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feat: support biglake tables in pandas_gbq.sample#1014
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Summary of ChangesHello@tswast, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request enhances the Highlights
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Code Review
This pull request adds the initial structure to support sampling from BigLake tables inpandas_gbq.sample. It refactors the existing sampling logic into separate functions for BigQuery and BigLake tables and introduces a newbiglake.py module to interact with the BigLake REST API.
My review has identified a few critical issues. The core logic for sampling BigLake tables is not yet implemented. The newbiglake.py module contains a critical bug in URL construction and lacks proper error handling for API requests. I've also noted some areas for improvement regarding the robustness of table ID parsing and documentation. Please address these points to complete the feature.
| return session.get( | ||
| f"{_ICEBERG_REST_CATALOG_URI}.{path}", | ||
| headers={ | ||
| "x-goog-user-project": billing_project_id, | ||
| "Content-Type": "application/json; charset=utf-8", | ||
| # TODO(tswast): parameter for this option (or get from catalog metadata?) | ||
| # /iceberg/{$api_version}/restcatalog/extensions/{name=projects/*/catalogs/*} | ||
| "X-Iceberg-Access-Delegation": "vended-credentials", | ||
| }, | ||
| ).json() |
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The URL for the BigLake REST API is constructed incorrectly. There's an extra. between the base URI and the path, which will lead to a404 Not Found error. The path already starts with a/. Additionally, the response fromsession.get is not checked for errors before attempting to parse it as JSON. This can lead to unhelpfulJSONDecodeError exceptions on HTTP failures. You should callresponse.raise_for_status() to handle non-2xx responses gracefully.
response=session.get(f"{_ICEBERG_REST_CATALOG_URI}{path}",headers={"x-goog-user-project":billing_project_id,"Content-Type":"application/json; charset=utf-8",# TODO(tswast): parameter for this option (or get from catalog metadata?)# /iceberg/{$api_version}/restcatalog/extensions/{name=projects/*/catalogs/*}"X-Iceberg-Access-Delegation":"vended-credentials", }, )response.raise_for_status()returnresponse.json()
| def _sample_biglake_table( | ||
| *, | ||
| table_id: str, | ||
| credentials: google.oauth2.credentials.Credentials, | ||
| bqclient: google.cloud.bigquery.Client, | ||
| target_bytes: int, | ||
| progress_bar_type: str | None, | ||
| use_bqstorage_api: bool, | ||
| ) -> Optional[pandas.DataFrame]: | ||
| pass |
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The function_sample_biglake_table is a stub with apass statement. The PR title suggests support for BigLake tables is being added, but the core implementation is missing. This function needs to be implemented to fulfill the feature's goal. Additionally, corresponding unit tests should be added to verify its functionality.
| """ | ||
| # https://iceberg.apache.org/spec/#metrics | ||
| # total-files-size | ||
| project, catalog, namespace, table = table_id.split(".") |
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Unpackingtable_id.split('.') directly into four variables is unsafe. Iftable_id doesn't contain exactly three dots, this will raise aValueError. It's better to validate the number of parts before unpacking to provide a more informative error message and prevent unexpected crashes.
parts=table_id.split(".")iflen(parts)!=4:raiseValueError("Invalid BigLake table ID. Expected format: ""project.catalog.namespace.table" )project,catalog,namespace,table=parts
| """ | ||
| Docstring for get_table_metadata |
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The docstring forget_table_metadata is a placeholder. Please add a proper docstring explaining the function's purpose, its parameters (table_id,credentials,billing_project_id), and what it returns. This is important for maintainability and usability.
| # BigLake tables can't be read directly by the BQ Storage Read API, so make | ||
| # sure we run a query first. | ||
| parts = table_id.split(".") | ||
| if len(parts) == 4: |
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Relying onlen(table_id.split('.')) == 4 to identify a BigLake table is fragile. BigQuery identifiers can contain dots if they are quoted (e.g.,my-project.dataset.with.dot.table). This could lead to misidentifying a standard BigQuery table as a BigLake table. Consider a more robust detection mechanism. For example, you could attempt to parse the ID as a BigLake ID and handle failure, or introduce an explicit parameter to specify the table type.
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Fixes #<issue_number_goes_here> 🦕