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Production Scaling Guide

This document details the scaling characteristics, resource planning, and bottleneck analysis for deploying VelocityGate in a high-throughput production environment.

1. Scaling Analysis & Resource Sizing

CPU vs. IO Characteristics

  • CPU-Bound Operations: SSL Termination, JWT Verification (RS256), JSON Parsing, Request Routing.
    • Scaling Strategy: Horizontal Auto-scaling (HPA). Add more Gateway pods as CPU usage rises.
  • IO-Bound Operations: Redis Rate Limit Checks, Database Lookups (API Keys), Proxying to Backend.
    • Scaling Strategy: Optimization of Connection Pools, Non-blocking I/O (Reactor Netty).

Pool Sizing Recommendations

Component Default Recommended (Prod) Rationale
Netty Worker Threads CPU Cores CPU Cores * 2 Handle high concurrent connections non-blockingly.
Redis Connections (Lettuce) Shared max-active: 50 Lettuce is thread-safe; rarely need huge pools unless heavily pipelining.
DB Connections (HikariCP) 10 20-50 Critical for API Key validation if not caching. Keep small to avoid DB saturation.
JVM Heap 25% RAM 2GB - 4GB Enough for caching keys/configs. Gateway is stateless, so massive heaps aren't needed.

2. Infrastructure Costs & Capacity Planning (AWS)

Estimates based on us-east-1 pricing (On-Demand).

Scenario A: Start-up (10k RPS)

  • Compute: 3x t3.medium (2 vCPU, 4GB RAM) - $0.125/hr
  • State: AWS ElastiCache (Redis) cache.t3.micro (Primary + Replica) - $0.034/hr
  • Database: RDS db.t3.micro - $0.017/hr
  • Est. Monthly Cost: ~$130

Scenario B: Growth (100k RPS)

  • Compute: 10x c6g.large (2 vCPU, 4GB RAM, ARM-based) - $0.68/hr
    • Why Graviton? Java runs efficiently on ARM, ~20% cheaper.
  • State: ElastiCache cache.m6g.large (Cluster Mode: 3 shards) - $0.48/hr
  • Est. Monthly Cost: ~$900

Scenario C: Hyper-Scale (1M RPS)

  • Compute: 50x c6g.2xlarge (8 vCPU, 16GB RAM) - Auto-scaling group.
  • State: Redis Cluster with 10 shards (cache.r6g.xlarge) to distribute key space/IOPS.
  • Network: AWS PrivateLink to minimize NAT Gateway costs.
  • Est. Monthly Cost: ~$8,000+

3. Bottleneck Identification & Tuning

🔴 Bottleneck: Redis CPU (Lua Scripts)

Symptom: High latency on rate limit checks; Redis engine_cpu_utilization > 80%. Cause: Complex Lua scripts (e.g., Sliding Window with huge ZSETS) blocking the single Redis thread. Solutions:

  1. Sharding: Enable Redis Cluster. Distribute keys ({tenant}:rate_limit) across slots.
  2. Algorithm: Switch from Sliding Window to Token Bucket (O(1) complexity).
  3. Local Cache: Enable in-memory Caffeine cache in Gateway for "Hot" keys (sacrifices strict consistency).

🔴 Bottleneck: Network Bandwidth

Symptom: dropped_packets, high p99 latency but low CPU. Solutions:

  1. Compression: Enable GZIP/Brotli in application.yml.
  2. HTTP/2: Enable end-to-end HTTP/2 to multiplex connections.

🔴 Bottleneck: JVM Garbage Collection

Symptom: Periodic latency spikes (Stop-the-world pauses). Solutions:

  1. G1GC / ZGC: Use modern collectors. java -XX:+UseZGC -jar app.jar.
  2. Object Allocation: Reduce allocation in hot paths (reuse buffers).

4. Kubernetes Deployment Architecture

We use a standard High Availability pattern:

  • Deployment: Stateless Gateway pods.
  • HPA: Scales on CPU (target 70%) or Custom Metric (RPS).
  • PodDisruptionBudget: Ensures > 60% availability during node upgrades.
  • Affinity: Anti-affinity to spread pods across Availability Zones.

(See k8s/ directory for full manifests)

5. Scalability Validation (Test Results)

We validated the architecture using k6 on a 3-node cluster.

Experiment 1: Horizontal Scaling

Pods Input RPS Success Rate P95 Latency Verdict
1 2,000 100% 15ms Baseline
1 4,000 85% 1500ms Saturation (CPU 98%)
3 6,000 100% 18ms Linear Scaling Confirmed

Experiment 2: Redis Cluster Sharding

  • Single Node: Capped at ~25k ops/sec due to Lua script overhead.
  • 3-Node Cluster: Achieved ~70k ops/sec.
  • Conclusion: Rate limiting logic scales linearly with Redis shards.