Extremely Slow Loading of Parquet Dataset with datasets

John6666 · April 30, 2025, 1:23am

It may indeed be a parquet issue. Some say that it works fine with numpy. @lhoestq

Slow iteration speeds when using IterableDataset.shuffle with load_dataset(data_files=..., streaming=True)

opened 09:44PM - 14 Aug 24 UTC

### Describe the bug When I load a dataset from a number of arrow files, as i…n: ``` random_dataset = load_dataset( "arrow", data_files={split: shard_filepaths}, streaming=True, split=split, ) ``` I'm able to get fast iteration speeds when iterating over the dataset without shuffling. When I shuffle the dataset, the iteration speed is reduced by ~1000x. It's very possible the way I'm loading dataset shards is not appropriate; if so please advise! Thanks for the help ### Steps to reproduce the bug Here's full code to reproduce the issue: - Generate a random dataset - Create shards of data independently using Dataset.save_to_disk() - The below will generate 16 shards (arrow files), of 512 examples each ``` import time from pathlib import Path from multiprocessing import Pool, cpu_count import torch from datasets import Dataset, load_dataset split = "train" split_save_dir = "/tmp/random_split" def generate_random_example(): return { 'inputs': torch.randn(128).tolist(), 'indices': torch.randint(0, 10000, (2, 20000)).tolist(), 'values': torch.randn(20000).tolist(), } def generate_shard_dataset(examples_per_shard: int = 512): dataset_dict = { 'inputs': [], 'indices': [], 'values': [] } for _ in range(examples_per_shard): example = generate_random_example() dataset_dict['inputs'].append(example['inputs']) dataset_dict['indices'].append(example['indices']) dataset_dict['values'].append(example['values']) return Dataset.from_dict(dataset_dict) def save_shard(shard_idx, save_dir, examples_per_shard): shard_dataset = generate_shard_dataset(examples_per_shard) shard_write_path = Path(save_dir) / f"shard_{shard_idx}" shard_dataset.save_to_disk(shard_write_path) return str(Path(shard_write_path) / "data-00000-of-00001.arrow") def generate_split_shards(save_dir, num_shards: int = 16, examples_per_shard: int = 512): with Pool(cpu_count()) as pool: args = [(m, save_dir, examples_per_shard) for m in range(num_shards)] shard_filepaths = pool.starmap(save_shard, args) return shard_filepaths shard_filepaths = generate_split_shards(split_save_dir) ``` Load the dataset as IterableDataset: ``` random_dataset = load_dataset( "arrow", data_files={split: shard_filepaths}, streaming=True, split=split, ) random_dataset = random_dataset.with_format("numpy") ``` Observe the iterations/second when iterating over the dataset directly, and applying shuffling before iterating: Without shuffling, this gives ~1500 iterations/second ``` start_time = time.time() for count, item in enumerate(random_dataset): if count > 0 and count % 100 == 0: elapsed_time = time.time() - start_time iterations_per_second = count / elapsed_time print(f"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second") ``` ``` Processed 100 items at an average of 705.74 iterations/second Processed 200 items at an average of 1169.68 iterations/second Processed 300 items at an average of 1497.97 iterations/second Processed 400 items at an average of 1739.62 iterations/second Processed 500 items at an average of 1931.11 iterations/second` ``` When shuffling, this gives ~3 iterations/second: ``` random_dataset = random_dataset.shuffle(buffer_size=100,seed=42) start_time = time.time() for count, item in enumerate(random_dataset): if count > 0 and count % 100 == 0: elapsed_time = time.time() - start_time iterations_per_second = count / elapsed_time print(f"Processed {count} items at an average of {iterations_per_second:.2f} iterations/second") ``` ``` Processed 100 items at an average of 3.75 iterations/second Processed 200 items at an average of 3.93 iterations/second ``` ### Expected behavior Iterations per second should be barely affected by shuffling, especially with a small buffer size ### Environment info Datasets version: 2.21.0 Python 3.10 Ubuntu 22.04

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Extremely Slow Loading of Parquet Dataset with datasets

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