Use single lakebase instance for all integration tests and add retry logic #618
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Pull Request Overview
This PR adds retry logic to handle workspace quota limits when creating lakebase database instances during integration tests. The change introduces a timeout-based retry mechanism that specifically handles BadRequest exceptions, which commonly occur when the workspace quota for lakebase instances is exceeded during concurrent test runs.
- Added retry logic with 6-minute timeout for lakebase database instance creation
- Refactored database creation into a separate helper function to support retry mechanism
- Removed unused RETRY_INTERVAL_SECONDS constants from test files
Reviewed Changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| tests/conftest.py | Added retry logic for lakebase database creation and refactored creation into helper function |
| tests/e2e/test_run_demos.py | Removed unused RETRY_INTERVAL_SECONDS constant |
| tests/e2e/conftest.py | Removed unused RETRY_INTERVAL_SECONDS constant |
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Pull Request Overview
Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.
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| ✅ 403/403 passed, 2 flaky, 1 skipped, 4h24m17s total Flaky tests:
Running from acceptance #2864 |
mwojtyczka added a commit that referenced this pull request Nov 6, 2025
* Added Data Quality Summary Metrics ([#553](#553)). The data quality engine has been enhanced with the ability to track and manage summary metrics for data quality validation, leveraging Spark's Observation feature. A new `DQMetricsObserver` class has been introduced to manage Spark observations and track summary metrics on datasets checked with the engine. The `DQEngine` class has been updated to optionally return the Spark observation associated with a given run, allowing users to access and save summary metrics. The engine now supports also writing summary metrics to a table using the `metrics_config` parameter, and a new `save_summary_metrics` method has been added to save data quality summary metrics to a table. Additionally, the engine has been updated to include a unique `run_id` field in the detailed per-row quality results, enabling cross-referencing with summary metrics. The changes also include updates to the configuration file to support the storage of summary metrics. Overall, these enhancements provide a more comprehensive and flexible data quality checking capability, allowing users to track and analyze data quality issues more effectively. * LLM assisted rules generation ([#577](#577)). This release introduces a significant enhancement to the data quality rules generation process with the integration of AI-assisted rules generation using large language models (LLMs). The `DQGenerator` class now includes a `generate_dq_rules_ai_assisted` method, which takes user input in natural language and optionally a schema from an input table to generate data quality rules. These rules are then validated for correctness. The AI-assisted rules generation feature supports both programmatic and no-code approaches. Additionally, the feature enables the use of different LLM models and gives the possibility to use custom check functions. The release also includes various updates to the documentation, configuration files, and testing framework to support the new AI-assisted rules generation feature, ensuring a more streamlined and efficient process for defining and applying data quality rules. * Added Lakebase checks storage backend ([#550](#550)). A Lakebase checks storage backend was added, allowing users to store and manage their data quality rules in a centralized lakabase table, in addition to the existing Delta table storage. The `checks_location` resolution has been updated to accommodate Lakebase, supporting both table and file storage, with flexible formatting options, including "catalog.schema.table" and "database.schema.table". The Lakebase checks storage backend is configurable through the `LakebaseChecksStorageConfig` class, which includes fields for instance name, user, location, port, run configuration name, and write mode. This update provides users with more flexibility in storing and loading quality checks, ensuring that checks are saved correctly regardless of the specified location format. * Added runtime validation of sql expressions ([#625](#625)). The data quality check functionality has been enhanced with runtime validation of SQL expressions, ensuring that specified fields can be resolved in the input DataFrame and that SQL expressions are valid before evaluation. If an SQL expression is invalid, the check evaluation is skipped and the results include a check failure with a descriptive message. Additionally, the configuration validation for Unity Catalog volume file paths has been improved to enforce a specific format, preventing invalid configurations and providing more informative error messages. * Fixed docs ([#598](#598)). The documentation build process has undergone significant improvements to enhance efficiency and maintainability. * Improved Config Serialization ([#676](#676)). Several updates have been made to improve the functionality, consistency, and maintainability of the codebase. The configuration loading functionality has been refactored to utilize the `ConfigSerializer` class, which handles the serialization and deserialization of workspace and run configurations. * Restore use of `hatch-fancy-pypi-readme` to fix images in PyPi ([#601](#601)). The image source path for the logo in the README has been modified to correctly display the logo image when rendered, particularly on PyPi. * Skip check evaluation if columns or filter cannot be resolved in the input DataFrame ([#609](#609)). DQX now skip check evaluation if columns or filters are incorrect allowing other checks to proceed even if one rule fails. The DQX engine validates specified column, columns and filter fields against the input DataFrame before applying checks, skipping evaluation and providing informative error messages if any fields are invalid. * Updated user guide docs ([#607](#607)). The documentation for quality checking and integration options has been updated to provide accurate and detailed information on supported types and approaches. Quality checking can be performed in-transit (pre-commit), validating data on the fly during processing, or at-rest, checking existing data stored in tables. * Improved build process ([#618](#618)). The hatch version has been updated to 1.15.0 to avoid compatibility issues with click version 8.3 and later, which introduced a bug affecting hatch. Additionally, the project's dependencies have been updated, including bumping the `databricks-labs-pytester` version from 0.7.2 to 0.7.4, and code refactoring has been done to use a single Lakebase instance for all integration tests, with retry logic added to handle cases where the workspace quota limit for the number of Lakebase instances is exceeded, enhancing the testing infrastructure and improving test reliability. Furthermore, documentation updates have been made to clarify the application of quality checks to data using DQX. These changes aim to improve the efficiency, reliability, and clarity of the project's testing and documentation infrastructure.
Merged
mwojtyczka added a commit that referenced this pull request Nov 7, 2025
* Added Data Quality Summary Metrics ([#553](#553)). The data quality engine has been enhanced with the ability to track and manage summary metrics for data quality validation, leveraging Spark's Observation feature. A new `DQMetricsObserver` class has been introduced to manage Spark observations and track summary metrics on datasets checked with the engine. The `DQEngine` class has been updated to optionally return the Spark observation associated with a given run, allowing users to access and save summary metrics. The engine now supports also writing summary metrics to a table using the `metrics_config` parameter, and a new `save_summary_metrics` method has been added to save data quality summary metrics to a table. Additionally, the engine has been updated to include a unique `run_id` field in the detailed per-row quality results, enabling cross-referencing with summary metrics. The changes also include updates to the configuration file to support the storage of summary metrics. Overall, these enhancements provide a more comprehensive and flexible data quality checking capability, allowing users to track and analyze data quality issues more effectively. * LLM assisted rules generation ([#577](#577)). This release introduces a significant enhancement to the data quality rules generation process with the integration of AI-assisted rules generation using large language models (LLMs). The `DQGenerator` class now includes a `generate_dq_rules_ai_assisted` method, which takes user input in natural language and optionally a schema from an input table to generate data quality rules. These rules are then validated for correctness. The AI-assisted rules generation feature supports both programmatic and no-code approaches. Additionally, the feature enables the use of different LLM models and gives the possibility to use custom check functions. The release also includes various updates to the documentation, configuration files, and testing framework to support the new AI-assisted rules generation feature, ensuring a more streamlined and efficient process for defining and applying data quality rules. * Added Lakebase checks storage backend ([#550](#550)). A Lakebase checks storage backend was added, allowing users to store and manage their data quality rules in a centralized lakabase table, in addition to the existing Delta table storage. The `checks_location` resolution has been updated to accommodate Lakebase, supporting both table and file storage, with flexible formatting options, including "catalog.schema.table" and "database.schema.table". The Lakebase checks storage backend is configurable through the `LakebaseChecksStorageConfig` class, which includes fields for instance name, user, location, port, run configuration name, and write mode. This update provides users with more flexibility in storing and loading quality checks, ensuring that checks are saved correctly regardless of the specified location format. * Added runtime validation of sql expressions ([#625](#625)). The data quality check functionality has been enhanced with runtime validation of SQL expressions, ensuring that specified fields can be resolved in the input DataFrame and that SQL expressions are valid before evaluation. If an SQL expression is invalid, the check evaluation is skipped and the results include a check failure with a descriptive message. Additionally, the configuration validation for Unity Catalog volume file paths has been improved to enforce a specific format, preventing invalid configurations and providing more informative error messages. * Fixed docs ([#598](#598)). The documentation build process has undergone significant improvements to enhance efficiency and maintainability. * Improved Config Serialization ([#676](#676)). Several updates have been made to improve the functionality, consistency, and maintainability of the codebase. The configuration loading functionality has been refactored to utilize the `ConfigSerializer` class, which handles the serialization and deserialization of workspace and run configurations. * Restore use of `hatch-fancy-pypi-readme` to fix images in PyPi ([#601](#601)). The image source path for the logo in the README has been modified to correctly display the logo image when rendered, particularly on PyPi. * Skip check evaluation if columns or filter cannot be resolved in the input DataFrame ([#609](#609)). DQX now skip check evaluation if columns or filters are incorrect allowing other checks to proceed even if one rule fails. The DQX engine validates specified column, columns and filter fields against the input DataFrame before applying checks, skipping evaluation and providing informative error messages if any fields are invalid. * Updated user guide docs ([#607](#607)). The documentation for quality checking and integration options has been updated to provide accurate and detailed information on supported types and approaches. Quality checking can be performed in-transit (pre-commit), validating data on the fly during processing, or at-rest, checking existing data stored in tables. * Improved build process ([#618](#618)). The hatch version has been updated to 1.15.0 to avoid compatibility issues with click version 8.3 and later, which introduced a bug affecting hatch. Additionally, the project's dependencies have been updated, including bumping the `databricks-labs-pytester` version from 0.7.2 to 0.7.4, and code refactoring has been done to use a single Lakebase instance for all integration tests, with retry logic added to handle cases where the workspace quota limit for the number of Lakebase instances is exceeded, enhancing the testing infrastructure and improving test reliability. Furthermore, documentation updates have been made to clarify the application of quality checks to data using DQX. These changes aim to improve the efficiency, reliability, and clarity of the project's testing and documentation infrastructure.
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