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Use pyarrow.Table for handling of dependencies #356
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We decided against storing the dependency table internally as |
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This pull request main goal is to speed up loading, saving, and parsing of the dependency table.
To achieve this we switch to use
pyarrow.Table
to represent the dependencies.Benchmark loading and saving dependency files
Reading a dependency file with 1,000,000 entries from CSV, pickle, or parquet
Writing a dependency file with 1,000,000 entries to CSV, pickle, or parquet
Conclusions
pyarrow.Table
should be used when reading/writing CSV filespyarrow.Table
instead ofpandas.DataFrame
Benchmarking single methods
Dependency.__call__()
Dependency.__contains__()
Dependency.__get_item__()
Dependency.__len__()
Dependency.__str__()
Dependency.archives
Dependency.attachments
Dependency.attachment_ids
Dependency.files
Dependency.media
Dependency.removed_media
Dependency.table_ids
Dependency.tables
Dependency.archive(1000 files)
Dependency.bit_depth(1000 files)
Dependency.channels(1000 files)
Dependency.checksum(1000 files)
Dependency.duration(1000 files)
Dependency.format(1000 files)
Dependency.removed(1000 files)
Dependency.sampling_rate(1000 files)
Dependency.type(1000 files)
Dependency.version(1000 files)
Dependency._add_attachment()
Dependency._add_media(1000 files)
Dependency._add_meta()
Dependency._drop()
Dependency._remove()
Dependency._update_media()
Dependency._update_media_version(1000 files)
Conclusion
Using
pyarrow.Table
(or apolars.DataFrame
) is faster for certain column based operations, but it is way too slow when addressing single rows. So we should not use it, but stay withpandas.DataFrame
to represent the dependency table.