Filter on dataset too much slowww

I have a dataset with 50M rows.
For pre-processing, I need to tokenize this and filter rows with the large sequence.

My tokenization took roughly 12mins. I used `map()` with batch size 1024 and multi-process with 96 processes.

When I applied the `filter()` function it is taking too much time. I need to filter sequences based on a boolean column.
Below are the variants I tried.
1. filter() with batch size 1024, single process (takes roughly 3 hr)
2. filter() with batch size 1024, 96 processes (takes 5-6 hrs ¯\\\_(ツ)\_/¯)
3. filter() with loading all data in memory, only a single boolean column (never ends).

Can someone please help?

Below is a sample code for small dataset.

```
from datasets import load_dataset
dataset = load_dataset('glue', 'mrpc', split='train')
dataset = dataset.map(lambda x: {'flag': random.randint(0,1)==1})

def _amplify(data):
        return data

dataset = dataset.filter(_amplify, batch_size=1024, keep_in_memory=False, input_columns=['flag'])
```



When I use the filter on the arrow table directly, it works like butter. But I can't find a way to update the table in `Dataset` object.

```
ds_table = dataset.data.filter(mask=dataset['flag'])
```

@thomwolf @lhoestq can you guys please take a look and recommend some solution.

Hi ! Currently the filter method reads the dataset batch by batch to write a new, filtered, arrow file on disk. Therefore all the reading + writing can take some time.
Using a mask directly on the arrow table doesn't do any read or write operation therefore it's way quicker.

Replacing the old table by the new one should do the job:
```python
dataset._data = dataset._data.filter(...)
```

Note: this is a **workaround** and in general users shouldn't have to do that. In particular if you did some `shuffle` or `select` before that then it would not work correctly since the indices mapping (index from `__getitem__` -> index in the table) would not be valid anymore. But if you haven't done any `shuffle`, `select`, `shard`, `train_test_split` etc. then it should work.

Ideally it would be awesome to update the filter function to allow masking this way !
If you would like to give it a shot I will be happy to help :) 

Yes, would be happy to contribute. Thanks

Hi @lhoestq @ayubSubhaniya,

If there's no progress on this one, can I try working on it?

Thanks,
Gunjan

Sure @gchhablani feel free to start working on it, this would be very appreciated :)
This feature is would be really awesome, especially since arrow allows to mask really quickly and without having to rewrite the dataset on disk

Hi @lhoestq, any updates on this issue? The `filter` method is still veryyy slow 😕 

No update so far, we haven't worked on this yet :/

Though PyArrow is much more stable than 3 years ago so it would be a good time to dive into this

Hi @lhoestq, thanks a lot for the update! 

I would like to work on this(if possible). Could you please give me some steps regarding how should I approach this? Also any references would be great! 

I just played a bit with it to make sure using `table.filter()` is fine, but actually it seems to create a new table **in memory** :/
This is an issue since it can quickly fill the RAM, and `datasets`'s role is to make sure you can load bigger-than-memory datasets. Therefore I don't think it's a good idea in the end to use `table.filter()`

Anyway I just ran OP's code an it runs in 20ms now on my side thanks to the I/O optimizations we did.

Another way to speed up `filter` is to add support pyarrow expressions though, using e.g. arrow formatting + dataset.filter (runs in 10ms on my side):

```python
import pyarrow.dataset as pds
import pyarrow.compute as pc

expr = pc.field("flag") == True  # or any condition

filtered = dataset.with_format("arrow").filter(
    lambda t: pds.dataset(t).to_table(columns={"mask": expr})[0].to_numpy(),
    batched=True,
).with_format(None)
```

or simply with numpy:

```python
filtered = dataset.with_format("np").filter(
    lambda flags_array: flags_array == True,  # or any condition
    input_columns=["flag"]
    batched=True,
).with_format(None)

I can confirm that it's still extremely slow to use the `filter` function, and even trying workarounds like iterating on the dataset to create a mask for the `select` function is very slow (10-20 it/s) on a very small dataset with 5 fields.

You can iterate faster on the dataset if you format it with zero-copy formats like `with_format("arrow")`, `with_format("numpy")` or `with_format("polars")`. This is useful to use arrow/numpy/polars functions in map() or filter() and accelerate your data pipeline

I have also noticed that passing the input_columns parameter speeds up the operation by a few orders of magnitude. It was especially helpful with my image-containing dataset.

Hi! Triaging older issues this week — I think this one can be closed.

- Filed Jan 2021 against an old `datasets` version. The Arrow-backed `filter()` path was substantially reworked since: the current implementation at [`src/datasets/arrow_dataset.py`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py) (search for `def filter` / `def _map_single`) supports multiprocessing via `num_proc`, batched filtering via `batched=True`, and writes the result through the same Arrow writer pipeline as `map()`.
- For "filter on a boolean column", the recommended fast path today is either `.filter(lambda x: x["col"], num_proc=...)` with batching, or simply [`Dataset.from_pandas(df[df.col])`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py) if the data fits in memory. Both are dramatically faster than the original ~3h figure quoted here.

Would you mind closing this out? If you have a current-version repro where filter is still pathologically slow on a boolean column, happy to look at it as a fresh issue.

(Disclosure: I drafted this comment with help from Claude Code while triaging stale issues; the references above were verified manually against the current `main` branch at commit 19302564.)