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Efficient data storage format optimized for random-access reads, especially in machine learning workflows

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# Barecat

**[Documentation](https://barecat.readthedocs.io)** |
**[API Reference](https://istvansarandi.com/docs/barecat/api/barecat/Barecat.html)** |
**[GitHub](https://github.com/isarandi/barecat)**

Barecat (**bare** con**cat**enation) is a highly scalable, simple aggregate storage format for
storing many (tens of millions and more) small files, with focus on fast random access and
minimal overhead.

Barecat can be thought of as a simple filesystem, or as something akin to an indexed tarball, or a
key-value store. Indeed, it can be [mounted via FUSE](https://github.com/isarandi/barecat-mount), converted to a tarball, or used like a dictionary
within Python.

Barecat associates strings (file paths) with binary data (file contents). It's like a dictionary,
but it has some special handling for '/' characters in the keys, supporting a filesystem-like
experience (`listdir`, `walk`, `glob`, etc).

Internally, all the data is simply concatenated one after another into one or more data shard files.
Additionally, an index is maintained in an SQLite database, which stores the shard number, the offset
and the size of each inner file (as well as a checksum, and further filesystem-like metadata
like modification time). Barecat also maintains aggregate statistics for each directory, such as the
total number of files and total file size.

![Architecture](https://raw.githubusercontent.com/isarandi/barecat/main/figure.png)

As you can see, the Barecat format is very simple. Readers/writers are easy to write in any language, since
SQLite is a widely-supported format.

## Installation

```bash
pip install barecat
```

Optional extras:

```bash
pip install barecat[mount]   # FUSE mount support
```

Requires Python 3.9+.

## Background

A typical use case for Barecat is storing image files for training deep learning models, where the
files are accessed randomly during training. The files are typically stored on a network file
system, where accessing many small files can be slow, and clusters often put a limit on the number
of files of a user. So it is necessary to somehow merge the small files into big ones.
However, typical archive formats such as tar are not suitable, since they don't allow fast random
lookups. In tar, one has to scan the entire archive as there is no central directory.
Zip is better, but still requires scanning the central directory, which can be slow for very large
archives with millions or tens of millions of files.

We need an index into the archive, and the index itself cannot be required to be loaded
into memory, to support very large datasets.

Therefore, in this format the metadata is indexed separately in an SQLite database for fast lookup
based on paths. The index also allows fast listing of directory contents and contains aggregate
statistics (total file size, number of files) for each directory.

## Features

- **Fast random access**: The archive can be accessed randomly, addressed by filepath,
  without having to scan the entire archive or all the metadata.
  The index is stored in a separate SQLite database file, which itself does not need to be loaded
  entirely into memory. Ideal for storing training image data for deep learning jobs.
- **Sharding**: To make it easier to move the data around or to distribute it across multiple
  storage devices, the archive can be split into multiple files of equal size (shards, or volumes).
  The shards do not have to be concatenated to be used, the library will keep all shard files open
  and load data from the appropriate one during normal operations.
- **Fast browsing**: The SQLite database contains an index for the parent directories, allowing
  fast listing of directory contents and aggregate statistics (total file size, number of files).
- **Intuitive API**: Familiar filesystem-like API, as well as a dictionary-like one.
- **Mountable**: The archive can be efficiently mounted in readonly or read-write mode via FUSE.
- **Simple storage format**: The files are simply concatenated after each other and the index contains
  the offsets and sizes of each file. There is no header format to understand. The index can be
  dumped into any format with simple SQL queries.

## Command Line Interface

Barecat provides a unified `barecat` command with subcommands:

```bash
# Create archive from files and directories
barecat create mydata.barecat /path/to/images/

# Create from find output
find /data -name '*.jpg' -print0 | barecat create -0 mydata.barecat -T -

# Extract archive
barecat extract mydata.barecat -C /output/

# List contents
barecat list mydata.barecat
barecat list -l mydata.barecat         # Long format with sizes

# Add files to an existing archive
barecat add mydata.barecat newdir/

# Print a file to stdout
barecat cat mydata.barecat path/to/file.txt

# Convert from/to tar or zip
barecat convert data.tar.gz data.barecat

# Interactive shell
barecat shell mydata.barecat

# Merge multiple archives
barecat merge -o combined.barecat archive1.barecat archive2.barecat

# Verify integrity
barecat verify mydata.barecat

# Defragment after deletions
barecat defrag mydata.barecat

# Mount via FUSE (requires barecat[mount])
barecat mount mydata.barecat /mnt/mydata
```

This may yield the following files:

```
mydata.barecat                # SQLite index database
mydata.barecat-shard-00000    # Data shard 0
mydata.barecat-shard-00001    # Data shard 1
```

The files can be extracted back out. Unix-like permissions, modification times, and owner info are
preserved.

See the [documentation](https://barecat.readthedocs.io) for the full CLI reference.

## Python API

```python
import barecat

# Write
with barecat.Barecat('mydata.barecat', readonly=False) as bc:
    bc['path/to/file.jpg'] = binary_file_data
    bc.add_by_path('path/to/file/on/disk.jpg')

with open('local_file.bin', 'rb') as f:
        bc.add('path/in/archive.bin', fileobj=f)

# Read
with barecat.Barecat('mydata.barecat') as bc:
    binary_file_data = bc['path/to/file.jpg']
    entrynames = bc.listdir('path/to')

for root, dirs, files in bc.walk('path/to/something'):
        print(root, dirs, files)

paths = bc.glob('path/to/**/*.jpg', recursive=True)

with bc.open('path/to/file.jpg', 'rb') as f:
        data = f.read(123)
```

### Automatic Encoding/Decoding

Wrap the archive with `DecodedView` for automatic serialization based on file extension:

```python
from barecat import Barecat, DecodedView
import numpy as np

with Barecat('data.barecat', readonly=False) as bc:
    dec = DecodedView(bc)
    dec['config.json'] = {'learning_rate': 0.001}
    dec['weights.npy'] = np.random.randn(100, 100)

with Barecat('data.barecat') as bc:
    dec = DecodedView(bc)
    config = dec['config.json']   # Returns dict
    weights = dec['weights.npy']  # Returns numpy array
```

Supported formats: `.json`, `.pkl`, `.npy`, `.npz`, `.msgpack`, images (`.png`, `.jpg`, etc.),
with stackable compression (`.gz`, `.bz2`, `.xz`).

## Documentation

Full documentation is available at [barecat.readthedocs.io](https://barecat.readthedocs.io).

## License

MIT

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