- Add support for parsing Snowflake timestamp structs with epoch and fraction fields
- Implement handling of Snowflake timestamp logical types (with and without timezone)
- Enhance JSON value processing to detect and convert Snowflake timestamp objects
- Add error handling and logging for timestamp parsing
* xAxisTickinerval
* fix(visualization): Add x-axis time unit configuration for bar and combo charts
- Extend chart configuration to support optional x-axis time unit
- Update modify visualization agent to dynamically set x-axis time interval
- Modify bar line and combo chart prompts to include x_axis_time_unit parameter
* only use valid time units
* refactor(visualization): Simplify x-axis time unit configuration
- Modify modify_visualization_agent to extract and remove x-axis time unit more efficiently
- Update global styling result structure for x-axis time interval
- Adjust format_label_prompt comment to clarify date format default behavior
---------
Co-authored-by: Nate Kelley <nate@buster.so>
- Restructure deploy_datasets function to separate concerns
- Improve column and relationship validation in dataset deployment
- Enhance error handling and validation result generation
- Add support for more comprehensive column and relationship checks
- Refactor validation logic to handle multiple error types
- Add support for capturing source type (table, view, materialized view)
- Improve column metadata queries for Postgres, MySQL, BigQuery, and Snowflake
- Include more comprehensive column information during dataset import
- Extend DatasetColumnRecord to include source_type field
- Add comprehensive column validation before dataset deployment
- Validate existence of all required columns in source database
- Simplify column type and nullability retrieval
- Enhance error reporting for missing columns
- Update deployment logic to use pre-validated column information
- Add detailed validation error logging in CLI
- Improve type compatibility checks in dataset validation
- Modify deployment process to handle and report validation errors more comprehensively
- Add Hash derive for Verification enum
- Update API and CLI to support more informative validation results
- Introduce helper functions for processing string and JSON values
- Implement case-insensitive string and JSON value transformations
- Add robust timestamp parsing with error handling
- Enhance Snowflake query result processing with consistent data normalization
- Refactor stored values processing in dataset deployment to use background task
- Add `StoredValueColumn` struct to encapsulate column processing details
- Implement `process_stored_values_background` for parallel and resilient value storage
- Add logging for successful and failed stored value processing
- Update CLI to handle optional SQL definitions and improve file processing
* fix: add stored values support for dataset columns
This commit introduces stored values functionality for dataset columns, including:
- Adding a `stored_values` flag to column deployment requests
- Implementing a mechanism to store column values during dataset deployment
- Updating data analyst and SQL generation agents to leverage stored values
- Creating a new utility module for stored values search and management
* refactor(stored_values): improve stored values implementation and schema management
This commit enhances the stored values functionality with several key improvements:
- Update schema and table creation to use organization ID as schema name
- Modify stored values storage to include column ID
- Improve value extraction and embedding generation process
- Remove unnecessary distance calculation in search results
- Clean up unused values_engine module
- Added support for retrieving and passing data source type during SQL agent generation
- Updated SQL generation prompts to include data source type in system messages
- Modified `generate_sql_agent` function to fetch and utilize data source type information
- Improved SQL generation context by dynamically incorporating data source type details
* feat: enhance deploy command with skip_dbt option
- Updated the Deploy command to accept a `skip_dbt` boolean argument, allowing users to bypass the dbt run during deployment.
- Refactored the deploy function to conditionally execute the dbt command based on the `skip_dbt` flag, improving deployment flexibility.
* Refactor query engine and CLI commands for improved functionality and error handling
- Updated `get_bigquery_columns` and `get_snowflake_columns` functions to enhance column name handling and ensure proper error reporting.
- Modified `get_snowflake_client` to accept a database ID for better connection management.
- Enhanced the `deploy` command in the CLI to include additional parameters (`path`, `data_source_name`, `schema`, `env`) for more flexible deployments.
- Improved error handling and reporting in the `deploy` function, including detailed summaries of deployment errors and successful file processing.
- Updated `get_model_files` to accept a directory path and added checks for file existence, enhancing robustness.
- Adjusted model file structures to include schema information and refined the upload process to handle optional parameters more effectively.
These changes collectively improve the usability and reliability of the query engine and deployment process.
* Update dataset DDL generation to include optional YML file content
- Modified `generate_sql_agent` to append optional YML file content to dataset DDL
- Ensures more comprehensive dataset representation during SQL agent generation
- Handles cases where YML file might be present or absent gracefully
- Added `html-escape` crate to `Cargo.toml` for HTML escaping.
- Updated email template processing to escape HTML in message and button text, preventing potential XSS vulnerabilities.
- Modified test cases to include HTML content in email parameters, ensuring proper handling and escaping.
This change improves security by sanitizing user input in email communications.
* fix permission check on post_dataset rest
* refactor: enhance dataset overview access lineage and permission checks
- Updated the `get_dataset_overview` function to conditionally add default access lineage based on user roles and existing access paths.
- Simplified the logic for adding user roles to the lineage, ensuring clarity and maintainability.
- Improved handling for the `RestrictedQuerier` role to include checks for existing access before adding default lineage, enhancing permission accuracy.
- Streamlined code by removing redundant checks and consolidating role handling, optimizing overall readability.
* feat: Enhance permission group handling and data retrieval
- Introduced a new `PermissionGroupInfo` struct to encapsulate detailed information about permission groups, including user and dataset counts.
- Updated the `get_permission_group` and `list_permission_groups` functions to improve data retrieval and error handling.
- Refactored SQL queries in `list_permission_groups` to include additional joins for counting users and datasets associated with permission groups, enhancing the overall functionality and clarity of the API.
- Streamlined code for better readability and maintainability, ensuring consistent handling of user and permission group data.
* refactor: Improve dataset access handling and permission checks
- Enhanced the `get_restricted_user_datasets` and `get_restricted_user_datasets_with_metadata` functions to include additional permission checks for dataset groups and permission groups.
- Consolidated SQL queries to ensure proper filtering of deleted records and improved clarity in dataset retrieval logic.
- Introduced new joins and filters to handle dataset group permissions, ensuring accurate access control for users.
- Streamlined code for better readability and maintainability, enhancing overall functionality in dataset access management.
* fix: Update SQL migration and seed data for user attributes
- Modified the SQL migration to specify the schema for the `users` table, ensuring clarity in the update statement.
- Adjusted the seed data for `users_to_organizations` to change the `organization_id` from 'public' to 'none', reflecting a more accurate state for user roles and organization associations.
- Ensured consistency in the formatting of SQL insert statements for better readability.
* fix: Prevent users from updating their own profiles
- Added a check in the `update_user_handler` to prevent users from updating their own information, returning an error if they attempt to do so.
- This change enhances security by ensuring that users cannot modify their own records, which could lead to unauthorized changes.
* refactor: Simplify dashboard permission queries by removing team-based joins
- Removed left joins with `teams_to_users` table in dashboard permission queries
- Simplified permission checks to only filter by direct user ID
- Updated queries in `get_user_dashboard_permission`, `get_bulk_user_dashboard_permission`, and `list_dashboards_handler`
- Streamlined SQL query logic for more direct and efficient permission checks
- Enhanced the `get_restricted_user_datasets` and `get_restricted_user_datasets_with_metadata` functions to include additional permission checks for dataset groups and permission groups.
- Consolidated SQL queries to ensure proper filtering of deleted records and improved clarity in dataset retrieval logic.
- Introduced new joins and filters to handle dataset group permissions, ensuring accurate access control for users.
- Streamlined code for better readability and maintainability, enhancing overall functionality in dataset access management.
- Introduced a new `PermissionGroupInfo` struct to encapsulate detailed information about permission groups, including user and dataset counts.
- Updated the `get_permission_group` and `list_permission_groups` functions to improve data retrieval and error handling.
- Refactored SQL queries in `list_permission_groups` to include additional joins for counting users and datasets associated with permission groups, enhancing the overall functionality and clarity of the API.
- Streamlined code for better readability and maintainability, ensuring consistent handling of user and permission group data.
- Updated the `get_dataset_overview` function to conditionally add default access lineage based on user roles and existing access paths.
- Simplified the logic for adding user roles to the lineage, ensuring clarity and maintainability.
- Improved handling for the `RestrictedQuerier` role to include checks for existing access before adding default lineage, enhancing permission accuracy.
- Streamlined code by removing redundant checks and consolidating role handling, optimizing overall readability.
- Updated the `get_snowflake_client` function to no longer require `warehouse_id` and `database_id`, simplifying the connection process.
- This change enhances flexibility in client initialization and aligns with recent updates to Snowflake API handling.
- Downgraded the `base64` crate version in `Cargo.toml` from `0.22.1` to `0.21`.
- Refactored the `snowflake_query` function in `snowflake_query.rs` to improve data type handling, including support for additional Arrow data types and enhanced null value checks.
- Updated the `route_to_query` function in `query_router.rs` to use mutable `snowflake_client` for better state management during query execution.
- Improved error handling for closing the Snowflake client session, ensuring proper logging of any issues encountered.
- Streamlined the `get_dataset_overview` function to improve access control for the `RestrictedQuerier` role, ensuring more precise permission checks.
- Updated the `get_user_information` function to optimize dataset processing, categorizing datasets based on direct access and permission group access.
- Removed redundant code and improved readability by consolidating logic for user roles, enhancing maintainability.
- Enhanced lineage tracking for datasets, providing a clearer representation of user permissions across different access types.
- Enhanced the `get_dataset_overview` function to refine access control for the `RestrictedQuerier` role, allowing for more granular permission checks based on various access paths.
- Updated the `get_user_information` function to streamline dataset processing, ensuring that datasets are categorized correctly based on direct access and permission group access.
- Removed redundant code and improved readability by consolidating logic for user roles, enhancing maintainability of both functions.
- Improved lineage tracking for datasets, providing a clearer representation of user permissions across different access types.
- Introduced a default access lineage for users, ensuring consistent representation of user permissions.
- Simplified the addition of user roles to the lineage, consolidating logic for WorkspaceAdmin, DataAdmin, Querier, and Viewer roles.
- Enhanced lineage tracking for RestrictedQuerier role to include direct dataset access and permission group lineage, improving granularity of dataset permissions.
- Removed redundant code related to dataset and permission group lineage, optimizing readability and maintainability of the `get_dataset_overview` function.
- Simplified access control logic for datasets based on user roles, consolidating conditions for WorkspaceAdmin, DataAdmin, Querier, Viewer, and RestrictedQuerier.
- Enhanced dataset lineage tracking to provide clearer representation of user permissions across various dataset access types.
- Removed redundant code related to dataset processing, improving readability and maintainability of the `get_user_information` function.
- Ensured that datasets are correctly categorized based on direct access, permission group access, and organization datasets, optimizing the overall data retrieval process.
- Updated `get_dataset_overview` to include dataset group access and permission group to dataset group access, improving the granularity of dataset permissions.
- Introduced new queries to fetch dataset groups and their associated permissions, enhancing the dataset overview for users.
- Refactored `get_user_information` to concurrently retrieve dataset groups and permission group datasets, optimizing performance with `tokio::spawn`.
- Enhanced lineage tracking for datasets, allowing for better representation of user permissions across dataset groups and permission groups.
- Improved error handling during database queries to ensure robust data retrieval.
- Added new structs `DatasetLineage` and `DatasetInfo` to represent dataset details and lineage.
- Updated `UserResponse` to include a list of datasets associated with the user.
- Refactored `get_user_information` function to concurrently fetch user info, direct datasets, permission group datasets, and organization datasets using `tokio::spawn` for improved performance.
- Implemented logic to compile datasets based on direct access and permission group access, including lineage tracking for better data representation.
- Enhanced error handling during database queries to ensure robust user information retrieval.
- Introduced a new `DatasetToDatasetGroup` struct to represent the relationship between datasets and dataset groups, including fields for timestamps and optional deletion.
- Updated the database schema to include `updated_at` and `deleted_at` fields for the `datasets_to_dataset_groups` table, enhancing data tracking capabilities.
- Refactored the routing in `mod.rs` to include a nested router for assets, improving the organization of dataset group routes.
- Introduced new PUT routes for managing users and dataset groups in the assets module.
- Updated the router to support PUT requests for `/users`, `/dataset_groups`, and `/datasets`, enhancing the API's functionality for resource updates.
- Improved modularity by organizing related routes within the assets module.
- Changed the parameter in the SQL query from `user.id` to `user_id` for consistency with the updated user ID parameter naming convention.
- Enhanced the SQL query to count distinct dataset permissions and utilize `bool_or` for identity checks, improving accuracy and performance.
- Cleaned up the grouping in the SQL query by removing unnecessary fields, streamlining the data retrieval process.
dal