Load Balancing for Distributed Databases

As your app scales beyond a single database node, bottlenecks build up gradually through uneven workloads, lagging replicas, and overloaded primaries. Load balancing smooths those out before they cause outages. Here are a few common strategies and when they make sense:

• Round-robin load balancing: The simplest approach distributes queries evenly across replicas. It is easy to implement (think HAProxy or PgBouncer) but ignores query cost and replica lag. Reliable for read-heavy, low-variance traffic but risky when workloads skew.
• Read/write splitting: Middleware such as ProxySQL, Vitess, or Pgpool-II inspects queries and routes reads to replicas while keeping writes on the primary. It works well for read-heavy e-commerce or SaaS workloads, but replica lag can break read-after-write consistency for user-facing queries.
• Latency-aware routing: Common in globally distributed systems, this approach routes queries to the nearest healthy node to minimize round-trip time. It keeps latency low for users worldwide but adds replication and consistency complexity, especially with asynchronous cross-region replication.
• Adaptive or feedback-driven balancing: This approach uses telemetry to monitor CPU, query latency, and replica lag in real time and rebalance traffic dynamically. It requires strong monitoring and orchestration but can significantly reduce tail latency during traffic spikes.

Load balancing is more than spreading queries; it is about eliminating redundant work. Readyset takes that further with QueryPilot, which caches frequent reads and routes them intelligently across replicas to offload primaries and keep latency low.

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