📦 deps(thirdparty): update snapshots

2026-05-29 08:33:53 +00:00
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+# Scalability
+
+## Performance Modeling
+
+Key metrics:
+- **Latency**: p50, p95, p99 response times
+- **Throughput**: requests per second
+- **Resource utilization**: CPU, memory, network, disk I/O
+- **Error rates**: 4xx, 5xx responses
+- **Saturation**: queue depths, connection pools
+
+Capacity planning:
+1. **Baseline**: measure current performance under normal load
+2. **Load test**: use realistic traffic patterns (gradual ramp, spike, sustained)
+3. **Find limits**: identify bottlenecks (CPU? DB? Network?)
+4. **Model growth**: project based on business metrics (users, transactions)
+5. **Plan headroom**: maintain 30-50% capacity buffer
+
+## Bottleneck Identification
+
+| Resource | Symptoms | Solutions |
+|----------|----------|-----------|
+| Database | High query latency, connection pool exhaustion | Indexing, query optimization, read replicas, caching, sharding |
+| CPU | High utilization, slow processing | Horizontal scaling, algorithm optimization, caching, async processing |
+| Memory | OOM errors, high GC pressure | Memory profiling, data structure optimization, streaming processing |
+| Network | High bandwidth, slow transfers | Compression, CDN, protocol optimization (HTTP/2, gRPC) |
+| I/O | Disk queue depth, slow reads/writes | SSD, batching, async I/O, caching |
+
+## Scaling Strategies
+
+### Vertical scaling (bigger machines)
+
+- **Pros**: Simple, no code changes
+- **Cons**: Expensive, hard limits, single point of failure
+- **Use when**: Quick fix needed, not yet optimized
+
+### Horizontal scaling (more machines)
+
+- **Pros**: Cost-effective, no hard limits, fault tolerant
+- **Cons**: Requires stateless design, load balancing complexity
+- **Requirements**: Stateless services, shared state in DB/cache
+
+### Caching layers
+
+| Layer | Location | Tradeoffs |
+|-------|----------|-----------|
+| L1 | Application | Fastest, stale risk |
+| L2 | Distributed (Redis, Memcached) | Shared across instances |
+| L3 | CDN (CloudFlare, CloudFront) | Edge caching |
+
+Strategies: cache-aside, write-through, write-behind based on needs
+
+### Database scaling
+
+- **Read replicas**: Route reads to replicas, writes to primary
+- **Sharding**: Partition data (customer, geography, hash)
+- **Connection pooling**: PgBouncer, connection reuse
+- **Query optimization**: Indexes, query tuning, explain plans