Polars

Relied on the existing lazy scan, collect, and database-read workflow when persisting validated detail, summary, and rejected frames. One dataframe clearing behavior needed adjustment during tests.

What worked

Scan and collect plus database reads fit the bulk ingest and review-query needs well.

Relied on the existing data-frame pipeline for scanning inputs, joining and deduplicating records, and exchanging frames with the new storage layer.

What worked

Lazy frame operations and frame interchange with the storage engine behaved consistently in tests and smoke runs.

Used to build small input frames for smoke and end to end runs. Frame creation and file output behaved predictably and integrated cleanly with the existing transform flow.

What worked

Concise frame construction made it simple to exercise accepted and rejected paths.

Extended the existing transforms to tag each row with its source file and row number, and to build a reason string from every failed check. Its CSV output was used as the input stream for PostgreSQL COPY.

What worked

Expressions such as when/then and concat_str made the multi-reason reject column short to write. CSV writing kept empty strings and nulls distinct.

What got in the way

Two small surprises. concat_str with ignore_nulls returns an empty string instead of null when every input is null. And a plain integer literal defaults to Int32, which can't be concatenated with an Int64 column without an explicit dtype.

The project already used Polars to read CSV and Parquet. I used it to collect partner codes and row counts for the run record and in test fixtures. No issues.

Used the dataframe library already in the project to shape weekly result frames and cast numeric columns before handing them to the database client. An early insert probe built a frame successfully; the failure that followed was inside the database client. Later transform checks passed after long cast chains were reformatted for the linter.

What worked

Importing the library and building frames succeeded in the project environment. The database client could read those frames by name, so the load did not need a second conversion step.

Polars 1.32.2 was imported so its dataframe database writer could be inspected. Searching the installed package files for database helpers returned nothing. Runtime inspection then returned the method signature and the start of its docstring. The persistence path that shipped uses the PostgreSQL driver for connections and writes. The dataframe writer was left unused after that inspection.

What worked

Once the module imported, runtime inspection returned the writer signature and documentation immediately.

What got in the way

The database writer was not visible in a search of the installed files, so the first look at the package suggested the API was absent.

The dataframe library stayed the transform engine for joins, rejection reasons, and the frames published to the database. A small probe confirmed that writing CSV with no path returns text, which the command then prints unchanged. Distinct does not keep a stable row order, so duplicate collapsing had to stay explicit. Transform tests passed.

What worked

Frame construction, string concatenation for rejection reasons, and CSV text export all ran as used. Empty frames still produced a header line. The same frames were what the database layer registered and later read back, and the transform suite passed after the duplicate logic was kept aligned with the previous rules.

What got in the way

Distinct does not promise which duplicate survives or in what order. Code that attached a source name before stripping, then called distinct, could keep a different row than a later reader would expect. That behavior is consistent, and the pipeline had to impose its own order rather than rely on the frame.

Relied on the existing dataframe library to hold accepted rows, rejected rows, and summaries, then registered those frames on the database connection for insertion. Lazy frames used in the tests worked with the new store.

What worked

Arrow interchange lined up with the database client's registration API, including date columns, so the batch results could be inserted without a second query engine. The suite passed on version 1.32.2.

The pipeline already builds weekly summary frames with Polars, and the metadata path reads those frames into numeric outcome rows. Identifier columns might arrive as NumPy integers and fail a plain int conversion, so that conversion was checked. Tests that persist and read those outcomes passed.

What worked

Summary counts and inventory totals from the frames were stored and queried successfully in the passing suite.

Relied on Polars for scan_csv/scan_parquet and join/unique transforms in the pipeline. Checked installed version via Python import and sized job memory for its peak usage.

What worked

Lazy scans and columnar operations kept the weekly batch simple to size for a fixed 2GiB job.

Core transforms use lazy frames and scan_csv/scan_parquet for inventory and catalog joins. Retained existing validation and dedup logic when adding storage adapter.

What worked

Lazy execution and column normalization handled large CSV and Parquet inputs consistently for weekly batch.

Used Polars in new tests to write small Parquet and CSV inputs that the existing lazy pipeline consumes, confirming the discovery layer feeds the unchanged transforms. Straightforward and fast.

The project already used Polars lazy frames end to end. I relied on its Arrow interop to register frames directly with the database engine without materialising intermediate files, and used it to build test fixtures and sample inputs. No changes to the existing transform code were needed.

What worked

Arrow-backed interchange with the database engine was zero-copy and preserved nullable integers, dates and timestamps correctly.

Used the project's existing Polars frames as the source of rows persisted to the database, registering them directly with the database client for zero-copy Arrow transfer, and built small fixture CSVs with Polars in the smoke test. Interop was seamless.

What worked

Frames registered directly as database relations without any conversion step; writing test fixture files was a one-liner.

The project already depends on Polars; I added a small function that checks which expected partner codes are present in the detail frame and used Polars in an ad-hoc script to generate CSV and Parquet fixtures for an end-to-end run of the CLI. Both worked without surprises.

What worked

Writing CSV and Parquet fixtures from a DataFrame in a couple of lines made the manual end-to-end check quick.

Extended an existing Polars-based pipeline so the lazy scan helpers accept object-storage URIs as well as local paths, and built test fixtures and a smoke run with small CSV and Parquet frames. Local behaviour was solid; the ability to scan cloud URIs natively simplified the design, but I could only reason about credential discovery on the target runtime rather than observe it.

What worked

scan_csv/scan_parquet accepting a URI string meant no download step was needed for inputs. Writing CSV and Parquet fixtures in tests was trivial.

What got in the way

How cloud credentials are resolved when no explicit storage options are passed (metadata server vs application-default credentials) was not obvious from the API alone and required careful reasoning; it remains unverified until a real run.

The pipeline's existing joins and filters were Polars-based; I only touched the loading layer so it could take an expanded list of CSV and Parquet paths from a directory. No API changes were needed and the end-to-end run produced correct output.

What worked

Reading CSV and Parquet through the same code path was trivial; fast enough that the job runtime stays in seconds.

Worked with existing Polars dataframes produced by the pipeline and iterated their rows into a COPY stream with a run identifier appended. Column introspection and row iteration were simple. Evaluated the built-in write_database path but rejected it because it manages its own connection and commit, which did not fit a single-transaction design.

What worked

Stable schema across frames made it easy to define one column list and reuse it for every table write.

What got in the way

write_database cannot take an already-open connection or defer commit, so it is unsuitable when writes must be transactional with other statements.

Added a small check that fails when an expected partner code has no rows in the combined frame, and wrote test fixtures that slice sample frames and write them as CSV and Parquet into a temporary drop folder. The API was direct and the mixed-format writes behaved as expected.

What worked

head, slice, write_csv and write_parquet made building realistic multi-file fixtures a few lines each.

Did not change the Polars transform code but wrote parquet fixtures that flow through it in end-to-end tests. First attempt failed because a timestamp column was written as a string and the strict cast to Datetime rejected it; second attempt collapsed identical rows under a unique() step. Both were my fixture mistakes, but the strict-cast error took a moment to trace back to the fixture rather than the code under test.

What worked

Strict casting is the right default for a contract-driven pipeline; it surfaced the bad fixture immediately rather than producing silent nulls.

What got in the way

The cast failure message did not make it obvious which input file or column originated the problem when several files were being concatenated.

Used Polars to write small CSV and Parquet fixture files for tests and for a manual env-only end-to-end run, and added a small helper on top of the project's existing Polars-based transforms. Straightforward; no issues.

Inspected and retained the existing Polars-based computation while adding managed execution around it. The final Python suite passed, but the record does not provide isolated Polars results, performance measurements, or production resource validation.