v0.40.0

• Added google-cloud-core to known list (#2826). In this release, we have incorporated the google-cloud-core library into our project's configuration file, specifying several modules from this library. This change is part of the resolution of issue #1931, which pertains to working with Google Cloud services. The google-cloud-core library offers core functionalities for Google Cloud client libraries, including helper functions, HTTP-related functionalities, testing utilities, client classes, environment variable handling, exceptions, obsolete features, operation tracking, and version management. By adding these new modules to the known list in the configuration file, we can now utilize them in our project as needed, thereby enhancing our ability to work with Google Cloud services.
• Added gviz-api to known list (#2831). In this release, we have added the gviz-api library to our known library list, specifically specifying the gviz_api package within it. This addition enables the proper handling and recognition of components from the gviz-api library in the system, thereby addressing a portion of issue #1931. While the specifics of the gviz-api library's implementation and usage are not described in the commit message, it is expected to provide functionality related to data visualization. This enhancement will enable us to expand our system's capabilities and provide more comprehensive solutions for our users.
• Added export CLI functionality for assessment results (#2553). A new export command-line interface (CLI) function has been added to the open-source library to export assessment results. This feature includes the addition of a new AssessmentExporter class in the export.py module, which is responsible for exporting assessment results to CSV files inside a ZIP archive. Users can specify the destination path and type of report for the exported results. A notebook utility is also included to run the export from the workspace environment, with default location, unit tests, and integration tests for the notebook utility. The acl_migrator method has been optimized for better performance. This new functionality provides more flexibility in exporting assessment results and improves the overall assessment functionality of the library.
• Added functional test related to bug #2850 (#2880). A new functional test has been added to address a bug fix related to issue #2850, which involves reading data from a CSV file located in a volume using Spark's readStream function. The test specifies various options including file format, schema location, header, and compression. The CSV file is loaded from '/Volumes/playground/test/demo_data/' and the schema location is set to '/Volumes/playground/test/schemas/'. Additionally, a unit test has been added and is referenced in the commit. This functional test will help ensure that the bug fix for issue #2850 is working as expected.
• Added handling for PermissionDenied when retrieving WorkspaceClients from account (#2877). In this release, the workspace_clients method of the Account class in workspaces.py has been updated to handle PermissionDenied exceptions when retrieving WorkspaceClients. This change introduces a try-except block around the command retrieving the workspace client, which catches the PermissionDenied exception and logs a warning message if access to a workspace is denied. If no exception is raised, the workspace client is added to the list of clients as before. The commit also includes a new unit test to verify this functionality. This update addresses issue #2874 and enhances the robustness of the databricks labs ucx sync-workspace-info command by ensuring it gracefully handles permission errors during workspace retrieval.
• Added testing with Python 3.13 (#2878). The project has been updated to include testing with Python 3.13, in addition to the previously supported versions of Python 3.10, 3.11, and 3.12. This update is reflected in the .github/workflows/push.yml file, which now includes '3.13' in the pyVersion matrix for the jobs. This addition expands the range of Python versions that the project can be tested and run on, providing increased flexibility and compatibility for users, as well as ensuring continued support for the latest versions of the Python programming language.
• Added used tables in assessment dashboard (#2836). In this update, we introduce a new widget to the assessment dashboard for displaying used tables, enhancing visibility into how tables are utilized within the Databricks environment. This change includes the addition of the UsedTable class in the databricks.labs.ucx.source_code.base module, which tracks table usage details in the inventory database. Two new methods, collect_dfsas_from_query and collect_used_tables_from_query, have been implemented to collect data source access and used tables information from a query, with lineage information added to the table details. Additionally, a test function, test_dashboard_with_prepopulated_data, has been introduced to prepopulate data for use in the dashboard, ensuring proper functionality of the new feature.
• Avoid resource conflicts in integration tests by using a random dir name (#2865). In this release, we have implemented changes to address resource conflicts in integration tests by introducing random directory names. The save_locations method in conftest.py has been updated to generate random directory names using the tempfile.mkdtemp function, based on the value of the new make_random parameter. Additionally, in the test_migrate.py file located in the tests/integration/hive_metastore directory, the hard-coded directory name has been replaced with a random one generated by the make_random function, which is used when creating external tables and specifying the external delta location. Lastly, the test_move_tables_table_properties_mismatch_preserves_original function in test_table_move.py has been updated to include a randomly generated directory name in the table's external delta and storage location, ensuring that tests can run concurrently without conflicting with each other. These changes resolve the issue described in #2797 and improve the reliability of integration tests.
• Exclude dfsas from used tables (#2841). In this release, we've made significant improvements to the accuracy of table identification and handling in our system. We've excluded certain direct filesystem access patterns from being treated as tables in the current implementation, correcting a previous error. The collect_tables method has been updated to exclude table names matching defined direct filesystem access patterns. Additionally, we've added a new method TableInfoNode to wrap used tables and the nodes that use them. We've also introduced changes to handle direct filesystem access patterns more accurately, ensuring that the DataFrame API's spark.table() function is identified correctly, while the spark.read.parquet() function, representing direct filesystem access, is now ignored. These changes are supported by new unit tests to ensure correctness and reliability, enhancing the overall functionality and behavior of the system.
• Fixed known matches false postives for libraries starting with the same name as a library in the known.json (#2860). This commit addresses an issue of false positives in known matches for libraries that have the same name as a library in the known.json file. The module_compatibility function in the known.py file was updated to look for exact matches or parent module matches, rather than just matches at the beginning of the name. This more nuanced approach ensures that libraries with similar names are not incorrectly flagged as having compatibility issues. Additionally, the known.json file is now sorted when constructing module problems, indicating that the order of the entries in this file may have been relevant to the issue being resolved. To ensure the accuracy of the changes, new unit tests were added. The test suite was expanded to include tests for known and unknown compatibility, and a new load test was added for the known.json file. These changes improve the reliability of the known matches feature, which is critical for ensuring the correct identification of compatibility issues.
• Make delta format case sensitive (#2861). In this commit, the delta format is made case sensitive to enhance the robustness and reliability of the code. The TableInMount class has been updated with a __post_init__ method to convert the format attribute to uppercase, ensuring case sensitivity. Additionally, the Table class in the tables.py file has been modified to include a __post_init__ method that converts the table_format attribute to uppercase during object creation, making format comparisons case insensitive. New properties, is_delta and is_hive, have been added to the Table class to check if the table format is delta or hive, respectively. These changes affect the what method of the AclMigrationWhat enum class, which now checks for is_delta and is_hive instead of comparing table_format with DELTA and "HIVE". Relevant issues #2858 and #2840 have been addressed, and unit tests have been included to verify the behavior. However, the changes have not been verified on the staging environment yet.
• Make delta format case sensitive (#2862). The recent update, derived from the resolution of issue #2861, introduces a case-sensitive delta format to our open-source library, enhancing the precision of delta table tracking. This change impacts all table format-related code and is accompanied by additional tests for robustness. A new location column has been incorporated into the table_estimates view, facilitating the determination of delta table location. Furthermore, a new method has been implemented to extract the location column from the table_estimates view, further refining the project's functionality and accuracy in managing delta tables.
• Verify UCX catalog is accessible at start of migration-progress-experimental workflow (#2851). In this release, we have introduced a new verify_has_ucx_catalog method in the Application class of the databricks.labs.ucx.contexts module, which checks for the presence of a UCX catalog in the workspace and returns an instance of the VerifyHasCatalog class. This method is used in the migration-progress-experimental workflow to verify UCX catalog accessibility, addressing issues #2577 and #2848 and progressing work on #2816. The verify_has_ucx_catalog method is decorated with @cached_property and takes workspace_client and ucx_catalog as arguments. Additionally, we have added a new VerifyHasCatalog class that checks if a specified Unity Catalog (UC) catalog exists in the workspace and updated the import statement to include a NotFound exception. We have also added a timeout parameter to the validate_step function in the workflows.py file, modified the migration-progress-experimental workflow to include a new step verify_prerequisites in the table_migration job cluster, and added unit tests to ensure the proper functioning of these changes. These updates improve the application's ability to interact with UCX catalogs and ensure their presence and accessibility during workflow execution, while also enhancing the robustness and reliability of the migration-progress-experimental workflow.

Contributors: @ericvergnaud, @JCZuurmond, @asnare, @pritishpai, @nfx, @rportilla-databricks