OZIP Static Dictionary Compression for Random-Access Workloads
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Solution Overview
Problem
Existing data compression codecs, such as ZLIB and LZO, are inefficient for high-performance, latency-sensitive applications with random data access due to high processing overhead and unsuitability for hardware implementation, particularly in systems requiring low latency and random access support.
Innovation Solution
OZIP, a data compression codec that uses direct token encoding to a static dictionary, enabling low-cost and high-performance hardware implementations by avoiding expensive conditional branching and providing granular block mapping for quick random access, thus supporting highly parallel processing and low latency random data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If common compression codecs (ZLIB, LZO) are used to achieve higher compression ratios, then storage footprint is reduced, but processing overhead increases significantly
Solution Approach 1:
The patent changes the fundamental parameters of the compression approach by using a static dictionary with direct token encoding instead of dynamic dictionary building. This parameter change enables constant-time encoding operations and eliminates conditional branching, thereby reducing processing overhead while maintaining compression effectiveness through optimized token representation
Solution Approach 2:
The patent extracts and eliminates the expensive conditional branching logic from the compression algorithm. By using a static dictionary where token positions are predetermined, the algorithm removes the need for runtime conditional checks that plague traditional Lempel-Ziv implementations, thus reducing processing overhead
2Speed
If common compression codecs are implemented in hardware to reduce latency, then processing speed improves, but hardware complexity and fabrication costs increase
Solution Approach 1:
The patent replaces expensive, complex hardware structures with simpler, more economical implementations. The static dictionary approach allows for straightforward hardware mapping where token positions are fixed and predictable, enabling implementation with basic logic elements rather than complex state machines or memory structures required by traditional codecs
Solution Approach 2:
The patent segments the compression process into independent, parallelizable operations. The static dictionary allows different token encodings to be computed independently without dependency on conditional outcomes, enabling efficient parallel hardware implementation and pipelining to achieve high throughput
3Ease of manufacture
If sequential access compression algorithms are used to simplify implementation, then ease of implementation improves, but random data access capability deteriorates
Solution Approach 1:
The patent performs preliminary organization of the dictionary structure during system initialization or compression setup. By pre-computing and storing the static dictionary with fixed token positions, the system prepares the data structure in advance to enable O(1) random access operations, combining implementation simplicity with random access capability
Data Source
AI summary
A method, apparatus, and system for OZIP, a data compression and decompression codec, is provided. OZIP utilizes a fixed size static dictionary, which may be generated from a random sampling of input data to be compressed. Compression by direct token encoding to the static dictionary streamlines the encoding and avoids expensive conditional branching, facilitating hardware implementation and high parallelism. By bounding token definition sizes and static dictionary sizes to hardware architecture constraints such as word size or processor cache size, hardware implementation can be made fast and cost effective. For example, decompression may be accelerated by using SIMD instruction processor extensions. A highly granular block mapping in optional stored metadata allows compressed data to be accessed quickly at random, bypassing the processing overhead of dynamic dictionaries. Thus, OZIP can support low latency random data access for highly random workloads, such as for OLTP systems.