The Case for Shared Storage

The discussion contrasts "shared nothing" and "shared storage" architectures, particularly in data streaming contexts, highlighting the foundational philosophies that led to WarpStream's architecture. Shared-nothing architectures, characterized by node-level sharding, offer scalable performance by minimizing contention, but face challenges like hotspotting, especially when workloads don't shard well. This architecture is prominent in systems like Apache Kafka, which scales by balancing topic-partitions across brokers but requires careful capacity management. Conversely, shared storage systems separate data from metadata, using remote storage and centralized metadata stores to manage coordination, making them more flexible and easier to scale despite higher latency. WarpStream embraces shared storage, borrowing elements from data warehousing to overcome limitations of shared-nothing systems, such as topic-partition limits and heat management. Its architecture allows for dynamic load distribution across stateless agents, enhancing scalability and simplifying management. Although shared storage systems face challenges with metadata scaling, they often offer a more practical and adaptable solution for various workloads, making them preferable for many applications outside of latency-sensitive contexts.