Hybird-App离线缓存系统

本文介绍了Hybrid-App离线缓存系统的设计,包括背景、接口格式、资源包格式、资源下发和缓存策略。离线资源包通过增量和全量更新方式管理,启动时检查更新,下载后解压并校验。在容错处理中,系统设置标志位,确保升级过程的稳定性和恢复机制。此外,详细阐述了如何在特定时机使用缓存资源,并处理缓存文件的异常情况。

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Hybird-App离线缓存系统

目录

  • 背景
  • 接口格式
  • 离线资源包格式
  • 离线资源下发
  • 离线资源缓存

背景

由于线上乐刻客户端 App 第一次打开平台 H5 需要几秒的加载时间,这个体验对用户来说并不友好,为了让用户跳转 H5 和跳转到原生一样的用户体验,就需要把 H5 相关的离线资源包下发给客户端,客户端就可以使用离线资源来代替实际网络请求,节省用户等待时间和流量消耗。并且随着业务的发展,不同的业务升级进度不一样,就需要 App 支持模块化升级。

接口格式

offlineResourceInfo 接口请求方法: POST

offlineResourceInfo 接口请求参数:

Json 形式:

{
    //"appVersion": "2.4.0", 可以去掉,因为请求头会包含
    "resourceversionList": [{
        "name": "m",
        "version": "1.0.0"
    },{
        "name": "coach",
        "version": "1.0.0"
    },{
        "name": "activity",
        "version": "1.0.0"
    }]
}

Form 表单形式:

resourceNames=m,coach,activity&resourceVersions=1.0.0,1.0.0,1.0.0

offlineResourceInfo 接口返回结构体:


                
### Hybrid Sort Algorithm and Total Cache Miss Hybrid sorting algorithms combine two or more different sorting methods to achieve better performance characteristics than using a single approach alone. One common hybrid sort is TimSort, which combines merge sort and insertion sort techniques[^1]. The choice of combining specific sorts depends on the data distribution and size. In computer science, cache misses occur when the processor cannot find required data in the cache memory, leading to fetching from slower main memory. This phenomenon significantly impacts program execution speed. Total Cache Miss (TCM), therefore, refers to all instances where requested data must be retrieved directly from RAM instead of faster caches. The relationship between hybrid sort algorithms and total cache miss involves optimizing how elements are accessed during sorting operations: - **Data Access Patterns**: Efficient hybrids like TimSort aim at minimizing random accesses by leveraging natural runs within datasets, thereby reducing TCMs. - **Memory Hierarchy Utilization**: By adapting based on input sizes and patterns, these algorithms can better utilize multi-level caching architectures present in modern CPUs, thus lowering overall TCM rates. For implementing such an optimization-focused version of a hybrid sort considering TCM reduction: ```python def optimized_hybrid_sort(arr): min_run = calculate_minrun(len(arr)) # Calculate optimal run length for start in range(0, len(arr), min_run): end = min(start + min_run - 1, len(arr) - 1) # Insertion sort each small segment individually insertion_sort(arr, start, end) size = min_run while size < len(arr): for left in range(0, len(arr), 2 * size): mid = min((left + size - 1), (len(arr)-1)) right = min((left + 2*size - 1), (len(arr)-1)) if mid < right: merge(arr, left, mid, right) size *= 2 def calculate_minrun(n): r = 0 while n >= MIN_MERGE: r |= n & 1 n >>= 1 return n + r ``` This code snippet demonstrates part of what might go into creating a hybrid sorter that considers both efficiency gains through adaptive behavior as well as potential reductions in total cache misses due to improved locality properties.
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