Modern NoSQL database systems use log-structured merge (LSM) storage architectures to support high write throughput. LSM architectures aggregate writes in a mutable MemTable (stored in memory), which is regularly flushed to disk, creating a new immutable file called an SSTable. Some of the SSTables are chosen to be periodically merged—replaced with a single SSTable containing their union. A merge policy (a.k.a. compaction policy) specifies when to do merges and which SSTables to combine. A bounded depth merge policy is one that guarantees that the number of SSTables never exceeds a given parameter k, typically in the range 3–10. Bounded depth policies are useful in applications where low read latency is crucial, but they and their underlying combinatorics are not yet well understood. This paper compares several bounded depth policies, including representative policies from industrial NoSQL databases and two new ones based on recent theoretical modeling, as well as the standard Tiered policy and Leveled policy. The results validate the proposed theoretical model and show that, compared to the existing policies, the newly proposed policies can have substantially lower write amplification with comparable read amplification.

现代NoSQL数据库系统使用日志结构合并（LSM）存储体系结构来支持高写入吞吐量。LSM体系结构将写操作聚合到一个可变的MemTable（存储在内存中）中，该表定期刷新到磁盘上，从而创建一个称为SSTable的新的不可变文件。选择一些SSTable进行定期合并-替换为包含其联合的单个SSTable。一个合并 的政策何时做（又名压实政策）规定合并和SSTables结合。有界深度合并策略是一种保证SSTable的数量永远不会超过给定参数k的策略。，通常在3-10之间。深度限制策略在低读取延迟至关重要的应用程序中很有用，但尚不十分了解它们及其底层组合。本文比较了几种有界深度策略，包括来自工业NoSQL数据库的代表性策略和基于最新理论模型的两个新策略，以及标准的分层策略和分层策略。结果验证了所提出的理论模型，并表明，与现有策略相比，新提出的策略可以具有与可比的读取放大率相比更低的写入放大率。