Raise subprocesses traceback when interrupting
_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) | 0.008959 / 0.011353 (-0.002394) | 0.005804 / 0.011008 (-0.005204) | 0.112663 / 0.038508 (0.074155) | 0.043406 / 0.023109 (0.020297) | 0.348582 / 0.275898 (0.072684) | 0.382332 / 0.323480 (0.058852) | 0.007469 / 0.007986 (-0.000517) | 0.006211 / 0.004328 (0.001883) | 0.086576 / 0.004250 (0.082326) | 0.059223 / 0.037052 (0.022170) | 0.361051 / 0.258489 (0.102562) | 0.411359 / 0.293841 (0.117518) | 0.043640 / 0.128546 (-0.084906) | 0.014239 / 0.075646 (-0.061408) | 0.389729 / 0.419271 (-0.029542) | 0.072319 / 0.043533 (0.028786) | 0.351025 / 0.255139 (0.095886) | 0.371893 / 0.283200 (0.088693) | 0.125994 / 0.141683 (-0.015688) | 1.675249 / 1.452155 (0.223094) | 1.808740 / 1.492716 (0.316024) |

### 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) | 0.255172 / 0.018006 (0.237166) | 0.536003 / 0.000490 (0.535514) | 0.000365 / 0.000200 (0.000165) | 0.000070 / 0.000054 (0.000015) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031989 / 0.037411 (-0.005423) | 0.126854 / 0.014526 (0.112328) | 0.142458 / 0.176557 (-0.034098) | 0.207821 / 0.737135 (-0.529314) | 0.145610 / 0.296338 (-0.150728) |

### 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) | 0.468924 / 0.215209 (0.253715) | 4.696677 / 2.077655 (2.619023) | 2.183133 / 1.504120 (0.679013) | 1.994219 / 1.541195 (0.453024) | 2.101375 / 1.468490 (0.632885) | 0.827168 / 4.584777 (-3.757609) | 4.710167 / 3.745712 (0.964455) | 2.377062 / 5.269862 (-2.892800) | 1.712245 / 4.565676 (-2.853431) | 0.100620 / 0.424275 (-0.323655) | 0.014302 / 0.007607 (0.006695) | 0.590813 / 0.226044 (0.364769) | 5.871991 / 2.268929 (3.603063) | 2.722229 / 55.444624 (-52.722395) | 2.323585 / 6.876477 (-4.552892) | 2.503289 / 2.142072 (0.361217) | 0.983644 / 4.805227 (-3.821583) | 0.193942 / 6.500664 (-6.306722) | 0.076493 / 0.075469 (0.001024) |

### 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) | 1.463107 / 1.841788 (-0.378681) | 17.876918 / 8.074308 (9.802610) | 16.755740 / 10.191392 (6.564348) | 0.167556 / 0.680424 (-0.512868) | 0.020514 / 0.534201 (-0.513687) | 0.508385 / 0.579283 (-0.070898) | 0.505873 / 0.434364 (0.071509) | 0.603630 / 0.540337 (0.063293) | 0.708856 / 1.386936 (-0.678080) |

</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) | 0.008504 / 0.011353 (-0.002849) | 0.005894 / 0.011008 (-0.005114) | 0.085523 / 0.038508 (0.047015) | 0.038780 / 0.023109 (0.015671) | 0.402869 / 0.275898 (0.126971) | 0.423819 / 0.323480 (0.100339) | 0.006427 / 0.007986 (-0.001559) | 0.004598 / 0.004328 (0.000269) | 0.079807 / 0.004250 (0.075556) | 0.050852 / 0.037052 (0.013799) | 0.403232 / 0.258489 (0.144743) | 0.452489 / 0.293841 (0.158648) | 0.041501 / 0.128546 (-0.087045) | 0.014996 / 0.075646 (-0.060650) | 0.101548 / 0.419271 (-0.317724) | 0.056993 / 0.043533 (0.013461) | 0.403153 / 0.255139 (0.148014) | 0.424587 / 0.283200 (0.141388) | 0.114507 / 0.141683 (-0.027176) | 1.707098 / 1.452155 (0.254943) | 1.799008 / 1.492716 (0.306291) |

### 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) | 0.288003 / 0.018006 (0.269996) | 0.496526 / 0.000490 (0.496036) | 0.010923 / 0.000200 (0.010723) | 0.000159 / 0.000054 (0.000105) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.033948 / 0.037411 (-0.003463) | 0.142343 / 0.014526 (0.127817) | 0.143862 / 0.176557 (-0.032695) | 0.202655 / 0.737135 (-0.534480) | 0.151177 / 0.296338 (-0.145162) |

### 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) | 0.508003 / 0.215209 (0.292794) | 5.320394 / 2.077655 (3.242740) | 2.409854 / 1.504120 (0.905734) | 2.190656 / 1.541195 (0.649462) | 2.272171 / 1.468490 (0.803681) | 0.809492 / 4.584777 (-3.775285) | 4.554412 / 3.745712 (0.808699) | 4.413643 / 5.269862 (-0.856218) | 2.374034 / 4.565676 (-2.191642) | 0.099458 / 0.424275 (-0.324817) | 0.014553 / 0.007607 (0.006946) | 0.613916 / 0.226044 (0.387871) | 6.121430 / 2.268929 (3.852502) | 2.945661 / 55.444624 (-52.498964) | 2.595247 / 6.876477 (-4.281230) | 2.734047 / 2.142072 (0.591975) | 0.952217 / 4.805227 (-3.853010) | 0.196933 / 6.500664 (-6.303731) | 0.073391 / 0.075469 (-0.002078) |

### 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) | 1.475666 / 1.841788 (-0.366122) | 18.564281 / 8.074308 (10.489973) | 16.865259 / 10.191392 (6.673867) | 0.166494 / 0.680424 (-0.513930) | 0.020655 / 0.534201 (-0.513546) | 0.495120 / 0.579283 (-0.084163) | 0.502602 / 0.434364 (0.068238) | 0.622448 / 0.540337 (0.082110) | 0.721036 / 1.386936 (-0.665900) |

</details>
</details>

![](https://cml.dev/watermark.png#40c204c777793d64e8bb8ce357e9c07b3b303e41 "CML watermark")
 Whoops mario you're off this week sorry. I'm taking the liberty to merge this one <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) | 0.009079 / 0.011353 (-0.002274) | 0.005960 / 0.011008 (-0.005049) | 0.116530 / 0.038508 (0.078022) | 0.046649 / 0.023109 (0.023540) | 0.391906 / 0.275898 (0.116008) | 0.438892 / 0.323480 (0.115412) | 0.007134 / 0.007986 (-0.000851) | 0.004997 / 0.004328 (0.000668) | 0.085947 / 0.004250 (0.081697) | 0.059814 / 0.037052 (0.022762) | 0.396423 / 0.258489 (0.137934) | 0.455941 / 0.293841 (0.162100) | 0.042535 / 0.128546 (-0.086011) | 0.014667 / 0.075646 (-0.060980) | 0.402023 / 0.419271 (-0.017249) | 0.060381 / 0.043533 (0.016848) | 0.393829 / 0.255139 (0.138690) | 0.426557 / 0.283200 (0.143358) | 0.131519 / 0.141683 (-0.010163) | 1.758098 / 1.452155 (0.305943) | 1.848194 / 1.492716 (0.355478) |

### 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) | 0.236405 / 0.018006 (0.218399) | 0.611442 / 0.000490 (0.610952) | 0.005143 / 0.000200 (0.004943) | 0.000146 / 0.000054 (0.000092) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034317 / 0.037411 (-0.003094) | 0.182485 / 0.014526 (0.167959) | 0.183149 / 0.176557 (0.006592) | 0.293592 / 0.737135 (-0.443543) | 0.197137 / 0.296338 (-0.099202) |

### 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) | 0.475690 / 0.215209 (0.260481) | 4.757344 / 2.077655 (2.679690) | 2.184079 / 1.504120 (0.679959) | 1.956599 / 1.541195 (0.415404) | 2.043041 / 1.468490 (0.574551) | 0.817602 / 4.584777 (-3.767175) | 6.432267 / 3.745712 (2.686555) | 5.999402 / 5.269862 (0.729541) | 3.095970 / 4.565676 (-1.469706) | 0.181589 / 0.424275 (-0.242686) | 0.023286 / 0.007607 (0.015679) | 1.090318 / 0.226044 (0.864274) | 7.919330 / 2.268929 (5.650401) | 2.702821 / 55.444624 (-52.741804) | 2.375442 / 6.876477 (-4.501034) | 2.543075 / 2.142072 (0.401003) | 1.011763 / 4.805227 (-3.793464) | 0.203676 / 6.500664 (-6.296988) | 0.080075 / 0.075469 (0.004606) |

### 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) | 1.875420 / 1.841788 (0.033632) | 23.059278 / 8.074308 (14.984970) | 19.250807 / 10.191392 (9.059415) | 0.323678 / 0.680424 (-0.356746) | 0.028682 / 0.534201 (-0.505519) | 0.698231 / 0.579283 (0.118948) | 0.668129 / 0.434364 (0.233765) | 0.831218 / 0.540337 (0.290880) | 0.941191 / 1.386936 (-0.445745) |

</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) | 0.013122 / 0.011353 (0.001769) | 0.006123 / 0.011008 (-0.004886) | 0.090493 / 0.038508 (0.051985) | 0.070660 / 0.023109 (0.047551) | 0.413486 / 0.275898 (0.137588) | 0.450364 / 0.323480 (0.126884) | 0.010288 / 0.007986 (0.002302) | 0.006590 / 0.004328 (0.002261) | 0.087174 / 0.004250 (0.082923) | 0.077304 / 0.037052 (0.040252) | 0.428480 / 0.258489 (0.169991) | 0.459872 / 0.293841 (0.166032) | 0.060477 / 0.128546 (-0.068069) | 0.014859 / 0.075646 (-0.060788) | 0.103915 / 0.419271 (-0.315356) | 0.087466 / 0.043533 (0.043933) | 0.418644 / 0.255139 (0.163505) | 0.433409 / 0.283200 (0.150209) | 0.166716 / 0.141683 (0.025033) | 1.712068 / 1.452155 (0.259914) | 1.827869 / 1.492716 (0.335153) |

### 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) | 0.372491 / 0.018006 (0.354484) | 0.493426 / 0.000490 (0.492937) | 0.005497 / 0.000200 (0.005297) | 0.000129 / 0.000054 (0.000074) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.036531 / 0.037411 (-0.000880) | 0.142152 / 0.014526 (0.127626) | 0.148183 / 0.176557 (-0.028373) | 0.212918 / 0.737135 (-0.524217) | 0.154092 / 0.296338 (-0.142246) |

### 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) | 0.551733 / 0.215209 (0.336524) | 5.421498 / 2.077655 (3.343843) | 2.418848 / 1.504120 (0.914728) | 2.213185 / 1.541195 (0.671991) | 2.294881 / 1.468490 (0.826391) | 0.827031 / 4.584777 (-3.757746) | 6.365622 / 3.745712 (2.619910) | 4.927996 / 5.269862 (-0.341866) | 2.756133 / 4.565676 (-1.809544) | 0.101474 / 0.424275 (-0.322801) | 0.014523 / 0.007607 (0.006916) | 0.619082 / 0.226044 (0.393037) | 6.200132 / 2.268929 (3.931204) | 3.015590 / 55.444624 (-52.429034) | 2.711181 / 6.876477 (-4.165296) | 2.857157 / 2.142072 (0.715084) | 0.993329 / 4.805227 (-3.811898) | 0.203364 / 6.500664 (-6.297301) | 0.079167 / 0.075469 (0.003698) |

### 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) | 1.709881 / 1.841788 (-0.131907) | 24.867536 / 8.074308 (16.793228) | 21.755361 / 10.191392 (11.563969) | 0.295837 / 0.680424 (-0.384586) | 0.031934 / 0.534201 (-0.502267) | 0.709994 / 0.579283 (0.130711) | 0.779656 / 0.434364 (0.345293) | 0.780669 / 0.540337 (0.240331) | 0.712808 / 1.386936 (-0.674128) |

</details>
</details>

![](https://cml.dev/watermark.png#cf4a1951bdca7175adac9c8b85550e89dcceb6fa "CML watermark")

When a subprocess hangs in `filter` or `map`, one should be able to get the subprocess' traceback when interrupting the main process. Right now it shows nothing.

To do so I `.get()` the subprocesses async results even the main process is stopped with e.g. `KeyboardInterrupt`. I added a timeout in case the subprocess is hanging or crashed.