Improve default patterns resolution
Opened by mariosasko (closed)

Separate the default patterns that match directories from the ones matching files and ensure directories are checked first (reverts the change from https://github.com/huggingface/datasets/pull/6244, which merged these patterns). Also, ensure that the glob patterns do not overlap to avoid duplicates in the result. 

Additionally, replace `get_fs_token_paths` with `url_to_fs` to avoid [unnecessary glob calls](https://github.com/fsspec/filesystem_spec/blob/14dce8ca78f7aa509a20edb263bff83a7760c24d/fsspec/core.py#L655-L656).

fix https://github.com/huggingface/datasets/issues/6259
fix https://github.com/huggingface/datasets/issues/6272

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6704). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Awesome !

Note that it can still create duplicates if a path matches several dir patterns, e.g.

```
data/train-train/data/txt
```
matches two dir patterns:
```
**/{keyword}[{sep}]*/**
**/*[{sep}]{keyword}/**
```

PS: feel free to update your branch, I just updated ruff on `main`

Yes, I didn't mention that case on purpose 🙂. One solution would be deprecating the `**/*[{sep}]{keyword}/**` pattern (and eventually removing it). This way, the directory patterns would align more with the filename ones. Or do you think this is too big of a breaking change?

I think it's too big of a breaking change yes :/ (and would make the docs / logic more complex for users to get imo) Though I think your approach is already a nice step in the right direction

These changes to the `resolve_pattern` function lead to 20-30x faster local file resolution in my benchmarks.

Nice ! Though since `fsspec` caches the filesystem, is there a risk when adding new files and reloading a dataset ?


```python
with open("my/local/dir/0000.txt", "w") as f:
    f.write("Hello there")
d1 = load_dataset("my/local/dir")
with open("my/local/dir/0001.txt", "w") as f:
    f.write("General Kenobi")
d2 = load_dataset("my/local/dir")
assert list(d1) != list(d2)
```

Yes. But I think I have a solution for this.

I'm not satisfied with the context manager approach...

A clean solution would require a bigger rewrite of the resolution logic (e.g., merging `get_data_patterns` and `DataFilesDict.from_patterns` into a `get_data_files` function that would build the `DataFilesDict` by matching the paths using `fs.find` and `fsspec.utils.glob_translate` (available in `fsspec>=2023.12.0`))

The current changes make the local resolution 2-3x faster, which is good enough for now, I think.

<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) | 0.004888 / 0.011353 (-0.006465) | 0.003267 / 0.011008 (-0.007742) | 0.065117 / 0.038508 (0.026609) | 0.029416 / 0.023109 (0.006306) | 0.232021 / 0.275898 (-0.043877) | 0.258053 / 0.323480 (-0.065427) | 0.003971 / 0.007986 (-0.004014) | 0.002550 / 0.004328 (-0.001779) | 0.049126 / 0.004250 (0.044876) | 0.040620 / 0.037052 (0.003568) | 0.253437 / 0.258489 (-0.005052) | 0.273583 / 0.293841 (-0.020258) | 0.026775 / 0.128546 (-0.101771) | 0.010073 / 0.075646 (-0.065573) | 0.219089 / 0.419271 (-0.200183) | 0.035047 / 0.043533 (-0.008486) | 0.247661 / 0.255139 (-0.007478) | 0.258674 / 0.283200 (-0.024525) | 0.018428 / 0.141683 (-0.123255) | 1.130394 / 1.452155 (-0.321761) | 1.173167 / 1.492716 (-0.319549) |

### 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) | 0.092581 / 0.018006 (0.074574) | 0.303657 / 0.000490 (0.303167) | 0.000215 / 0.000200 (0.000015) | 0.000051 / 0.000054 (-0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018640 / 0.037411 (-0.018771) | 0.062032 / 0.014526 (0.047506) | 0.073982 / 0.176557 (-0.102575) | 0.121499 / 0.737135 (-0.615636) | 0.076780 / 0.296338 (-0.219559) |

### 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) | 0.279411 / 0.215209 (0.064202) | 2.737977 / 2.077655 (0.660322) | 1.454135 / 1.504120 (-0.049985) | 1.343144 / 1.541195 (-0.198051) | 1.339876 / 1.468490 (-0.128614) | 0.567306 / 4.584777 (-4.017471) | 2.372569 / 3.745712 (-1.373143) | 2.716810 / 5.269862 (-2.553052) | 1.697895 / 4.565676 (-2.867782) | 0.061804 / 0.424275 (-0.362471) | 0.004986 / 0.007607 (-0.002622) | 0.332721 / 0.226044 (0.106676) | 3.274572 / 2.268929 (1.005644) | 1.789900 / 55.444624 (-53.654725) | 1.536346 / 6.876477 (-5.340131) | 1.551940 / 2.142072 (-0.590132) | 0.634539 / 4.805227 (-4.170688) | 0.115860 / 6.500664 (-6.384805) | 0.041737 / 0.075469 (-0.033732) |

### 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) | 1.024469 / 1.841788 (-0.817319) | 11.327496 / 8.074308 (3.253188) | 9.265855 / 10.191392 (-0.925537) | 0.142200 / 0.680424 (-0.538224) | 0.013945 / 0.534201 (-0.520256) | 0.289670 / 0.579283 (-0.289614) | 0.269240 / 0.434364 (-0.165124) | 0.324748 / 0.540337 (-0.215590) | 0.421393 / 1.386936 (-0.965543) |

</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) | 0.005284 / 0.011353 (-0.006069) | 0.003351 / 0.011008 (-0.007658) | 0.049973 / 0.038508 (0.011465) | 0.030257 / 0.023109 (0.007148) | 0.273660 / 0.275898 (-0.002238) | 0.300328 / 0.323480 (-0.023152) | 0.004133 / 0.007986 (-0.003852) | 0.002614 / 0.004328 (-0.001715) | 0.048055 / 0.004250 (0.043804) | 0.044731 / 0.037052 (0.007678) | 0.290257 / 0.258489 (0.031768) | 0.321243 / 0.293841 (0.027402) | 0.029542 / 0.128546 (-0.099004) | 0.010074 / 0.075646 (-0.065573) | 0.057944 / 0.419271 (-0.361327) | 0.051267 / 0.043533 (0.007734) | 0.276278 / 0.255139 (0.021139) | 0.302464 / 0.283200 (0.019264) | 0.018231 / 0.141683 (-0.123452) | 1.140782 / 1.452155 (-0.311373) | 1.182991 / 1.492716 (-0.309725) |

### 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) | 0.092325 / 0.018006 (0.074319) | 0.302610 / 0.000490 (0.302121) | 0.000202 / 0.000200 (0.000002) | 0.000049 / 0.000054 (-0.000005) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021458 / 0.037411 (-0.015954) | 0.074883 / 0.014526 (0.060357) | 0.085747 / 0.176557 (-0.090809) | 0.125506 / 0.737135 (-0.611629) | 0.086921 / 0.296338 (-0.209417) |

### 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) | 0.290485 / 0.215209 (0.075276) | 2.853898 / 2.077655 (0.776243) | 1.615606 / 1.504120 (0.111486) | 1.491797 / 1.541195 (-0.049397) | 1.515981 / 1.468490 (0.047491) | 0.566760 / 4.584777 (-4.018017) | 2.462593 / 3.745712 (-1.283119) | 2.765516 / 5.269862 (-2.504345) | 1.755078 / 4.565676 (-2.810598) | 0.063614 / 0.424275 (-0.360661) | 0.005040 / 0.007607 (-0.002567) | 0.347957 / 0.226044 (0.121912) | 3.464258 / 2.268929 (1.195330) | 1.992273 / 55.444624 (-53.452351) | 1.699147 / 6.876477 (-5.177330) | 1.868438 / 2.142072 (-0.273635) | 0.660756 / 4.805227 (-4.144471) | 0.118142 / 6.500664 (-6.382522) | 0.041974 / 0.075469 (-0.033495) |

### 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) | 1.012206 / 1.841788 (-0.829581) | 12.343735 / 8.074308 (4.269427) | 10.321975 / 10.191392 (0.130583) | 0.140007 / 0.680424 (-0.540417) | 0.015755 / 0.534201 (-0.518446) | 0.291978 / 0.579283 (-0.287305) | 0.278792 / 0.434364 (-0.155572) | 0.325366 / 0.540337 (-0.214972) | 0.439403 / 1.386936 (-0.947533) |

</details>
</details>

![](https://cml.dev/watermark.png#d1d3c06a651c6ad5142f331cb5dc0008ddcade33 "CML watermark")


This change is breaking in 

https://github.com/huggingface/datasets/blob/f96e74d5c633cd5435dd526adb4a74631eb05c43/src/datasets/arrow_dataset.py#L1515

when the input is `pathlib.Path`. The issue is that `url_to_fs` expects a `str` and cannot deal with `Path`. `get_fs_token_paths` converts to `str` so it is not a problem

I opened https://github.com/huggingface/datasets/pull/6828 to add proper Path support to save_to_disk / load_from_disk