heatshrink
A data compression library for embedded and real-time systems with minimal memory usage.
What is heatshrink?
heatshrink is a data compression library specifically designed for embedded systems and real-time applications. It implements LZSS compression with minimal memory requirements and incremental processing capabilities, solving the problem of adding compression to resource-constrained environments where traditional libraries are too heavy.
Embedded systems engineers and firmware developers working on memory-constrained devices like microcontrollers, IoT sensors, and real-time systems that require data compression.
Developers choose heatshrink because it provides usable compression with predictable resource consumption—operating in as little as 50 bytes of RAM with bounded CPU usage—unlike general-purpose compression libraries that are too resource-intensive for embedded contexts.
Overview
data compression library for embedded/real-time systems
Use Cases
Best For
- Adding compression to firmware with less than 1KB of available RAM
- Real-time systems requiring predictable, incremental compression processing
- IoT devices that need to compress sensor data before transmission
- Microcontroller projects where memory is severely constrained
- Embedded applications needing configurable compression trade-offs
- Systems requiring both static and dynamic memory allocation options
Not Ideal For
- Desktop or server applications where memory and CPU are abundant and higher compression ratios are needed
- Projects requiring advanced compression features like streaming APIs, multiple algorithm support, or error correction
- Teams looking for drop-in libraries with pre-built binaries and package manager integration
Pros & Cons
Pros
Can operate with as little as 50 bytes of RAM, making it ideal for severely memory-constrained embedded systems, as highlighted in the key features.
Handles input data in arbitrarily small chunks, ensuring bounded and predictable CPU usage for hard real-time environments, per the philosophy.
Supports both static and dynamic allocation, allowing developers full control over memory management in embedded contexts.
Adjustable window and lookahead sizes let developers balance memory usage against compression effectiveness, as detailed in the configuration section.
Cons
Optimal compression requires manual configuration of window and lookahead sizes, and the README admits that settings must be checked with representative data.
Based solely on LZSS, it lacks modern compression techniques, which may result in lower compression ratios compared to more advanced libraries.
Requires copying source files and configuring heatshrink_config.h manually, lacking pre-built binaries or easy package management.
Frequently Asked Questions
Related Projects
zstdZstandard - Fast real-time compression algorithm
brotliBrotli compression format
LZ4Extremely Fast Compression algorithm
libarchiveMulti-format archive and compression library
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