Title: Deprecate `download_custom`
State: closed

Deprecate `DownloadManager.download_custom`. Users should use `fsspec` URLs (cacheable) or make direct requests with `fsspec`/`requests` (not cacheable) instead.

We should deprecate this method as it's not compatible with streaming, and implementing the streaming version of it is hard/impossible. There have been requests to implement the streaming version of this method on the forum, but the reason for this seems to be a tip in the docs that "promotes" this method (this PR removes it). 



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) | 0.007498 / 0.011353 (-0.003855) | 0.004158 / 0.011008 (-0.006850) | 0.087568 / 0.038508 (0.049060) | 0.083265 / 0.023109 (0.060156) | 0.378505 / 0.275898 (0.102607) | 0.399025 / 0.323480 (0.075545) | 0.006173 / 0.007986 (-0.001813) | 0.003743 / 0.004328 (-0.000586) | 0.071958 / 0.004250 (0.067707) | 0.059323 / 0.037052 (0.022271) | 0.377084 / 0.258489 (0.118595) | 0.408358 / 0.293841 (0.114517) | 0.035191 / 0.128546 (-0.093356) | 0.009408 / 0.075646 (-0.066238) | 0.312587 / 0.419271 (-0.106685) | 0.058073 / 0.043533 (0.014540) | 0.381977 / 0.255139 (0.126838) | 0.395611 / 0.283200 (0.112411) | 0.024191 / 0.141683 (-0.117491) | 1.572735 / 1.452155 (0.120581) | 1.687186 / 1.492716 (0.194470) |

### 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) | 0.208886 / 0.018006 (0.190879) | 0.474625 / 0.000490 (0.474135) | 0.006261 / 0.000200 (0.006061) | 0.000093 / 0.000054 (0.000038) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031401 / 0.037411 (-0.006011) | 0.086433 / 0.014526 (0.071907) | 0.108405 / 0.176557 (-0.068152) | 0.174564 / 0.737135 (-0.562571) | 0.099932 / 0.296338 (-0.196407) |

### 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) | 0.407059 / 0.215209 (0.191850) | 4.102056 / 2.077655 (2.024401) | 1.975397 / 1.504120 (0.471277) | 1.807117 / 1.541195 (0.265922) | 1.908667 / 1.468490 (0.440177) | 0.525880 / 4.584777 (-4.058897) | 3.899639 / 3.745712 (0.153927) | 4.358664 / 5.269862 (-0.911198) | 2.586185 / 4.565676 (-1.979492) | 0.061967 / 0.424275 (-0.362308) | 0.007656 / 0.007607 (0.000049) | 0.504851 / 0.226044 (0.278807) | 5.004429 / 2.268929 (2.735500) | 2.515540 / 55.444624 (-52.929084) | 2.183142 / 6.876477 (-4.693334) | 2.369835 / 2.142072 (0.227763) | 0.623527 / 4.805227 (-4.181700) | 0.145105 / 6.500664 (-6.355559) | 0.063924 / 0.075469 (-0.011546) |

### 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) | 1.472661 / 1.841788 (-0.369126) | 21.781655 / 8.074308 (13.707347) | 15.628820 / 10.191392 (5.437428) | 0.182342 / 0.680424 (-0.498082) | 0.021139 / 0.534201 (-0.513062) | 0.438610 / 0.579283 (-0.140673) | 0.451343 / 0.434364 (0.016979) | 0.563320 / 0.540337 (0.022983) | 0.740976 / 1.386936 (-0.645960) |

</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) | 0.007492 / 0.011353 (-0.003861) | 0.004429 / 0.011008 (-0.006579) | 0.068517 / 0.038508 (0.030008) | 0.078533 / 0.023109 (0.055424) | 0.383530 / 0.275898 (0.107632) | 0.435061 / 0.323480 (0.111581) | 0.005955 / 0.007986 (-0.002030) | 0.003645 / 0.004328 (-0.000683) | 0.068792 / 0.004250 (0.064541) | 0.062452 / 0.037052 (0.025399) | 0.408768 / 0.258489 (0.150279) | 0.438538 / 0.293841 (0.144697) | 0.032038 / 0.128546 (-0.096508) | 0.009196 / 0.075646 (-0.066450) | 0.074495 / 0.419271 (-0.344776) | 0.051322 / 0.043533 (0.007789) | 0.394458 / 0.255139 (0.139319) | 0.424763 / 0.283200 (0.141564) | 0.024890 / 0.141683 (-0.116793) | 1.568322 / 1.452155 (0.116167) | 1.703903 / 1.492716 (0.211187) |

### 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) | 0.249630 / 0.018006 (0.231624) | 0.471412 / 0.000490 (0.470923) | 0.000435 / 0.000200 (0.000235) | 0.000060 / 0.000054 (0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.033054 / 0.037411 (-0.004358) | 0.100150 / 0.014526 (0.085624) | 0.101704 / 0.176557 (-0.074853) | 0.164031 / 0.737135 (-0.573104) | 0.112497 / 0.296338 (-0.183841) |

### 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) | 0.487150 / 0.215209 (0.271941) | 4.662335 / 2.077655 (2.584681) | 2.477285 / 1.504120 (0.973165) | 2.294033 / 1.541195 (0.752838) | 2.380143 / 1.468490 (0.911653) | 0.519182 / 4.584777 (-4.065595) | 3.983589 / 3.745712 (0.237877) | 3.669895 / 5.269862 (-1.599967) | 2.267147 / 4.565676 (-2.298529) | 0.063300 / 0.424275 (-0.360975) | 0.008839 / 0.007607 (0.001232) | 0.566766 / 0.226044 (0.340721) | 5.533475 / 2.268929 (3.264546) | 3.033412 / 55.444624 (-52.411212) | 2.701793 / 6.876477 (-4.174684) | 2.899444 / 2.142072 (0.757372) | 0.614236 / 4.805227 (-4.190991) | 0.139533 / 6.500664 (-6.361131) | 0.067537 / 0.075469 (-0.007932) |

### 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) | 1.505572 / 1.841788 (-0.336216) | 22.859062 / 8.074308 (14.784754) | 15.044777 / 10.191392 (4.853385) | 0.169153 / 0.680424 (-0.511271) | 0.021027 / 0.534201 (-0.513174) | 0.447979 / 0.579283 (-0.131304) | 0.460676 / 0.434364 (0.026312) | 0.506327 / 0.540337 (-0.034010) | 0.737880 / 1.386936 (-0.649057) |

</details>
</details>

![](https://cml.dev/watermark.png#db7180eb7e3ebf52b9d1f2c6629db6d92d8a29ba "CML watermark")


<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) | 0.006118 / 0.011353 (-0.005235) | 0.003692 / 0.011008 (-0.007316) | 0.080606 / 0.038508 (0.042098) | 0.062014 / 0.023109 (0.038905) | 0.391886 / 0.275898 (0.115988) | 0.423978 / 0.323480 (0.100498) | 0.004968 / 0.007986 (-0.003017) | 0.002911 / 0.004328 (-0.001417) | 0.062867 / 0.004250 (0.058617) | 0.049493 / 0.037052 (0.012441) | 0.395656 / 0.258489 (0.137167) | 0.432406 / 0.293841 (0.138565) | 0.027242 / 0.128546 (-0.101304) | 0.007938 / 0.075646 (-0.067709) | 0.261703 / 0.419271 (-0.157569) | 0.045922 / 0.043533 (0.002389) | 0.391544 / 0.255139 (0.136405) | 0.417902 / 0.283200 (0.134703) | 0.021339 / 0.141683 (-0.120344) | 1.508391 / 1.452155 (0.056236) | 1.518970 / 1.492716 (0.026254) |

### 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) | 0.181159 / 0.018006 (0.163153) | 0.431402 / 0.000490 (0.430912) | 0.003849 / 0.000200 (0.003649) | 0.000068 / 0.000054 (0.000014) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024498 / 0.037411 (-0.012914) | 0.072758 / 0.014526 (0.058233) | 0.084910 / 0.176557 (-0.091646) | 0.148314 / 0.737135 (-0.588821) | 0.085212 / 0.296338 (-0.211126) |

### 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) | 0.386693 / 0.215209 (0.171484) | 3.852652 / 2.077655 (1.774997) | 1.891758 / 1.504120 (0.387638) | 1.718793 / 1.541195 (0.177598) | 1.747595 / 1.468490 (0.279104) | 0.498593 / 4.584777 (-4.086184) | 3.057907 / 3.745712 (-0.687805) | 4.728449 / 5.269862 (-0.541413) | 2.966368 / 4.565676 (-1.599308) | 0.057538 / 0.424275 (-0.366737) | 0.006415 / 0.007607 (-0.001192) | 0.461652 / 0.226044 (0.235608) | 4.625944 / 2.268929 (2.357015) | 2.306938 / 55.444624 (-53.137686) | 1.974670 / 6.876477 (-4.901806) | 2.146327 / 2.142072 (0.004254) | 0.585033 / 4.805227 (-4.220195) | 0.125936 / 6.500664 (-6.374728) | 0.062365 / 0.075469 (-0.013104) |

### 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) | 1.263415 / 1.841788 (-0.578373) | 18.380651 / 8.074308 (10.306343) | 13.853410 / 10.191392 (3.662018) | 0.144674 / 0.680424 (-0.535749) | 0.016833 / 0.534201 (-0.517368) | 0.330812 / 0.579283 (-0.248471) | 0.357553 / 0.434364 (-0.076810) | 0.383529 / 0.540337 (-0.156809) | 0.558923 / 1.386936 (-0.828013) |

</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) | 0.006074 / 0.011353 (-0.005278) | 0.003655 / 0.011008 (-0.007353) | 0.062981 / 0.038508 (0.024473) | 0.061457 / 0.023109 (0.038348) | 0.366471 / 0.275898 (0.090573) | 0.408463 / 0.323480 (0.084983) | 0.004854 / 0.007986 (-0.003132) | 0.002916 / 0.004328 (-0.001412) | 0.062745 / 0.004250 (0.058494) | 0.051136 / 0.037052 (0.014084) | 0.380313 / 0.258489 (0.121824) | 0.416945 / 0.293841 (0.123104) | 0.027228 / 0.128546 (-0.101318) | 0.008031 / 0.075646 (-0.067615) | 0.067941 / 0.419271 (-0.351331) | 0.042886 / 0.043533 (-0.000647) | 0.370112 / 0.255139 (0.114973) | 0.397111 / 0.283200 (0.113911) | 0.023063 / 0.141683 (-0.118620) | 1.476955 / 1.452155 (0.024800) | 1.534783 / 1.492716 (0.042066) |

### 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) | 0.231462 / 0.018006 (0.213456) | 0.439559 / 0.000490 (0.439069) | 0.000364 / 0.000200 (0.000164) | 0.000056 / 0.000054 (0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026925 / 0.037411 (-0.010486) | 0.079623 / 0.014526 (0.065097) | 0.088694 / 0.176557 (-0.087862) | 0.143163 / 0.737135 (-0.593972) | 0.089900 / 0.296338 (-0.206438) |

### 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) | 0.451429 / 0.215209 (0.236220) | 4.510723 / 2.077655 (2.433069) | 2.491853 / 1.504120 (0.987733) | 2.334670 / 1.541195 (0.793475) | 2.395519 / 1.468490 (0.927029) | 0.501369 / 4.584777 (-4.083408) | 3.014019 / 3.745712 (-0.731693) | 2.809199 / 5.269862 (-2.460662) | 1.842195 / 4.565676 (-2.723481) | 0.057675 / 0.424275 (-0.366600) | 0.006742 / 0.007607 (-0.000865) | 0.524402 / 0.226044 (0.298358) | 5.245296 / 2.268929 (2.976367) | 2.957990 / 55.444624 (-52.486634) | 2.649807 / 6.876477 (-4.226670) | 2.755909 / 2.142072 (0.613836) | 0.589610 / 4.805227 (-4.215617) | 0.125708 / 6.500664 (-6.374956) | 0.062237 / 0.075469 (-0.013232) |

### 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) | 1.362758 / 1.841788 (-0.479030) | 18.343694 / 8.074308 (10.269386) | 13.621521 / 10.191392 (3.430129) | 0.128866 / 0.680424 (-0.551558) | 0.016608 / 0.534201 (-0.517593) | 0.333071 / 0.579283 (-0.246212) | 0.341917 / 0.434364 (-0.092447) | 0.381075 / 0.540337 (-0.159263) | 0.512485 / 1.386936 (-0.874451) |

</details>
</details>

![](https://cml.dev/watermark.png#ab3f0165d4a2a8ab1aee1ebc4628893e17e27387 "CML watermark")


I forgot to mention this in the initial comment, but only one public dataset (excluding gated) uses this method - `pg19`, which I just fixed.


<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) | 0.007838 / 0.011353 (-0.003515) | 0.004791 / 0.011008 (-0.006217) | 0.102596 / 0.038508 (0.064088) | 0.087678 / 0.023109 (0.064569) | 0.373858 / 0.275898 (0.097960) | 0.416643 / 0.323480 (0.093163) | 0.006147 / 0.007986 (-0.001839) | 0.003837 / 0.004328 (-0.000491) | 0.076706 / 0.004250 (0.072456) | 0.063449 / 0.037052 (0.026396) | 0.378392 / 0.258489 (0.119903) | 0.431768 / 0.293841 (0.137927) | 0.036648 / 0.128546 (-0.091898) | 0.010042 / 0.075646 (-0.065604) | 0.350277 / 0.419271 (-0.068995) | 0.062892 / 0.043533 (0.019359) | 0.376151 / 0.255139 (0.121012) | 0.420929 / 0.283200 (0.137729) | 0.027816 / 0.141683 (-0.113867) | 1.791607 / 1.452155 (0.339452) | 1.903045 / 1.492716 (0.410328) |

### 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) | 0.224688 / 0.018006 (0.206682) | 0.491941 / 0.000490 (0.491451) | 0.004482 / 0.000200 (0.004282) | 0.000102 / 0.000054 (0.000048) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.033495 / 0.037411 (-0.003917) | 0.099855 / 0.014526 (0.085329) | 0.114593 / 0.176557 (-0.061964) | 0.190947 / 0.737135 (-0.546189) | 0.116202 / 0.296338 (-0.180136) |

### 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) | 0.488581 / 0.215209 (0.273372) | 4.869531 / 2.077655 (2.791876) | 2.527920 / 1.504120 (1.023800) | 2.340021 / 1.541195 (0.798826) | 2.432661 / 1.468490 (0.964171) | 0.569646 / 4.584777 (-4.015131) | 4.392036 / 3.745712 (0.646324) | 4.987253 / 5.269862 (-0.282608) | 2.866604 / 4.565676 (-1.699073) | 0.067393 / 0.424275 (-0.356882) | 0.008759 / 0.007607 (0.001152) | 0.584327 / 0.226044 (0.358283) | 5.853000 / 2.268929 (3.584072) | 3.206721 / 55.444624 (-52.237904) | 2.730867 / 6.876477 (-4.145610) | 2.944814 / 2.142072 (0.802742) | 0.703336 / 4.805227 (-4.101891) | 0.173985 / 6.500664 (-6.326679) | 0.075333 / 0.075469 (-0.000137) |

### 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) | 1.519755 / 1.841788 (-0.322033) | 22.918038 / 8.074308 (14.843730) | 17.211160 / 10.191392 (7.019768) | 0.196941 / 0.680424 (-0.483483) | 0.021833 / 0.534201 (-0.512368) | 0.476835 / 0.579283 (-0.102448) | 0.464513 / 0.434364 (0.030149) | 0.559180 / 0.540337 (0.018843) | 0.748232 / 1.386936 (-0.638704) |

</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) | 0.008461 / 0.011353 (-0.002892) | 0.004799 / 0.011008 (-0.006209) | 0.077466 / 0.038508 (0.038958) | 0.103562 / 0.023109 (0.080453) | 0.453661 / 0.275898 (0.177763) | 0.531126 / 0.323480 (0.207647) | 0.006618 / 0.007986 (-0.001367) | 0.004048 / 0.004328 (-0.000280) | 0.075446 / 0.004250 (0.071196) | 0.072815 / 0.037052 (0.035762) | 0.497145 / 0.258489 (0.238656) | 0.533828 / 0.293841 (0.239987) | 0.037657 / 0.128546 (-0.090890) | 0.010139 / 0.075646 (-0.065507) | 0.083759 / 0.419271 (-0.335512) | 0.061401 / 0.043533 (0.017868) | 0.441785 / 0.255139 (0.186646) | 0.491678 / 0.283200 (0.208479) | 0.033100 / 0.141683 (-0.108583) | 1.753612 / 1.452155 (0.301458) | 1.838956 / 1.492716 (0.346240) |

### 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) | 0.395023 / 0.018006 (0.377017) | 0.509362 / 0.000490 (0.508872) | 0.060742 / 0.000200 (0.060542) | 0.000545 / 0.000054 (0.000491) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.039327 / 0.037411 (0.001916) | 0.117345 / 0.014526 (0.102819) | 0.124540 / 0.176557 (-0.052017) | 0.200743 / 0.737135 (-0.536392) | 0.126750 / 0.296338 (-0.169589) |

### 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) | 0.488597 / 0.215209 (0.273388) | 4.875534 / 2.077655 (2.797880) | 2.714364 / 1.504120 (1.210244) | 2.603707 / 1.541195 (1.062513) | 2.733547 / 1.468490 (1.265057) | 0.575183 / 4.584777 (-4.009594) | 4.126096 / 3.745712 (0.380384) | 3.853803 / 5.269862 (-1.416058) | 2.395160 / 4.565676 (-2.170516) | 0.067391 / 0.424275 (-0.356884) | 0.009108 / 0.007607 (0.001501) | 0.585865 / 0.226044 (0.359820) | 5.864878 / 2.268929 (3.595949) | 3.153369 / 55.444624 (-52.291256) | 2.759064 / 6.876477 (-4.117413) | 3.032489 / 2.142072 (0.890416) | 0.702615 / 4.805227 (-4.102613) | 0.160034 / 6.500664 (-6.340630) | 0.077294 / 0.075469 (0.001825) |

### 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) | 1.595069 / 1.841788 (-0.246719) | 23.231191 / 8.074308 (15.156883) | 16.365137 / 10.191392 (6.173745) | 0.188360 / 0.680424 (-0.492063) | 0.021704 / 0.534201 (-0.512497) | 0.469996 / 0.579283 (-0.109287) | 0.463255 / 0.434364 (0.028891) | 0.560506 / 0.540337 (0.020169) | 0.751006 / 1.386936 (-0.635930) |

</details>
</details>

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@mariosasko How would you stream a split zip file with just [download_and_extract or download](https://github.com/huggingface/datasets/blob/main/src/datasets/download/download_manager.py#L353)? With download_custom, it is possible to combine a split zip file. Perhaps add an option in [download](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.DownloadManager.download) to combine split zips. This issue may apply to other multipart file-types.

Edit - 
In case asked why I use split zips, I haven't been able to upload zips larger than 50 GB to HuggingFace.

Edit2 -
Issue is [tackled](https://discuss.huggingface.co/t/download-custom-method-of-streamingdownloadmanager-not-implemented/28298/8) for split zips.