Support streaming datasets with pyarrow.parquet.read_table
State: closed

Support streaming datasets with `pyarrow.parquet.read_table`.

See: https://huggingface.co/datasets/uonlp/CulturaX/discussions/2

CC: @AndreaFrancis 

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

This function reads an entire Arrow table in one go, which is not ideal memory-wise, so I don't think we should encourage using this function, considering we want to keep RAM usage as low as possible in the streaming mode. 

(Note that Parquet files are compressed, meaning the loaded table can be significantly larger than the size in Parquet.)

Instead, we should suggest the authors to use:
```python
with open(doc_path, "rb") as f:
    parquet_file = pq.ParquetFile(f)
    for batch in parquet_file.iter_batches():
        pa_table = pa.Table.from_batches([batch])
        yield idx, pa_table
        idx += 1
```

@mariosasko I think the potential problem you evoke is independent of whether or not we support streaming mode:
- if the user's script with `read_table` works in non-streaming mode, it will also work in streaming mode after this PR

In fact, what we should suggest instead is to follow the scriptless approach, so that our `parquet` packaged module is used, with all the optimizations implemented. But this approach is not possible in all cases, and some use cases need to implement a script. And if they have small Parquet files and use `read_table`, I think we should support streaming.

In summary, let me clarify the goal and the scope of this PR:
- a user needs using a loading script
- their files are small enough so that they use `read_table`
- their loading script works in non-streaming mode
- therefore, this PR allows loading their dataset in streaming mode as well

Yes, the no-script approach with metadata configs makes the most sense.

> their files are small enough so that they use read_table

Some of the Parquet files in that repo are larger than 1 GB ...

Also, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.

@mariosasko, yes, this solution is not specifically for the "uonlp/CulturaX" dataset, but for other use cases as I explained above: indeed, they finally removed the use of `read_table` because their data files are too large.

> Also, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.

Do you know how many datasets are currently using `read_table`?

> Do you know how many datasets are currently using read_table?

Zero (based on the script that checks the script contents of the public Hub datasets). 

I see... Thanks! :hugs: 

@mariosasko thanks for pointing the script! :hugs: 

However, I have found some Hub datasets that are using `read_table`, e.g.:
- https://huggingface.co/datasets/jglaser/protein_ligand_contacts
- https://huggingface.co/datasets/AresEkb/prof_standards_sbert_large_mt_nlu_ru
- https://huggingface.co/datasets/victorcosta/pt_legislation
- https://huggingface.co/datasets/jglaser/binding_affinity
- https://huggingface.co/datasets/jglaser/pdbbind_complexes
- https://huggingface.co/datasets/victorcosta/ria_pt__proems_format

I'm merging this PR as discussed in private.

<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) | 0.008267 / 0.011353 (-0.003086) | 0.005813 / 0.011008 (-0.005195) | 0.108802 / 0.038508 (0.070294) | 0.093996 / 0.023109 (0.070886) | 0.403115 / 0.275898 (0.127217) | 0.457299 / 0.323480 (0.133819) | 0.006277 / 0.007986 (-0.001709) | 0.004701 / 0.004328 (0.000373) | 0.080700 / 0.004250 (0.076449) | 0.077906 / 0.037052 (0.040854) | 0.409972 / 0.258489 (0.151483) | 0.477707 / 0.293841 (0.183867) | 0.041816 / 0.128546 (-0.086731) | 0.011250 / 0.075646 (-0.064397) | 0.390634 / 0.419271 (-0.028637) | 0.065361 / 0.043533 (0.021828) | 0.404501 / 0.255139 (0.149362) | 0.448162 / 0.283200 (0.164962) | 0.032823 / 0.141683 (-0.108860) | 1.899892 / 1.452155 (0.447737) | 2.044561 / 1.492716 (0.551844) |

### 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) | 0.241093 / 0.018006 (0.223086) | 0.482111 / 0.000490 (0.481622) | 0.005505 / 0.000200 (0.005305) | 0.000094 / 0.000054 (0.000039) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.034861 / 0.037411 (-0.002551) | 0.109296 / 0.014526 (0.094770) | 0.127594 / 0.176557 (-0.048962) | 0.191815 / 0.737135 (-0.545320) | 0.122630 / 0.296338 (-0.173709) |

### 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) | 0.452194 / 0.215209 (0.236985) | 4.486200 / 2.077655 (2.408545) | 2.155635 / 1.504120 (0.651515) | 2.004569 / 1.541195 (0.463374) | 2.142570 / 1.468490 (0.674080) | 0.561488 / 4.584777 (-4.023289) | 4.381102 / 3.745712 (0.635390) | 3.914920 / 5.269862 (-1.354942) | 2.474271 / 4.565676 (-2.091406) | 0.067528 / 0.424275 (-0.356747) | 0.008723 / 0.007607 (0.001116) | 0.536077 / 0.226044 (0.310033) | 5.342050 / 2.268929 (3.073122) | 2.735747 / 55.444624 (-52.708877) | 2.353938 / 6.876477 (-4.522539) | 2.442878 / 2.142072 (0.300805) | 0.685404 / 4.805227 (-4.119823) | 0.156657 / 6.500664 (-6.344007) | 0.071714 / 0.075469 (-0.003755) |

### 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) | 1.562852 / 1.841788 (-0.278935) | 24.538203 / 8.074308 (16.463895) | 16.857777 / 10.191392 (6.666385) | 0.184221 / 0.680424 (-0.496203) | 0.021688 / 0.534201 (-0.512513) | 0.470700 / 0.579283 (-0.108583) | 0.470593 / 0.434364 (0.036229) | 0.645066 / 0.540337 (0.104729) | 0.756075 / 1.386936 (-0.630861) |

</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) | 0.009486 / 0.011353 (-0.001867) | 0.004694 / 0.011008 (-0.006314) | 0.080216 / 0.038508 (0.041708) | 0.093479 / 0.023109 (0.070369) | 0.537353 / 0.275898 (0.261455) | 0.551631 / 0.323480 (0.228151) | 0.007373 / 0.007986 (-0.000613) | 0.004044 / 0.004328 (-0.000285) | 0.075301 / 0.004250 (0.071051) | 0.069408 / 0.037052 (0.032355) | 0.527962 / 0.258489 (0.269473) | 0.559423 / 0.293841 (0.265582) | 0.039351 / 0.128546 (-0.089195) | 0.010801 / 0.075646 (-0.064845) | 0.092803 / 0.419271 (-0.326468) | 0.058876 / 0.043533 (0.015343) | 0.513742 / 0.255139 (0.258603) | 0.574666 / 0.283200 (0.291466) | 0.030277 / 0.141683 (-0.111406) | 1.884936 / 1.452155 (0.432782) | 2.008260 / 1.492716 (0.515543) |

### 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) | 0.242162 / 0.018006 (0.224156) | 0.467400 / 0.000490 (0.466910) | 0.005348 / 0.000200 (0.005148) | 0.000103 / 0.000054 (0.000049) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.038022 / 0.037411 (0.000611) | 0.108239 / 0.014526 (0.093713) | 0.121514 / 0.176557 (-0.055042) | 0.184951 / 0.737135 (-0.552184) | 0.123138 / 0.296338 (-0.173200) |

### 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) | 0.558587 / 0.215209 (0.343377) | 5.740312 / 2.077655 (3.662657) | 3.172164 / 1.504120 (1.668044) | 2.852908 / 1.541195 (1.311713) | 2.894435 / 1.468490 (1.425945) | 0.586399 / 4.584777 (-3.998378) | 4.498342 / 3.745712 (0.752630) | 4.000569 / 5.269862 (-1.269292) | 2.610988 / 4.565676 (-1.954688) | 0.068415 / 0.424275 (-0.355860) | 0.008602 / 0.007607 (0.000994) | 0.614731 / 0.226044 (0.388686) | 6.068158 / 2.268929 (3.799229) | 3.301070 / 55.444624 (-52.143554) | 2.868034 / 6.876477 (-4.008443) | 2.959072 / 2.142072 (0.816999) | 0.684174 / 4.805227 (-4.121053) | 0.154099 / 6.500664 (-6.346565) | 0.070641 / 0.075469 (-0.004828) |

### 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) | 1.835667 / 1.841788 (-0.006120) | 24.981645 / 8.074308 (16.907337) | 17.218517 / 10.191392 (7.027125) | 0.197055 / 0.680424 (-0.483368) | 0.025465 / 0.534201 (-0.508736) | 0.523498 / 0.579283 (-0.055785) | 0.528268 / 0.434364 (0.093904) | 0.599518 / 0.540337 (0.059180) | 0.887206 / 1.386936 (-0.499730) |

</details>
</details>

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