Compression Support

def register_compression(name, callback, extensions, force=False):
    """
    Register a compression format.
    
    Parameters:
    - name: str, compression format name
    - callback: callable, function that returns file-like object for decompression
    - extensions: list of str, file extensions associated with this format
    - force: bool, whether to overwrite existing registration
    """

def available_compressions():
    """
    List all available compression formats.
    
    Returns:
    list of str, compression format names
    """

# GZIP compression (.gz files)
'gzip': Uses gzip module for compression/decompression

# BZIP2 compression (.bz2 files)  
'bz2': Uses bz2 module for compression/decompression

# LZMA compression (.lzma, .xz files)
'lzma': Uses lzma module for compression/decompression

# ZIP archive format (.zip files)
'zip': Uses zipfile module for archive access

# Snappy compression (requires python-snappy)
'snappy': Fast compression optimized for speed over ratio

# LZ4 compression (requires lz4) 
'lz4': Ultra-fast compression with .lz4 extension

# Zstandard compression (requires zstandard)
'zstd': Modern compression with .zst extension, good speed/ratio balance

# fsspec automatically detects compression from file extensions

# Reading compressed files
with fsspec.open('data.csv.gz', 'rt') as f:
    # Automatically decompressed
    content = f.read()

with fsspec.open('logs.txt.bz2', 'rt') as f:
    for line in f:
        process_line(line)

with fsspec.open('archive.tar.xz', 'rb') as f:
    # LZMA decompression
    data = f.read()

# Force specific compression format
with fsspec.open('data.csv', 'rt', compression='gzip') as f:
    content = f.read()

# Override automatic detection
with fsspec.open('file.gz', 'rt', compression='bz2') as f:
    # Treats .gz file as bz2 compressed
    content = f.read()

# Disable compression for .gz file
with fsspec.open('not-compressed.gz', 'rt', compression=None) as f:
    # Reads raw file without decompression
    content = f.read()

# Write compressed data
with fsspec.open('output.csv.gz', 'wt') as f:
    # Automatically compressed using gzip
    f.write('column1,column2\n')
    f.write('value1,value2\n')

# Write with explicit compression
with fsspec.open('output.txt', 'wt', compression='bz2') as f:
    f.write('This will be bz2 compressed\n')

# S3 files with compression
with fsspec.open('s3://bucket/data.csv.gz', 'rt') as f:
    df = pd.read_csv(f)

# HTTP files with compression  
with fsspec.open('https://example.com/data.json.gz', 'rt') as f:
    data = json.load(f)

# GCS files with compression
with fsspec.open('gcs://bucket/logs.txt.bz2', 'rt') as f:
    for line in f:
        process_log(line)

# Process files with different compression formats
files = [
    's3://bucket/data1.csv.gz',     # gzip
    's3://bucket/data2.csv.bz2',    # bzip2
    's3://bucket/data3.csv.xz',     # lzma
    's3://bucket/data4.csv'         # uncompressed
]

dataframes = []
for file_path in files:
    with fsspec.open(file_path, 'rt') as f:
        # Compression automatically handled
        df = pd.read_csv(f)
        dataframes.append(df)

combined_df = pd.concat(dataframes)

# Access files within ZIP archives
with fsspec.open('zip://data.csv::archive.zip', 'rt') as f:
    # Reads data.csv from within archive.zip
    content = f.read()

# Remote ZIP archives
with fsspec.open('zip://data.csv::s3://bucket/archive.zip', 'rt') as f:
    content = f.read()

import fsspec
import my_compression_lib

def my_compression_opener(file, mode='rb'):
    """Custom compression opener function."""
    if 'r' in mode:
        return my_compression_lib.decompress_file(file)
    elif 'w' in mode:
        return my_compression_lib.compress_file(file)
    else:
        raise ValueError(f"Unsupported mode: {mode}")

# Register custom compression format
fsspec.compression.register_compression(
    name='myformat',
    callback=my_compression_opener,
    extensions=['.mycomp', '.mc']
)

# Now use custom compression
with fsspec.open('data.txt.mycomp', 'rt') as f:
    content = f.read()

# Choose compression based on use case

# For speed-critical applications
with fsspec.open('data.csv.lz4', 'rt') as f:  # Fast decompression
    df = pd.read_csv(f)

# For space-critical applications  
with fsspec.open('data.csv.xz', 'rt') as f:   # High compression ratio
    df = pd.read_csv(f)

# For general use
with fsspec.open('data.csv.gz', 'rt') as f:   # Good balance
    df = pd.read_csv(f)

# Compression works with caching layers
with fsspec.open('s3://bucket/large-data.csv.gz', 
                 'rt',
                 cache_type='blockcache',
                 block_size=1024*1024) as f:
    # Compressed data is cached, decompression happens after cache
    df = pd.read_csv(f)

import concurrent.futures

def process_compressed_file(file_path):
    with fsspec.open(file_path, 'rt') as f:
        return len(f.read())

# Process multiple compressed files in parallel
compressed_files = [
    's3://bucket/file1.csv.gz',
    's3://bucket/file2.csv.bz2', 
    's3://bucket/file3.csv.xz'
]

with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
    results = list(executor.map(process_compressed_file, compressed_files))

# Check what compression formats are available
available = fsspec.compression.available_compressions()
print("Available compression formats:", available)

# Check for specific format
if 'lz4' in available:
    print("LZ4 compression is available")
    with fsspec.open('data.csv.lz4', 'rt') as f:
        content = f.read()
else:
    print("LZ4 not available, using gzip")
    with fsspec.open('data.csv.gz', 'rt') as f:
        content = f.read()

try:
    with fsspec.open('data.csv.gz', 'rt') as f:
        content = f.read()
except ImportError as e:
    print(f"Compression library not available: {e}")
except OSError as e:
    print(f"Compression error (possibly corrupted file): {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

# Read compressed CSV directly into pandas
df = pd.read_csv(fsspec.open('s3://bucket/data.csv.gz', 'rt'))

# Write compressed CSV from pandas
with fsspec.open('output.csv.gz', 'wt') as f:
    df.to_csv(f, index=False)

# Read compressed JSON
with fsspec.open('config.json.gz', 'rt') as f:
    config = json.load(f)

# Write compressed JSON
with fsspec.open('output.json.bz2', 'wt') as f:
    json.dump(data, f, indent=2)

# Read compressed numpy array
with fsspec.open('array.npy.gz', 'rb') as f:
    array = np.load(f)

# Write compressed numpy array  
with fsspec.open('output.npy.gz', 'wb') as f:
    np.save(f, array)