Make IterableDataset.from_spark more efficient
Opened by mathewjacob1002 (closed)

Moved the code from using collect() to using toLocalIterator, which allows for prefetching partitions that will be selected next, thus allowing for better performance when iterating. 

Comments: @lhoestq would you be able to review this please and also approve the workflow?

Sounds good to me :) feel free to run `make style` to apply code formatting

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

cool ! I think we can merge once all comments have been addressed

@lhoestq I just addressed the comments and I think we can move ahead with this! 


<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) | 0.007734 / 0.011353 (-0.003619) | 0.004608 / 0.011008 (-0.006400) | 0.094466 / 0.038508 (0.055958) | 0.086477 / 0.023109 (0.063368) | 0.410311 / 0.275898 (0.134413) | 0.455560 / 0.323480 (0.132080) | 0.006112 / 0.007986 (-0.001874) | 0.003845 / 0.004328 (-0.000483) | 0.072506 / 0.004250 (0.068256) | 0.066721 / 0.037052 (0.029669) | 0.409967 / 0.258489 (0.151478) | 0.460480 / 0.293841 (0.166639) | 0.036700 / 0.128546 (-0.091847) | 0.009854 / 0.075646 (-0.065792) | 0.320936 / 0.419271 (-0.098335) | 0.061002 / 0.043533 (0.017469) | 0.413963 / 0.255139 (0.158824) | 0.426787 / 0.283200 (0.143588) | 0.029182 / 0.141683 (-0.112501) | 1.685136 / 1.452155 (0.232981) | 1.754590 / 1.492716 (0.261873) |

### 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) | 0.222698 / 0.018006 (0.204692) | 0.505929 / 0.000490 (0.505440) | 0.005291 / 0.000200 (0.005091) | 0.000097 / 0.000054 (0.000042) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032527 / 0.037411 (-0.004884) | 0.094842 / 0.014526 (0.080317) | 0.110138 / 0.176557 (-0.066418) | 0.193786 / 0.737135 (-0.543349) | 0.112593 / 0.296338 (-0.183745) |

### 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) | 0.441671 / 0.215209 (0.226461) | 4.392961 / 2.077655 (2.315306) | 2.161111 / 1.504120 (0.656991) | 1.967080 / 1.541195 (0.425885) | 2.065411 / 1.468490 (0.596920) | 0.561080 / 4.584777 (-4.023697) | 4.159612 / 3.745712 (0.413900) | 6.435248 / 5.269862 (1.165386) | 3.732338 / 4.565676 (-0.833339) | 0.066156 / 0.424275 (-0.358119) | 0.008030 / 0.007607 (0.000423) | 0.532182 / 0.226044 (0.306137) | 5.315142 / 2.268929 (3.046213) | 2.680157 / 55.444624 (-52.764467) | 2.303799 / 6.876477 (-4.572677) | 2.530911 / 2.142072 (0.388838) | 0.669504 / 4.805227 (-4.135723) | 0.151940 / 6.500664 (-6.348724) | 0.066999 / 0.075469 (-0.008470) |

### 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) | 1.424275 / 1.841788 (-0.417513) | 21.550742 / 8.074308 (13.476434) | 16.031414 / 10.191392 (5.840022) | 0.194681 / 0.680424 (-0.485743) | 0.020389 / 0.534201 (-0.513812) | 0.429808 / 0.579283 (-0.149475) | 0.457503 / 0.434364 (0.023139) | 0.511522 / 0.540337 (-0.028816) | 0.682621 / 1.386936 (-0.704315) |

</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) | 0.007519 / 0.011353 (-0.003834) | 0.004445 / 0.011008 (-0.006563) | 0.071946 / 0.038508 (0.033438) | 0.082982 / 0.023109 (0.059873) | 0.459938 / 0.275898 (0.184040) | 0.504875 / 0.323480 (0.181395) | 0.005805 / 0.007986 (-0.002181) | 0.003740 / 0.004328 (-0.000589) | 0.071998 / 0.004250 (0.067747) | 0.062580 / 0.037052 (0.025527) | 0.462263 / 0.258489 (0.203774) | 0.506355 / 0.293841 (0.212514) | 0.036321 / 0.128546 (-0.092225) | 0.009830 / 0.075646 (-0.065816) | 0.079810 / 0.419271 (-0.339461) | 0.055291 / 0.043533 (0.011758) | 0.464093 / 0.255139 (0.208954) | 0.481109 / 0.283200 (0.197910) | 0.026909 / 0.141683 (-0.114774) | 1.652538 / 1.452155 (0.200383) | 1.750713 / 1.492716 (0.257997) |

### 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) | 0.267552 / 0.018006 (0.249546) | 0.502021 / 0.000490 (0.501531) | 0.001635 / 0.000200 (0.001435) | 0.000099 / 0.000054 (0.000044) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.033747 / 0.037411 (-0.003665) | 0.104242 / 0.014526 (0.089716) | 0.113829 / 0.176557 (-0.062728) | 0.176242 / 0.737135 (-0.560893) | 0.117002 / 0.296338 (-0.179336) |

### 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) | 0.476731 / 0.215209 (0.261522) | 4.727054 / 2.077655 (2.649399) | 2.589396 / 1.504120 (1.085276) | 2.511180 / 1.541195 (0.969985) | 2.634122 / 1.468490 (1.165632) | 0.563840 / 4.584777 (-4.020937) | 4.140212 / 3.745712 (0.394500) | 6.188789 / 5.269862 (0.918928) | 3.716897 / 4.565676 (-0.848780) | 0.065823 / 0.424275 (-0.358452) | 0.007705 / 0.007607 (0.000098) | 0.566580 / 0.226044 (0.340535) | 5.653306 / 2.268929 (3.384377) | 3.028756 / 55.444624 (-52.415868) | 2.592319 / 6.876477 (-4.284158) | 2.614250 / 2.142072 (0.472178) | 0.667135 / 4.805227 (-4.138093) | 0.153455 / 6.500664 (-6.347209) | 0.069321 / 0.075469 (-0.006148) |

### 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) | 1.541978 / 1.841788 (-0.299810) | 21.747360 / 8.074308 (13.673052) | 15.963657 / 10.191392 (5.772265) | 0.192843 / 0.680424 (-0.487581) | 0.020702 / 0.534201 (-0.513499) | 0.433620 / 0.579283 (-0.145663) | 0.467327 / 0.434364 (0.032963) | 0.507398 / 0.540337 (-0.032940) | 0.692797 / 1.386936 (-0.694140) |

</details>
</details>

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