the log structure has been achieved, the official development process will commence. This will involve writing code to parse the raw log text, extract warning information, and integrate it into the get_job_run_error Admin API tool. The development process will also include thorough testing to ensure that the parsing logic is accurate, reliable, and performant. This testing will involve running dbt jobs with fusion enabled, generating a variety of warning messages, and then using the get_job_run_error tool to verify that the warnings are correctly parsed and displayed. Furthermore, the implementation should be modular and well-documented, making it easy to maintain and extend in the future. This will involve using clear and concise code, adding comments to explain the purpose of each section, and providing detailed documentation on the API and the parsing logic. By following these best practices, the implementation team can ensure that the solution is robust, scalable, and easy to maintain, allowing dbt Cloud to continue providing accurate and reliable warning information to its users.

Alternatives Considered

Currently, no alternatives have been formally considered. This indicates a clear focus on directly addressing the problem by developing a parsing solution specific to dbt Fusion logs. While exploring alternatives is generally a good practice, the unique nature of Fusion logs and the need for accurate warning information likely make a custom parsing solution the most effective approach. However, it may be worthwhile to briefly consider alternative approaches in the future, such as using a more generic log parsing tool or relying on structured logging from dbt Fusion itself. These alternatives could potentially offer some advantages in terms of reduced development effort or improved maintainability, but they would also need to be carefully evaluated to ensure that they can provide the same level of accuracy and completeness as a custom parsing solution. Ultimately, the decision of whether to pursue alternative approaches will depend on the specific requirements of dbt Cloud and the trade-offs between cost, accuracy, and maintainability.

Additional Context

No additional context was provided, suggesting that the problem and solution are well-defined within the existing information. This focus allows for a targeted approach to addressing the parsing issue without the need for further clarification or investigation. However, it is important to remain open to new information and changing requirements as dbt Fusion evolves. As Fusion matures and its log outputs become more stable, the specific details of the parsing solution may need to be adjusted to ensure continued accuracy and compatibility. Therefore, it is crucial to maintain ongoing communication with the dbt Fusion development team and to monitor the evolution of Fusion's log outputs. This will allow the dbt Cloud team to proactively identify any potential issues and to adapt the parsing solution as needed, ensuring that users continue to receive accurate and reliable warning information from all dbt jobs.

Conclusion

In conclusion, supporting the parsing of fusion logs for warnings is crucial for dbt Cloud to provide accurate and comprehensive feedback to its users. The proposed solution involves implementing a new parsing mechanism tailored to dbt Fusion's log outputs, ensuring that warnings are accurately captured and displayed. While the implementation will commence once Fusion reaches GA and its logs stabilize, initial experimentation will be conducted to understand the log structure. This effort will enhance the overall user experience and maintain the reliability of dbt Cloud as it integrates with dbt Fusion.

For more information on dbt and its features, visit the dbt Labs website.