indexed-parquet-dataset 0.3.10.dev0

High-performance O(1) random access indexer for Parquet datasets in PyTorch

Indexed Parquet Dataset

Indexed Parquet Dataset is a high-performance Python library for O(1) random access to massive datasets in Parquet format.

It is specifically optimized for Deep Learning (PyTorch), consumes minimal memory, and supports advanced features such as Schema Evolution (working with files of different schemas in a single dataset).

Key Features

• ⚡ O(1) Random Access: Instantly navigate to any row in a multi-gigabyte dataset without scanning files.
• 🔄 Schema Evolution: Work with datasets where files have different schemas, missing columns, or renamed fields.
• 📦 Lazy Loading: Files are opened only when data is requested. Features an efficient LRU handle cache.
• 🔥 PyTorch Integration: Native support for torch.utils.data.Dataset, including adaptive collate_fn generation.
• 🛠️ Fluent API: Method chaining: shuffle (global or locality-aware), filter, alias, split, limit, rename, cast, map.
• 💾 Index Persistence: Save and fast-load the index from a file.
• 🏗️ Materialization: "Bake" all transformations into new Parquet files via clone().

Architecture

The library remains lightweight, storing only metadata and a row map in RAM:

graph TD
    subgraph RAM ["Application (RAM - Lightweight)"]
        direction TB
        subgraph DS ["IndexedParquetDataset"]
            Indices["Indices Array [np.ndarray]<br/>(Shuffled/Filtered indices)"]
            Meta["Metadata & Schema<br/>(File offsets, column mapping)"]
            Cache["File Handle Cache<br/>(Lazy Loading LRU)"]
        end
        
        User["User Code / PyTorch DataLoader"] -- "dataset[idx]" --> Indices
        Indices -- "Global Index" --> Meta
        Meta -- "Find File & Row Offset" --> Cache
    end
    
    subgraph Storage ["Storage (HDD/SSD/S3-over-FUSE)"]
        F1["data_part_1.parquet"]
        F2["data_part_2.parquet"]
        FN["data_part_N.parquet"]
    end
    
    Cache -- "Lazy Read" --> F1
    Cache -- "Lazy Read" --> F2
    Cache -- "Lazy Read" --> FN
    
    F1 -. "O(1) Row Retrieval" .-> User
    F2 -. "O(1) Row Retrieval" .-> User
    FN -. "O(1) Row Retrieval" .-> User

Installation

From PyPI:

pip install indexed-parquet-dataset

For PyTorch support:

pip install "indexed-parquet-dataset[torch]"

Quickstart

Basic Initialization

from indexed_parquet_dataset import IndexedParquetDataset

# Scans the folder and builds a global row index
ds = IndexedParquetDataset.from_folder("./path/to/data")

print(f"Total rows: {len(ds)}")
print(f"First row: {ds[0]}") # {'id': 1, 'text': '...', ...}

# Random access to any row is instant
sample = ds[999_999]

Transformations (Fluent API)

ds = (IndexedParquetDataset.from_folder("./data")
      .filter(lambda x: x["score"] > 0.5)
      .shuffle(seed=42, rg_buffer=32) # Locality-aware shuffle for best I/O performance
      .alias("text_len", lambda x: len(x["text"]))
      .limit(10000))

# Each row now has a virtual 'text_len' column
print(ds[0]["text_len"])

Usage with PyTorch

from torch.utils.data import DataLoader

ds = IndexedParquetDataset.from_folder("./data", auto_fill=True)
train_ds, val_ds = ds.train_test_split(test_size=0.1)

loader = DataLoader(
    train_ds, 
    batch_size=32, 
    shuffle=True, 
    num_workers=4,
    collate_fn=ds.generate_collate_fn(on_none='fill')
)

Documentation

Full documentation is available on GitHub Pages.

License

Apache 2.0 License