【数据结构】深入浅出LSM-技术圈

前言

在数据库或搜索引擎的存储过程中会很多数据结构,而NoSql数据库对LMS结构非常青睐，如RocksDB、LevelDB、HBase以及Prometheus等其底层的存储引擎都是基于LSM树。本文就来详细了解一下何为LMS，以及它的优缺点和使用范围。

简介

LSM一般的名字叫 Log Structured-Merge Tree（日志结构合并树），来源于分布式数据库领域，也是BigTable 的论文中所使用的文件组织方式。它的特点在于写入的时候是append only的形式，就像名字所显示的那样，跟日志一样只在文件后面追加。LSM树并不像B+树、红黑树一样是一颗严格的树状数据结构，它其实是一种存储结构。LSM树的核心特点是利用顺序写来提高写性能，但因为分层(此处分层是指的分为内存和文件两部分)的设计会稍微降低读性能，但是通过牺牲小部分读性能换来高性能写，使得LSM树成为非常流行的存储结构。

LSM树的核心思想

如图所示,在 LSM 树中，MemTable、Immutable MemTable 和 SSTable（Sorted String Table）是三个重要的概念。

Memtable

LSM树中的内存缓存，用于暂存新插入的键值对，以减少频繁写入磁盘的IO操作。MemTable通常采用哈希表或平衡树等数据结构实现，可以很快速地进行键值对的插入和查找。当MemTable达到容量限制后，其中的键值对将会被排序后写入Immutable MemTable或SSTable中。

Immutable MemTable

一旦MemTable达到容量限制，它就会被“冻结”成为一个不可修改的immutable MemTable，同时系统会在后台创建一个新的空白的Mutable MemTable供后续写入使用。Immutable MemTable中的键值对是按照键升序排列的，这样可以方便地进行二分查找和归并操作。

SSTable

LSM树中持久化存储的部分，包含一组已经排好序的键值对列表。由于SSTable中的键值对是有序的，因此可以采用较为高效的二分查找算法进行查询。另外，SSTable还支持key-range删除操作，即可以通过标记某个键值对被删除，在合并操作时统一进行处理。除此之外，SSTable还拥有其他高级特性，如布隆过滤器和索引块等。

总结

MemTable、Immutable MemTable和SSTable都是LSM树中非常重要的概念。其中MemTable和Immutable MemTable用于加速新数据的写入操作，而SSTable则是LSM树中持久化存储部分的核心，通过SSTable中的键值对列表，支持高效的查询和删除操作。

LSM树的Compact策略

在LSM树中，Compact是一种重要的策略，用于减少存储空间的浪费和提高查询性能。Compact操作主要通过合并多个SSTables以及删除已经过期或无效的数据来实现。

常见的Compact策略有以下几种：

1. 1. Level 0 Compaction：Level 0 Compaction是指将相邻的Immutable MemTable和SSTable进行归并操作。这种方式能够在磁盘上创建更大的文件块，从而提高查询性能，但可能会导致数据重复和存储空间的浪费。
  2. Level N Compaction：Level N Compaction是指将位于同一层级（Level）的多个SSTable进行归并操作。这种方式能够在不同的层级之间移动数据，从而进一步减少冗余数据和存储空间的浪费，同时也能够降低查询延迟。
  3. Size-Tiered Compaction：Size-Tiered Compaction是一种基于SSTable大小的Compaction策略，即按照SSTable大小分成不同的层级进行归并。这种策略可以有效地减少存储空间的浪费，但可能会导致数据读取性能下降。
  4. Time-Based Compaction：Time-Based Compaction是一种基于时间戳和数据过期时间的Compaction策略，即将已经过期或无效的数据从SSTable中删除。这种策略可以有效地减少不必要的磁盘I/O和存储空间使用，同时也能够确保数据的正确性和一致性。

综上所述，LSM树的Compact策略可以根据具体应用场景和需求选择不同的方式进行优化。同时，在实际应用中，还需要考虑到吞吐量、查询延迟、存储空间等多个因素来选择最合适的Compact策略。

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Compacttion

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