Developer
Chelsea Lin
chelsealin@google.com
Performance
YoY:+642%Key patterns and highlights from this developer's activity.
Breakdown of growth, maintenance, and fixes effort over time.
Bugs introduced vs. fixed over time.
Reclassifies engineering effort based on bug attribution. Commits that introduced bugs are retrospectively counted as poor investments.
Investment Quality reclassifies engineering effort based on bug attribution data. Commits identified as buggy origins (those that introduced bugs later fixed by someone) have their grow and maintenance time moved into the Wasted Time category. Their waste (fix commits) remains counted as productive. All other commits retain their standard classification: grow is productive, maintenance is maintenance, and waste (fixes) is productive.
The standard model classifies commits as Growth, Maintenance, or Fixes. Investment Quality adds a quality lens: a commit that introduced a bug is retrospectively counted as a poor investment — the engineering time spent on it was wasted because it ultimately required additional fix work. Fix commits (Fixes in the standard model) are reframed as productive, because fixing bugs is valuable work.
Currently computed client-side from commit and bug attribution data. Ideal server-side endpoint:
POST /v1/organizations/{orgId}/investment-quality
Content-Type: application/json
Request:
{
"startTime": "2025-01-01T00:00:00Z",
"endTime": "2025-12-31T23:59:59Z",
"bucketSize": "BUCKET_SIZE_MONTH",
"groupBy": ["repository_id" | "deliverer_email"]
}
Response:
{
"productivePct": 74,
"maintenancePct": 18,
"wastedPct": 8,
"buckets": [
{
"bucketStart": "2025-01-01T00:00:00Z",
"productive": 4.2,
"maintenance": 1.8,
"wasted": 0.6
}
]
}Latest analyzed commits from this developer.
| Hash | Message | Date | Files |
|---|
Commit activity distribution by hour and day of week. Shows when this developer is most active.
Developers who frequently work on the same files and symbols. Higher score means stronger code collaboration.
| Effort |
|---|
| 86a83fba | This commit **excludes version 2026.3.0 of the `gcsfs` dependency** from being installed or used within the project's dependency resolution system. This is a crucial **dependency management** update, acting as a **bug fix** to address an identified internal issue. By preventing the use of this specific `gcsfs` release, the change ensures greater stability and compatibility for all components interacting with Google Cloud Storage. This action helps to avoid potential runtime problems or unexpected behavior that might arise from the problematic `gcsfs` version, thereby improving overall system reliability. | Mar 31 | 3 | – |
| ff49d0bb | This commit delivers a crucial **bug fix** to the **`bigframes.core.compile.sqlglot` compiler**, specifically enhancing its handling of `NULL` values and string operations. It **corrects the SQL generation** for boolean, comparison, and numeric expressions by consistently employing `sql.is_null_literal` for more robust null literal checks across `bool_ops.py`, `comparison_ops.py`, and `numeric_ops.py`. Furthermore, it **fixes an edge case in `string_ops.py`** where the `string_slice` function incorrectly calculated `length_expr` for negative end indices. These improvements ensure the **accuracy and reliability of generated SQL** for BigQuery DataFrames, preventing unexpected behavior when operations involve `NULL`s or complex string manipulations. | Mar 31 | 10 | waste |
| 9f03614b | This commit **fixes a bug** in the **`to_gbq`** function of the **BigQuery Dataframes library** that could lead to data column swaps when replacing an existing table. The internal **schema matching logic** in `bq_caching_executor.py`, specifically the `_is_schema_match` function, was refactored to correctly compare column names and types while preserving order. This ensures that `DataFrame.to_gbq(if_exists='replace')` operations now maintain **data integrity and column alignment**, preventing unintended data corruption. A new system test was added to verify this correct behavior when replacing tables with reordered columns. | Mar 26 | 2 | waste |
| 25c23093 | This commit primarily focuses on **maintenance** by significantly **increasing test coverage** for the **`bigframes.core.compile.sqlglot` compiler**. It introduces comprehensive new unit tests and updates SQL snapshots for various expression types, including JSON, boolean, comparison, datetime, string, numeric, array, and blob operations, with a particular emphasis on null value handling and edge cases. Additionally, it adds a **new capability** to the **`sqlglot` and `ibis` compilers** by expanding support for non-fixed frequency datetime to integer label conversions to include monthly, quarterly, and yearly rules. This work enhances the robustness and correctness of the SQL generation for complex data manipulations within BigQuery DataFrames. | Mar 23 | 40 | maint |
| 5519f4c3 | This commit **refactors** the **BigFrames DDL generation** logic to leverage `sqlglot` for constructing `CREATE EXTERNAL TABLE` statements. It introduces a new `create_external_table` function and its corresponding `sqlglot` transform within the `bigframes.core.compile.sqlglot.sql.ddl` module. The `bigframes.bigquery._operations.table.create_external_table` function is updated to utilize this new `sqlglot`-based generation, ensuring more consistent and robust DDL output. This change standardizes the creation of external tables, improving the maintainability and reliability of BigFrames' interaction with BigQuery. | Mar 19 | 9 | grow |
| 18476ca8 | This commit **refactors** the **BigFrames BigQuery I/O operations** to utilize `sqlglot` for generating `LOAD DATA` DDL statements. The `bigframes.bigquery._operations.io.load_data` function now leverages a **new `sqlglot`-based DDL generation module** (`packages/bigframes/bigframes/core/compile/sqlglot/sql/ddl.py`) for constructing these SQL commands, replacing the previous internal implementation. This **refactoring** improves the robustness and maintainability of SQL generation within the `bigframes.bigquery` module by standardizing on `sqlglot` for DDL construction. The change includes comprehensive unit and snapshot tests to validate the correctness of the newly generated SQL, ensuring no functional impact on users while enhancing the underlying **SQL compilation layer**. | Mar 16 | 14 | grow |
| b6e47514 | This commit **fixes** an issue within the **SQLGlot compiler's datetime operations** by extending conversion logic. Specifically, it ensures that datetime and timestamp conversion operations in `bigframes.core.compile.sqlglot.expressions.datetime_ops` **correctly handle `DATETIME_DTYPE` and `DATE_DTYPE` inputs**. This **bug fix** addresses a previous oversight, improving the accuracy and reliability of date and time data processing. The change prevents incorrect type conversions for users interacting with BigFrames, enhancing the overall robustness of the compiler's datetime handling. | Mar 16 | 1 | waste |
| 9590f90b | This commit **refactors** the `top_down` tree traversal method within the `bigframes.core.bigframe_node.py` module, which is central to how BigQuery DataFrame nodes are processed. The change replaces the previous recursive implementation with an **iterative, stack-based approach** for improved stability. This **maintenance** effort enhances the robustness and performance of tree traversals, particularly for deep trees, by mitigating potential recursion depth limits and stack overflow issues. The core logic for navigating BigQuery DataFrame nodes is now more stable and efficient, impacting any operations that rely on this traversal mechanism. | Mar 12 | 1 | maint |
| 9966d2d9 | This commit introduces a comprehensive suite of **golden SQL tests** for the TPC-H benchmark queries within the `bigframes` package. It adds new test infrastructure in `packages/bigframes/tests/unit/core/compile/sqlglot/tpch/conftest.py` and parameterized tests in `packages/bigframes/tests/unit/core/compile/sqlglot/tpch/test_tpch.py` to generate and validate SQL output against predefined snapshots for TPC-H queries 1 through 22. This **maintenance** effort significantly **improves the test coverage** for the `sqlglot` compilation process, ensuring the correct translation of complex analytical queries. The added tests will help prevent regressions and maintain the integrity of the SQL generation for the **`bigframes` core compilation module**. | Mar 11 | 25 | maint |
| a39313f5 | This commit performs a significant **refactoring** of the `bigframes` library to standardize SQL literal generation by consistently leveraging `sqlglot`. It **enhances** the `sqlglot` literal function to support a broader range of Python data types, including `NaN`, `Inf`, `pd.NA`, and `pa.Scalar`. This change impacts core **AI operations**, **ML model generation**, **BigQuery I/O**, and various **SQL utility functions**, ensuring more robust and consistent SQL output. The deprecated `simple_literal` function is removed, streamlining the codebase and improving maintainability. | Mar 10 | 22 | maint |
| 24bb505d | This commit performs a major **refactoring** to decouple SQL building logic from the `SQLGlotIR` module, centralizing it into a new `sql` module within `bigframes.core.compile.sqlglot`. This new module provides core utilities for generating SQL literals, identifiers, and casting, alongside new Data Manipulation Language (DML) operations like `insert` and `replace`. Consequently, numerous components across **SQL generation** in `bigframes.bigquery`, `bigframes.core.sql`, `bigframes.ml`, and `bigframes.session` are updated to leverage these centralized functions. This enhances the modularity and maintainability of the **SQLGlot-based compilation system** and introduces **new DML capabilities** for more consistent SQL output. | Mar 4 | 22 | maint |
| c62cfd95 | This commit **updates system tests** for **BigQuery remote functions** within the `bigframes` library. It introduces a **new test case** in `test_remote_function.py` to specifically verify that setting remote function ingress to 'all' correctly triggers an expected failure, aligning with new **GCP policies**. This **test enhancement** ensures the system's compliance and robust error handling for ingress configurations. Additionally, a redundant `pytest` parameter is removed, contributing to **test suite maintenance**. | Mar 4 | 1 | maint |
| 72b1c96a | This commit **releases version 2.37.0 of the `bigframes` library**, incorporating a range of **new features, bug fixes, and documentation updates**. Key enhancements include adding a `cloud_function_cpus` option to `remote_function`, introducing `display.render_mode` for DataFrame/Series visualization, and supporting `pd.col` expressions with `.loc` and `getitem`. It also adds support for Python 3.14 and `dt.tz_localize()`, alongside fixes for uploading local data with nested JSONs and `IsInOp` type handling. This **minor version upgrade** provides significant improvements and expanded capabilities for `bigframes` users. The update is reflected in `packages/bigframes/.librarian/state.yaml`, `packages/bigframes/bigframes/version.py`, and `packages/bigframes/third_party/bigframes_vendored/version.py`. | Mar 4 | 4 | maint |
| 6a377608 | This commit performs a **maintenance update** to the internal tooling configuration for the **`bigframes`** package. It **updates the Docker image reference** for the `python-librarian-generator` within the `packages/bigframes/.librarian/state.yaml` file. This ensures that the `bigframes` package leverages the most recent version of the generator for its internal code generation and dependency management processes. The update is a **chore** that keeps the development infrastructure current, potentially incorporating new features or bug fixes from the generator tool. | Mar 3 | 1 | maint |
| 88178f29 | This commit performs **maintenance** by **removing all remaining Python 3.9 support code** from the `bigframes` package. Specifically, it updates a **generative AI notebook** to reflect a Python 3.12.1 kernel, and removes Python 3.9 from the **`noxfile` configurations** for unit, system, sample, and snippet tests across various modules. Additionally, comments in the `constraints-3.10.txt` file are updated to reflect this change. This **cleanup** officially **deprecates Python 3.9 compatibility**, streamlining the project's testing and development environments. | Mar 2 | 7 | maint |
| 5ee79921 | This commit performs a significant **refactoring** by **removing the deprecated `googlesql` module** and migrating all SQL generation logic to use `sqlglot` expressions and utilities. The change impacts core **SQL compilation and generation** across the `bigframes` package, affecting methods like `to_sql`, various SQL utility functions in `bigframes.core.sql`, and ML-related SQL generation. This internal migration standardizes the SQL construction mechanism, enhancing maintainability and consistency for all generated BigQuery SQL queries. New utility functions like `identifier` and `_escape_chars` were introduced in `sqlglot_ir.py` to support this transition. | Mar 2 | 20 | maint |
| 1ac793b4 | This commit delivers a **bug fix** to improve **BigFrames session data loading** by correctly handling local data uploads that contain **unsupported nested JSON types**. It introduces a new mechanism within `bigframes.session.loader` to detect these complex data structures using `_is_dtype_can_load` and then routes them through the `load_data_or_write_data` method, which leverages the write API for successful ingestion. This ensures that users can seamlessly upload local datasets with nested JSONs, preventing previous loading failures and enhancing the overall robustness of **data ingestion** capabilities. The `bigframes.session.bq_caching_executor` is also updated to utilize this new loading strategy, with corresponding test updates for expected error messages. | Feb 27 | 3 | waste |
| 004e3c69 | This commit **fixes a bug** in the `IsInOp` (SQL `IN` operator) by improving type compatibility checks, allowing comparisons between columns and literals with different nullability. It updates the **Ibis compilation layer**'s `isin_op_impl` to compare types by name and enhances the **SQLGlot expression handling** for `IsInOp` to use a more robust `dtypes.can_compare` function. The core **data type system**'s `dtypes.can_compare` is also improved to gracefully handle type coercion failures, leading to more reliable query generation and **fewer type-related errors** for users. Additionally, as a **maintenance task**, the TPC-H verification script was migrated to `tests/system/large/test_tpch.py` for better test suite integration. | Feb 20 | 5 | maint |
| 0addff98 | This commit **refactors the SQL generation logic** within the **BigFrames SQLGlot IR compiler** to **prevent ambiguity in generated SQL queries** by consistently adding table aliases. This **bug fix** addresses issues such as ambiguous table references, particularly for `SYSTEM_TIME` expressions, and resolves an `UnboundLocalError` in the vendored SQLGlot generator's `_generate_table` function. The changes lead to **more robust and correct SQL output** across various operations, as confirmed by extensive updates to SQL snapshot tests. | Feb 17 | 285 | maint |
| 8c8fe539 | This commit **refactors** the `bigframes` **SQLGlot compiler** to support **resampling operations**, specifically migrating existing sampling logic to use `sqlglot`. It introduces the `compile_fromrange` function in `compiler.py` to translate `FromRangeNode` objects into SQLGlot intermediate representation, enabling range-based SQL generation. Concurrently, the `SQLGlotIR` module gains a new `resample` method, which generates SQL for resampling operations utilizing `GENERATE_ARRAY` and cross joins. This work enhances the `bigframes` compiler's ability to leverage `sqlglot` for complex data manipulation, with new unit tests validating the correctness of the generated SQL for resampling. | Feb 13 | 4 | maint |
This commit **excludes version 2026.3.0 of the `gcsfs` dependency** from being installed or used within the project's dependency resolution system. This is a crucial **dependency management** update, acting as a **bug fix** to address an identified internal issue. By preventing the use of this specific `gcsfs` release, the change ensures greater stability and compatibility for all components interacting with Google Cloud Storage. This action helps to avoid potential runtime problems or unexpected behavior that might arise from the problematic `gcsfs` version, thereby improving overall system reliability.
This commit delivers a crucial **bug fix** to the **`bigframes.core.compile.sqlglot` compiler**, specifically enhancing its handling of `NULL` values and string operations. It **corrects the SQL generation** for boolean, comparison, and numeric expressions by consistently employing `sql.is_null_literal` for more robust null literal checks across `bool_ops.py`, `comparison_ops.py`, and `numeric_ops.py`. Furthermore, it **fixes an edge case in `string_ops.py`** where the `string_slice` function incorrectly calculated `length_expr` for negative end indices. These improvements ensure the **accuracy and reliability of generated SQL** for BigQuery DataFrames, preventing unexpected behavior when operations involve `NULL`s or complex string manipulations.
This commit **fixes a bug** in the **`to_gbq`** function of the **BigQuery Dataframes library** that could lead to data column swaps when replacing an existing table. The internal **schema matching logic** in `bq_caching_executor.py`, specifically the `_is_schema_match` function, was refactored to correctly compare column names and types while preserving order. This ensures that `DataFrame.to_gbq(if_exists='replace')` operations now maintain **data integrity and column alignment**, preventing unintended data corruption. A new system test was added to verify this correct behavior when replacing tables with reordered columns.
This commit primarily focuses on **maintenance** by significantly **increasing test coverage** for the **`bigframes.core.compile.sqlglot` compiler**. It introduces comprehensive new unit tests and updates SQL snapshots for various expression types, including JSON, boolean, comparison, datetime, string, numeric, array, and blob operations, with a particular emphasis on null value handling and edge cases. Additionally, it adds a **new capability** to the **`sqlglot` and `ibis` compilers** by expanding support for non-fixed frequency datetime to integer label conversions to include monthly, quarterly, and yearly rules. This work enhances the robustness and correctness of the SQL generation for complex data manipulations within BigQuery DataFrames.
This commit **refactors** the **BigFrames DDL generation** logic to leverage `sqlglot` for constructing `CREATE EXTERNAL TABLE` statements. It introduces a new `create_external_table` function and its corresponding `sqlglot` transform within the `bigframes.core.compile.sqlglot.sql.ddl` module. The `bigframes.bigquery._operations.table.create_external_table` function is updated to utilize this new `sqlglot`-based generation, ensuring more consistent and robust DDL output. This change standardizes the creation of external tables, improving the maintainability and reliability of BigFrames' interaction with BigQuery.
This commit **refactors** the **BigFrames BigQuery I/O operations** to utilize `sqlglot` for generating `LOAD DATA` DDL statements. The `bigframes.bigquery._operations.io.load_data` function now leverages a **new `sqlglot`-based DDL generation module** (`packages/bigframes/bigframes/core/compile/sqlglot/sql/ddl.py`) for constructing these SQL commands, replacing the previous internal implementation. This **refactoring** improves the robustness and maintainability of SQL generation within the `bigframes.bigquery` module by standardizing on `sqlglot` for DDL construction. The change includes comprehensive unit and snapshot tests to validate the correctness of the newly generated SQL, ensuring no functional impact on users while enhancing the underlying **SQL compilation layer**.
This commit **fixes** an issue within the **SQLGlot compiler's datetime operations** by extending conversion logic. Specifically, it ensures that datetime and timestamp conversion operations in `bigframes.core.compile.sqlglot.expressions.datetime_ops` **correctly handle `DATETIME_DTYPE` and `DATE_DTYPE` inputs**. This **bug fix** addresses a previous oversight, improving the accuracy and reliability of date and time data processing. The change prevents incorrect type conversions for users interacting with BigFrames, enhancing the overall robustness of the compiler's datetime handling.
This commit **refactors** the `top_down` tree traversal method within the `bigframes.core.bigframe_node.py` module, which is central to how BigQuery DataFrame nodes are processed. The change replaces the previous recursive implementation with an **iterative, stack-based approach** for improved stability. This **maintenance** effort enhances the robustness and performance of tree traversals, particularly for deep trees, by mitigating potential recursion depth limits and stack overflow issues. The core logic for navigating BigQuery DataFrame nodes is now more stable and efficient, impacting any operations that rely on this traversal mechanism.
This commit introduces a comprehensive suite of **golden SQL tests** for the TPC-H benchmark queries within the `bigframes` package. It adds new test infrastructure in `packages/bigframes/tests/unit/core/compile/sqlglot/tpch/conftest.py` and parameterized tests in `packages/bigframes/tests/unit/core/compile/sqlglot/tpch/test_tpch.py` to generate and validate SQL output against predefined snapshots for TPC-H queries 1 through 22. This **maintenance** effort significantly **improves the test coverage** for the `sqlglot` compilation process, ensuring the correct translation of complex analytical queries. The added tests will help prevent regressions and maintain the integrity of the SQL generation for the **`bigframes` core compilation module**.
This commit performs a significant **refactoring** of the `bigframes` library to standardize SQL literal generation by consistently leveraging `sqlglot`. It **enhances** the `sqlglot` literal function to support a broader range of Python data types, including `NaN`, `Inf`, `pd.NA`, and `pa.Scalar`. This change impacts core **AI operations**, **ML model generation**, **BigQuery I/O**, and various **SQL utility functions**, ensuring more robust and consistent SQL output. The deprecated `simple_literal` function is removed, streamlining the codebase and improving maintainability.
This commit performs a major **refactoring** to decouple SQL building logic from the `SQLGlotIR` module, centralizing it into a new `sql` module within `bigframes.core.compile.sqlglot`. This new module provides core utilities for generating SQL literals, identifiers, and casting, alongside new Data Manipulation Language (DML) operations like `insert` and `replace`. Consequently, numerous components across **SQL generation** in `bigframes.bigquery`, `bigframes.core.sql`, `bigframes.ml`, and `bigframes.session` are updated to leverage these centralized functions. This enhances the modularity and maintainability of the **SQLGlot-based compilation system** and introduces **new DML capabilities** for more consistent SQL output.
This commit **updates system tests** for **BigQuery remote functions** within the `bigframes` library. It introduces a **new test case** in `test_remote_function.py` to specifically verify that setting remote function ingress to 'all' correctly triggers an expected failure, aligning with new **GCP policies**. This **test enhancement** ensures the system's compliance and robust error handling for ingress configurations. Additionally, a redundant `pytest` parameter is removed, contributing to **test suite maintenance**.
This commit **releases version 2.37.0 of the `bigframes` library**, incorporating a range of **new features, bug fixes, and documentation updates**. Key enhancements include adding a `cloud_function_cpus` option to `remote_function`, introducing `display.render_mode` for DataFrame/Series visualization, and supporting `pd.col` expressions with `.loc` and `getitem`. It also adds support for Python 3.14 and `dt.tz_localize()`, alongside fixes for uploading local data with nested JSONs and `IsInOp` type handling. This **minor version upgrade** provides significant improvements and expanded capabilities for `bigframes` users. The update is reflected in `packages/bigframes/.librarian/state.yaml`, `packages/bigframes/bigframes/version.py`, and `packages/bigframes/third_party/bigframes_vendored/version.py`.
This commit performs a **maintenance update** to the internal tooling configuration for the **`bigframes`** package. It **updates the Docker image reference** for the `python-librarian-generator` within the `packages/bigframes/.librarian/state.yaml` file. This ensures that the `bigframes` package leverages the most recent version of the generator for its internal code generation and dependency management processes. The update is a **chore** that keeps the development infrastructure current, potentially incorporating new features or bug fixes from the generator tool.
This commit performs **maintenance** by **removing all remaining Python 3.9 support code** from the `bigframes` package. Specifically, it updates a **generative AI notebook** to reflect a Python 3.12.1 kernel, and removes Python 3.9 from the **`noxfile` configurations** for unit, system, sample, and snippet tests across various modules. Additionally, comments in the `constraints-3.10.txt` file are updated to reflect this change. This **cleanup** officially **deprecates Python 3.9 compatibility**, streamlining the project's testing and development environments.
This commit performs a significant **refactoring** by **removing the deprecated `googlesql` module** and migrating all SQL generation logic to use `sqlglot` expressions and utilities. The change impacts core **SQL compilation and generation** across the `bigframes` package, affecting methods like `to_sql`, various SQL utility functions in `bigframes.core.sql`, and ML-related SQL generation. This internal migration standardizes the SQL construction mechanism, enhancing maintainability and consistency for all generated BigQuery SQL queries. New utility functions like `identifier` and `_escape_chars` were introduced in `sqlglot_ir.py` to support this transition.
This commit delivers a **bug fix** to improve **BigFrames session data loading** by correctly handling local data uploads that contain **unsupported nested JSON types**. It introduces a new mechanism within `bigframes.session.loader` to detect these complex data structures using `_is_dtype_can_load` and then routes them through the `load_data_or_write_data` method, which leverages the write API for successful ingestion. This ensures that users can seamlessly upload local datasets with nested JSONs, preventing previous loading failures and enhancing the overall robustness of **data ingestion** capabilities. The `bigframes.session.bq_caching_executor` is also updated to utilize this new loading strategy, with corresponding test updates for expected error messages.
This commit **fixes a bug** in the `IsInOp` (SQL `IN` operator) by improving type compatibility checks, allowing comparisons between columns and literals with different nullability. It updates the **Ibis compilation layer**'s `isin_op_impl` to compare types by name and enhances the **SQLGlot expression handling** for `IsInOp` to use a more robust `dtypes.can_compare` function. The core **data type system**'s `dtypes.can_compare` is also improved to gracefully handle type coercion failures, leading to more reliable query generation and **fewer type-related errors** for users. Additionally, as a **maintenance task**, the TPC-H verification script was migrated to `tests/system/large/test_tpch.py` for better test suite integration.
This commit **refactors the SQL generation logic** within the **BigFrames SQLGlot IR compiler** to **prevent ambiguity in generated SQL queries** by consistently adding table aliases. This **bug fix** addresses issues such as ambiguous table references, particularly for `SYSTEM_TIME` expressions, and resolves an `UnboundLocalError` in the vendored SQLGlot generator's `_generate_table` function. The changes lead to **more robust and correct SQL output** across various operations, as confirmed by extensive updates to SQL snapshot tests.
This commit **refactors** the `bigframes` **SQLGlot compiler** to support **resampling operations**, specifically migrating existing sampling logic to use `sqlglot`. It introduces the `compile_fromrange` function in `compiler.py` to translate `FromRangeNode` objects into SQLGlot intermediate representation, enabling range-based SQL generation. Concurrently, the `SQLGlotIR` module gains a new `resample` method, which generates SQL for resampling operations utilizing `GENERATE_ARRAY` and cross joins. This work enhances the `bigframes` compiler's ability to leverage `sqlglot` for complex data manipulation, with new unit tests validating the correctness of the generated SQL for resampling.