Issue: concatenate_datasets loads all the data into memory
State: closed
Labels: name: bug

## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.

Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.

![image](https://user-images.githubusercontent.com/7063207/116420321-2b21b480-a83e-11eb-9006-8f6ca729fb6f.png)


## Steps to reproduce the bug
```python
from datasets import concatenate_datasets, load_from_disk

test_sampled_pro = load_from_disk("test_sampled_pro")
val_sampled_pro = load_from_disk("val_sampled_pro")

big_set = concatenate_datasets([test_sampled_pro, val_sampled_pro])

# Loaded to memory
big_set.save_to_disk("big_set")

# Loaded to memory
big_set = concatenate_datasets([big_set, val_sampled_pro])
```

## Expected results
The data should be loaded into memory in batches and then saved directly to disk.

## Actual results
The entire data set is loaded into the memory and then saved to the hard disk.

## Versions
Paste the output of the following code:
```python
- Datasets: 1.6.1
- Python: 3.8.8 (default, Apr 13 2021, 19:58:26) 
[GCC 7.3.0]
- Platform: Linux-5.4.72-microsoft-standard-WSL2-x86_64-with-glibc2.10
```


Therefore, when I try to concatenate larger datasets (5x 35GB data sets) I also get an out of memory error, since over 90GB of swap space was used at the time of the crash:

```
---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-6-9766d77530b9> in <module>
     20         print(file_name)
     21         cv_batch = load_from_disk(file_name)
---> 22         cv_sampled_train = concatenate_datasets([cv_sampled_train, cv_batch])
     23 
     24 print("Saving to disk!")

C:\ProgramData\Anaconda3\lib\site-packages\datasets\arrow_dataset.py in concatenate_datasets(dsets, info, split, axis)
   2891 
   2892     # Concatenate tables
-> 2893     table = concat_tables([dset._data for dset in dsets if len(dset._data) > 0], axis=axis)
   2894     table = update_metadata_with_features(table, None)
   2895 

C:\ProgramData\Anaconda3\lib\site-packages\datasets\table.py in concat_tables(tables, axis)
    837     if len(tables) == 1:
    838         return tables[0]
--> 839     return ConcatenationTable.from_tables(tables, axis=axis)
    840 
    841 

C:\ProgramData\Anaconda3\lib\site-packages\datasets\table.py in from_tables(cls, tables, axis)
    697             return result
    698 
--> 699         blocks = to_blocks(tables[0])
    700         for table in tables[1:]:
    701             table_blocks = to_blocks(table)

C:\ProgramData\Anaconda3\lib\site-packages\datasets\table.py in to_blocks(table)
    669                 return [[InMemoryTable(table)]]
    670             elif isinstance(table, ConcatenationTable):
--> 671                 return copy.deepcopy(table.blocks)
    672             else:
    673                 return [[table]]

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_list(x, memo, deepcopy)
    203     append = y.append
    204     for a in x:
--> 205         append(deepcopy(a, memo))
    206     return y
    207 d[list] = _deepcopy_list

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_list(x, memo, deepcopy)
    203     append = y.append
    204     for a in x:
--> 205         append(deepcopy(a, memo))
    206     return y
    207 d[list] = _deepcopy_list

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    151             copier = getattr(x, "__deepcopy__", None)
    152             if copier is not None:
--> 153                 y = copier(memo)
    154             else:
    155                 reductor = dispatch_table.get(cls)

C:\ProgramData\Anaconda3\lib\site-packages\datasets\table.py in __deepcopy__(self, memo)
    143         # by adding it to the memo, self.table won't be copied
    144         memo[id(self.table)] = self.table
--> 145         return _deepcopy(self, memo)
    146 
    147     def __getstate__(self):

C:\ProgramData\Anaconda3\lib\site-packages\datasets\table.py in _deepcopy(x, memo)
     62     memo[id(x)] = result
     63     for k, v in x.__dict__.items():
---> 64         setattr(result, k, copy.deepcopy(v, memo))
     65     return result
     66 

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_list(x, memo, deepcopy)
    203     append = y.append
    204     for a in x:
--> 205         append(deepcopy(a, memo))
    206     return y
    207 d[list] = _deepcopy_list

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    170                     y = x
    171                 else:
--> 172                     y = _reconstruct(x, memo, *rv)
    173 
    174     # If is its own copy, don't memoize.

C:\ProgramData\Anaconda3\lib\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    262     if deep and args:
    263         args = (deepcopy(arg, memo) for arg in args)
--> 264     y = func(*args)
    265     if deep:
    266         memo[id(x)] = y

C:\ProgramData\Anaconda3\lib\copy.py in <genexpr>(.0)
    261     deep = memo is not None
    262     if deep and args:
--> 263         args = (deepcopy(arg, memo) for arg in args)
    264     y = func(*args)
    265     if deep:

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_list(x, memo, deepcopy)
    203     append = y.append
    204     for a in x:
--> 205         append(deepcopy(a, memo))
    206     return y
    207 d[list] = _deepcopy_list

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    170                     y = x
    171                 else:
--> 172                     y = _reconstruct(x, memo, *rv)
    173 
    174     # If is its own copy, don't memoize.

C:\ProgramData\Anaconda3\lib\copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    262     if deep and args:
    263         args = (deepcopy(arg, memo) for arg in args)
--> 264     y = func(*args)
    265     if deep:
    266         memo[id(x)] = y

C:\ProgramData\Anaconda3\lib\copy.py in <genexpr>(.0)
    261     deep = memo is not None
    262     if deep and args:
--> 263         args = (deepcopy(arg, memo) for arg in args)
    264     y = func(*args)
    265     if deep:

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_tuple(x, memo, deepcopy)
    208 
    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 210     y = [deepcopy(a, memo) for a in x]
    211     # We're not going to put the tuple in the memo, but it's still important we
    212     # check for it, in case the tuple contains recursive mutable structures.

C:\ProgramData\Anaconda3\lib\copy.py in <listcomp>(.0)
    208 
    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 210     y = [deepcopy(a, memo) for a in x]
    211     # We're not going to put the tuple in the memo, but it's still important we
    212     # check for it, in case the tuple contains recursive mutable structures.

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_list(x, memo, deepcopy)
    203     append = y.append
    204     for a in x:
--> 205         append(deepcopy(a, memo))
    206     return y
    207 d[list] = _deepcopy_list

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_tuple(x, memo, deepcopy)
    208 
    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 210     y = [deepcopy(a, memo) for a in x]
    211     # We're not going to put the tuple in the memo, but it's still important we
    212     # check for it, in case the tuple contains recursive mutable structures.

C:\ProgramData\Anaconda3\lib\copy.py in <listcomp>(.0)
    208 
    209 def _deepcopy_tuple(x, memo, deepcopy=deepcopy):
--> 210     y = [deepcopy(a, memo) for a in x]
    211     # We're not going to put the tuple in the memo, but it's still important we
    212     # check for it, in case the tuple contains recursive mutable structures.

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    144     copier = _deepcopy_dispatch.get(cls)
    145     if copier is not None:
--> 146         y = copier(x, memo)
    147     else:
    148         if issubclass(cls, type):

C:\ProgramData\Anaconda3\lib\copy.py in _deepcopy_list(x, memo, deepcopy)
    203     append = y.append
    204     for a in x:
--> 205         append(deepcopy(a, memo))
    206     return y
    207 d[list] = _deepcopy_list

C:\ProgramData\Anaconda3\lib\copy.py in deepcopy(x, memo, _nil)
    159                     reductor = getattr(x, "__reduce_ex__", None)
    160                     if reductor is not None:
--> 161                         rv = reductor(4)
    162                     else:
    163                         reductor = getattr(x, "__reduce__", None)

C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\io.pxi in pyarrow.lib.Buffer.__reduce_ex__()

C:\ProgramData\Anaconda3\lib\site-packages\pyarrow\io.pxi in pyarrow.lib.Buffer.to_pybytes()

MemoryError: 

```

Hi ! this looks like an important issue. Let me try to reproduce this.
Cc @samsontmr this might be related to the memory issue you have in #2134 

@lhoestq Just went to open a similar issue.

It seems like deep copying (tested on master) the dataset object writes the table's record batches (`dset._data._batches`) into RAM.

To find the bug, I modified the `_deepcopy` function in `table.py` as follows:
```python
def _deepcopy(x, memo: dict):
    """deepcopy a regular class instance"""
    import psutil # pip install this package
    import time
    cls = x.__class__
    result = cls.__new__(cls)
    memo[id(x)] = result
    for k, v in x.__dict__.items():
        print("="* 50)
        print("Current memory:", psutil.virtual_memory().percent)
        print(f"Saving object {k} with value {v}")
        setattr(result, k, copy.deepcopy(v, memo))
        time.sleep(5)
        print("Memory after copy:", psutil.virtual_memory().percent)
    return result
```
Test script:
```python
import copy
from datasets import load_dataset
bk = load_dataset("bookcorpus", split="train")
bk_copy = copy.deepcopy(bk)
```

Thanks for the insights @mariosasko ! I'm working on a fix.
Since this is a big issue I'll make a patch release as soon as this is fixed

Hi @samsontmr @TaskManager91 the fix is on the master branch, feel free to install `datasets` from source and let us know if you still have issues

We just released `datasets` 1.6.2 that includes the fix :)

thanks it works like a charm! :)