Consistent Hashing

Part 8: Redis Distributed State vs. Dapr Virtual Actors Showdown

📖 Series Navigation: ← Previous Chapter: Modular Monolith vs Microservices vs SpinKube Wasm | Series Hub Part 8: Redis Distributed State vs. Dapr Virtual Actors Showdown Answer-first: Redis in-memory state with Lua scripts excels at high-throughput (100k+ QPS), low-latency caching and raw data manipulation. However, for complex distributed state machines, turn-based concurrency, and long-lived stateful AI agent context, Dapr Virtual Actors eliminate race conditions, distributed locking overhead, and manual lifecycle plumbing via single-threaded mailboxes and automatic hydration. ...

Part 9: Consistent Hashing & Dynamic Sharding in Go

← Previous Chapter: Part 8: Saga Pattern & Distributed Transactions in Go | Series Hub: System Design Masterclass | Next Chapter: Part 10: Observability, Continuous Profiling & Pprof in Go → Prerequisite: Read Part 8: Saga Pattern & Distributed Transactions in Go to understand distributed consistency models before engineering dynamic key partitioning and topology rebalancing. Answer-first: Consistent hashing minimizes partition rebalancing overhead during distributed node scaling by mapping keys and nodes onto a circular continuum using virtual nodes and monotonic hashing algorithms like Ketama or Google Maglev. When cluster membership changes, only K/N keys are migrated, preventing catastrophic cache stampedes and balancing partition variance to within three percent. ...

Chapter 9: Database Sharding & Read-Write Splitting at Scale

Answer-first: Scaling relational databases beyond vertical hardware limits requires read/write splitting with session pinning to eliminate replication lag anomalies, followed by horizontal sharding across isolated partitions. The 2027 SOTA architecture pairs consistent hashing with virtual nodes, 64-bit monotonic Snowflake IDs to prevent B-Tree index fragmentation, and saga orchestration over blocking two-phase commits for cross-shard consistency. Prerequisite: Advanced knowledge of relational database internals (WAL logs, B-Tree indexes, replication lag), consistent hashing algorithms, and distributed transaction semantics is required for this chapter. ...