Title: [docs] Split pattern search order
State: closed

This PR addresses #5681 about the order of split patterns 🤗 Datasets searches for when generating dataset splits.

Comments:
_The documentation is not available anymore as the PR was closed or merged._

<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) | 0.007841 / 0.011353 (-0.003512) | 0.005640 / 0.011008 (-0.005368) | 0.096465 / 0.038508 (0.057957) | 0.036476 / 0.023109 (0.013367) | 0.306431 / 0.275898 (0.030533) | 0.339545 / 0.323480 (0.016065) | 0.006064 / 0.007986 (-0.001922) | 0.004404 / 0.004328 (0.000076) | 0.073130 / 0.004250 (0.068879) | 0.052765 / 0.037052 (0.015713) | 0.309895 / 0.258489 (0.051406) | 0.354037 / 0.293841 (0.060196) | 0.037127 / 0.128546 (-0.091420) | 0.012387 / 0.075646 (-0.063260) | 0.333503 / 0.419271 (-0.085769) | 0.059799 / 0.043533 (0.016266) | 0.305496 / 0.255139 (0.050358) | 0.324122 / 0.283200 (0.040922) | 0.107007 / 0.141683 (-0.034676) | 1.416743 / 1.452155 (-0.035411) | 1.520772 / 1.492716 (0.028055) |

### 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) | 0.261233 / 0.018006 (0.243227) | 0.573806 / 0.000490 (0.573316) | 0.000390 / 0.000200 (0.000190) | 0.000058 / 0.000054 (0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027672 / 0.037411 (-0.009740) | 0.112803 / 0.014526 (0.098278) | 0.121085 / 0.176557 (-0.055471) | 0.176056 / 0.737135 (-0.561080) | 0.127171 / 0.296338 (-0.169167) |

### 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) | 0.414756 / 0.215209 (0.199547) | 4.148743 / 2.077655 (2.071088) | 1.883940 / 1.504120 (0.379820) | 1.698771 / 1.541195 (0.157576) | 1.811926 / 1.468490 (0.343436) | 0.708293 / 4.584777 (-3.876484) | 3.780456 / 3.745712 (0.034744) | 2.098556 / 5.269862 (-3.171306) | 1.323512 / 4.565676 (-3.242164) | 0.086253 / 0.424275 (-0.338022) | 0.012587 / 0.007607 (0.004980) | 0.514824 / 0.226044 (0.288779) | 5.157415 / 2.268929 (2.888487) | 2.382519 / 55.444624 (-53.062105) | 2.014539 / 6.876477 (-4.861938) | 2.215239 / 2.142072 (0.073166) | 0.847178 / 4.805227 (-3.958049) | 0.170053 / 6.500664 (-6.330611) | 0.066461 / 0.075469 (-0.009008) |

### 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) | 1.199056 / 1.841788 (-0.642732) | 15.244999 / 8.074308 (7.170691) | 14.661593 / 10.191392 (4.470201) | 0.168855 / 0.680424 (-0.511569) | 0.017889 / 0.534201 (-0.516312) | 0.424961 / 0.579283 (-0.154322) | 0.428632 / 0.434364 (-0.005732) | 0.502680 / 0.540337 (-0.037658) | 0.597827 / 1.386936 (-0.789109) |

</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) | 0.007749 / 0.011353 (-0.003604) | 0.005527 / 0.011008 (-0.005482) | 0.074774 / 0.038508 (0.036266) | 0.035367 / 0.023109 (0.012258) | 0.340594 / 0.275898 (0.064696) | 0.373970 / 0.323480 (0.050490) | 0.006094 / 0.007986 (-0.001892) | 0.004428 / 0.004328 (0.000100) | 0.074120 / 0.004250 (0.069869) | 0.054852 / 0.037052 (0.017800) | 0.357173 / 0.258489 (0.098684) | 0.388877 / 0.293841 (0.095036) | 0.037002 / 0.128546 (-0.091545) | 0.012337 / 0.075646 (-0.063309) | 0.086962 / 0.419271 (-0.332310) | 0.050370 / 0.043533 (0.006837) | 0.342989 / 0.255139 (0.087850) | 0.358065 / 0.283200 (0.074865) | 0.111063 / 0.141683 (-0.030620) | 1.516704 / 1.452155 (0.064549) | 1.634359 / 1.492716 (0.141643) |

### 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) | 0.261493 / 0.018006 (0.243487) | 0.566288 / 0.000490 (0.565799) | 0.000439 / 0.000200 (0.000239) | 0.000056 / 0.000054 (0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030426 / 0.037411 (-0.006985) | 0.114606 / 0.014526 (0.100080) | 0.126134 / 0.176557 (-0.050423) | 0.175324 / 0.737135 (-0.561812) | 0.132766 / 0.296338 (-0.163573) |

### 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) | 0.426785 / 0.215209 (0.211576) | 4.243555 / 2.077655 (2.165900) | 2.089631 / 1.504120 (0.585511) | 1.994562 / 1.541195 (0.453367) | 2.140284 / 1.468490 (0.671794) | 0.698645 / 4.584777 (-3.886132) | 3.807471 / 3.745712 (0.061759) | 3.275343 / 5.269862 (-1.994519) | 1.796756 / 4.565676 (-2.768921) | 0.085986 / 0.424275 (-0.338289) | 0.012213 / 0.007607 (0.004606) | 0.536815 / 0.226044 (0.310771) | 5.344611 / 2.268929 (3.075683) | 2.498578 / 55.444624 (-52.946047) | 2.153260 / 6.876477 (-4.723217) | 2.251310 / 2.142072 (0.109237) | 0.839104 / 4.805227 (-3.966123) | 0.169639 / 6.500664 (-6.331025) | 0.065880 / 0.075469 (-0.009589) |

### 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) | 1.268610 / 1.841788 (-0.573178) | 15.624915 / 8.074308 (7.550606) | 15.163684 / 10.191392 (4.972292) | 0.172992 / 0.680424 (-0.507432) | 0.018154 / 0.534201 (-0.516047) | 0.440485 / 0.579283 (-0.138798) | 0.431949 / 0.434364 (-0.002415) | 0.547935 / 0.540337 (0.007597) | 0.662442 / 1.386936 (-0.724494) |

</details>
</details>

![](https://cml.dev/watermark.png#5c8a6ba43c4aaa0ca0665d8dadd87ef33e28e8e4 "CML watermark")