`sort` after `filter` unreasonably  slow
`filter` add an indices mapping on top of the dataset, so `sort` has to gather all the rows that are kept to form a new Arrow table and sort the table. Gathering all the rows can take some time, but is a necessary step. You can try calling `ds = ds.flatten_indices()` before sorting to remove the indices mapping. > `filter` add an indices mapping on top of the dataset, so `sort` has to gather all the rows that are kept to form a new Arrow table and sort the table. Gathering all the rows can take some time, but is a necessary step. You can try calling `ds = ds.flatten_indices()` before sorting to remove the indices mapping.

This worked, thank you so much.
### Describe the bug

as the tittle says ...

### Steps to reproduce the bug

`sort` seems to be normal.

```python
from datasets import Dataset
import random

nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]

ds = Dataset.from_list(nums)

print("start sort")

ds = ds.sort("k")

print("finish sort")
```

but `sort` after `filter` is extremely slow.

```python
from datasets import Dataset
import random

nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]

ds = Dataset.from_list(nums)

ds = ds.filter(lambda x:x > 100, input_columns="k")

print("start sort")

ds = ds.sort("k")

print("finish sort")
```


### Expected behavior

Is this a bug, or is it a misuse of the `sort` function?

### Environment info

- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0