In the field of log compression, the prevailing "parse-then-compress" paradigm fundamentally limits effectiveness by treating log parsing and compression as isolated objectives. While parsers prioritize semantic accuracy (i.e., event identification), they often obscure deep correlations between static templates and dynamic variables that are critical for storage efficiency. In this paper, we investigate this misalignment through a comprehensive empirical study and propose LogPrism, a framework that bridges the gap via unified redundancy encoding. Rather than relying on a rigid pre-parsing step, LogPrism dynamically integrates structural extraction with variable encoding by constructing a Unified Redundancy Tree (URT). This hierarchical approach effectively mines "structure+variable" co-occurrence patterns, capturing deep contextual redundancies while accelerating processing through pre-emptive pattern encoding. Extensive experiments on 16 benchmark datasets confirm that LogPrism establishes a new state-of-the-art. It achieves the highest compression ratio on 14 datasets, surpassing existing baselines by margins of 6.12% to 83.34%, while delivering superior throughput at 29.87 MB/s (1.68$\times$~43.04$\times$ faster than competitors). Moreover, when configured in single-archive mode to maximize global pattern discovery, LogPrism boosts its compression ratio by 273.27%, outperforming the best baseline by 19.39% with a 2.62$\times$ speed advantage.

翻译：在日志压缩领域，主流的“先解析后压缩”范式将日志解析与压缩视为孤立目标，从根本上限制了压缩效能。解析器虽优先考虑语义准确性（即事件识别），却常常掩盖了静态模板与动态变量之间对存储效率至关重要的深层关联。本文通过全面的实证研究剖析了这种错位问题，并提出LogPrism框架——通过统一冗余编码弥合此间隙。该框架摒弃僵化的预解析步骤，通过构建统一冗余树动态整合结构提取与变量编码。这种分层方法能有效挖掘“结构+变量”共现模式，在捕获深层上下文冗余的同时，通过预置模式编码加速处理流程。基于16个基准数据集的大规模实验证实，LogPrism确立了全新的性能标杆：在14个数据集上取得最高压缩率，较现有基线提升6.12%至83.34%，同时以29.87 MB/s的吞吐量实现性能领先（达到对比方法的1.68$\times$~43.04$\times$）。此外，当采用单归档模式配置以最大化全局模式发现时，LogPrism的压缩率提升达273.27%，以19.39%的优势超越最佳基线方法，并保持2.62$\times$的速度优势。