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Mini-LSM: Building an LSM-Tree Storage Engine in a Week
Explore the internals of modern databases by building a simplified Log-Structured Merge-Tree (LSM-Tree) storage engine in a week. This project serves as a hands-on guide to key concepts like memtables, SSTables, and compaction.
Rust
Added on 2025年6月10日3,456
Stars511
ForksRust
LanguageProject Introduction
Summary
This project guides you through building a mini LSM-Tree storage engine from scratch over approximately one week. It covers the essential components necessary to create a functional key-value store optimized for write throughput.
Problem Solved
Understanding the core mechanics of high-performance, write-optimized databases like Cassandra or RocksDB can be challenging. This project provides a practical, accelerated approach to learning LSM-Tree fundamentals.
Core Features
Memtable Implementation
In-memory write buffer for high-speed data ingestion.
SSTable Management
Immutable, sorted files storing flushed data segments.
Compaction Strategies
Processes to merge and compact SSTables, optimizing read performance and reducing space amplification.
Read Path Implementation
Layered lookup across Memtable and SSTables for efficient key retrieval.
Tech Stack
Rust
Standard Library
Use Cases
While this specific project is for educational purposes, the LSM-Tree architecture is widely used in production systems for scenarios requiring high write throughput and efficient handling of large datasets.
Scenario 1: Time-Series Data Storage
Details
Storing rapidly arriving data points, such as sensor readings or application metrics, where write speed is critical.
User Value
Enables high-volume data ingestion typical in monitoring and IoT applications.
Scenario 2: Log Processing and Storage
Details
Persistent storage for logs generated by various applications and services.
User Value
Optimized for append-heavy workloads characteristic of logging systems.
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