Support working_dir in from_spark
State: closed

Accept `working_dir` as an argument to `Dataset.from_spark`. Setting a non-NFS working directory for Spark workers to materialize to will improve write performance.

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

Added env var

@lhoestq would you or another maintainer be able to review please? :)

I removed the env var

<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) | 0.005771 / 0.011353 (-0.005582) | 0.004086 / 0.011008 (-0.006922) | 0.097170 / 0.038508 (0.058661) | 0.027464 / 0.023109 (0.004355) | 0.305425 / 0.275898 (0.029527) | 0.343869 / 0.323480 (0.020389) | 0.004899 / 0.007986 (-0.003087) | 0.003294 / 0.004328 (-0.001034) | 0.074710 / 0.004250 (0.070459) | 0.034982 / 0.037052 (-0.002070) | 0.306063 / 0.258489 (0.047574) | 0.343115 / 0.293841 (0.049274) | 0.025155 / 0.128546 (-0.103392) | 0.008429 / 0.075646 (-0.067217) | 0.318680 / 0.419271 (-0.100591) | 0.043304 / 0.043533 (-0.000229) | 0.306703 / 0.255139 (0.051564) | 0.335535 / 0.283200 (0.052335) | 0.087428 / 0.141683 (-0.054255) | 1.483769 / 1.452155 (0.031614) | 1.538753 / 1.492716 (0.046037) |

### 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) | 0.203313 / 0.018006 (0.185307) | 0.413864 / 0.000490 (0.413375) | 0.003186 / 0.000200 (0.002986) | 0.000068 / 0.000054 (0.000013) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022862 / 0.037411 (-0.014550) | 0.097306 / 0.014526 (0.082780) | 0.102823 / 0.176557 (-0.073733) | 0.162803 / 0.737135 (-0.574333) | 0.106311 / 0.296338 (-0.190028) |

### 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) | 0.451710 / 0.215209 (0.236501) | 4.508520 / 2.077655 (2.430865) | 2.181118 / 1.504120 (0.676998) | 1.977607 / 1.541195 (0.436412) | 2.008366 / 1.468490 (0.539876) | 0.565388 / 4.584777 (-4.019389) | 3.439318 / 3.745712 (-0.306394) | 1.747512 / 5.269862 (-3.522349) | 1.102124 / 4.565676 (-3.463553) | 0.069212 / 0.424275 (-0.355063) | 0.011926 / 0.007607 (0.004318) | 0.553414 / 0.226044 (0.327370) | 5.548959 / 2.268929 (3.280031) | 2.628769 / 55.444624 (-52.815856) | 2.301003 / 6.876477 (-4.575473) | 2.341744 / 2.142072 (0.199672) | 0.673092 / 4.805227 (-4.132135) | 0.137722 / 6.500664 (-6.362942) | 0.066909 / 0.075469 (-0.008560) |

### 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) | 1.196854 / 1.841788 (-0.644934) | 13.421776 / 8.074308 (5.347468) | 13.839760 / 10.191392 (3.648368) | 0.140557 / 0.680424 (-0.539867) | 0.016619 / 0.534201 (-0.517582) | 0.357985 / 0.579283 (-0.221298) | 0.387018 / 0.434364 (-0.047346) | 0.452798 / 0.540337 (-0.087540) | 0.542085 / 1.386936 (-0.844851) |

</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) | 0.005868 / 0.011353 (-0.005484) | 0.004103 / 0.011008 (-0.006905) | 0.076126 / 0.038508 (0.037618) | 0.027744 / 0.023109 (0.004635) | 0.357257 / 0.275898 (0.081359) | 0.387981 / 0.323480 (0.064501) | 0.004807 / 0.007986 (-0.003178) | 0.003337 / 0.004328 (-0.000991) | 0.075486 / 0.004250 (0.071236) | 0.035121 / 0.037052 (-0.001931) | 0.361385 / 0.258489 (0.102896) | 0.399346 / 0.293841 (0.105505) | 0.025263 / 0.128546 (-0.103284) | 0.008571 / 0.075646 (-0.067075) | 0.081815 / 0.419271 (-0.337457) | 0.041114 / 0.043533 (-0.002418) | 0.362840 / 0.255139 (0.107701) | 0.380926 / 0.283200 (0.097727) | 0.092728 / 0.141683 (-0.048955) | 1.517647 / 1.452155 (0.065492) | 1.534914 / 1.492716 (0.042198) |

### 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) | 0.199669 / 0.018006 (0.181663) | 0.399070 / 0.000490 (0.398580) | 0.002014 / 0.000200 (0.001814) | 0.000079 / 0.000054 (0.000024) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024541 / 0.037411 (-0.012870) | 0.099676 / 0.014526 (0.085151) | 0.106503 / 0.176557 (-0.070054) | 0.153755 / 0.737135 (-0.583380) | 0.108564 / 0.296338 (-0.187775) |

### 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) | 0.443842 / 0.215209 (0.228633) | 4.441158 / 2.077655 (2.363503) | 2.159496 / 1.504120 (0.655376) | 1.955358 / 1.541195 (0.414163) | 1.973864 / 1.468490 (0.505374) | 0.550467 / 4.584777 (-4.034310) | 3.381831 / 3.745712 (-0.363881) | 2.561192 / 5.269862 (-2.708670) | 1.361684 / 4.565676 (-3.203992) | 0.068140 / 0.424275 (-0.356135) | 0.012005 / 0.007607 (0.004398) | 0.551921 / 0.226044 (0.325877) | 5.503591 / 2.268929 (3.234662) | 2.591609 / 55.444624 (-52.853015) | 2.246681 / 6.876477 (-4.629796) | 2.290941 / 2.142072 (0.148868) | 0.655212 / 4.805227 (-4.150015) | 0.136013 / 6.500664 (-6.364651) | 0.066995 / 0.075469 (-0.008474) |

### 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) | 1.300438 / 1.841788 (-0.541350) | 13.866224 / 8.074308 (5.791916) | 13.932624 / 10.191392 (3.741232) | 0.144345 / 0.680424 (-0.536079) | 0.016623 / 0.534201 (-0.517578) | 0.357629 / 0.579283 (-0.221654) | 0.389759 / 0.434364 (-0.044605) | 0.417704 / 0.540337 (-0.122633) | 0.501358 / 1.386936 (-0.885578) |

</details>
</details>

![](https://cml.dev/watermark.png#89f775226321ba94e5bf4670a323c0fb44f5f65c "CML watermark")


Thank you!