1 Introduction

LSM-tree stands for Log-Structured Merge Tree. It is a data structure used in computer systems, particularly in storage and database systems, to efficiently manage and store large volumes of data with high write throughput.

The LSM-tree structure is designed to optimize write operations, which are typically more expensive in traditional storage structures. It achieves this by leveraging the characteristics of sequential disk I/O and reducing the number of random disk accesses.

The basic idea behind LSM-trees is to separate write and read operations by maintaining multiple levels of data structures. When data is written, it is first inserted into an in-memory component called a memtable, which serves as a write buffer. The memtable is typically implemented as a skip list or a hash table for efficient insertion and retrieval.

Periodically, the contents of the memtable are flushed to disk as sorted run files, also known as SSTables (Sorted String Tables). SSTables are immutable and ordered by the keys, which allows efficient sequential reads. Each SSTable corresponds to a specific time interval or a size limit.

To handle read operations efficiently, LSM-trees use a combination of the in-memory memtable and the on-disk SSTables. When a read request arrives, the LSM-tree first checks the memtable for the requested data. If the data is not found there, it searches the SSTables in a bottom-up manner, starting from the most recent SSTable to the oldest. This process is known as a merge operation.

To maintain consistency and prevent data inconsistencies between the memtable and SSTables, LSM-trees employ background compaction processes. Compaction merges overlapping or redundant data from SSTables into a new, compacted SSTable. This process reduces the number of SSTables and helps to optimize read performance.

LSM-trees provide several advantages:

  1. Efficient write performance: The LSM-tree structure optimizes write operations by buffering writes in an in-memory component and reducing disk random accesses.
  2. Scalability: LSM-trees can efficiently handle large volumes of data and scale horizontally by adding more machines to the system.
  3. Crash recovery: The LSM-tree’s append-only design and use of SSTables make crash recovery simpler and faster by replaying the write-ahead logs.
  4. Flexibility: LSM-trees can be tuned to balance the trade-offs between write and read performance based on system requirements.

LSM-trees are widely used in various storage and database systems, including Apache Cassandra, LevelDB, RocksDB, and HBase, to name a few.