Remove `Table.__getstate__` and `Table.__setstate__`
Thanks for working on this! The [issue](https://bugs.python.org/issue24658) with pickling objects larger than 4GB seems to be patched in Python 3.8 (the minimal supported version was 3.6 at the time of implementing this), so a simple solution would be removing the `Table.__setstate__` and `Table.__getstate__` overrides. @mariosasko 
Cool!
I removed these overrides, and it worked.

All modifications are committed. Ready for review! _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) | 0.005251 / 0.011353 (-0.006102) | 0.003804 / 0.011008 (-0.007204) | 0.063143 / 0.038508 (0.024635) | 0.059409 / 0.023109 (0.036300) | 0.255319 / 0.275898 (-0.020579) | 0.279194 / 0.323480 (-0.044285) | 0.004643 / 0.007986 (-0.003343) | 0.002560 / 0.004328 (-0.001768) | 0.047490 / 0.004250 (0.043240) | 0.039034 / 0.037052 (0.001982) | 0.257352 / 0.258489 (-0.001137) | 0.293029 / 0.293841 (-0.000812) | 0.027548 / 0.128546 (-0.100998) | 0.011307 / 0.075646 (-0.064339) | 0.210325 / 0.419271 (-0.208946) | 0.035161 / 0.043533 (-0.008372) | 0.253491 / 0.255139 (-0.001648) | 0.272085 / 0.283200 (-0.011115) | 0.018924 / 0.141683 (-0.122759) | 1.111148 / 1.452155 (-0.341007) | 1.178076 / 1.492716 (-0.314641) |

### 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) | 0.092447 / 0.018006 (0.074441) | 0.303680 / 0.000490 (0.303190) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019087 / 0.037411 (-0.018325) | 0.062663 / 0.014526 (0.048137) | 0.074651 / 0.176557 (-0.101905) | 0.121334 / 0.737135 (-0.615802) | 0.076703 / 0.296338 (-0.219636) |

### 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) | 0.286505 / 0.215209 (0.071295) | 2.804942 / 2.077655 (0.727287) | 1.481930 / 1.504120 (-0.022190) | 1.369485 / 1.541195 (-0.171710) | 1.424467 / 1.468490 (-0.044023) | 0.556810 / 4.584777 (-4.027967) | 2.416338 / 3.745712 (-1.329374) | 2.901869 / 5.269862 (-2.367992) | 1.827007 / 4.565676 (-2.738669) | 0.062252 / 0.424275 (-0.362024) | 0.005076 / 0.007607 (-0.002531) | 0.343850 / 0.226044 (0.117805) | 3.377611 / 2.268929 (1.108683) | 1.860214 / 55.444624 (-53.584410) | 1.595146 / 6.876477 (-5.281331) | 1.627234 / 2.142072 (-0.514838) | 0.651027 / 4.805227 (-4.154200) | 0.119214 / 6.500664 (-6.381450) | 0.043342 / 0.075469 (-0.032127) |

### 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) | 0.942863 / 1.841788 (-0.898924) | 12.484633 / 8.074308 (4.410324) | 10.560668 / 10.191392 (0.369276) | 0.144647 / 0.680424 (-0.535777) | 0.014734 / 0.534201 (-0.519466) | 0.286575 / 0.579283 (-0.292708) | 0.270913 / 0.434364 (-0.163451) | 0.323792 / 0.540337 (-0.216545) | 0.419186 / 1.386936 (-0.967750) |

</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) | 0.005315 / 0.011353 (-0.006038) | 0.003548 / 0.011008 (-0.007460) | 0.049271 / 0.038508 (0.010763) | 0.055198 / 0.023109 (0.032089) | 0.275940 / 0.275898 (0.000042) | 0.307637 / 0.323480 (-0.015843) | 0.003997 / 0.007986 (-0.003988) | 0.002544 / 0.004328 (-0.001785) | 0.050381 / 0.004250 (0.046130) | 0.041158 / 0.037052 (0.004105) | 0.281519 / 0.258489 (0.023030) | 0.308085 / 0.293841 (0.014244) | 0.030464 / 0.128546 (-0.098083) | 0.010690 / 0.075646 (-0.064957) | 0.057458 / 0.419271 (-0.361814) | 0.032814 / 0.043533 (-0.010719) | 0.282435 / 0.255139 (0.027296) | 0.301342 / 0.283200 (0.018142) | 0.017556 / 0.141683 (-0.124127) | 1.159423 / 1.452155 (-0.292732) | 1.177344 / 1.492716 (-0.315372) |

### 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) | 0.091086 / 0.018006 (0.073079) | 0.305316 / 0.000490 (0.304826) | 0.000218 / 0.000200 (0.000019) | 0.000054 / 0.000054 (-0.000000) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021832 / 0.037411 (-0.015579) | 0.071055 / 0.014526 (0.056529) | 0.082982 / 0.176557 (-0.093574) | 0.119966 / 0.737135 (-0.617169) | 0.083539 / 0.296338 (-0.212800) |

### 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) | 0.302501 / 0.215209 (0.087292) | 2.936347 / 2.077655 (0.858692) | 1.601658 / 1.504120 (0.097538) | 1.467267 / 1.541195 (-0.073928) | 1.514656 / 1.468490 (0.046166) | 0.563934 / 4.584777 (-4.020843) | 2.513715 / 3.745712 (-1.231997) | 2.813014 / 5.269862 (-2.456847) | 1.773243 / 4.565676 (-2.792433) | 0.063208 / 0.424275 (-0.361067) | 0.004979 / 0.007607 (-0.002628) | 0.360694 / 0.226044 (0.134650) | 3.520578 / 2.268929 (1.251650) | 1.975369 / 55.444624 (-53.469255) | 1.691257 / 6.876477 (-5.185220) | 1.730872 / 2.142072 (-0.411200) | 0.655366 / 4.805227 (-4.149861) | 0.146043 / 6.500664 (-6.354621) | 0.041386 / 0.075469 (-0.034083) |

### 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) | 0.979840 / 1.841788 (-0.861948) | 12.456924 / 8.074308 (4.382616) | 10.938595 / 10.191392 (0.747203) | 0.133853 / 0.680424 (-0.546571) | 0.015744 / 0.534201 (-0.518457) | 0.289585 / 0.579283 (-0.289698) | 0.291143 / 0.434364 (-0.143221) | 0.328109 / 0.540337 (-0.212228) | 0.561897 / 1.386936 (-0.825039) |

</details>
</details>

![](https://cml.dev/watermark.png#05ec66cc1abc20bd13d02c681b7be372ae084a4f "CML watermark")

When using distributed training, the code of `os.remove(filename)` may be executed separately by each rank, leading to `FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmprxxxxxxx.arrow'`

```python
from torch import distributed as dist

if dist.get_rank() == 0:
    dataset = process_dataset(*args, **kwargs)
    objects = [dataset]
else:
    objects = [None]
dist.broadcast_object_list(objects, src=0)
dataset = objects[0]
```
