Speedup _add_meta(), _add_attachment()

[hagenw]

Speedup audb.Dependencies._add_meta() and audb.Dependencies._add_attachment() by treating them in the same way as audb.Dependencies._add_media().

Currently, this fails as it would require that the meta or attachment to be added is not already stored in the dependency table. It seems our implementation makes use of the fact that we can overwrite the row for meta and attachment.

Summary by Sourcery

Unify how dependency rows are added for media, meta, and attachments by introducing a shared row insertion helper.

Enhancements:

• Introduce a generic _add_rows() helper for inserting dependency table rows.
• Update _add_meta() and _add_attachment() to use the same bulk row insertion logic as _add_media() for consistency and potential performance improvements.

[sourcery-ai]

Reviewer's Guide

Refactors how meta and attachment dependencies are added by routing them, along with media entries, through a shared _add_rows() helper that builds and appends a typed DataFrame of new rows for performance and consistency.

Class diagram for updated Dependencies row insertion helpers

classDiagram
    class Dependencies {
        DataFrame _df
        _set_dtypes(df)
        _add_attachment(file, archive, checksum, version)
        _add_media(values)
        _add_meta(file, checksum, version)
        _add_rows(rows)
    }

    class DataFrame
    class DefineModule {
        DEPENDENCY_TABLE
        DEPENDENCY_TYPE
    }

    Dependencies --> DataFrame : uses
    Dependencies --> DefineModule : uses

    class AddRowsTuple {
        str file
        str archive
        int bit_depth
        int channels
        str checksum
        float duration
        str format
        int removed
        float sampling_rate
        int type
        str version
    }

    Dependencies ..> AddRowsTuple : constructs rows

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Flow diagram for adding dependency rows via _add_rows

flowchart TD
    A[_add_attachment] --> B[build single attachment tuple list]
    A2[_add_meta] --> C[build single meta tuple list]
    A3[_add_media] --> D[receive list of media tuples]

    B --> E[_add_rows]
    C --> E
    D --> E

    E --> F[build DataFrame from records
columns: file + DEPENDENCY_TABLE keys]
    F --> G[set dtypes via _set_dtypes]
    G --> H[concat new DataFrame to _df]

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File-Level Changes

Change	Details	Files
Route _add_attachment() through a new shared row-adding helper instead of assigning directly into the dependencies DataFrame.	Compute the attachment format from the file extension as before. Build a single-row values list containing file and dependency metadata fields. Call the shared _add_rows() helper with the constructed values instead of using DataFrame .loc assignment.	audb/core/dependencies.py
Route _add_meta() through the shared row-adding helper and keep archive resolution logic intact.	Determine the archive name from the meta file path, preserving the existing filename parsing logic for different meta prefixes. Construct a values tuple with file and dependency metadata fields including type set to meta. Call the shared _add_rows() helper with the constructed values instead of using DataFrame .loc assignment.	audb/core/dependencies.py
Introduce _add_rows() as a common implementation used by _add_media(), _add_meta(), and _add_attachment() to efficiently append dependency rows.	Define _add_rows() to accept a sequence of tuples representing full dependency table rows including the file name. Create a DataFrame from the given rows with "file" as index and dependency table columns derived from define.DEPENDENCY_TABLE. Apply _set_dtypes() to the new DataFrame and concatenate it with the existing dependencies DataFrame to append rows in batch. Refactor _add_media() to delegate to _add_rows() instead of duplicating DataFrame construction logic.	audb/core/dependencies.py

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[hagenw]

The current implementation does not really bring a boost, so we will close here.