Scalable Architecture

QuantaDB's architecture is designed to scale efficiently, allowing your applications to handle increasing amounts of data and user load without compromising performance.

The scalability of QuantaDB is attributed to several factors inherent in its design:

LSM-Tree Structure: The append-only nature of the LSM-Tree simplifies data management and allows for efficient handling of large datasets over time. As data grows, new data is written sequentially, and background compaction processes merge and optimize data in a way that is less disruptive than in-place updates of B-trees.
Tiered Storage: LSM-Trees typically utilize a tiered storage structure (in-memory MemTable and on-disk SSTables). This allows for fast writes to memory and efficient querying of sorted data on disk.
Background Compaction: Compaction processes run in the background to merge SSTables, remove obsolete data, and maintain performance. This prevents read performance degradation as the database grows and the number of SSTables increases.
Pure Dart Implementation: Being written entirely in Dart allows QuantaDB to leverage Dart's efficiency and concurrency features (like Isolates for background tasks), which contributes to its ability to handle scaling workloads.

These architectural choices ensure that QuantaDB can effectively manage large and growing datasets, making it suitable for applications with increasing data storage demands.