Change default compression argument for JsonDatasetWriter
State: closed

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"](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html) 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.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6659). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Can someone check this out?

<details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) |

</details>
</details>

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