Cassandra, a distributed NoSQL database, relies on the Log-Structured Merge-Tree (LSM Tree) architecture to manage data efficiently. This design separates write and read operations, with writes initially stored in memory (MemTable) and later flushed to immutable SSTable files. Over time, the accumulation of SSTables necessitates compaction—a process that reorganizes data to maintain performance. Traditional compaction strategies like Size-Tiered Compaction (STC) and Level Compaction (LC) have limitations in balancing read and write amplification. The Unified Compaction Strategy (UCS), introduced in Cassandra 5, addresses these challenges by integrating density-based layering and sharding mechanisms, offering a more adaptive and scalable solution.
Cassandra's LSM Tree architecture ensures efficient write operations by batching data into MemTables, which are then flushed to SSTables. These SSTables are immutable and organized in tiers, with compaction merging and reorganizing them to reduce redundancy and improve query performance. Traditional strategies like STC and LC manage these tiers differently, but UCS introduces a novel approach by leveraging density and sharding to optimize compaction.
UCS determines the tier level based on density, defined as the ratio of SSTable size to the token range it covers. This metric ensures that SSTables are distributed across tiers according to their data density, rather than fixed size thresholds. By prioritizing density, UCS avoids the inefficiencies of STC (which leads to high write amplification) and LC (which causes excessive read amplification), creating a more balanced compaction process.
UCS employs sharding to divide data across multiple directories, enabling parallel compaction operations. Each shard operates independently, reducing contention and improving throughput. The number of shards is dynamically adjusted based on the target SSTable size, ensuring even distribution of data across token ranges. This mechanism also minimizes unnecessary compaction by focusing only on overlapping SSTables, reducing read amplification.
UCS triggers compaction based on overlap sections—regions where SSTables intersect. This approach ensures that only necessary SSTables are merged, avoiding redundant operations. The compaction threshold is dynamically adjusted using a parameter W, which can be tuned to favor either STC (positive values) or LC (negative values), or a balanced hybrid strategy (neutral values). This flexibility allows UCS to adapt to varying workloads, from write-heavy to read-heavy scenarios.
UCS supports datasets up to 10TB and beyond, with a design that minimizes both read and write amplification. By reducing the size of high-tier SSTables through density-based sharding, UCS lowers the memory and disk I/O overhead associated with compaction. This results in faster query performance and reduced operational costs, making it ideal for large-scale distributed systems.
UCS is designed to coexist with legacy compaction strategies. Existing SSTables generated by STC or LC are gradually integrated into the UCS framework, preserving data integrity while enabling seamless upgrades. The system automatically maps LCS/STCS levels to UCS parameters, ensuring compatibility without requiring manual intervention.
While UCS reduces compaction overhead, it requires careful monitoring of disk space and concurrency. The size of SSTables and the number of active compaction tasks must be balanced to prevent resource contention. For example, a 1TB target space with a 4:1 compression ratio requires 4GB per thread, necessitating a controlled number of parallel operations to avoid overloading the system.
UCS's parameter W and shard size adjustments must be tailored to the specific workload. Time-series data, for instance, benefits from periodic compaction to eliminate outdated entries, while high-throughput applications may prioritize minimizing write amplification. These tuning considerations ensure optimal performance across diverse use cases.
The Unified Compaction Strategy (UCS) represents a significant advancement in Cassandra's data management capabilities. By integrating density-driven layering and sharding, UCS optimizes compaction to reduce both read and write amplification, enhancing scalability and performance. Its adaptive nature allows it to balance the trade-offs between different workloads, making it a versatile solution for modern distributed systems. As Cassandra continues to evolve, UCS sets a new standard for efficient data storage and retrieval in LSM-based architectures.