Troubleshooting `test_profiler_when_run_config_missing` Failure In Databricks DQX Node Type Compatibility Issue

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databricks.sdk.errors.platform.InvalidParameterValue: Node type Standard_D4ads_v6 is not supported. Supported node types: Standard_DS3_v2, Standard_DS4_v2, Standard_DS5_v2, Standard_D4s_v3, Standard_D8s_v3, Standard_D16s_v3, Standard_D32s_v3, Standard_D64s_v3, Standard_D4a_v4, Standard_D8a_v4, Standard_D16a_v4, Standard_D32a_v4, Standard_D48a_v4, Standard_D64a_v4, Standard_D96a_v4, Standard_D8as_v4, Standard_D16as_v4, Standard_D32as_v4, Standard_D48as_v4, Standard_D64as_v4, Standard_D96as_v4, Standard_D4ds_v4, Standard_D8ds_v4, Standard_D16ds_v4, Standard_D32ds_v4, Standard_D48ds_v4, Standard_D64ds_v4, Standard_D3_v2, Standard_D4_v2, Standard_D5_v2, Standard_D8_v3, Standard_D16_v3, Standard_D32_v3, Standard_D64_v3, Standard_D4s_v5, Standard_D8s_v5, Standard_D16s_v5, Standard_D32s_v5, Standard_D48s_v5, Standard_D64s_v5, Standard_D96s_v5, Standard_D4ds_v5, Standard_D8ds_v5, Standard_D16ds_v5, Standard_D32ds_v5, Standard_D48ds_v5, Standard_D64ds_v5, Standard_D96ds_v5, Standard_D4as_v5, Standard_D8as_v5, Standard_D16as_v5, Standard_D32as_v5, Standard_D48as_v5, Standard_D64as_v5, Standard_D96as_v5, Standard_D4ads_v5, Standard_D8ads_v5, Standard_D16ads_v5, Standard_D32ads_v5, Standard_D48ads_v5, Standard_D64ads_v5, Standard_D96ads_v5, Standard_D4d_v4, Standard_D8d_v4, Standard_D16d_v4, Standard_D32d_v4, Standard_D48d_v4, Standard_D64d_v4, Standard_D12_v2, Standard_D13_v2, Standard_D14_v2, Standard_D15_v2, Standard_DS12_v2, Standard_DS13_v2, Standard_DS14_v2, Standard_DS15_v2, Standard_E8_v3, Standard_E16_v3, Standard_E32_v3, Standard_E64_v3, Standard_E8s_v3, Standard_E16s_v3, Standard_E32s_v3, Standard_E64s_v3, Standard_E4d_v4, Standard_E8d_v4, Standard_E16d_v4, Standard_E20d_v4, Standard_E32d_v4, Standard_E48d_v4, Standard_E64d_v4, Standard_E4ds_v4, Standard_E8ds_v4, Standard_E16ds_v4, Standard_E20ds_v4, Standard_E32ds_v4, Standard_E48ds_v4, Standard_E64ds_v4, Standard_E80ids_v4, Standard_E4a_v4, Standard_E8a_v4, Standard_E16a_v4, Standard_E20a_v4, Standard_E32a_v4, Standard_E48a_v4, Standard_E64a_v4, Standard_E96a_v4, Standard_E4as_v4, Standard_E8as_v4, Standard_E16as_v4, Standard_E20as_v4, Standard_E32as_v4, Standard_E48as_v4, Standard_E64as_v4, Standard_E96as_v4, Standard_E4s_v4, Standard_E8s_v4, Standard_E16s_v4, Standard_E20s_v4, Standard_E32s_v4, Standard_E48s_v4, Standard_E64s_v4, Standard_E80is_v4, Standard_E4s_v5, Standard_E8s_v5, Standard_E16s_v5, Standard_E20s_v5, Standard_E32s_v5, Standard_E48s_v5, Standard_E64s_v5, Standard_E96s_v5, Standard_E4ds_v5, Standard_E8ds_v5, Standard_E16ds_v5, Standard_E20ds_v5, Standard_E32ds_v5, Standard_E48ds_v5, Standard_E64ds_v5, Standard_E96ds_v5, Standard_E4as_v5, Standard_E8as_v5, Standard_E16as_v5, Standard_E20as_v5, Standard_E32as_v5, Standard_E48as_v5, Standard_E64as_v5, Standard_E96as_v5, Standard_E4ads_v5, Standard_E8ads_v5, Standard_E16ads_v5, Standard_E20ads_v5, Standard_E32ads_v5, Standard_E48ads_v5, Standard_E64ads_v5, Standard_E96ads_v5, Standard_L4s, Standard_L8s, Standard_L16s, Standard_L32s, Standard_F4, Standard_F8, Standard_F16, Standard_F4s, Standard_F8s, Standard_F16s, Standard_H8, Standard_H16, Standard_F4s_v2, Standard_F8s_v2, Standard_F16s_v2, Standard_F32s_v2, Standard_F64s_v2, Standard_F72s_v2, Standard_NC12, Standard_NC24, Standard_NC6s_v3, Standard_NC12s_v3, Standard_NC24s_v3, Standard_NC4as_T4_v3, Standard_NC8as_T4_v3, Standard_NC16as_T4_v3, Standard_NC64as_T4_v3, Standard_ND96asr_v4, Standard_L8s_v2, Standard_L16s_v2, Standard_L32s_v2, Standard_L64s_v2, Standard_L80s_v2, Standard_L8s_v3, Standard_L16s_v3, Standard_L32s_v3, Standard_L48s_v3, Standard_L64s_v3, Standard_L80s_v3, Standard_L8as_v3, Standard_L16as_v3, Standard_L32as_v3, Standard_L48as_v3, Standard_L64as_v3, Standard_L80as_v3, Standard_DC4as_v5, Standard_DC8as_v5, Standard_DC16as_v5, Standard_DC32as_v5, Standard_EC8as_v5, Standard_EC16as_v5, Standard_EC32as_v5, Standard_EC8ads_v5, Standard_EC16ads_v5, Standard_EC32ads_v5, Standard_NV36ads_A10_v5, Standard_NV36adms_A10_v5, Standard_NV72ads_A10_v5, Standard_NC24ads_A100_v4, Standard_NC48ads_A100_v4, Standard_NC96ads_A100_v4, Standard_D4pds_v6, Standard_D8pds_v6, Standard_D16pds_v6, Standard_D32pds_v6, Standard_D48pds_v6, Standard_D64pds_v6, Standard_D96pds_v6, Standard_D4plds_v6, Standard_D8plds_v6, Standard_D16plds_v6, Standard_D32plds_v6, Standard_D48plds_v6, Standard_D64plds_v6, Standard_D96plds_v6, Standard_E4pds_v6, Standard_E8pds_v6, Standard_E16pds_v6, Standard_E32pds_v6, Standard_E48pds_v6, Standard_E64pds_v6, Standard_E96pds_v6, Standard_E4ps_v6, Standard_E8ps_v6, Standard_E16ps_v6, Standard_E32ps_v6, Standard_E48ps_v6, Standard_E64ps_v6, Standard_E96ps_v6, Standard_D4pls_v6, Standard_D8pls_v6, Standard_D16pls_v6, Standard_D32pls_v6, Standard_D48pls_v6, Standard_D64pls_v6, Standard_D96pls_v6, Standard_D4ps_v6, Standard_D8ps_v6, Standard_D16ps_v6, Standard_D32ps_v6, Standard_D48ps_v6, Standard_D64ps_v6, Standard_D96ps_v6, Standard_E20ads_v6, Standard_E48ads_v6, Standard_E96ads_v6, Standard_D48ads_v6, Standard_D96ads_v6, Standard_NC40ads_H100_v5, Standard_NC80adis_H100_v5, Standard_D48ds_v6, Standard_D96ds_v6, Standard_D128ds_v6, Standard_E20ds_v6, Standard_E48ds_v6, Standard_E96ds_v6, Standard_E128ds_v6, Standard_ND96isr_H100_v5, Standard_D4s_v4, Standard_D8s_v4, Standard_D16s_v4, Standard_D32s_v4, Standard_D48s_v4, Standard_D64s_v4, Standard_E2ps_v6, Standard_E2pds_v6

This article delves into a specific test failure encountered within the Databricks Labs DQX (Data Quality Experience) project. The failure, identified as test_profiler_when_run_config_missing, stems from an invalid parameter value related to the node type specified in the test configuration. Specifically, the error message indicates that the Standard_D4ads_v6 node type is not supported, and a comprehensive list of supported node types is provided. This issue falls under the databrickslabs and dqx discussion categories, highlighting its relevance to the Databricks ecosystem and data quality initiatives.

Understanding the test_profiler_when_run_config_missing Failure

To effectively address this failure, it's crucial to understand the context in which it occurs. The test, named test_profiler_when_run_config_missing, likely aims to verify the behavior of the DQX profiler when a run configuration is either missing or misconfigured. The profiler is a critical component of DQX, responsible for analyzing data and generating insightful statistics about its quality and characteristics. A missing or invalid run configuration can lead to unexpected behavior, and this test is designed to catch such scenarios.

The error message provides key insights into the root cause:

  • databricks.sdk.errors.platform.InvalidParameterValue: This clearly indicates that an invalid parameter has been passed during the execution of a Databricks SDK operation.
  • Node type Standard_D4ads_v6 is not supported: This pinpoints the specific parameter causing the issue – the node type Standard_D4ads_v6. Node types in Databricks define the compute resources allocated to a cluster, influencing performance and cost. The error message reveals that this particular node type is not among the supported options for the DQX profiler.
  • Supported node types: ...: The extensive list of supported node types provides a clear path for resolving the issue. By selecting a node type from this list, the configuration can be corrected to align with the platform's requirements.

Analyzing the Error Log

The provided error log offers a detailed trace of the events leading to the failure. Let's break down the relevant sections:

  • DQX Installation: The log shows that the DQX installation process is initiated, including the creation of dashboards and job configurations.
  • Dashboard Creation: The system attempts to create dashboards, parsing assets and installing the 'DQX_Quality_Dashboard'. Warnings related to parsing expressions and unsupported fields in the dashboard configuration are also logged. While these warnings may not be directly related to the node type error, they warrant further investigation as they could indicate other potential issues.
  • Workflow Installation: The log indicates that a new job configuration is being created for the profiler step. This is where the error occurs, highlighting that the node type issue arises during the workflow deployment phase.
  • Parallel Task Failure: The error is flagged within a parallel task execution framework (databricks.labs.blueprint.parallel), suggesting that multiple components are being installed concurrently. The failure of the profiler job configuration impacts the overall installation process.
  • Traceback: The traceback provides a stack trace of the function calls that led to the error. It clearly shows that the databricks.sdk.errors.platform.InvalidParameterValue exception is raised during the jobs.create operation, which is part of deploying the workflow. The root cause is the attempt to create a Databricks job with the unsupported node type Standard_D4ads_v6.

Diagnosing the Root Cause and Identifying Solutions

Based on the error message and log analysis, the root cause of the test failure is the use of an unsupported node type (Standard_D4ads_v6) in the DQX profiler's run configuration. This could stem from several factors:

  1. Outdated Configuration: The configuration file used for the test might contain an outdated node type that is no longer supported by the Databricks platform.
  2. Incorrect Default Value: The default node type setting within the DQX installation scripts or configuration templates might be set to Standard_D4ads_v6, leading to this error if not explicitly overridden.
  3. Environment-Specific Issue: The supported node types can vary depending on the Databricks environment or region. The Standard_D4ads_v6 node type might be valid in some environments but not in the one where the test is being executed.

To address this issue, the following solutions can be considered:

  1. Update the Run Configuration: The most direct solution is to modify the DQX profiler's run configuration to use a supported node type. This involves identifying the configuration file or setting that specifies the node type and replacing Standard_D4ads_v6 with a valid option from the list provided in the error message. It's crucial to select a node type that meets the resource requirements of the profiler while also being cost-effective.
  2. Fix the Default Node Type: If the default node type is the source of the problem, the DQX installation scripts or configuration templates should be updated to use a supported node type. This will prevent the error from recurring in future installations.
  3. Implement Environment-Aware Configuration: To handle environment-specific variations in supported node types, the configuration system can be made environment-aware. This could involve using environment variables or configuration files specific to each environment to define the appropriate node type.
  4. Validate Node Type in Code: As a preventative measure, the DQX installation code can be enhanced to validate the configured node type against the list of supported types. This would catch the error early in the process and provide a more informative error message to the user.

Implementing the Solution and Preventing Future Failures

To implement the solution effectively, follow these steps:

  1. Identify the Configuration Source: Determine where the node type is configured. This could be a configuration file (e.g., a .yaml or .json file), an environment variable, or a default value within the DQX codebase.
  2. Modify the Configuration: Replace Standard_D4ads_v6 with a supported node type. Choose a node type that aligns with the performance needs of the DQX profiler and the available resources in your Databricks environment. Some commonly used and generally supported node types include Standard_DS3_v2, Standard_D4s_v3, and Standard_D8s_v3.
  3. Test the Changes: After modifying the configuration, rerun the test_profiler_when_run_config_missing test to verify that the issue is resolved. Also, consider running other DQX tests to ensure that the change does not introduce any regressions.
  4. Update Default Settings (If Necessary): If the default node type was the issue, update the DQX installation scripts or configuration templates to prevent future occurrences of the error. This might involve changing a default value in a Python script or modifying a configuration file that is used during installation.
  5. Implement Validation (Optional but Recommended): Add code to validate the node type during DQX installation or configuration. This can help catch errors early and provide users with clear guidance on how to fix them. The validation code should check the configured node type against a list of supported types and raise an error if it's invalid.
  6. Consider Environment-Specific Configurations: If your DQX deployments span multiple Databricks environments, consider using environment variables or separate configuration files to specify node types. This allows you to tailor the configuration to each environment's specific capabilities.

By implementing these steps, you can resolve the test_profiler_when_run_config_missing failure and prevent similar issues from occurring in the future. Consistent configuration management and validation are key to ensuring the stability and reliability of DQX deployments.

Addressing Dashboard Parsing Warnings

In addition to the primary node type error, the error log also includes warnings related to dashboard parsing. While these warnings may not be directly causing the test failure, they indicate potential issues with the DQX dashboard configuration and should be addressed to ensure the dashboard functions correctly.

The warnings include:

  • WARNING [databricks.labs.lsql.dashboards] Parsing : No expression was parsed from ''
  • WARNING [databricks.labs.lsql.dashboards] Parsing unsupported field in dashboard.yml: tiles.00_2_dq_error_types.hidden

The first warning suggests that there are empty expressions in the dashboard configuration, which might lead to unexpected behavior or missing data in the dashboard. The second warning indicates that an unsupported field (tiles.00_2_dq_error_types.hidden) is being used in the dashboard.yml file. This field might be a deprecated feature or a custom extension that is not recognized by the dashboard parsing library.

To address these warnings, follow these steps:

  1. Locate the dashboard.yml File: Identify the dashboard.yml file that is being parsed during the DQX installation process. The log message Reading dashboard assets from /home/runner/work/dqx/dqx/src/databricks/labs/dqx/queries/quality/dashboard... provides a clue to the file's location.
  2. Inspect the File: Open the dashboard.yml file and examine its contents. Look for empty expressions or the tiles.00_2_dq_error_types.hidden field.
  3. Remove Empty Expressions: If you find empty expressions, remove them from the file. Empty expressions do not contribute to the dashboard's functionality and can be safely removed.
  4. Address Unsupported Fields: For the tiles.00_2_dq_error_types.hidden field, consider the following options:
    • If the field is no longer needed, remove it from the file.
    • If the field is intended to hide a tile in the dashboard, use the supported mechanism for hiding tiles in the dashboarding framework. Refer to the DQX documentation or the dashboarding library's documentation for guidance on how to hide tiles correctly.
    • If the field is a custom extension, ensure that the dashboard parsing library is configured to recognize it. This might involve adding a custom parser or handler for the field.
  5. Test the Dashboard: After making changes to the dashboard.yml file, reinstall DQX or redeploy the dashboard to verify that the warnings are resolved and the dashboard functions as expected.

By addressing these dashboard parsing warnings, you can ensure that the DQX dashboard is configured correctly and provides accurate and reliable data quality insights.

Conclusion

The test_profiler_when_run_config_missing failure, caused by an unsupported node type, highlights the importance of careful configuration management in Databricks environments. By updating the run configuration, addressing default settings, and implementing validation, you can resolve this issue and prevent similar failures in the future. Additionally, addressing dashboard parsing warnings ensures the overall health and reliability of the DQX system.

Key Takeaways:

  • Node type compatibility is critical for successful DQX deployments.
  • Configuration validation can prevent many common errors.
  • Addressing warnings proactively improves system stability.

By following the steps outlined in this article, you can ensure the smooth operation of DQX and effectively leverage its capabilities for data quality monitoring and improvement.