Running `datase.map` with `num_proc > 1` uses a lot of memory
Thanks for reporting ! We are working on this and we'll do a patch release very soon.
## Describe the bug
Running `datase.map` with `num_proc > 1`  leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.

## Steps to reproduce the bug
```python
from datasets import load_dataset

dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False)


def _prepare_sample(batch):
    return {"input_ids": list(), "attention_mask": list()}


for split_name, dataset_split in list(dstc8_datset.items()):
    print(f"Processing {split_name}")
    encoded_dataset_split = dataset_split.map(
        function=_prepare_sample,
        batched=True,
        num_proc=4,
        remove_columns=dataset_split.column_names,
        batch_size=10,
        writer_batch_size=10,
        keep_in_memory=False,
    )
    print(encoded_dataset_split)

    path = f"./data/encoded_{split_name}"

    encoded_dataset_split.save_to_disk(path)
```

## Expected results
Memory usage should stay within reasonable boundaries.


## Actual results
This is htop-output from running the provided script.

![image](https://user-images.githubusercontent.com/8143425/115954836-66954980-a4f3-11eb-8340-0153bdc3a475.png)

## Versions
```
- Datasets: 1.6.0
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10
```
Running on WSL2

Running `datase.map` with `num_proc > 1` uses a lot of memory 
 ## Describe the bug
Running `datase.map` with `num_proc > 1`  leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.

## Steps to reproduce the bug
```python
from datasets import load_dataset

dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False)


def _prepare_sample(batch):
    return {"input_ids": list(), "attention_mask": list()}


for split_name, dataset_split in list(dstc8_datset.items()):
    print(f"Processing {split_name}")
    encoded_dataset_split = dataset_split.map(
        function=_prepare_sample,
        batched=True,
        num_proc=4,
        remove_columns=dataset_split.column_names,
        batch_size=10,
        writer_batch_size=10,
        keep_in_memory=False,
    )
    print(encoded_dataset_split)

    path = f"./data/encoded_{split_name}"

    encoded_dataset_split.save_to_disk(path)
```

## Expected results
Memory usage should stay within reasonable boundaries.


## Actual results
This is htop-output from running the provided script.

![image](https://user-images.githubusercontent.com/8143425/115954836-66954980-a4f3-11eb-8340-0153bdc3a475.png)

## Versions
```
- Datasets: 1.6.0
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26)
[GCC 7.3.0]
- Platform: Linux-4.19.128-microsoft-standard-x86_64-with-glibc2.10
```
Running on WSL2
 
 Thanks for reporting ! We are working on this and we'll do a patch release very soon.