Change default compression argument for JsonDatasetWriter

[Rexhaif]

Change default compression type from None to "infer", to align with pandas' defaults.

Documentation asks the user to supply to_json_kwargs with arguments suitable for pandas' to_json method. At the same time, while pandas' by default uses "infer" for compression, datasets enforce None as default. This, likely, confuses user, as they expect the same behaviour, i.e they expect that if they name their output file as "dataset.jsonl.zst" then the compression would be inferred as "zstd" and file will be compressed before writing.

Moreover, while it is probably outside of the scope of this pull request, compression argument needs to be capable of taking dict as input (along with str), as it does in pandas, in order to allow user to specify compression parameters. Current implementation will likely fail with NotImplementedError, as it expects either None or str specifying compression algo.

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[Rexhaif]

Can someone check this out?

[lhoestq]

Thanks for the improvement ! :)

I agree it is more intuitive to fit pandas' behavior

[github-actions]

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric	read_batch_formatted_as_numpy after write_array2d	read_batch_formatted_as_numpy after write_flattened_sequence	read_batch_formatted_as_numpy after write_nested_sequence	read_batch_unformated after write_array2d	read_batch_unformated after write_flattened_sequence	read_batch_unformated after write_nested_sequence	read_col_formatted_as_numpy after write_array2d	read_col_formatted_as_numpy after write_flattened_sequence	read_col_formatted_as_numpy after write_nested_sequence	read_col_unformated after write_array2d	read_col_unformated after write_flattened_sequence	read_col_unformated after write_nested_sequence	read_formatted_as_numpy after write_array2d	read_formatted_as_numpy after write_flattened_sequence	read_formatted_as_numpy after write_nested_sequence	read_unformated after write_array2d	read_unformated after write_flattened_sequence	read_unformated after write_nested_sequence	write_array2d	write_flattened_sequence	write_nested_sequence
new / old (diff)	0.005008 / 0.011353 (-0.006345)	0.003267 / 0.011008 (-0.007741)	0.064140 / 0.038508 (0.025632)	0.027419 / 0.023109 (0.004309)	0.246692 / 0.275898 (-0.029206)	0.271303 / 0.323480 (-0.052177)	0.004127 / 0.007986 (-0.003859)	0.002698 / 0.004328 (-0.001631)	0.050415 / 0.004250 (0.046165)	0.040323 / 0.037052 (0.003271)	0.265738 / 0.258489 (0.007249)	0.291556 / 0.293841 (-0.002285)	0.027924 / 0.128546 (-0.100622)	0.010206 / 0.075646 (-0.065441)	0.207106 / 0.419271 (-0.212165)	0.036087 / 0.043533 (-0.007446)	0.250412 / 0.255139 (-0.004727)	0.269014 / 0.283200 (-0.014186)	0.018102 / 0.141683 (-0.123581)	1.135137 / 1.452155 (-0.317018)	1.177718 / 1.492716 (-0.314998)

Benchmark: benchmark_getitem_100B.json

metric	get_batch_of_1024_random_rows	get_batch_of_1024_rows	get_first_row	get_last_row
new / old (diff)	0.095557 / 0.018006 (0.077550)	0.306235 / 0.000490 (0.305745)	0.000214 / 0.000200 (0.000014)	0.000044 / 0.000054 (-0.000011)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018217 / 0.037411 (-0.019194)	0.060993 / 0.014526 (0.046467)	0.072748 / 0.176557 (-0.103808)	0.119357 / 0.737135 (-0.617778)	0.073719 / 0.296338 (-0.222619)

Benchmark: benchmark_iterating.json

metric	read 5000	read 50000	read_batch 50000 10	read_batch 50000 100	read_batch 50000 1000	read_formatted numpy 5000	read_formatted pandas 5000	read_formatted tensorflow 5000	read_formatted torch 5000	read_formatted_batch numpy 5000 10	read_formatted_batch numpy 5000 1000	shuffled read 5000	shuffled read 50000	shuffled read_batch 50000 10	shuffled read_batch 50000 100	shuffled read_batch 50000 1000	shuffled read_formatted numpy 5000	shuffled read_formatted_batch numpy 5000 10	shuffled read_formatted_batch numpy 5000 1000
new / old (diff)	0.295924 / 0.215209 (0.080715)	2.901071 / 2.077655 (0.823417)	1.497316 / 1.504120 (-0.006804)	1.371232 / 1.541195 (-0.169962)	1.395643 / 1.468490 (-0.072847)	0.577548 / 4.584777 (-4.007229)	2.383813 / 3.745712 (-1.361899)	2.764451 / 5.269862 (-2.505411)	1.733074 / 4.565676 (-2.832602)	0.063730 / 0.424275 (-0.360545)	0.004933 / 0.007607 (-0.002674)	0.347135 / 0.226044 (0.121090)	3.390814 / 2.268929 (1.121885)	1.849454 / 55.444624 (-53.595170)	1.561801 / 6.876477 (-5.314675)	1.587818 / 2.142072 (-0.554254)	0.652061 / 4.805227 (-4.153166)	0.117195 / 6.500664 (-6.383469)	0.041922 / 0.075469 (-0.033548)

Benchmark: benchmark_map_filter.json

metric	filter	map fast-tokenizer batched	map identity	map identity batched	map no-op batched	map no-op batched numpy	map no-op batched pandas	map no-op batched pytorch	map no-op batched tensorflow
new / old (diff)	0.949050 / 1.841788 (-0.892738)	11.353664 / 8.074308 (3.279355)	9.261581 / 10.191392 (-0.929811)	0.140374 / 0.680424 (-0.540050)	0.014254 / 0.534201 (-0.519946)	0.288124 / 0.579283 (-0.291159)	0.262888 / 0.434364 (-0.171476)	0.330774 / 0.540337 (-0.209564)	0.444777 / 1.386936 (-0.942159)

PyArrow==latest

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric	read_batch_formatted_as_numpy after write_array2d	read_batch_formatted_as_numpy after write_flattened_sequence	read_batch_formatted_as_numpy after write_nested_sequence	read_batch_unformated after write_array2d	read_batch_unformated after write_flattened_sequence	read_batch_unformated after write_nested_sequence	read_col_formatted_as_numpy after write_array2d	read_col_formatted_as_numpy after write_flattened_sequence	read_col_formatted_as_numpy after write_nested_sequence	read_col_unformated after write_array2d	read_col_unformated after write_flattened_sequence	read_col_unformated after write_nested_sequence	read_formatted_as_numpy after write_array2d	read_formatted_as_numpy after write_flattened_sequence	read_formatted_as_numpy after write_nested_sequence	read_unformated after write_array2d	read_unformated after write_flattened_sequence	read_unformated after write_nested_sequence	write_array2d	write_flattened_sequence	write_nested_sequence
new / old (diff)	0.005162 / 0.011353 (-0.006191)	0.003418 / 0.011008 (-0.007591)	0.049764 / 0.038508 (0.011256)	0.029336 / 0.023109 (0.006226)	0.278570 / 0.275898 (0.002672)	0.300676 / 0.323480 (-0.022804)	0.004292 / 0.007986 (-0.003694)	0.002745 / 0.004328 (-0.001584)	0.049194 / 0.004250 (0.044943)	0.044036 / 0.037052 (0.006984)	0.299258 / 0.258489 (0.040769)	0.324451 / 0.293841 (0.030610)	0.029777 / 0.128546 (-0.098769)	0.010426 / 0.075646 (-0.065221)	0.057267 / 0.419271 (-0.362004)	0.051276 / 0.043533 (0.007743)	0.278012 / 0.255139 (0.022873)	0.297099 / 0.283200 (0.013899)	0.018340 / 0.141683 (-0.123343)	1.179255 / 1.452155 (-0.272899)	1.231536 / 1.492716 (-0.261180)

Benchmark: benchmark_getitem_100B.json

metric	get_batch_of_1024_random_rows	get_batch_of_1024_rows	get_first_row	get_last_row
new / old (diff)	0.092546 / 0.018006 (0.074540)	0.299959 / 0.000490 (0.299469)	0.000220 / 0.000200 (0.000020)	0.000043 / 0.000054 (-0.000012)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.021657 / 0.037411 (-0.015755)	0.075440 / 0.014526 (0.060914)	0.086246 / 0.176557 (-0.090310)	0.126511 / 0.737135 (-0.610624)	0.091303 / 0.296338 (-0.205036)

Benchmark: benchmark_iterating.json

metric	read 5000	read 50000	read_batch 50000 10	read_batch 50000 100	read_batch 50000 1000	read_formatted numpy 5000	read_formatted pandas 5000	read_formatted tensorflow 5000	read_formatted torch 5000	read_formatted_batch numpy 5000 10	read_formatted_batch numpy 5000 1000	shuffled read 5000	shuffled read 50000	shuffled read_batch 50000 10	shuffled read_batch 50000 100	shuffled read_batch 50000 1000	shuffled read_formatted numpy 5000	shuffled read_formatted_batch numpy 5000 10	shuffled read_formatted_batch numpy 5000 1000
new / old (diff)	0.294775 / 0.215209 (0.079566)	2.868973 / 2.077655 (0.791319)	1.666971 / 1.504120 (0.162851)	1.545680 / 1.541195 (0.004486)	1.559983 / 1.468490 (0.091493)	0.572191 / 4.584777 (-4.012586)	2.429317 / 3.745712 (-1.316395)	2.673334 / 5.269862 (-2.596527)	1.758114 / 4.565676 (-2.807563)	0.063766 / 0.424275 (-0.360509)	0.005070 / 0.007607 (-0.002537)	0.345488 / 0.226044 (0.119443)	3.464525 / 2.268929 (1.195596)	1.975717 / 55.444624 (-53.468908)	1.686671 / 6.876477 (-5.189806)	1.825434 / 2.142072 (-0.316638)	0.655853 / 4.805227 (-4.149374)	0.116372 / 6.500664 (-6.384292)	0.040647 / 0.075469 (-0.034822)

Benchmark: benchmark_map_filter.json

metric	filter	map fast-tokenizer batched	map identity	map identity batched	map no-op batched	map no-op batched numpy	map no-op batched pandas	map no-op batched pytorch	map no-op batched tensorflow
new / old (diff)	1.014080 / 1.841788 (-0.827707)	12.038496 / 8.074308 (3.964188)	10.354536 / 10.191392 (0.163144)	0.130285 / 0.680424 (-0.550139)	0.015514 / 0.534201 (-0.518687)	0.284743 / 0.579283 (-0.294540)	0.280275 / 0.434364 (-0.154088)	0.321175 / 0.540337 (-0.219162)	0.425840 / 1.386936 (-0.961096)