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feat(interface): accept in-memory text records

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@afourniernv is already working on this.

Since Sep 21, 2026.

  • #284 by @afourniernv — open

Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
58/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
python
Domain
api, backend-api-design, data

Research direction

Start with the Python API's existing AnonymizerInput path and its handling of the public result dataframe, then compare the current file-input validation and schema behavior. Done means typed TextRecord and TextRecordsInput support in the API, preserved IDs and input order, validated configured ID columns, and unchanged file-backed and CLI behavior.

Written by the indexing model from the issue text.

Description

triaged

Priority

Medium

Problem

Applications that already hold text in memory must write temporary CSV or Parquet files before calling Anonymizer. That adds avoidable I/O and complicates service integrations. Callers also need a stable way to correlate completed rows with their own request or event IDs, including when an input column name collides with a generated output column.

Proposed solution

  • Add typed TextRecord(id, text) and TextRecordsInput input shapes.
  • Let the Python API accept TextRecordsInput alongside the existing file-backed AnonymizerInput without changing AnonymizerInput.source or its JSON schema.
  • Preserve caller IDs and input order in the public result dataframe.
  • Keep the CLI file-only; do not expose nested record objects as command-line flags.
  • Retain existing file input behavior and validate configured ID columns.

Alternatives considered

  • Temporary files: workable, but expensive and awkward for request-oriented services.
  • Accepting a raw dataframe: broader and less explicit than the two fields the pipeline needs.
  • Reading private trace_dataframe state: not an appropriate public correlation contract.

Compatibility

Existing AnonymizerInput(source=...) calls, generated schemas, and CLI behavior remain unchanged. The new shape is additive.

Dominant language
Python
Stars
129
Forks
21
Avg merge
1d 8h
Merged PRs (30d)
20

Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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