Cassandra, a distributed NoSQL database, has long been celebrated for its scalability and fault tolerance. However, as data volumes grow exponentially, storage density challenges have emerged as a critical barrier to achieving higher node capacities. This article explores the technical hurdles in increasing Cassandra's storage density to 20TB per node, focusing on hardware advancements, optimization strategies, and cost-efficiency measures. By addressing these challenges, organizations can unlock new possibilities for scalable, high-performance data management.
Modern NVMe storage technology now supports up to 60TB of capacity with sustained throughput of 2GB/s, far surpassing the 16-32GB RAM limits of traditional HDDs from a decade ago. High-speed network infrastructure has also evolved, necessitating parallel improvements in query throughput to accommodate increased node density. As node density rises, the number of queries per node escalates exponentially, demanding optimized CPU and I/O efficiency to maintain performance.
Data compression is a cornerstone of storage density optimization. Compressing 20TB of data can reduce its footprint significantly, enabling higher storage utilization. Advanced compression algorithms, such as Zstandard (Zstd), further enhance this by achieving up to 30% better compression ratios for small payloads like time-series or key-value workloads.
Compaction, the process of merging and cleaning up SSTables, has been refined with innovations like the BTI format and tri-index structures. These improvements reduce memory overhead and minimize read/write amplification. Techniques such as TryM tables and optimized compaction buffering further lower unnecessary I/O operations. Direct I/O usage is also promoted to avoid memory wastage from page caching. Virtual memory optimizations remain under evaluation to further enhance efficiency.
To eliminate GC pauses, the adoption of Java 21 with ZGC (Z Garbage Collector) is strongly recommended. ZGC ensures minimal pause times, critical for maintaining low-latency operations. Additionally, the Vector API in Java 17+ improves CPU utilization, enabling more efficient data processing.
EBS (Elastic Block Store) performance bottlenecks, such as the 256KB payload limit per I/O operation, can lead to excessive system calls during compaction. To mitigate this, intelligent read strategies are implemented to reduce I/O operations. Lowering system call frequency minimizes Java thread context switches, while optimizing CPU and system resource utilization ensures smoother performance. These optimizations result in increased disk throughput during compaction and reduced system call overhead across all nodes.
Increasing node density significantly reduces operational costs. For instance, reducing from 100 nodes ($137,000/month) to 20 nodes ($40,000/month) demonstrates a 70% cost saving. Disaggregated storage models, such as AWS EBS, allow flexible instance type selection. The c512 XL instance, for example, offers cost savings compared to I3/4 XL instances, with total costs approximately halved. Workload-specific instance selection and tuning of concurrent reads are essential to balance performance and cost.
Adopting a unified compaction strategy as the default configuration will streamline operations. Enhancements to the Time Window Compaction Strategy aim to resolve layered compaction issues, ensuring more efficient data management.
Integrating Project Panama's Vector API with Cassandra will further boost data processing efficiency. Java 17+ virtual memory management optimizations will also play a role in reducing memory overhead and improving performance.
Cassandra 5.0 is expected to incorporate these optimizations, with ongoing efforts to address EBS performance issues (Jira #15452). Community-driven development will be crucial in refining these features for broader adoption.
Achieving 20TB per node in Cassandra requires a multifaceted approach, combining hardware advancements, storage optimization, and cost-effective resource management. By leveraging compression, compaction improvements, and disaggregated storage models, organizations can overcome storage density challenges while maintaining high performance. Future enhancements, including unified compaction strategies and advanced GC techniques, will further solidify Cassandra's position as a scalable, high-performance database solution. Careful tuning of instance types, concurrent reads, and resource isolation will ensure optimal results in real-world deployments.