计算机科学原理的速查表（Cheat Code）-Computer science principles (cheat code)

计算机科学原理的速查表

• 哈希：实现快速查找。
• 排序：实现快速搜索。
• 仅追加写入：实现高吞吐、低延迟的写入。
• 内存存储：实现极速读写。
• 概率型数据结构：以可接受的小概率误报换取极速查找。
• B-树：兼顾快速查找与磁盘友好访问模式。
• 布隆过滤器：以极小空间实现高效成员测试，允许可控误报。
• 预写日志（WAL）：在保障数据持久性的同时不牺牲写入性能。
• 缓存：把热点数据放到更快介质，实现快速读取。
• 索引：无需全表扫描即可快速定位。
• 压缩：用 CPU 换存储成本，显著减少空间占用。
• 分片：横向扩展，把数据分布到多节点。
• 复制：通过冗余提升可用性与读性能。
• 列式存储：把相关列放一起，加速分析型查询。
• LSM 树：批量写入 + 定期合并，实现高写入吞吐。
• 跳表：用更简单的无锁实现达到平衡树性能。
• 一致性哈希：扩容时只需极小数据迁移，实现均匀分布。
• 前缀树（Trie）：快速前缀匹配与自动补全。
• 环形缓冲区：固定内存、高效循环访问。
• 写时复制（COW）：修改前共享数据，节省内存。
• Merkle 树：通过哈希实现篡改检测与高效同步。
• 线段树：支持快速区间查询与对数级更新。
• 树状数组（Fenwick）：用极小空间实现高效前缀和。
• 并查集：路径压缩 + 按秩合并，极速连通性查询。
• 后缀数组：比后缀树更省内存的高效字符串匹配。
• 倒排索引：把词映射到文档位置，实现全文快速检索。
• 空间索引：多维划分，加速地理查询。
• 时序数据库：针对时间线数据优化存储与压缩。
• 事件溯源：记录状态变化而非当前状态，完整可审计。
• CRDT（无冲突可复制数据类型）：无需协调即可达成最终一致。
• 无锁数据结构：利用原子操作与内存序实现高并发。
• 分区：按访问模式切分数据，提升性能。
• 物化视图：预计算并存储结果，加速复杂查询。
• 增量压缩：只存版本差异，极大节省空间。
• 堆：常数时间 peek，高效实现优先队列。
• rope：高效 giant 文本的拼接与修改。
• 基数树（Radix Tree）：路径压缩，节省前缀存储空间。
• 批量处理：把多个操作打包，摊销开销，提升吞吐。

原文：

Computer science principles (cheat code)

-Hashing to get quick lookups

• Sorting to get quick searches.
• Append only to get fast and high throughput writes.
• In-memory to get ultra fast writes/reads.
• Probabilistic data structures to get fast lookups with chances offalse positives.
• B-trees to get quick lookups with disk-friendly access patterns.
• Bloom filters to get space-efficient membership testing with acceptable false positives.
• Write-ahead logging to get durability without sacrificing write performance.
• Caching to get fast reads by storing frequently accessed data in faster storage.
• Indexing to get quick searches without scanning entire datasets.
• Compression to get reduced storage costs at the expense of CPU overhead.
• Sharding to get horizontal scalability by distributing data across multiple nodes.
• Replication to get high availability and read performance through data redundancy.
• Columnar storage to get fast analytical queries by storing related data together.
• LSM trees to get high write throughput by batching writes and periodic merging
• Skip lists to get balanced tree performance with simpler lock-free implementations.
• Consistent hashing to get even data distribution with minimal reshuffling during scaling.
• Trie structures to get fast prefix matching and ocomnletefinctional
• Ring buffers to get bounded memory usage with efficient circular data access.
• Copy-on-write to get memory efficiency by sharing data until modifications occur.
• Merkle trees to get tamper detection and efficient synchronization through cryptographic hashing.
• Segment trees to get fast range queries with logarithmic updatecomplexity
• Fenwick trees to get efficient prefix sum calculations with minimal memory overhead.
• Union-find to get fast connectivity queries through path compression andunion by rank
• Suffix arrays to get efficient string matching with reduced memory compared to suffix trees.
• Inverted indexes to get fast full-text search by mapping terms to document locations.
• Spatial indexing to get quick geographic queries through multi-dimensional partitioning.
• Time-series databases to get optimized storage for chronological data with compression.
• Event sourcing to get complete audit trails by storing state changes instead of current state.
• CRDT (Conflict-free Replicated Data Types) to get eventual consistency without coordination overhead.
• Lockless data structures to get high concurrency through atomic operations and memory ordering.
• Partitioning to get improved performance by dividing data based on access patterns.
• Materialized views to get fast complex queries by pre- computing and storing results
• Delta compression to get reduced storage by storing only differences between versions.
• Heap data structures to get efficient priority queue operations withconstant-timepeek
• Rope data structures to get efficient string concatenation and manipulation for large texts.
• Radix trees to get memory-efficient prefix storage through pathcompression. dantivedatast performance based on access patterns.
• Batching to get improved throughput by amortizing overhead across multiple operations.