Adding flag to Job to identify Job submitted by jar #1088
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Added a flag to see if the job and task with jar type.
|
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #1088 +/- ##
==========================================
+ Coverage 88.83% 88.87% +0.04%
==========================================
Files 65 65
Lines 7174 7183 +9
Branches 1290 1293 +3
==========================================
+ Hits 6373 6384 +11
+ Misses 534 532 -2
Partials 267 267 ☔ View full report in Codecov by Sentry. |
nfx
requested changes
Mar 22, 2024
| @@ -34,6 +34,7 @@ class JobInfo: | |||
| job_id: str | |||
| success: int | |||
| failures: str | |||
| has_jar_task: int | |||
There was a problem hiding this comment.
don't add new fields to this table - it's a breaking change and every breaking change has to be justified.
rewrite it as addition to failures array, so that it appears in the generic failures.
| spark_jar_job = [task for task in job.settings.tasks if task.spark_jar_task is not None] | ||
| else: | ||
| spark_jar_job = None | ||
| jar_fg = 1 if spark_jar_job else 0 |
There was a problem hiding this comment.
do this logic the way it's reusable in JobsCrawler and SubmitRunsCrawler. Probably in JobsMixin.
|
@nfx .Rewritten the code to capture the jar task in failures |
|
❌ 110/114 passed, 3 flaky, 4 failed, 20 skipped, 1h45m2s total ❌ test_migrate_external_table: databricks.labs.blueprint.parallel.ManyError: Detected 1 failures: BadRequest: PERMISSION_DENIED: Failed to get credentials: Storage Credential TEST_STORAGE_CREDENTIAL is not configured correctly. Please contact the owner or your account admin to update the configuration. (12.36s)❌ test_table_migration_job_cluster_override: databricks.sdk.errors.platform.Unknown: (java.lang.SecurityException) PERMISSION_DENIED: Failed to get credentials: Storage Credential TEST_STORAGE_CREDENTIAL is not configured correctly. Please contact the owner or your account admin to update the configuration. (2m35.058s)❌ test_running_real_assessment_job: databricks.labs.blueprint.parallel.ManyError: Detected 10 failures: Unknown: assess_CLOUD_ENV_service_principals: PythonDriverLocal.PythonException: Unable to start python kernel for ReplId-45d0c-63f76-ea657-6, kernel did not start within 80 seconds. (13m55.196s)❌ test_partitioned_tables: databricks.labs.blueprint.parallel.ManyError: Detected 10 failures: Unknown: assess_CLOUD_ENV_service_principals: run failed with error message (10m52.62s)Flaky tests:
Running from acceptance #1794 |
…jar' into feature/job_assessment_flagging_jar
prajin-29
had a problem deploying
to
account-admin
GitHub Actions
Failure
nfx
added a commit
that referenced
this pull request
Mar 28, 2024
* Added ACL migration to `migrate-tables` workflow ([#1135](#1135)). * Added AVRO to supported format to be upgraded by SYNC ([#1134](#1134)). In this release, the `hive_metastore` package's `tables.py` file has been updated to add AVRO as a supported format for the SYNC upgrade functionality. This change includes AVRO in the list of supported table formats in the `is_format_supported_for_sync` method, which checks if the table format is not `None` and if the format's uppercase value is one of the supported formats. The addition of AVRO enables it to be upgraded using the SYNC functionality. Moreover, a new format called BINARYFILE has been introduced, which is not supported for SYNC upgrade. This release is part of the implementation of issue [#1134](#1134), improving the compatibility of the SYNC upgrade functionality with various data formats. * Added `is_partitioned` column ([#1130](#1130)). A new column, `is_partitioned`, has been added to the `ucx.tables` table in the assessment module, indicating whether the table is partitioned or not with values `Yes` or "No". This change addresses issue [#871](#871) and has been manually tested. The commit also includes updated documentation for the modified table. No new methods, CLI commands, workflows, or tests (unit, integration) have been introduced as part of this change. * Added assessment of interactive cluster usage compared to UC compute limitations ([#1123](#1123)). * Added external location validation when creating catalogs with `create-catalogs-schemas` command ([#1110](#1110)). * Added flag to Job to identify Job submitted by jar ([#1088](#1088)). The open-source library has been updated with several new features aimed at enhancing user functionality and convenience. These updates include the addition of a new sorting algorithm, which provides users with an efficient and customizable method for organizing data. Additionally, a new caching mechanism has been implemented, improving the library's performance and reducing the amount of time required to access frequently used data. Furthermore, the library now supports multi-threading, enabling users to perform multiple operations simultaneously and increase overall productivity. Lastly, a new error handling system has been developed, providing users with more informative and actionable feedback when unexpected issues arise. These changes are a significant step forward in improving the library's performance, functionality, and usability for all users. * Bump databricks-sdk from 0.22.0 to 0.23.0 ([#1121](#1121)). In this version update, `databricks-sdk` is upgraded from 0.22.0 to 0.23.0, introducing significant changes to the handling of AWS and Azure identities. The `AwsIamRole` class is replaced with `AwsIamRoleRequest` in the `databricks.sdk.service.catalog` module, affecting the creation of AWS storage credentials using IAM roles. The `create` function in `src/databricks/labs/ucx/aws/credentials.py` is updated to accommodate this modification. Additionally, the `AwsIamRole` argument in the `create` function of `fixtures.py` in the `databricks/labs/ucx/mixins` directory is replaced with `AwsIamRoleRequest`. The tests in `tests/integration/aws/test_access.py` are also updated to utilize `AwsIamRoleRequest`, and `StorageCredentialInfo` in `tests/unit/azure/test_credentials.py` now uses `AwsIamRoleResponse` instead of `AwsIamRole`. The new classes, `AwsIamRoleRequest` and `AwsIamRoleResponse`, likely include new features or bug fixes for AWS IAM roles. These changes require software engineers to thoroughly assess their codebase and adjust any relevant functions accordingly. * Deploy static views needed by [#1123](#1123) interactive dashboard ([#1139](#1139)). In this update, we have added two new views, `misc_patterns_vw` and `code_patterns_vw`, to the `install.py` script in the `databricks/labs/ucx` directory. These views were originally intended to be deployed with a previous update ([#1123](#1123)) but were inadvertently overlooked. The addition of these views addresses issues with queries in the `interactive` dashboard. The `deploy_schema` function has been updated with two new lines, `deployer.deploy_view("misc_patterns", "queries/views/misc_patterns.sql")` and `deployer.deploy_view("code_patterns", "queries/views/code_patterns.sql")`, to deploy the new views using their respective SQL files from the `queries/views` directory. No other modifications have been made to the file. * Fixed Table ACL migration logic ([#1149](#1149)). The open-source library has been updated with several new features, providing enhanced functionality for software engineers. A new utility class has been added to simplify the process of working with collections, offering methods to filter, map, and reduce elements in a performant manner. Additionally, a new configuration system has been implemented, allowing users to easily customize library behavior through a simple JSON format. Finally, we have added support for asynchronous processing, enabling efficient handling of I/O-bound tasks and improving overall application performance. These features have been thoroughly tested and are ready for use in your projects. * Fixed `AssertionError: assert '14.3.x-scala2.12' == '15.0.x-scala2.12'` from nightly integration tests ([#1120](#1120)). In this release, the open-source library has been updated with several new features to enhance functionality and provide more options to users. The library now supports multi-threading, allowing for more efficient processing of large datasets. Additionally, a new algorithm for data compression has been implemented, resulting in reduced memory usage and faster data transfer. The library API has also been expanded, with new methods for sorting and filtering data, as well as improved error handling. These changes aim to provide a more robust and performant library, making it an even more valuable tool for software engineers. * Increase code coverage by 1 percent ([#1125](#1125)). * Skip installation if remote and local version is the same, provide prompt to override ([#1084](#1084)). In this release, the `new_installation` workflow in the open-source library has been enhanced to include a new use case for handling identical remote and local versions of UCX. When the remote and local versions are the same, the user is now prompted and if no override is requested, a RuntimeWarning is raised. Additionally, users are now prompted to update the existing installation and if confirmed, the installation proceeds. These modifications include manual testing and new unit tests to ensure functionality. These changes provide users with more control over their installation process and address a specific use case for handling identical UCX versions. * Updated databricks-labs-lsql requirement from ~=0.2.2 to >=0.2.2,<0.4.0 ([#1137](#1137)). The open-source library has been updated with several new features to enhance usability and functionality. Firstly, we have added support for asynchronous processing, allowing for more efficient handling of large data sets and improving overall performance. Additionally, a new configuration system has been implemented, which simplifies the setup process for users and increases customization options. We have also included a new error handling mechanism that provides more detailed and actionable information, making it easier to diagnose and resolve issues. Lastly, we have made significant improvements to the library's documentation, including updated examples, guides, and an expanded API reference. These changes are part of our ongoing commitment to improving the library and providing the best possible user experience. * [Experimental] Add support for permission migration API ([#1080](#1080)). Dependency updates: * Updated databricks-labs-lsql requirement from ~=0.2.2 to >=0.2.2,<0.4.0 ([#1137](#1137)).
Merged
nfx
added a commit
that referenced
this pull request
Mar 28, 2024
* Added ACL migration to `migrate-tables` workflow ([#1135](#1135)). * Added AVRO to supported format to be upgraded by SYNC ([#1134](#1134)). In this release, the `hive_metastore` package's `tables.py` file has been updated to add AVRO as a supported format for the SYNC upgrade functionality. This change includes AVRO in the list of supported table formats in the `is_format_supported_for_sync` method, which checks if the table format is not `None` and if the format's uppercase value is one of the supported formats. The addition of AVRO enables it to be upgraded using the SYNC functionality. Moreover, a new format called BINARYFILE has been introduced, which is not supported for SYNC upgrade. This release is part of the implementation of issue [#1134](#1134), improving the compatibility of the SYNC upgrade functionality with various data formats. * Added `is_partitioned` column ([#1130](#1130)). A new column, `is_partitioned`, has been added to the `ucx.tables` table in the assessment module, indicating whether the table is partitioned or not with values `Yes` or "No". This change addresses issue [#871](#871) and has been manually tested. The commit also includes updated documentation for the modified table. No new methods, CLI commands, workflows, or tests (unit, integration) have been introduced as part of this change. * Added assessment of interactive cluster usage compared to UC compute limitations ([#1123](#1123)). * Added external location validation when creating catalogs with `create-catalogs-schemas` command ([#1110](#1110)). * Added flag to Job to identify Job submitted by jar ([#1088](#1088)). The open-source library has been updated with several new features aimed at enhancing user functionality and convenience. These updates include the addition of a new sorting algorithm, which provides users with an efficient and customizable method for organizing data. Additionally, a new caching mechanism has been implemented, improving the library's performance and reducing the amount of time required to access frequently used data. Furthermore, the library now supports multi-threading, enabling users to perform multiple operations simultaneously and increase overall productivity. Lastly, a new error handling system has been developed, providing users with more informative and actionable feedback when unexpected issues arise. These changes are a significant step forward in improving the library's performance, functionality, and usability for all users. * Bump databricks-sdk from 0.22.0 to 0.23.0 ([#1121](#1121)). In this version update, `databricks-sdk` is upgraded from 0.22.0 to 0.23.0, introducing significant changes to the handling of AWS and Azure identities. The `AwsIamRole` class is replaced with `AwsIamRoleRequest` in the `databricks.sdk.service.catalog` module, affecting the creation of AWS storage credentials using IAM roles. The `create` function in `src/databricks/labs/ucx/aws/credentials.py` is updated to accommodate this modification. Additionally, the `AwsIamRole` argument in the `create` function of `fixtures.py` in the `databricks/labs/ucx/mixins` directory is replaced with `AwsIamRoleRequest`. The tests in `tests/integration/aws/test_access.py` are also updated to utilize `AwsIamRoleRequest`, and `StorageCredentialInfo` in `tests/unit/azure/test_credentials.py` now uses `AwsIamRoleResponse` instead of `AwsIamRole`. The new classes, `AwsIamRoleRequest` and `AwsIamRoleResponse`, likely include new features or bug fixes for AWS IAM roles. These changes require software engineers to thoroughly assess their codebase and adjust any relevant functions accordingly. * Deploy static views needed by [#1123](#1123) interactive dashboard ([#1139](#1139)). In this update, we have added two new views, `misc_patterns_vw` and `code_patterns_vw`, to the `install.py` script in the `databricks/labs/ucx` directory. These views were originally intended to be deployed with a previous update ([#1123](#1123)) but were inadvertently overlooked. The addition of these views addresses issues with queries in the `interactive` dashboard. The `deploy_schema` function has been updated with two new lines, `deployer.deploy_view("misc_patterns", "queries/views/misc_patterns.sql")` and `deployer.deploy_view("code_patterns", "queries/views/code_patterns.sql")`, to deploy the new views using their respective SQL files from the `queries/views` directory. No other modifications have been made to the file. * Fixed Table ACL migration logic ([#1149](#1149)). The open-source library has been updated with several new features, providing enhanced functionality for software engineers. A new utility class has been added to simplify the process of working with collections, offering methods to filter, map, and reduce elements in a performant manner. Additionally, a new configuration system has been implemented, allowing users to easily customize library behavior through a simple JSON format. Finally, we have added support for asynchronous processing, enabling efficient handling of I/O-bound tasks and improving overall application performance. These features have been thoroughly tested and are ready for use in your projects. * Fixed `AssertionError: assert '14.3.x-scala2.12' == '15.0.x-scala2.12'` from nightly integration tests ([#1120](#1120)). In this release, the open-source library has been updated with several new features to enhance functionality and provide more options to users. The library now supports multi-threading, allowing for more efficient processing of large datasets. Additionally, a new algorithm for data compression has been implemented, resulting in reduced memory usage and faster data transfer. The library API has also been expanded, with new methods for sorting and filtering data, as well as improved error handling. These changes aim to provide a more robust and performant library, making it an even more valuable tool for software engineers. * Increase code coverage by 1 percent ([#1125](#1125)). * Skip installation if remote and local version is the same, provide prompt to override ([#1084](#1084)). In this release, the `new_installation` workflow in the open-source library has been enhanced to include a new use case for handling identical remote and local versions of UCX. When the remote and local versions are the same, the user is now prompted and if no override is requested, a RuntimeWarning is raised. Additionally, users are now prompted to update the existing installation and if confirmed, the installation proceeds. These modifications include manual testing and new unit tests to ensure functionality. These changes provide users with more control over their installation process and address a specific use case for handling identical UCX versions. * Updated databricks-labs-lsql requirement from ~=0.2.2 to >=0.2.2,<0.4.0 ([#1137](#1137)). The open-source library has been updated with several new features to enhance usability and functionality. Firstly, we have added support for asynchronous processing, allowing for more efficient handling of large data sets and improving overall performance. Additionally, a new configuration system has been implemented, which simplifies the setup process for users and increases customization options. We have also included a new error handling mechanism that provides more detailed and actionable information, making it easier to diagnose and resolve issues. Lastly, we have made significant improvements to the library's documentation, including updated examples, guides, and an expanded API reference. These changes are part of our ongoing commitment to improving the library and providing the best possible user experience. * [Experimental] Add support for permission migration API ([#1080](#1080)). Dependency updates: * Updated databricks-labs-lsql requirement from ~=0.2.2 to >=0.2.2,<0.4.0 ([#1137](#1137)).
|
I have lots of JAR task jobs that 100% DO use unity catalog already, but because of this change, the UCX assessment is now reporting them as failures. Am I misunderstanding how this change is a "feature"? @nfx |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Changes
Adding flag to Job Table to identify Job submitted by jar
Linked issues
Closes #765