Title: Support for writing arrow files directly with BeamWriter
Author: mariosasko (COLLABORATOR)
State: closed
Created: 2022-12-15T12:38:05Z

Make it possible to write Arrow files directly with `BeamWriter` rather than converting from Parquet to Arrow, which is sub-optimal, especially for big datasets for which Beam is primarily used.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5364). All of your documentation changes will be reflected on that endpoint.

Deleting `BeamPipeline` and `upload_local_to_remote` would break the existing Beam scripts, so I reverted this change.

From what I understand, we need these components in our scripts for the pattern:
```python
if not pipeline.is_local():
    dl_manager.ship_files_with_pipeline()
```

I plan to address this in a subsequent PR by (implicitly) downloading the files directly to the remote storage of the non-local runners.

I got `AttributeError: 'Pipeline' object has no attribute 'is_local'` when running
```python
load_dataset("wikipedia", language="af", date="20230101", beam_runner="DirectRunner")
```
```python
~/.cache/huggingface/modules/datasets_modules/datasets/wikipedia/aa542ed919df55cc5d3347f42dd4521d05ca68751f50dbc32bae2a7f1e167559/wikipedia.py in _split_generators(self, dl_manager, pipeline)
    965             # Use dictionary since testing mock always returns the same result.
    966         downloaded_files = dl_manager.download({"xml": xml_urls})
--> 967         if not pipeline.is_local():
    968             downloaded_files = dl_manager.ship_files_with_pipeline(downloaded_files, pipeline)
    969 

AttributeError: 'Pipeline' object has no attribute 'is_local'
```

<details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<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) | 0.010649 / 0.011353 (-0.000704) | 0.006116 / 0.011008 (-0.004892) | 0.115568 / 0.038508 (0.077060) | 0.041704 / 0.023109 (0.018595) | 0.360459 / 0.275898 (0.084561) | 0.425679 / 0.323480 (0.102200) | 0.008992 / 0.007986 (0.001006) | 0.006321 / 0.004328 (0.001993) | 0.090223 / 0.004250 (0.085973) | 0.049877 / 0.037052 (0.012824) | 0.382447 / 0.258489 (0.123958) | 0.406567 / 0.293841 (0.112726) | 0.045138 / 0.128546 (-0.083409) | 0.014203 / 0.075646 (-0.061444) | 0.388897 / 0.419271 (-0.030375) | 0.057176 / 0.043533 (0.013644) | 0.358729 / 0.255139 (0.103590) | 0.386086 / 0.283200 (0.102887) | 0.119221 / 0.141683 (-0.022462) | 1.731574 / 1.452155 (0.279419) | 1.744103 / 1.492716 (0.251386) |

### 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) | 0.230380 / 0.018006 (0.212373) | 0.493690 / 0.000490 (0.493201) | 0.005150 / 0.000200 (0.004950) | 0.000097 / 0.000054 (0.000042) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030771 / 0.037411 (-0.006641) | 0.123196 / 0.014526 (0.108671) | 0.134097 / 0.176557 (-0.042459) | 0.190442 / 0.737135 (-0.546693) | 0.138416 / 0.296338 (-0.157923) |

### 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) | 0.469763 / 0.215209 (0.254554) | 4.682847 / 2.077655 (2.605192) | 2.076717 / 1.504120 (0.572597) | 1.843721 / 1.541195 (0.302527) | 1.923486 / 1.468490 (0.454996) | 0.817680 / 4.584777 (-3.767097) | 4.482409 / 3.745712 (0.736697) | 3.898695 / 5.269862 (-1.371167) | 2.078291 / 4.565676 (-2.487386) | 0.100285 / 0.424275 (-0.323990) | 0.014761 / 0.007607 (0.007154) | 0.611261 / 0.226044 (0.385217) | 5.926919 / 2.268929 (3.657990) | 2.685080 / 55.444624 (-52.759544) | 2.232179 / 6.876477 (-4.644298) | 2.305576 / 2.142072 (0.163504) | 0.993729 / 4.805227 (-3.811498) | 0.194491 / 6.500664 (-6.306173) | 0.074176 / 0.075469 (-0.001293) |

### 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) | 1.388592 / 1.841788 (-0.453196) | 17.146945 / 8.074308 (9.072636) | 15.989570 / 10.191392 (5.798178) | 0.200147 / 0.680424 (-0.480277) | 0.034009 / 0.534201 (-0.500192) | 0.517531 / 0.579283 (-0.061753) | 0.533966 / 0.434364 (0.099602) | 0.637024 / 0.540337 (0.096687) | 0.749166 / 1.386936 (-0.637770) |

</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) | 0.008240 / 0.011353 (-0.003113) | 0.006139 / 0.011008 (-0.004869) | 0.112258 / 0.038508 (0.073750) | 0.039001 / 0.023109 (0.015891) | 0.449467 / 0.275898 (0.173569) | 0.483422 / 0.323480 (0.159942) | 0.006176 / 0.007986 (-0.001810) | 0.006340 / 0.004328 (0.002012) | 0.083105 / 0.004250 (0.078855) | 0.047002 / 0.037052 (0.009950) | 0.458564 / 0.258489 (0.200075) | 0.513704 / 0.293841 (0.219863) | 0.041359 / 0.128546 (-0.087188) | 0.014515 / 0.075646 (-0.061131) | 0.392599 / 0.419271 (-0.026673) | 0.055222 / 0.043533 (0.011690) | 0.446956 / 0.255139 (0.191817) | 0.469194 / 0.283200 (0.185994) | 0.118212 / 0.141683 (-0.023471) | 1.682647 / 1.452155 (0.230492) | 1.780076 / 1.492716 (0.287360) |

### 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) | 0.259124 / 0.018006 (0.241117) | 0.507559 / 0.000490 (0.507069) | 0.001080 / 0.000200 (0.000880) | 0.000081 / 0.000054 (0.000026) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031969 / 0.037411 (-0.005442) | 0.126997 / 0.014526 (0.112471) | 0.139593 / 0.176557 (-0.036963) | 0.182735 / 0.737135 (-0.554400) | 0.145871 / 0.296338 (-0.150468) |

### 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) | 0.530894 / 0.215209 (0.315685) | 5.284979 / 2.077655 (3.207324) | 2.592886 / 1.504120 (1.088766) | 2.407202 / 1.541195 (0.866007) | 2.434079 / 1.468490 (0.965589) | 0.829382 / 4.584777 (-3.755395) | 4.481710 / 3.745712 (0.735998) | 3.912280 / 5.269862 (-1.357581) | 1.962291 / 4.565676 (-2.603386) | 0.101840 / 0.424275 (-0.322435) | 0.014528 / 0.007607 (0.006921) | 0.639956 / 0.226044 (0.413911) | 6.414685 / 2.268929 (4.145756) | 3.240290 / 55.444624 (-52.204334) | 2.795208 / 6.876477 (-4.081269) | 2.912122 / 2.142072 (0.770050) | 0.992188 / 4.805227 (-3.813039) | 0.200701 / 6.500664 (-6.299964) | 0.074235 / 0.075469 (-0.001234) |

### 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) | 1.455075 / 1.841788 (-0.386712) | 17.186669 / 8.074308 (9.112361) | 15.404357 / 10.191392 (5.212965) | 0.168267 / 0.680424 (-0.512157) | 0.020774 / 0.534201 (-0.513427) | 0.502603 / 0.579283 (-0.076680) | 0.506500 / 0.434364 (0.072136) | 0.624245 / 0.540337 (0.083907) | 0.735529 / 1.386936 (-0.651407) |

</details>
</details>

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


I think we could close this PR.

indeed, since Beam stuff are deprecated