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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.
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.
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.
In-memory write buffer for high-speed data ingestion.
Immutable, sorted files storing flushed data segments.
Processes to merge and compact SSTables, optimizing read performance and reducing space amplification.
Layered lookup across Memtable and SSTables for efficient key retrieval.
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.
Storing rapidly arriving data points, such as sensor readings or application metrics, where write speed is critical.
Enables high-volume data ingestion typical in monitoring and IoT applications.
Persistent storage for logs generated by various applications and services.
Optimized for append-heavy workloads characteristic of logging systems.
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