Log-Structured Merge Trees (LSM) for High-Throughput Databases

Modern data systems demand high write throughput, but traditional B-Trees often struggle under heavy load. The Log-Structured Merge Tree (LSM) offers a powerful solution by prioritizing sequential writes and delayed merging. By the end of this course, you will have a deep conceptual understanding of how LSM trees function, including their core components, write path, read path, and crucial compaction process. You will be prepared to analyze and configure databases utilizing this structure. ### What you'll learn * Understand the fundamental structure of LSM trees, including the mutable Memtable and immutable SSTables. * Master the write path process, from in-memory buffering and flushing to persistent disk storage. * Analyze the critical trade-offs between read amplification and write amplification in LSM structures. * Configure and evaluate different compaction strategies, such as size-tiered and leveled compaction. * Apply LSM principles to understand the core architecture of modern NoSQL and high-performance key-value stores. This text-only course begins with foundational concepts and definitions of database indexing structures. We then move into detailed explanations of LSM tree components and operations, followed by an analysis of performance characteristics and configuration choices. This course is designed for aspiring database administrators, backend engineers, and anyone seeking to understand the core mechanics powering modern high-performance data storage systems. No prior specialized knowledge of LSM trees is required. Start reading today and unlock the secrets of high-throughput data management.