fix: Improve IO Abstractions and cleanup #755
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- add abstract Reader/Writer contracts - add IOStats and base write_batches implementation - add unit tests for contracts and batching Refs: #718
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🛠 Docs available at: https://k.atlan.dev/application-sdk/refactor/io-too |
inishchith
commented
Oct 6, 2025
| parquet_output: Optional[ParquetOutput], | ||
| parquet_output: Optional[ParquetWriter], | ||
| ) -> Optional[Union[ActivityStatistics, "pd.DataFrame"]]: | ||
| """Execute multi-database flow with proper error handling and result finalization.""" |
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@abhishekagrawal-atlan to review
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what does effective_sql_client mean here? removing it
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🛠 Full Test Coverage Report: https://k.atlan.dev/coverage/application-sdk/pr/755 |
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OnkarVO7
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Oct 14, 2025
| ) | ||
| return ParquetOutput( | ||
| return ParquetWriter( | ||
| output_path=output_path, |
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Can we try to simplify or rename it better this output_path and prefix?
Just by looking at the names it is not self explanatory as to what is required to be passed into these values
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removed output_suffix, better let the developers pass output_path directly.
will remove other such patterns across
| if hasattr(dataframe, "empty") and getattr(dataframe, "empty"): | ||
| continue | ||
| valid_dataframes.append(dataframe) | ||
| # Handle both async and sync iterators |
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These multidb related all funtcions and logic should be in it's separate utility module
| with self.engine.connect() as conn: | ||
| return self._execute_pandas_query(conn, query, chunksize) | ||
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| async def get_batched_results( |
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We can combine a lot of code for get_batched_results and get_results
| from sqlalchemy.orm import Session | ||
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| class BaseSQLClient(ClientInterface): |
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Thinking out loud here
Should we keep the BaseSQLClient as a normal query executor using sqlalchemy
We create a PandasSQLClient that inherits from BaseSQLClient which uses pandas to execute queries
Similarly DaftSQLClient
This will make the all the code very modular since each executor will have it's own module and devs can choose which query executor they want to use
Also let's say there comes a source which is not supported for pandas or daft normal query executor will have the logic that will work
Dependency management for pandas and daft will also get simplified I feel
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Changelog
Note
Introduce unified I/O via Reader/Writer with JSON/Parquet/Iceberg readers/writers, migrate activities/handlers to use them, enhance SQL client APIs, remove legacy inputs/outputs/docgen, and update tests/tooling.
Reader/Writerbase classes and newiomodule (json,parquet,iceberg).io/_utils.py(download_files,find_local_files_by_extension,path_gen, size estimation, null/datetime processing,is_empty_dataframe).DataframeTypeenum; remove legacyinputs/*andoutputs/*modules.ParquetFileReader/Writer,JsonFileWriter, and shared utils.SQLQueryInputusage with SQL client batch/result APIs; adjust parquet/json paths.get_batched_results/get_results; expose chunking.interceptors.models.Written by Cursor Bugbot for commit 5c42896. This will update automatically on new commits. Configure here.
address: #718
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