Dictionary Compression

def train_dictionary(
    dict_size: int,
    samples: list[bytes],
    k: int = 0,
    d: int = 8,
    f: int = 20,
    split_point: float = 1.0,
    accel: int = 1,
    notifications: int = 0,
    dict_id: int = 0,
    level: int = 3,
    steps: int = 4,
    threads: int = 0
) -> ZstdCompressionDict:
    """
    Train a compression dictionary from sample data.

    Parameters:
    - dict_size: int, target dictionary size in bytes
    - samples: list[bytes], sample data for training
    - k: int, segment size parameter (0 = auto)
    - d: int, dmer size parameter (6-16, default 8)
    - f: int, log frequency parameter (default 20)
    - split_point: float, split point for training (0.0-1.0)
    - accel: int, acceleration parameter (1-10)
    - notifications: int, notification level (0=none, 1=basic, 2=verbose)
    - dict_id: int, dictionary ID (0 = auto-generate)
    - level: int, compression level for dictionary optimization
    - steps: int, training steps (1-4)
    - threads: int, number of threads (0 = auto)

    Returns:
    ZstdCompressionDict: Trained compression dictionary
    """

import zstandard as zstd

# Prepare sample data for training
samples = [
    b'{"name": "John", "age": 30, "city": "New York"}',
    b'{"name": "Jane", "age": 25, "city": "San Francisco"}',
    b'{"name": "Bob", "age": 35, "city": "Chicago"}',
    b'{"name": "Alice", "age": 28, "city": "Boston"}',
    # ... more similar JSON documents
]

# Train dictionary
dictionary = zstd.train_dictionary(
    dict_size=8192,  # 8KB dictionary
    samples=samples,
    level=5,
    threads=4
)

print(f"Dictionary size: {len(dictionary)} bytes")
print(f"Dictionary ID: {dictionary.dict_id()}")

class ZstdCompressionDict:
    def __init__(
        self,
        data: bytes,
        dict_type: int = DICT_TYPE_AUTO,
        k: int = 0,
        d: int = 0
    ):
        """
        Create a compression dictionary from raw dictionary data.

        Parameters:
        - data: bytes, raw dictionary data
        - dict_type: int, dictionary type (DICT_TYPE_AUTO, DICT_TYPE_RAWCONTENT, DICT_TYPE_FULLDICT)
        - k: int, segment size parameter
        - d: int, dmer size parameter
        """

    def __len__(self) -> int:
        """Get dictionary size in bytes."""

    def dict_id(self) -> int:
        """
        Get dictionary ID.

        Returns:
        int: Dictionary identifier
        """

    def as_bytes(self) -> bytes:
        """
        Get dictionary data as bytes.

        Returns:
        bytes: Raw dictionary data
        """

    def precompute_compress(
        self,
        level: int = 3,
        compression_params: ZstdCompressionParameters = None
    ):
        """
        Precompute compression tables for better performance.

        Parameters:
        - level: int, compression level to optimize for
        - compression_params: ZstdCompressionParameters, detailed parameters
        """

    # Properties
    k: int  # Segment size parameter
    d: int  # Dmer size parameter

import zstandard as zstd

# Load dictionary from file
with open('dictionary.zdict', 'rb') as f:
    dict_data = f.read()

# Create dictionary object
dictionary = zstd.ZstdCompressionDict(dict_data)

# Optimize for specific compression level
dictionary.precompute_compress(level=9)

# Get dictionary information
print(f"Dictionary size: {len(dictionary)} bytes")
print(f"Dictionary ID: {dictionary.dict_id()}")
print(f"Parameters: k={dictionary.k}, d={dictionary.d}")

# Save optimized dictionary
optimized_data = dictionary.as_bytes()
with open('optimized_dictionary.zdict', 'wb') as f:
    f.write(optimized_data)

class ZstdCompressor:
    def __init__(
        self,
        level: int = 3,
        dict_data: ZstdCompressionDict = None,
        **kwargs
    ):
        """
        Create compressor with dictionary support.

        Parameters:
        - level: int, compression level
        - dict_data: ZstdCompressionDict, compression dictionary
        - **kwargs: other compressor parameters
        """

import zstandard as zstd

# Train dictionary from samples
samples = [b"sample data 1", b"sample data 2", b"sample data 3"]
dictionary = zstd.train_dictionary(4096, samples)

# Create compressor with dictionary
compressor = zstd.ZstdCompressor(level=5, dict_data=dictionary)

# Compress new data (similar to training samples)
new_data = b"new sample data with similar patterns"
compressed = compressor.compress(new_data)

# Compare compression ratios
compressor_no_dict = zstd.ZstdCompressor(level=5)
compressed_no_dict = compressor_no_dict.compress(new_data)

print(f"With dictionary: {len(compressed)} bytes")
print(f"Without dictionary: {len(compressed_no_dict)} bytes")
print(f"Improvement: {(len(compressed_no_dict) - len(compressed)) / len(compressed_no_dict) * 100:.1f}%")

class ZstdDecompressor:
    def __init__(
        self,
        dict_data: ZstdCompressionDict = None,
        **kwargs
    ):
        """
        Create decompressor with dictionary support.

        Parameters:
        - dict_data: ZstdCompressionDict, decompression dictionary
        - **kwargs: other decompressor parameters
        """

import zstandard as zstd

# Load dictionary (same as used for compression)
dictionary = zstd.ZstdCompressionDict(dict_data)

# Create decompressor with dictionary
decompressor = zstd.ZstdDecompressor(dict_data=dictionary)

# Decompress dictionary-compressed data
decompressed = decompressor.decompress(compressed_data)
print(f"Decompressed: {decompressed}")

# Dictionary type constants
DICT_TYPE_AUTO: int        # Auto-detect dictionary type
DICT_TYPE_RAWCONTENT: int  # Raw content dictionary
DICT_TYPE_FULLDICT: int    # Full dictionary with headers

import zstandard as zstd

# Raw content dictionary (just the sample data)
raw_dict = zstd.ZstdCompressionDict(
    sample_data,
    dict_type=zstd.DICT_TYPE_RAWCONTENT
)

# Full dictionary (with zstd dictionary headers)
full_dict = zstd.ZstdCompressionDict(
    trained_dict_data,
    dict_type=zstd.DICT_TYPE_FULLDICT
)

# Auto-detect type
auto_dict = zstd.ZstdCompressionDict(
    dict_data,
    dict_type=zstd.DICT_TYPE_AUTO
)

import zstandard as zstd

# Use representative samples
samples = collect_representative_data()

# Aim for 100-1000 samples, each 1KB-64KB
filtered_samples = [s for s in samples if 1024 <= len(s) <= 65536]

# Dictionary size: typically 64KB-1MB
dictionary = zstd.train_dictionary(
    dict_size=min(65536, sum(len(s) for s in filtered_samples) // 100),
    samples=filtered_samples
)

import zstandard as zstd

# Precompute tables for target compression level
dictionary.precompute_compress(level=compression_level)

# Reuse compressor/decompressor objects
compressor = zstd.ZstdCompressor(level=5, dict_data=dictionary)
decompressor = zstd.ZstdDecompressor(dict_data=dictionary)

# Compress multiple items efficiently
for data in data_items:
    compressed = compressor.compress(data)
    process_compressed(compressed)

import zstandard as zstd

# Save dictionary for later use
dictionary_data = dictionary.as_bytes()
with open('model.zdict', 'wb') as f:
    f.write(dictionary_data)

# Load dictionary
with open('model.zdict', 'rb') as f:
    dict_data = f.read()
dictionary = zstd.ZstdCompressionDict(dict_data)