Preserve order of configs and splits when using Parquet exports
State: closed | Author: albertvillanova (MEMBER)

Preserve order of configs and splits, as defined in dataset infos.

Fix #6521.

Comments: The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6526). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.005101 / 0.011353 (-0.006252) | 0.003471 / 0.011008 (-0.007537) | 0.062293 / 0.038508 (0.023785) | 0.032650 / 0.023109 (0.009541) | 0.249241 / 0.275898 (-0.026657) | 0.277079 / 0.323480 (-0.046400) | 0.002971 / 0.007986 (-0.005015) | 0.002637 / 0.004328 (-0.001691) | 0.048415 / 0.004250 (0.044165) | 0.042832 / 0.037052 (0.005779) | 0.247840 / 0.258489 (-0.010649) | 0.283994 / 0.293841 (-0.009847) | 0.027764 / 0.128546 (-0.100782) | 0.010544 / 0.075646 (-0.065102) | 0.208810 / 0.419271 (-0.210462) | 0.035744 / 0.043533 (-0.007789) | 0.252811 / 0.255139 (-0.002328) | 0.276163 / 0.283200 (-0.007036) | 0.018581 / 0.141683 (-0.123102) | 1.130043 / 1.452155 (-0.322112) | 1.194298 / 1.492716 (-0.298418) |

### 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) | 0.004488 / 0.018006 (-0.013518) | 0.302072 / 0.000490 (0.301582) | 0.000211 / 0.000200 (0.000012) | 0.000044 / 0.000054 (-0.000010) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.017799 / 0.037411 (-0.019613) | 0.061146 / 0.014526 (0.046620) | 0.081796 / 0.176557 (-0.094761) | 0.120407 / 0.737135 (-0.616729) | 0.075211 / 0.296338 (-0.221127) |

### 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) | 0.295349 / 0.215209 (0.080140) | 2.953511 / 2.077655 (0.875857) | 1.495332 / 1.504120 (-0.008788) | 1.364144 / 1.541195 (-0.177051) | 1.429562 / 1.468490 (-0.038928) | 0.574325 / 4.584777 (-4.010452) | 2.384352 / 3.745712 (-1.361360) | 2.843625 / 5.269862 (-2.426236) | 1.806802 / 4.565676 (-2.758875) | 0.065076 / 0.424275 (-0.359199) | 0.004970 / 0.007607 (-0.002638) | 0.339935 / 0.226044 (0.113891) | 3.375103 / 2.268929 (1.106175) | 1.822921 / 55.444624 (-53.621703) | 1.546126 / 6.876477 (-5.330350) | 1.573630 / 2.142072 (-0.568442) | 0.655081 / 4.805227 (-4.150146) | 0.122446 / 6.500664 (-6.378218) | 0.042220 / 0.075469 (-0.033249) |

### 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) | 0.942127 / 1.841788 (-0.899661) | 11.470401 / 8.074308 (3.396093) | 10.025961 / 10.191392 (-0.165431) | 0.129087 / 0.680424 (-0.551337) | 0.014141 / 0.534201 (-0.520060) | 0.285470 / 0.579283 (-0.293813) | 0.266755 / 0.434364 (-0.167608) | 0.323391 / 0.540337 (-0.216947) | 0.427645 / 1.386936 (-0.959291) |

</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) | 0.005578 / 0.011353 (-0.005775) | 0.003734 / 0.011008 (-0.007274) | 0.049200 / 0.038508 (0.010692) | 0.030981 / 0.023109 (0.007872) | 0.281195 / 0.275898 (0.005297) | 0.309950 / 0.323480 (-0.013530) | 0.004046 / 0.007986 (-0.003939) | 0.002709 / 0.004328 (-0.001620) | 0.048505 / 0.004250 (0.044254) | 0.046245 / 0.037052 (0.009193) | 0.280130 / 0.258489 (0.021641) | 0.313739 / 0.293841 (0.019898) | 0.029828 / 0.128546 (-0.098718) | 0.011152 / 0.075646 (-0.064495) | 0.057753 / 0.419271 (-0.361518) | 0.055112 / 0.043533 (0.011580) | 0.281861 / 0.255139 (0.026722) | 0.304402 / 0.283200 (0.021203) | 0.019931 / 0.141683 (-0.121752) | 1.150585 / 1.452155 (-0.301570) | 1.217850 / 1.492716 (-0.274866) |

### 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) | 0.091552 / 0.018006 (0.073546) | 0.301772 / 0.000490 (0.301282) | 0.000225 / 0.000200 (0.000025) | 0.000046 / 0.000054 (-0.000009) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023189 / 0.037411 (-0.014223) | 0.078741 / 0.014526 (0.064216) | 0.092320 / 0.176557 (-0.084236) | 0.129636 / 0.737135 (-0.607500) | 0.091673 / 0.296338 (-0.204665) |

### 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) | 0.298542 / 0.215209 (0.083333) | 2.899358 / 2.077655 (0.821703) | 1.673896 / 1.504120 (0.169776) | 1.489518 / 1.541195 (-0.051677) | 1.542853 / 1.468490 (0.074363) | 0.559843 / 4.584777 (-4.024934) | 2.422101 / 3.745712 (-1.323611) | 2.844592 / 5.269862 (-2.425270) | 1.794527 / 4.565676 (-2.771150) | 0.064615 / 0.424275 (-0.359660) | 0.005078 / 0.007607 (-0.002530) | 0.355112 / 0.226044 (0.129068) | 3.462129 / 2.268929 (1.193200) | 1.975393 / 55.444624 (-53.469231) | 1.706513 / 6.876477 (-5.169963) | 1.716954 / 2.142072 (-0.425118) | 0.642094 / 4.805227 (-4.163133) | 0.119215 / 6.500664 (-6.381449) | 0.041941 / 0.075469 (-0.033528) |

### 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) | 0.986774 / 1.841788 (-0.855014) | 12.702049 / 8.074308 (4.627741) | 11.727663 / 10.191392 (1.536271) | 0.135008 / 0.680424 (-0.545416) | 0.016055 / 0.534201 (-0.518146) | 0.293564 / 0.579283 (-0.285719) | 0.284884 / 0.434364 (-0.149480) | 0.332524 / 0.540337 (-0.207814) | 0.425392 / 1.386936 (-0.961544) |

</details>
</details>

![](https://cml.dev/watermark.png#7b5fc585fcaf77b92839e82d0ce2c2fbf0d9ea95 "CML watermark")