Support streaming Beam datasets from HF GCS preprocessed data
_The documentation is not available anymore as the PR was closed or merged._ ```python
In [1]: from datasets import load_dataset

In [2]: ds = load_dataset("wikipedia", "20220301.en", split="train", streaming=True); item = next(iter(ds)); item
Out[2]: 
{'id': '12',
 'url': 'https://en.wikipedia.org/wiki/Anarchism',
 'title': 'Anarchism',
 'text': 'Anarchism is a political philosophy and movement that is sceptical of authority and rejects all involuntary, coercive forms of hierarchy. Anarchism calls for the abolition of the state, which it holds to be unnecessary, undesirable, and harmful. As a historically left-wing movement, placed on the farthest left of the political spectrum, it is usually described alongside communalism and libertarian Marxism as the libertarian wing (libertarian socialism) of the socialist movement,...}
``` I love your example 🏴‍🅰️ <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) | 0.007859 / 0.011353 (-0.003493) | 0.005129 / 0.011008 (-0.005879) | 0.098070 / 0.038508 (0.059562) | 0.036500 / 0.023109 (0.013391) | 0.311575 / 0.275898 (0.035677) | 0.338351 / 0.323480 (0.014872) | 0.005962 / 0.007986 (-0.002024) | 0.004060 / 0.004328 (-0.000268) | 0.072970 / 0.004250 (0.068719) | 0.049289 / 0.037052 (0.012237) | 0.310303 / 0.258489 (0.051814) | 0.347449 / 0.293841 (0.053608) | 0.046912 / 0.128546 (-0.081634) | 0.011952 / 0.075646 (-0.063694) | 0.333600 / 0.419271 (-0.085671) | 0.052700 / 0.043533 (0.009167) | 0.325486 / 0.255139 (0.070347) | 0.326920 / 0.283200 (0.043720) | 0.107683 / 0.141683 (-0.034000) | 1.416679 / 1.452155 (-0.035476) | 1.502418 / 1.492716 (0.009702) |

### 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) | 0.216520 / 0.018006 (0.198514) | 0.448450 / 0.000490 (0.447960) | 0.004213 / 0.000200 (0.004013) | 0.000082 / 0.000054 (0.000028) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027081 / 0.037411 (-0.010331) | 0.110989 / 0.014526 (0.096463) | 0.116087 / 0.176557 (-0.060470) | 0.173771 / 0.737135 (-0.563364) | 0.121240 / 0.296338 (-0.175099) |

### 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) | 0.399938 / 0.215209 (0.184729) | 4.017665 / 2.077655 (1.940010) | 1.782327 / 1.504120 (0.278207) | 1.612955 / 1.541195 (0.071761) | 1.698839 / 1.468490 (0.230349) | 0.706702 / 4.584777 (-3.878075) | 4.533425 / 3.745712 (0.787713) | 2.102611 / 5.269862 (-3.167250) | 1.461429 / 4.565676 (-3.104248) | 0.085719 / 0.424275 (-0.338556) | 0.012104 / 0.007607 (0.004497) | 0.507397 / 0.226044 (0.281352) | 5.061572 / 2.268929 (2.792643) | 2.272106 / 55.444624 (-53.172518) | 1.935575 / 6.876477 (-4.940901) | 2.102541 / 2.142072 (-0.039532) | 0.838395 / 4.805227 (-3.966832) | 0.168573 / 6.500664 (-6.332091) | 0.064234 / 0.075469 (-0.011235) |

### 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) | 1.190077 / 1.841788 (-0.651710) | 15.765587 / 8.074308 (7.691279) | 14.694626 / 10.191392 (4.503234) | 0.142912 / 0.680424 (-0.537512) | 0.017669 / 0.534201 (-0.516532) | 0.421502 / 0.579283 (-0.157781) | 0.452732 / 0.434364 (0.018368) | 0.497480 / 0.540337 (-0.042857) | 0.586310 / 1.386936 (-0.800626) |

</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) | 0.007629 / 0.011353 (-0.003724) | 0.005330 / 0.011008 (-0.005679) | 0.076366 / 0.038508 (0.037858) | 0.034703 / 0.023109 (0.011593) | 0.356300 / 0.275898 (0.080402) | 0.392909 / 0.323480 (0.069429) | 0.005959 / 0.007986 (-0.002026) | 0.004140 / 0.004328 (-0.000188) | 0.075289 / 0.004250 (0.071039) | 0.047880 / 0.037052 (0.010828) | 0.357289 / 0.258489 (0.098800) | 0.404554 / 0.293841 (0.110714) | 0.037182 / 0.128546 (-0.091365) | 0.012266 / 0.075646 (-0.063380) | 0.088554 / 0.419271 (-0.330718) | 0.049698 / 0.043533 (0.006165) | 0.353453 / 0.255139 (0.098314) | 0.373252 / 0.283200 (0.090052) | 0.101892 / 0.141683 (-0.039791) | 1.481534 / 1.452155 (0.029380) | 1.553818 / 1.492716 (0.061102) |

### 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) | 0.229891 / 0.018006 (0.211884) | 0.452444 / 0.000490 (0.451954) | 0.000434 / 0.000200 (0.000234) | 0.000058 / 0.000054 (0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030170 / 0.037411 (-0.007241) | 0.115097 / 0.014526 (0.100571) | 0.122094 / 0.176557 (-0.054463) | 0.171352 / 0.737135 (-0.565784) | 0.128441 / 0.296338 (-0.167898) |

### 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) | 0.428347 / 0.215209 (0.213138) | 4.266243 / 2.077655 (2.188588) | 2.148327 / 1.504120 (0.644207) | 1.874141 / 1.541195 (0.332946) | 1.968737 / 1.468490 (0.500246) | 0.715320 / 4.584777 (-3.869457) | 4.166097 / 3.745712 (0.420384) | 2.169550 / 5.269862 (-3.100312) | 1.377441 / 4.565676 (-3.188236) | 0.086376 / 0.424275 (-0.337899) | 0.012018 / 0.007607 (0.004411) | 0.517433 / 0.226044 (0.291388) | 5.167327 / 2.268929 (2.898398) | 2.545822 / 55.444624 (-52.898803) | 2.241726 / 6.876477 (-4.634751) | 2.327220 / 2.142072 (0.185147) | 0.841618 / 4.805227 (-3.963609) | 0.169473 / 6.500664 (-6.331191) | 0.065505 / 0.075469 (-0.009964) |

### 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) | 1.270476 / 1.841788 (-0.571312) | 17.049885 / 8.074308 (8.975577) | 14.847615 / 10.191392 (4.656223) | 0.168671 / 0.680424 (-0.511753) | 0.017564 / 0.534201 (-0.516637) | 0.424780 / 0.579283 (-0.154503) | 0.517392 / 0.434364 (0.083028) | 0.561197 / 0.540337 (0.020859) | 0.697792 / 1.386936 (-0.689144) |

</details>
</details>

![](https://cml.dev/watermark.png#ce06edf0afb70027ffbd3c2ddec5d28037e9bd31 "CML watermark")

This PR implements streaming Apache Beam datasets that are already preprocessed by us and stored in the HF Google Cloud Storage:
- natural_questions
- wiki40b
- wikipedia

This is done by streaming from the prepared Arrow files in HF Google Cloud Storage.

This will fix their corresponding dataset viewers. Related to:
- https://github.com/huggingface/datasets-server/pull/988#discussion_r1150767138

Related to:
- https://huggingface.co/datasets/natural_questions/discussions/4
- https://huggingface.co/datasets/wiki40b/discussions/2
- https://huggingface.co/datasets/wikipedia/discussions/9

CC: @severo 