Support parallelized downloading and processing in load_dataset with Spark

### Feature request

When calling `load_dataset` for datasets that have multiple files, support using Spark to distribute the downloading and processing job to worker nodes when `cache_dir` is a cloud file system shared among nodes.

```python
load_dataset(..., use_spark=True)
```

### Motivation

Further speed up `dl_manager.download` and `_prepare_split` by distributing the workloads to worker nodes. 

### Your contribution

I can submit a PR to support this.

Comments:
Hi ! We're using process pools for parallelism right now. I was wondering if there's a package that implements the same API as a process pool but runs with Spark under the hood ? That or something similar would be cool because users could use whatever distributed framework they want this way.

Feel free to ping us when you'd like to open PRs for this kind of things, so that we can discuss this before you start working on it ^^

Hi, thanks for taking a look and providing your input! I don't know of such packages, and even it exists, I don't think with the process pool API it's possible to run Spark as backend properly; otherwise I understand a unified API would be preferable.

The process pool API requires splitting the workload to a fixed number parts for multiprocessing; meanwhile distributed framework such as Spark has sophisticated scheduler to distribute the workload to the processes on multiple machines in a cluster, so the way of splitting things for `multiprocessing.pool` would not suit / be as flexible as directly calling the `sparkContext.parallelize` API.

I think this could be a good addition to scale the `datasets` implementation to distributed workers, and from my benchmark results so far it looks promising compared with multiprocessing.

I see ! I think we only need an equivalent of `pool.map`. We use it to run download and conversion of data files on disk. That would require less changes in the internal code - and therefore less tests to write ;)

We also use `pool.apply_async` in some places with a `Queue` to get progress updates of the running jobs. I'm mentioning this in case there's a way to get a python generator from a running spark job ? This is less important though

For Spark, `rdd.map` (where `rdd` can be created by `sparkContext.parallelize`) is the most similar as `pool.map`, but it requires creating a Spark RDD first that is used for distributing the `iterable` and the actual parallelization is managed by the Spark framework; `pool.map` takes the splits of `iterable` that are split into `num_proc` parts by the Python code. You can also check my PR #5807 in the `src/datasets/utils/py_utils.py` file to compare the differences of the APIs, it might make more sense than the the above description.

Given the different inputs and mechanisms of calling the `map` functions, this is why I think it's not that feasible to reuse most of the `multiprocessing` code.

Progress bar updating might be challenging with Spark, I'll consider it as a followup work.

Indeed I think the current use of multiprocessing.Pool in `map_nested` can be rewritten to work like `sparkContext.parallelize` - without splitting the iterable.

Maybe from the user's perspective it's ok to let multiprocessing.Pool or spark distribute the load on their own, as long as it takes a list and runs jobs in parallel in the end :)


From your feedback, seems to me there are two paths to consider now for supporting spark's `map` function in `map_nested` now:
1. Keep the current `pool.map` implementation, and add an if statement for the spark's `map` code (which is what I did in my current PR) -- the code change is just a few lines in the `map_nested` function, and it has been tested by unit tests + manual testing on real Spark clusters; if you have other concerns I'd also be happy to address them.
2. Rewrite the current `pool.map` implementation to remove splitting the iterable, and we will still need to add an if statement to use either
```python
with Pool(...) as pool:
    mapped = pool.map(_single_map_nested, iterable)
```
or
```python
rdd = spark.sparkContext.parallelize(iterable)
mapped = rdd.map(lambda obj: _single_map_nested((function, obj, types, None, True, None))).collect()
```
because there is no unified API that supports both `pool.map` and `rdd.map`. This can be more unified and flexible in the long run, but might require more work, and it will change the existing multiprocessing behavior, which is why I'm not leaning towards this option.

Am I understanding correctly?

Yup correct ! I think it's a nice path because it would be possible for users to define whatever parallel processing backend they want. I think we still need to discuss how that would look like in the `datasets` API : how to specify it has to use the "spark" parallel backend ? And how to specify the spark session parameters (number of executors etc.) ? Maybe there is something more practical than `use_spark=True`

I'll check with the team internally if they have some ideas, but feel free to share your thoughts here !

Sure, please let me know if you have more updates regarding the API and implementation from the team.

For parameters we don't need to worry about setting them for Spark, because Spark will figure out the environment / number of worker nodes by itself, so it's preferable to just provide some parameter such as `use_spark` to use the RDD `map` function.

Hi! I wanted to check in to see if there is any update from the team.

A potential change of API I can think of is change the argument to `distributed_backend=...`, which accepts `str`, such as `load_dataset(..., distributed_backend="spark")`.

Implementation wise, we can add a class / function to abstract away the details of using multiprocessing vs. spark vs. other parallel processing frameworks in `map_nested` and `_prepare_split`.

I found this quite interesting: https://github.com/joblib/joblib-spark with this syntax:

```python
with parallel_backend('spark', n_jobs=3):
    ...
```

cc @lu-wang-dl who might know better

Joblib spark is providing Spark backend for joblib. We can implement a general parallel backend like
```
with parallel_backend("<parallel-backedn>", n_jobs=..):
```

It can support multiprocessing , spark, ray, and etc. https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend

Thank you @lhoestq for finding this repo. I validated that it can distribute downloading jobs with Spark to arbitrary cluster worker nodes evenly with `n_jobs=-1`.

For the API, I think it makes sense to define it as
```python
load_dataset(..., parallel_backend=<str>)
```
where `parallel_backend` can be `spark`, `multiprocessing`,  and potentially other supported joblib backends including `ray` and `dask`.

Implementation-wise, do you think it is better to just use `joblib` for `spark` backend in `map_nested`, or also migrate the `multiprocessing.Pool` code to use `joblib`?

Hello @lhoestq, I wanted to follow up on my previous comment with some prototyping code that demonstrates how `map_nested` would be like if we unify `multiprocessing` and `spark` with `joblib`. The snippet hasn't hashed out the details such as dealing with `tqdm` yet.

In terms of API, the way of using multiprocessing is still the same; for Spark, the user sets `parallel_backend='spark'`  can reuse the `num_proc` argument to pass in the number of executors, or preferably, just set `num_proc=-1` and joblib is able to decide it (I've validated it by running it on a Spark cluster).

```python
def map_nested(
  # ... same args
  parallel_backend: Optional[str] = None, # proposed new argument
):

  # ... same code

  # allow user to specify num_proc=-1, so that joblib will optimize it
  if (num_proc <= 1 and num_proc != -1) or len(iterable) < parallel_min_length:
    # same code
    mapped = [
      _single_map_nested((function, obj, types, None, True, None))
      for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
    ]
  else:
    if not parallel_backend:
      parallel_backend = 'loky' # 'loky' is joblib's own implementation of robust multiprocessing
    
    n_jobs = min(num_proc, len(iterable))

    if parallel_backend == 'spark':
      n_jobs = -1 # 'loky' is joblib's own implementation of robust multiprocessing
      from joblibspark import register_spark
      register_spark()

    # parallelized with the same API
    with joblib.parallel_backend(parallel_backend, n_jobs=n_jobs):
      mapped = joblib.Parallel()(
        joblib.delayed(
          _single_map_nested((function, obj, types, None, True, None))
        )(obj) for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
      )
  
  # ... same code
```
We can always `joblib` for Spark and other distributed backends such as Ray if people want to support them later. It's worth noting that some distributed backends do not currently have `joblib` implementations.

I would appreciate your thoughts on this proposed new API. We can also discuss the pros and cons of migrating the `multiprocessing` code to `joblib` later.

Nice ! It should be quite easy to make the change then :)

I think adding spark support can actually be less than 20 lines of code and would roughly require one line of code to change in map_nested:

Maybe we can define a new `datasets.parallel` submodule that has the `parallel_backend()` context manager and a `parallel_map()` function that uses `Pool.map` by default and `joblib` otherwise.

`joblib` would be an optional dependency, and `joblib-spark` as well.

Then whenever someone wants to use Spark, they can do something like this (similar to scikit-learn parallel_backend):

```python
from datasets.parallel import parallel_backend

with parallel_backend("spark"):
    ds = load_dataset(...)
```

What do you think ?

Although until we've switched to all the steps in `load_dataset` to use `datasets.parallel`, I would require the user to explicitly say which step should use Spark. Maybe something like this, but I'm not sure yet:

```python
from datasets.parallel import parallel_backend

with parallel_backend("spark", steps=["download"]):
    ds = load_dataset(...)
```
for now some steps can be NotImplemented:
```python
from datasets.parallel import parallel_backend

with parallel_backend("spark", steps=["download", "prepare"]):
# NotImplementedError: the "prepare" step that converts the raw data files to Arrow is not compatible with the "spark" backend yet
```

This way we can progressively roll out Spark support for the other data loading/processing steps without breaking changes between `datasets` versions

Sounds good! I like the partial rollout idea.
So for example `map_nested` would call `parallel_map` under the hood if `num_proc != 1` or `parallel_backend` is specified right?
I would be happy to start a PR next week to explore this path.

Awesome ! I think map_nested can call `parallel_map()` if num_proc > 1, and `parallel_map` can be responsible to use Pool.map by default or joblib.