Faster column validation and reordering
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6636). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. Thanks @mariosasko, I made the changes. However, I did some tests with `map` and I still saw that it took ~3.5 minutes per batch on 6000 features when using `dataset.map(lambda x: x, batched=True)`. From the profile, the culprits were mainly with `ArrowWriter.write_batch` and `ArrowWriter._build_writer`. The slow down from `_build_writer` is due to updating existing features with the inferred ones. I don't think this can be optimized any further, but fortunately, I can avoid this by setting the `features` in `map`. On the other hand, `write_batch` selects cols based on intersection and difference between schema names and example keys using two for loops. The same exists in `ArrowWriter.write_examples_on_file`. Optimizing the column selection using set operations effectively brings it from 3.5 minutes per batch down to 6 seconds per batch. Can we add these changes along with this PR?

Edit: Ah just realized you can avoid the issue with inferring features altogether when you set the format to arrow (or pandas). <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) | 0.004990 / 0.011353 (-0.006363) | 0.003138 / 0.011008 (-0.007870) | 0.062368 / 0.038508 (0.023860) | 0.028634 / 0.023109 (0.005524) | 0.241297 / 0.275898 (-0.034601) | 0.264433 / 0.323480 (-0.059047) | 0.003133 / 0.007986 (-0.004852) | 0.003444 / 0.004328 (-0.000885) | 0.048522 / 0.004250 (0.044271) | 0.043700 / 0.037052 (0.006648) | 0.257054 / 0.258489 (-0.001435) | 0.277551 / 0.293841 (-0.016290) | 0.027132 / 0.128546 (-0.101414) | 0.010395 / 0.075646 (-0.065251) | 0.208003 / 0.419271 (-0.211269) | 0.035814 / 0.043533 (-0.007719) | 0.250098 / 0.255139 (-0.005041) | 0.266726 / 0.283200 (-0.016474) | 0.018424 / 0.141683 (-0.123259) | 1.129242 / 1.452155 (-0.322912) | 1.167674 / 1.492716 (-0.325042) |

### 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) | 0.091808 / 0.018006 (0.073802) | 0.298726 / 0.000490 (0.298236) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 0.000054 (-0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019119 / 0.037411 (-0.018292) | 0.061969 / 0.014526 (0.047443) | 0.073392 / 0.176557 (-0.103165) | 0.119460 / 0.737135 (-0.617675) | 0.074072 / 0.296338 (-0.222266) |

### 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) | 0.281435 / 0.215209 (0.066226) | 2.702094 / 2.077655 (0.624439) | 1.411541 / 1.504120 (-0.092579) | 1.284084 / 1.541195 (-0.257111) | 1.302638 / 1.468490 (-0.165852) | 0.562420 / 4.584777 (-4.022357) | 2.364890 / 3.745712 (-1.380822) | 2.744033 / 5.269862 (-2.525828) | 1.699000 / 4.565676 (-2.866677) | 0.062315 / 0.424275 (-0.361961) | 0.004982 / 0.007607 (-0.002625) | 0.334385 / 0.226044 (0.108341) | 3.203268 / 2.268929 (0.934339) | 1.766998 / 55.444624 (-53.677627) | 1.497164 / 6.876477 (-5.379313) | 1.509996 / 2.142072 (-0.632077) | 0.633014 / 4.805227 (-4.172213) | 0.115317 / 6.500664 (-6.385347) | 0.041120 / 0.075469 (-0.034349) |

### 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) | 0.965877 / 1.841788 (-0.875911) | 11.219909 / 8.074308 (3.145601) | 9.333822 / 10.191392 (-0.857570) | 0.136482 / 0.680424 (-0.543941) | 0.013632 / 0.534201 (-0.520569) | 0.287251 / 0.579283 (-0.292032) | 0.262786 / 0.434364 (-0.171578) | 0.322893 / 0.540337 (-0.217444) | 0.418180 / 1.386936 (-0.968756) |

</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) | 0.005444 / 0.011353 (-0.005909) | 0.003147 / 0.011008 (-0.007862) | 0.049242 / 0.038508 (0.010734) | 0.030944 / 0.023109 (0.007834) | 0.281901 / 0.275898 (0.006003) | 0.303820 / 0.323480 (-0.019660) | 0.004326 / 0.007986 (-0.003659) | 0.002696 / 0.004328 (-0.001632) | 0.048306 / 0.004250 (0.044055) | 0.044145 / 0.037052 (0.007093) | 0.297253 / 0.258489 (0.038764) | 0.324062 / 0.293841 (0.030221) | 0.046724 / 0.128546 (-0.081823) | 0.010079 / 0.075646 (-0.065567) | 0.057635 / 0.419271 (-0.361636) | 0.033621 / 0.043533 (-0.009912) | 0.282303 / 0.255139 (0.027164) | 0.300761 / 0.283200 (0.017561) | 0.017116 / 0.141683 (-0.124567) | 1.156519 / 1.452155 (-0.295636) | 1.216087 / 1.492716 (-0.276630) |

### 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) | 0.093011 / 0.018006 (0.075005) | 0.301310 / 0.000490 (0.300820) | 0.000223 / 0.000200 (0.000023) | 0.000053 / 0.000054 (-0.000001) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023112 / 0.037411 (-0.014299) | 0.075192 / 0.014526 (0.060666) | 0.086213 / 0.176557 (-0.090343) | 0.125853 / 0.737135 (-0.611282) | 0.087754 / 0.296338 (-0.208585) |

### 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) | 0.301095 / 0.215209 (0.085886) | 2.911769 / 2.077655 (0.834114) | 1.614708 / 1.504120 (0.110588) | 1.494497 / 1.541195 (-0.046698) | 1.506978 / 1.468490 (0.038488) | 0.572743 / 4.584777 (-4.012034) | 2.417142 / 3.745712 (-1.328570) | 2.755338 / 5.269862 (-2.514523) | 1.711026 / 4.565676 (-2.854650) | 0.062732 / 0.424275 (-0.361543) | 0.005031 / 0.007607 (-0.002576) | 0.352343 / 0.226044 (0.126298) | 3.465183 / 2.268929 (1.196255) | 1.958795 / 55.444624 (-53.485829) | 1.682239 / 6.876477 (-5.194238) | 1.688897 / 2.142072 (-0.453176) | 0.643311 / 4.805227 (-4.161916) | 0.115426 / 6.500664 (-6.385238) | 0.040338 / 0.075469 (-0.035131) |

### 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) | 1.005322 / 1.841788 (-0.836466) | 11.779380 / 8.074308 (3.705072) | 10.041574 / 10.191392 (-0.149818) | 0.127617 / 0.680424 (-0.552807) | 0.015840 / 0.534201 (-0.518361) | 0.286905 / 0.579283 (-0.292378) | 0.275180 / 0.434364 (-0.159183) | 0.332498 / 0.540337 (-0.207840) | 0.410719 / 1.386936 (-0.976217) |

</details>
</details>

![](https://cml.dev/watermark.png#32b206d47f582380f9c64578dcfa6c48252db3b8 "CML watermark")

I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by  `set(columns) - (self._data.column_names)` is more efficient. 