fix: tables schema command replace manual _delta_log JSON parsing with DeltaTable.schema() from the deltalake library#229
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Pull request overview
Fixes fab tables schema failing on Delta tables whose pre-checkpoint JSON logs were vacuumed, by switching schema extraction to the Delta protocol via the deltalake library.
Changes:
- Replaced manual
_delta_log/*.jsonscanning withdeltalake.DeltaTable(...).schema()for checkpoint-aware schema extraction. - Added
deltalake>=0.18.0to project dependencies. - Renamed typo’d error constant
ERROR_INVALID_DETLA_TABLEtoERROR_INVALID_DELTA_TABLEand wired it into the tables schema command.
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| src/fabric_cli/core/fab_constant.py | Fixes typo in error constant name; minor formatting cleanup. |
| src/fabric_cli/commands/tables/fab_tables_schema.py | Refactors schema retrieval to use deltalake over ABFSS + token, removing fragile log parsing. |
| pyproject.toml | Adds deltalake dependency required for robust schema extraction. |
| .changes/unreleased/fixed-20260430-130558.yaml | Adds release note entry for the schema extraction fix. |
Comment on lines
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| table = DeltaTable( | ||
| table_uri, | ||
| storage_options={ | ||
| "bearer_token": token, | ||
| "use_fabric_endpoint": "true", |
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This change introduces a new schema extraction path via deltalake.DeltaTable (ABFSS URI + token-based access) but there are no tests covering the success path or the TableNotFoundError -> FabricCLIError(ERROR_INVALID_DELTA_TABLE) mapping. Please add command-level tests that mock DeltaTable/schema() to validate output structure and error handling.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
pkontek
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📥 Pull Request
✨ Description of new changes
Summary: The
fab tables schemacommand failed with[InvalidDeltaTable] Failed to extract the table schemafor Delta tables where pre-checkpoint JSON commit log files had been cleaned up. The previous implementation manually scanned_delta_log/*.jsonfiles via the OneLake API looking for ametaDataentry — an approach that breaks once Delta Lake compacts logs into.checkpoint.parquetfiles and removes the preceding JSON entries.Context: Delta Lake creates a checkpoint every 10 transactions by default and retains log files according to the configured retention policy. Once older JSON commit files are removed, the
metaDataentry (which carries the schema) is no longer available in the remaining JSON logs, causing the command to fail even for healthy, accessible tables.Dependencies: Adds
deltalake>=0.18.0as a new dependency. Thedeltalakelibrary correctly resolves the table schema from both JSON logs and Parquet checkpoints using the standard Delta protocol, making the implementation robust and protocol-compliant.Changes:
_delta_logJSON parsing withDeltaTable.schema()from thedeltalakelibrarydeltalake>=0.18.0to project dependencies inpyproject.tomlfab_tables_schema.pyby removing ~50 lines of fragile log-parsing logic