miniz

Pros of zlib

• More mature and widely used, with extensive testing and optimization
• Offers a broader range of compression algorithms and options
• Better documentation and community support

Cons of zlib

• Larger codebase, potentially more complex to integrate
• May have higher memory usage for certain operations
• Slower compilation times due to its size and complexity

Code Comparison

zlib:

z_stream strm;
strm.zalloc = Z_NULL;
strm.zfree = Z_NULL;
strm.opaque = Z_NULL;
deflateInit(&strm, level);

miniz:

tdefl_compressor comp;
tdefl_init(&comp, NULL, NULL, tdefl_create_comp_flags_from_zip_params(level, -15, MZ_DEFAULT_STRATEGY));

Key Differences

• miniz is designed to be a lightweight, single-file implementation
• zlib offers more features and flexibility but with increased complexity
• miniz may be easier to integrate into small projects or embedded systems
• zlib is more suitable for large-scale applications requiring advanced compression options

Performance Considerations

• zlib generally offers better compression ratios due to its advanced algorithms
• miniz may have faster compression/decompression speeds in some scenarios
• zlib's memory usage can be more efficiently managed in long-running applications

Pros of lz4

• Extremely fast compression and decompression speeds
• Supports streaming compression and decompression
• Widely used in various applications and systems

Cons of lz4

• Lower compression ratio compared to other algorithms
• Limited support for older systems and platforms

Code Comparison

lz4:

char* compressed_data = (char*)malloc(LZ4_compressBound(input_size));
int compressed_size = LZ4_compress_default(input_data, compressed_data, input_size, LZ4_compressBound(input_size));

miniz:

unsigned char *pComp_data = (unsigned char *)malloc(compressBound(input_size));
mz_ulong compressed_size = compressBound(input_size);
compress(pComp_data, &compressed_size, (const unsigned char *)input_data, input_size);

Key Differences

• lz4 focuses on speed, while miniz offers a balance between speed and compression ratio
• miniz provides a more comprehensive set of compression utilities, including ZIP file support
• lz4 has a simpler API, making it easier to integrate into projects
• miniz is a single-file library, which can be more convenient for some developers

Use Cases

• lz4: Real-time compression scenarios, high-performance storage systems
• miniz: General-purpose compression, projects requiring ZIP file manipulation

Pros of Zstd

• Higher compression ratios and faster decompression speeds
• Supports dictionary compression for improved efficiency with small files
• More actively maintained with frequent updates and optimizations

Cons of Zstd

• Larger library size and more complex implementation
• Slightly slower compression speed for some use cases
• Steeper learning curve due to more advanced features

Code Comparison

Miniz (simple compression):

mz_ulong src_len = (mz_ulong)strlen(src_buf);
mz_ulong cmp_len = compressBound(src_len);
compress(cmp_buf, &cmp_len, (const unsigned char *)src_buf, src_len);

Zstd (simple compression):

size_t cmp_len = ZSTD_compressBound(src_len);
ZSTD_compress(cmp_buf, cmp_len, src_buf, src_len, 1);

Both libraries offer straightforward compression APIs, but Zstd provides more advanced options for fine-tuning compression parameters and performance.

Miniz is a lightweight, single-file zlib replacement focused on simplicity and ease of integration. It's ideal for projects requiring basic compression functionality with minimal overhead.

Zstd, developed by Facebook, offers state-of-the-art compression algorithms with a focus on high performance and advanced features. It's better suited for projects that prioritize compression efficiency and can handle a more complex library.

Pros of Snappy

• Higher compression speed, optimized for performance
• Better integration with Google's ecosystem and other Google projects
• More active development and maintenance

Cons of Snappy

• Lower compression ratio compared to Miniz
• Limited platform support, primarily focused on x86 architectures
• Larger codebase, potentially more complex to integrate

Code Comparison

Miniz (compression):

mz_uint8 *pDst_buf = (mz_uint8 *)malloc(src_len * 2);
mz_ulong dst_len = src_len * 2;
int status = mz_compress(pDst_buf, &dst_len, pSrc_buf, src_len);

Snappy (compression):

std::string output;
snappy::Compress(input_data.data(), input_data.size(), &output);

Summary

Snappy focuses on speed and integration with Google's ecosystem, making it ideal for projects prioritizing performance. Miniz offers better compression ratios and broader platform support, suitable for projects where file size reduction is crucial. Snappy's codebase is larger and more complex, while Miniz provides a simpler implementation. The choice between the two depends on specific project requirements, balancing compression speed, ratio, and ease of integration.

Pros of Brotli

• Higher compression ratios, especially for web content
• Faster decompression speed
• Wider adoption and support in modern web browsers

Cons of Brotli

• Slower compression speed compared to Miniz
• Larger library size and more complex implementation
• Limited to static compression scenarios (not suitable for real-time compression)

Code Comparison

Miniz (simple compression):

mz_ulong src_len = (mz_ulong)strlen(src_buf);
mz_ulong compressed_size = compressBound(src_len);
int status = compress(compressed_buf, &compressed_size, (const unsigned char *)src_buf, src_len);

Brotli (simple compression):

size_t encoded_size = BrotliEncoderMaxCompressedSize(input_size);
uint8_t* encoded_buffer = malloc(encoded_size);
BROTLI_BOOL result = BrotliEncoderCompress(quality, BROTLI_DEFAULT_WINDOW, BROTLI_DEFAULT_MODE,
                                           input_size, input_buffer, &encoded_size, encoded_buffer);

Both libraries offer compression functionality, but Brotli focuses on higher compression ratios at the cost of speed, while Miniz prioritizes simplicity and faster compression. Miniz is more suitable for real-time compression scenarios, while Brotli excels in static content compression, particularly for web assets.