v0.6.1

• Made ProductInfo.version a cached_property to avoid failure when comparing wheel uploads in development (#105). In this release, the apply method of a class has been updated to sort upgrade scripts in semantic versioning order before applying them, addressing potential issues with version comparison during development. The implementation of ProductInfo.version has been refactored to a cached_property called _version, which calculates and caches the project version, addressing a failure during wheel upload comparisons in development. The Wheels class constructor has also been updated to include explicit keyword-only arguments, and a deprecation warning has been added. These changes aim to improve the reliability and predictability of the upgrade process and the library as a whole.

Dependency updates:

• Bump actions/checkout from 4.1.5 to 4.1.6 (#106).

Contributors: @dependabot[bot], @nkvuong

v0.6.0

• Added upstream wheel uploads for Databricks Workspaces without Public Internet access (#99). This commit introduces a new feature for uploading upstream wheel dependencies to Databricks Workspaces without Public Internet access. A new flag has been added to upload functions, allowing users to include or exclude dependencies in the download list. The WheelsV2 class has been updated with a new method, upload_wheel_dependencies(prefixes), which checks if each wheel's name starts with any of the provided prefixes before uploading it to the Workspace File System (WSFS). This feature also includes two new tests to verify the functionality of uploading the main wheel package and dependent wheel packages, optimizing downloads based on specific use cases. This enables users to more easily use the package in offline environments with restricted internet access, particularly for Databricks Workspaces with extra layers of network security.
• Fixed bug for double-uploading of unreleased wheels in air-gapped setups (#103). In this release, we have addressed a bug in the upload_wheel_dependencies method of the WheelsV2 class, which caused double-uploading of unreleased wheels in air-gapped setups. This issue occurred due to the condition if wheel.name == self._local_wheel.name not being met, resulting in undefined behavior. We have introduced a cached property _current_version to tackle this bug for unreleased versions uploaded to air-gapped workspaces. We also added a new method, upload_to_wsfs(), that uploads files to the workspace file system (WSFS) in the integration test. This release also includes new tests to ensure that only the Databricks SDK is uploaded and that the number of installation files is correct. These changes have resolved the double-uploading issue, and the number of installation files, Databricks SDK, Blueprint, and version.json metadata are now uploaded correctly to WSFS.

Contributors: @aminmovahed-db, @nfx

v0.5.0

• Added content assertion for assert_file_uploaded and assert_file_dbfs_uploaded in MockInstallation (#101). The recent commit introduces a content assertion feature to the MockInstallation class, enhancing its testing capabilities. This is achieved by adding an optional expected parameter of type bytes to the assert_file_uploaded and assert_file_dbfs_uploaded methods, allowing users to verify the uploaded content's correctness. The _assert_upload method has also been updated to accept this new parameter, ensuring the actual uploaded content matches the expected content. Furthermore, the commit includes informative docstrings for the new and updated methods, providing clear explanations of their functionality and usage. To support these improvements, new test cases test_assert_file_uploaded and test_load_empty_data_class have been added to the tests/unit/test_installation.py file, enabling more rigorous testing of the MockInstallation class and ensuring that the expected content is uploaded correctly.
• Added handling for partial functions in parallel.Threads (#93). In this release, we have enhanced the parallel.Threads module with the ability to handle partial functions, addressing issue #93. This improvement includes the addition of a new static method, _get_result_function_signature, to obtain the signature of a function or a string representation of its arguments and keywords if it is a partial function. The _wrap_result class method has also been updated to log an error message with the function's signature if an exception occurs. Furthermore, we have added a new test case, test_odd_partial_failed, to the unit tests, ensuring that the gather function handles partial functions that raise errors correctly. The Python version required for this project remains at 3.10, and the pyproject.toml file has been updated to include "isort", "mypy", "types-PyYAML", and types-requests in the list of dependencies. These adjustments are aimed at improving the functionality and type checking in the parallel.Threads module.
• Align configurations with UCX project (#96). This commit brings project configurations in line with the UCX project through various fixes and updates, enhancing compatibility and streamlining collaboration. It addresses pylint configuration warnings, adjusts GitHub Actions workflows, and refines the pyproject.toml file. Additionally, the NiceFormatter class in logger.py has been improved for better code readability, and the versioning scheme has been updated to ensure SemVer and PEP440 compliance, making it easier to manage and understand the project's versioning. Developers adopting the project will benefit from these alignments, as they promote adherence to the project's standards and up-to-date best practices.
• Check backwards compatibility with UCX, Remorph, and LSQL (#84). This release includes an update to the dependabot configuration to check for daily updates in both the pip and github-actions package ecosystems, with a new directory parameter added for the pip ecosystem for more precise update management. Additionally, a new GitHub Actions workflow, "downstreams", has been added to ensure backwards compatibility with UCX, Remorph, and LSQL by running automated downstream checks on pull requests, merge groups, and pushes to the main branch. The workflow has appropriate permissions for writing id-tokens, reading contents, and writing pull-requests, and runs the downstreams action from the databrickslabs/sandbox repository using GITHUB_TOKEN for authentication. These changes improve the security and maintainability of the project by ensuring compatibility with downstream projects and staying up-to-date with the latest package versions, reducing the risk of potential security vulnerabilities and bugs.

Dependency updates:

• Bump actions/setup-python from 4 to 5 (#89).
• Bump softprops/action-gh-release from 1 to 2 (#87).
• Bump actions/checkout from 2.5.0 to 4.1.2 (#88).
• Bump codecov/codecov-action from 1 to 4 (#85).
• Bump actions/checkout from 4.1.2 to 4.1.3 (#95).
• Bump actions/checkout from 4.1.3 to 4.1.5 (#100).

Contributors: @dependabot[bot], @nfx, @grusin-db, @nkvuong

v0.4.4

• If Threads.strict() raises just one error, don't wrap it with ManyError (#79). The strict method in the gather function of the parallel.py module in the databricks/labs/blueprint package has been updated to change the way it handles errors. Previously, if any task in the tasks sequence failed, the strict method would raise a ManyError exception containing all the errors. With this change, if only one error occurs, that error will be raised directly without being wrapped in a ManyError exception. This simplifies error handling and avoids unnecessary nesting of exceptions. Additionally, the __tracebackhide__ dunder variable has been added to the method to improve the readability of tracebacks by hiding it from the user. This update aims to provide a more streamlined and user-friendly experience for handling errors in parallel processing tasks.

Contributors: @nfx

v0.4.3

• Fixed marshalling & unmarshalling edge cases (#76). The serialization and deserialization methods in the code have been updated to improve handling of edge cases during marshalling and unmarshalling of data. When encountering certain edge cases, the _marshal_list method will now return an empty list instead of None, and both the _unmarshal and _unmarshal_dict methods will return None as is if the input is None. Additionally, the _unmarshal method has been updated to call _unmarshal_generic instead of checking if the type reference is a dictionary or list when it is a generic alias. The _unmarshal_generic method has also been updated to handle cases where the input is None. A new test case, test_load_empty_data_class(), has been added to the tests/unit/test_installation.py file to verify this behavior, ensuring that the correct behavior is maintained when encountering these edge cases during the marshalling and unmarshalling processes. These changes increase the reliability of the serialization and deserialization processes.

Contributors: @nkvuong

v0.4.2

• Fixed edge cases when loading typing.Dict, typing.List and typing.ClassVar (#74). In this release, we have implemented changes to improve the handling of edge cases related to the Python typing.Dict, typing.List, and typing.ClassVar during serialization and deserialization of dataclasses and generic types. Specifically, we have modified the _marshal and _unmarshal functions to check for the __origin__ attribute to determine whether the type is a ClassVar and skip it if it is. The _marshal_dataclass and _unmarshal_dataclass functions now check for the __dataclass_fields__ attribute to ensure that only dataclass fields are marshaled and unmarshaled. We have also added a new unit test for loading a complex data class using the MockInstallation class, which contains various attributes such as a string, a nested dictionary, a list of Policy objects, and a dictionary mapping string keys to Policy objects. This test case checks that the installation object correctly serializes and deserializes the ComplexClass instance to and from JSON format according to the specified attribute types, including handling of the typing.Dict, typing.List, and typing.ClassVar types. These changes improve the reliability and robustness of our library in handling complex data types defined in the typing module.
• MockPrompts.extend() now returns a copy (#72). In the latest release, the extend() method in the MockPrompts class of the tui.py module has been enhanced. Previously, extend() would modify the original MockPrompts object, which could lead to issues when reusing the same object in multiple places within the same test, as its state would be altered each time extend() was called. This has been addressed by updating the extend() method to return a copy of the MockPrompts object with the updated patterns and answers, instead of modifying the original object. This change ensures that the original MockPrompts object can be securely reused in multiple test scenarios without unintended side effects, preserving the integrity of the original state. Furthermore, additional tests have been incorporated to verify the correct behavior of both the new and original prompts.

Contributors: @pritishpai, @nkvuong

v0.4.1

• Fixed MockInstallation to emulate workspace-global setup (#69). In this release, the MockInstallation class in the installation module has been updated to better replicate a workspace-global setup, enhancing testing and development accuracy. The is_global method now utilizes the product method instead of _product, and a new instance variable _is_global with a default value of True is introduced in the __init__ method. Moreover, a new product method is included, which consistently returns the string "mock". These enhancements resolve issue #69, "Fixed MockInstallation to emulate workspace-global setup", ensuring the MockInstallation instance behaves as a global installation, facilitating precise and reliable testing and development for our software engineering team.
• Improved MockPrompts with extend() method (#68). In this release, we've added an extend() method to the MockPrompts class in our library's TUI module. This new method allows developers to add new patterns and corresponding answers to the existing list of questions and answers in a MockPrompts object. The added patterns are compiled as regular expressions and the questions and answers list is sorted by the length of the regular expression patterns in descending order. This feature is particularly useful for writing tests where prompt answers need to be changed, as it enables better control and customization of prompt responses during testing. By extending the list of questions and answers, you can handle additional prompts without modifying the existing ones, resulting in more organized and maintainable test code. If a prompt hasn't been mocked, attempting to ask a question with it will raise a ValueError with an appropriate error message.
• Use Hatch v1.9.4 to as build machine requirement (#70). The Hatch package version for the build machine requirement has been updated from 1.7.0 to 1.9.4 in this change. This update streamlines the Hatch setup and version management, removing the specific installation step and listing hatch directly in the required field. The pre-setup command now only includes "hatch env create". Additionally, the acceptance tool version has been updated to ensure consistent project building and testing with the specified Hatch version. This change is implemented in the acceptance workflow file and the version of the acceptance tool used by the sandbox. This update ensures that the project can utilize the latest features and bug fixes available in Hatch 1.9.4, improving the reliability and efficiency of the build process. This change is part of the resolution of issue #70.

Contributors: @nfx, @pritishpai

v0.4.0

• Added commands with interactive prompts (#66). This commit introduces a new feature in the Databricks Labs project to support interactive prompts in the command-line interface (CLI) for enhanced user interactivity. The Prompts argument, imported from databricks.labs.blueprint.tui, is now integrated into the @app.command decorator, enabling the creation of commands with user interaction like confirmation prompts. An example of this is the me command, which confirms whether the user wants to proceed before displaying the current username. The commit also refactored the code to make it more efficient and maintainable, removing redundancy in creating client instances. The AccountClient and WorkspaceClient instances can now be provided automatically with the product name and version. These changes improve the CLI by making it more interactive, user-friendly, and adaptable to various use cases while also optimizing the codebase for better efficiency and maintainability.
• Added more code documentation (#64). This release introduces new features and updates to various files in the open-source library. The cli.py file in the src/databricks/labs/blueprint directory has been updated with a new decorator, command, which registers a function as a command. The entrypoint.py file in the databricks.labs.blueprint module now includes a module-level docstring describing its purpose, as well as documentation for the various standard libraries it imports. The Installation class in the installers.py file has new methods for handling files, such as load, load_or_default, upload, load_local, and files. The installers.py file also includes a new InstallationState dataclass, which is used to track installations. The limiter.py file now includes code documentation for the RateLimiter class and the rate_limited decorator, which are used to limit the rate of requests. The logger.py file includes a new NiceFormatter class, which provides a nicer format for logging messages with colors and bold text if the console supports it. The parallel.py file has been updated with new methods for running tasks in parallel and returning results and errors. The TUI.py file has been documented, and includes imports for logging, regular expressions, and collections abstract base class. Lastly, the upgrades.py file has been updated with additional code documentation and new methods for loading and applying upgrade scripts. Overall, these changes improve the functionality, maintainability, and usability of the open-source library.
• Fixed init-project command (#65). In this release, the init-project command has been improved with several bug fixes and new functionalities. A new import statement for the sys module has been added, and a docs directory is now included in the copied directories and files during initialization. The init_project function has been updated to open files using the default system encoding, ensuring proper reading and writing of file contents. The relative_paths function in the entrypoint.py file now returns absolute paths if the common path is the root directory, addressing issue #41. Additionally, several test functions have been added to tests/unit/test_entrypoint.py, enhancing the reliability and robustness of the init-project command by providing comprehensive tests for supporting functions. Overall, these changes significantly improve the functionality and reliability of the init-project command, ensuring a more consistent and accurate project initialization process.
• Using ProductInfo with integration tests (#63). In this update, the ProductInfo class has been enhanced with a new class method for_testing(klass) to facilitate effective integration testing. This method generates a new ProductInfo object with a random product_name, enabling the creation of distinct installation directories for each test execution. Prior to this change, conflicts and issues could arise when multiple test executions shared the same integration test folder. With the introduction of this new method, developers can now ensure that their integration tests run with unique product names and separate installation directories, enhancing testing isolation and accuracy. This update is demonstrated in the provided code snippet and includes a new test case to confirm the generation of unique product names. Furthermore, a pre-existing test case has been modified to provide a more specific error message related to the SingleSourceVersionError. This enhancement aims to improve the integration testing capabilities of the codebase and is designed to be easily adopted by other software engineers utilizing this project.

Contributors: @nfx

v0.3.1

• Fixed the order of marshal to handle Dataclass with as_dict before other types to avoid SerdeError (#60). In this release, we have addressed an issue that caused a SerdeError during the installation.save operation with a Dataclass object. The error was due to the order of evaluation in the _marshal_dataclass method. The order has been updated to evaluate the as_dict method first if it exists in the Dataclass, which resolves the SerdeError. To ensure the correctness of the fix, we have added a new test_data_class function that tests the save and load functionality with a Dataclass object. The test defines a Policy Dataclass with an as_dict method that returns a dictionary representation of the object and checks if the file is written correctly and if the loaded object matches the original object. This change has been thoroughly unit tested to ensure that it works as expected.

Contributors: @HariGS-DB

v0.3.0

• Added automated upgrade framework (#50). This update introduces an automated upgrade framework for managing and applying upgrades to the product, with a new upgrades.py file that includes a ProductInfo class having methods for version handling, wheel building, and exception handling. The test code organization has been improved, and new test cases, functions, and a directory structure for fixtures and unit tests have been added for the upgrades functionality. The test_wheels.py file now checks the version of the Databricks SDK and handles cases where the version marker is missing or does not contain the __version__ variable. Additionally, a new Application State Migrations section has been added to the README, explaining the process of seamless upgrades from version X to version Z through version Y, addressing the need for configuration or database state migrations as the application evolves. Users can apply these upgrades by following an idiomatic usage pattern involving several classes and functions. Furthermore, improvements have been made to the _trim_leading_whitespace function in the commands.py file of the databricks.labs.blueprint module, ensuring accurate and consistent removal of leading whitespace for each line in the command string, leading to better overall functionality and maintainability.
• Added brute-forcing SerdeError with as_dict() and from_dict() (#58). This commit introduces a brute-forcing approach for handling SerdeError using as_dict() and from_dict() methods in an open-source library. The new SomePolicy class demonstrates the usage of these methods for manual serialization and deserialization of custom classes. The as_dict() method returns a dictionary representation of the class instance, and the from_dict() method, decorated with @classmethod, creates a new instance from the provided dictionary. Additionally, the GitHub Actions workflow for acceptance tests has been updated to include the ready_for_review event type, ensuring that tests run not only for opened and synchronized pull requests but also when marked as "ready for review." These changes provide developers with more control over the deserialization process and facilitate debugging in cases where default deserialization fails, but should be used judiciously to avoid brittle code.
• Fixed nightly integration tests run as service principals (#52). In this release, we have enhanced the compatibility of our codebase with service principals, particularly in the context of nightly integration tests. The Installation class in the databricks.labs.blueprint.installation module has been refactored, deprecating the current method and introducing two new methods: assume_global and assume_user_home. These methods enable users to install and manage blueprint as either a global or user-specific installation. Additionally, the existing method has been updated to work with the new Installation methods. In the test suite, the test_installation.py file has been updated to correctly detect global and user-specific installations when running as a service principal. These changes improve the testability and functionality of our software, ensuring seamless operation with service principals during nightly integration tests.
• Made test_existing_installations_are_detected more resilient (#51). In this release, we have added a new test function test_existing_installations_are_detected that checks if existing installations are correctly detected and retries the test for up to 15 seconds if they are not. This improves the reliability of the test by making it more resilient to potential intermittent failures. We have also added an import from databricks.sdk.retries named retried which is used to retry the test function in case of an AssertionError. Additionally, the test function test_existing has been renamed to test_existing_installations_are_detected and the xfail marker has been removed. We have also renamed the test function test_dataclass to test_loading_dataclass_from_installation for better clarity. This change will help ensure that the library is correctly detecting existing installations and improve the overall quality of the codebase.

Contributors: @nfx