Caching System

class BaseCache:
    """Base class for caching implementations."""
    
    def __init__(self, blocksize, fetcher, size, **kwargs):
        """
        Initialize cache.
        
        Parameters:
        - blocksize: int, size of cache blocks
        - fetcher: callable, function to fetch data
        - size: int, total size of cached object
        - **kwargs: additional cache-specific options
        """
    
    def _fetch(self, start, end):
        """
        Fetch data range.
        
        Parameters:
        - start: int, start byte offset
        - end: int, end byte offset
        
        Returns:
        bytes, fetched data
        """
    
    def _read_cache(self, start, end):
        """
        Read from cache if available.
        
        Parameters:
        - start: int, start byte offset  
        - end: int, end byte offset
        
        Returns:
        bytes or None, cached data or None if not cached
        """

class MMapCache(BaseCache):
    """Memory-mapped file cache for efficient random access."""
    
    def __init__(self, blocksize, fetcher, size, location=None, blocks=None):
        """
        Initialize memory-mapped cache.
        
        Parameters:
        - blocksize: int, size of cache blocks
        - fetcher: callable, function to fetch data
        - size: int, total size of cached object
        - location: str, local file path for memory mapping
        - blocks: set, specific blocks to cache
        """

class ReadAheadCache(BaseCache):
    """Read-ahead cache optimized for sequential access patterns."""
    
    def __init__(self, blocksize, fetcher, size, maxblocks=32):
        """
        Initialize read-ahead cache.
        
        Parameters:
        - blocksize: int, size of cache blocks
        - fetcher: callable, function to fetch data
        - size: int, total size of cached object
        - maxblocks: int, maximum number of blocks to cache
        """

class BlockCache(BaseCache):
    """Block-based cache with LRU eviction policy."""
    
    def __init__(self, blocksize, fetcher, size, maxblocks=32):
        """
        Initialize block cache.
        
        Parameters:
        - blocksize: int, size of cache blocks
        - fetcher: callable, function to fetch data
        - size: int, total size of cached object
        - maxblocks: int, maximum number of blocks to keep in cache
        """

class BytesCache(BaseCache):
    """In-memory bytes cache for small files."""
    
    def __init__(self, blocksize, fetcher, size, **kwargs):
        """
        Initialize bytes cache.
        
        Parameters:
        - blocksize: int, size of cache blocks
        - fetcher: callable, function to fetch data
        - size: int, total size of cached object
        """

class BackgroundBlockCache(BaseCache):
    """Block cache with background prefetching capabilities."""
    
    def __init__(self, blocksize, fetcher, size, maxblocks=32):
        """
        Initialize background block cache.
        
        Parameters:
        - blocksize: int, size of cache blocks
        - fetcher: callable, function to fetch data
        - size: int, total size of cached object
        - maxblocks: int, maximum number of blocks to cache
        """

caches: dict
    """
    Mapping of cache names to cache classes.
    
    Available caches:
    - 'mmap': MMapCache
    - 'readahead': ReadAheadCache  
    - 'blockcache': BlockCache
    - 'bytes': BytesCache
    - 'background': BackgroundBlockCache
    """

# Use specific cache type when opening files
with fsspec.open('s3://bucket/large-file.dat', cache_type='mmap') as f:
    # File uses memory-mapped caching
    data = f.read(1024)

# Use block cache with custom parameters
with fsspec.open('s3://bucket/file.dat', 
                 cache_type='blockcache',
                 block_size=1024*1024,
                 maxblocks=64) as f:
    data = f.read()

# Sequential reading - use read-ahead cache
with fsspec.open('s3://bucket/log-file.txt', 
                 cache_type='readahead', 
                 block_size=64*1024) as f:
    for line in f:
        process_line(line)

# Random access - use memory-mapped cache
with fsspec.open('s3://bucket/database.dat',
                 cache_type='mmap',
                 block_size=4096) as f:
    # Jump to different positions efficiently
    f.seek(1000000)
    data1 = f.read(100)
    f.seek(5000000)
    data2 = f.read(100)

# Small files - use bytes cache
with fsspec.open('s3://bucket/config.json',
                 cache_type='bytes') as f:
    config = json.load(f)

# Use background cache for predictable access patterns
with fsspec.open('s3://bucket/time-series.dat',
                 cache_type='background',
                 block_size=1024*1024,
                 maxblocks=16) as f:
    # Cache will prefetch subsequent blocks in background
    for i in range(0, file_size, chunk_size):
        f.seek(i)
        chunk = f.read(chunk_size)
        process_chunk(chunk)

# Configure caching at filesystem level
s3 = fsspec.filesystem('s3', 
                      key='ACCESS_KEY', 
                      secret='SECRET_KEY',
                      default_cache_type='blockcache',
                      default_block_size=1024*1024)

# All files opened through this filesystem use the cache settings
with s3.open('bucket/file1.dat') as f:
    data1 = f.read()

with s3.open('bucket/file2.dat') as f:
    data2 = f.read()

# Tune cache parameters for specific workloads

# Large files with sequential access
large_file_cache = {
    'cache_type': 'readahead',
    'block_size': 8 * 1024 * 1024,  # 8MB blocks
    'maxblocks': 4  # Keep 32MB in memory
}

# Database-like files with random access
random_access_cache = {
    'cache_type': 'mmap',
    'block_size': 64 * 1024,  # 64KB blocks
    'maxblocks': 256  # Keep 16MB in memory
}

# Many small files
small_files_cache = {
    'cache_type': 'bytes'  # Cache entire file
}

# Open files with appropriate cache settings
with fsspec.open('s3://bucket/large.dat', **large_file_cache) as f:
    process_large_file(f)

with fsspec.open('s3://bucket/index.db', **random_access_cache) as f:
    lookup_data(f)

with fsspec.open('s3://bucket/config.json', **small_files_cache) as f:
    config = json.load(f)

# Access cache statistics (implementation-dependent)
with fsspec.open('s3://bucket/file.dat', cache_type='blockcache') as f:
    # Perform operations
    data = f.read(1024*1024)
    
    # Some cache implementations provide statistics
    if hasattr(f.cache, 'hit_count'):
        print(f"Cache hits: {f.cache.hit_count}")
        print(f"Cache misses: {f.cache.miss_count}")
        print(f"Hit ratio: {f.cache.hit_count / (f.cache.hit_count + f.cache.miss_count)}")

# Caching works with compression
with fsspec.open('s3://bucket/data.csv.gz',
                 compression='gzip',
                 cache_type='readahead',
                 block_size=1024*1024) as f:
    # Compressed data is cached, decompression happens after cache
    df = pd.read_csv(f)

# Control where cache files are stored (for persistent caches)
import tempfile

cache_dir = tempfile.mkdtemp()

with fsspec.open('s3://bucket/large-file.dat',
                 cache_type='mmap',
                 cache_storage=cache_dir) as f:
    # Memory-mapped cache file stored in cache_dir
    data = f.read()

# Cache files persist after closing
# Subsequent opens can reuse cached data

# Clear caches when needed
fs = fsspec.filesystem('s3')

# Clear cache for specific file
fs.invalidate_cache('bucket/file.dat')

# Clear all cached data for this filesystem
fs.invalidate_cache()

# Clear all filesystem instances (nuclear option)
fsspec.AbstractFileSystem.clear_instance_cache()