Reverse Proxy Deep Dive: Why Load Balancing at Scale Is Hard
13 days ago
- #reverse-proxy
- #scalability
- #load-balancing
- Load balancing is a critical function of reverse proxies, aiming for optimal resource utilization, resilience, and operational simplicity.
- Round-robin load balancing is simple but ineffective at scale due to unequal request types (e.g., read-heavy vs. write-heavy, CPU-bound vs. IO-bound).
- Alternatives to round-robin include Least Connections and Power of Two Choices (P2C), which offer better load distribution.
- Custom server requirements like caching, session persistence, and sharding complicate load balancing, requiring strategies like consistent hashing.
- Dynamic environments with constantly changing upstream hosts (e.g., Kubernetes) add complexity to load balancing.
- Adding a new host can cause traffic spikes and uneven load distribution, mitigated by techniques like slow start and weighted load balancing.
- Removing a host requires draining strategies to gracefully handle existing connections while excluding the host from new requests.
- Proxies often operate with a local view, leading to suboptimal decisions; global coordination mechanisms can help but are complex to implement.
- Proxy architecture (e.g., per-thread vs. shared views) impacts load balancing accuracy and efficiency.
- Common load balancing algorithms like Round-Robin, Least Connections, and Consistent Hashing each have unique challenges.
- Random Choice of Two (P2C) is surprisingly effective but still struggles with cold starts.
- Modern infrastructure's dynamic and ephemeral nature makes load balancing a complex problem requiring heuristics and partial information handling.