Optimizing Distributed Performance
1. Understanding Performance Bottlenecks
| Layer | Common Bottleneck | Diagnostic |
|---|---|---|
| CPU | Hot loops, GC | Profilers, perf, async-profiler |
| Memory | Allocations, leaks | Heap dumps, MAT |
| Disk I/O | fsync, random reads | iostat, fio |
| Network | Bandwidth, RTT, retransmits | tcpdump, ss, iperf |
| DB | Lock contention, slow queries | EXPLAIN, pg_stat_statements |
| Coordination | Locks, consensus | Tracing, lock wait monitors |
2. Implementing Connection Pooling
| Setting | Guidance |
|---|---|
| Min idle | 0-5; allow scale-down |
| Max size | ~ (cores × 2) to (cores × 4); not too large |
| Connection timeout | 1-5s |
| Idle timeout | 10 min typical |
| Max lifetime | 30 min — avoid stale connections |
| HikariCP | Default Java standard |
3. Implementing Request Batching
| Pattern | Detail |
|---|---|
| Bulk DB write | INSERT ... VALUES (...), (...), ... |
| Multi-get | Redis MGET, DynamoDB BatchGetItem |
| Producer batching | Kafka linger.ms |
| DataLoader (GraphQL) | Coalesce per-tick |
4. Implementing Response Compression
| Codec | Ratio | CPU | Use |
|---|---|---|---|
| gzip | Good | Medium | HTTP default |
| brotli | Best (text) | Higher (compress) | HTTPS-only browsers |
| zstd | Excellent + fast | Low | Kafka, ZFS, modern HTTP |
| snappy / lz4 | Lower ratio | Very low | Throughput-critical (Kafka) |
5. Implementing Query Optimization
| Technique | Detail |
|---|---|
| EXPLAIN ANALYZE | Inspect plan + actual rows |
| Avoid N+1 | Eager fetch / join / DataLoader |
| Project columns | Avoid SELECT * |
| Pushdown filters | Server-side WHERE |
| Statistics | ANALYZE / vacuum analyze |
6. Implementing Index Strategies
| Index Type | Best For |
|---|---|
| B-Tree | Equality, range, sort |
| Hash | Equality only |
| GIN / inverted | Full-text, JSONB, arrays |
| GIST | Geo, ranges |
| BRIN | Append-only large tables |
| Composite | Multi-column queries; respect leftmost prefix |
| Covering | Include columns to avoid heap lookup |
7. Implementing Caching Layers
| Layer | Latency | Tool |
|---|---|---|
| Browser | 0ms | Cache-Control, ETag |
| CDN | ~10ms | Cloudflare, CloudFront |
| Reverse proxy | ~1ms | NGINX, Varnish |
| App in-process | μs | Caffeine, Guava |
| Distributed | ~1ms LAN | Redis, Memcached |
| DB buffer pool | μs | InnoDB, shared_buffers |
8. Understanding Network Latency Optimization
| Technique | Detail |
|---|---|
| Co-locate (same AZ) | Sub-ms vs cross-region 50-150ms |
| Connection reuse | HTTP keep-alive, HTTP/2 |
| Pipelining / multiplex | HTTP/2, gRPC streams |
| Reduce round trips | Combine requests, GraphQL |
| QUIC / HTTP/3 | 0-RTT, no HOL blocking |
| Edge compute | Run close to user |
9. Implementing Lazy Loading
| Pattern | Detail |
|---|---|
| DB | Load relations on access (JPA FetchType.LAZY) |
| Frontend | Code splitting, image lazy loading |
| Risk | N+1 queries — mind the trade-off |
10. Implementing Prefetching and Preloading
| Pattern | Detail |
|---|---|
| Sequential scan prefetch | OS readahead; DB block prefetch |
| Predictive (ML) | Anticipate next page / object |
| Browser hints | <link rel="preload"> |
| DB warmup | Buffer pool preload after restart |
11. Understanding Performance Profiling Tools
| Tool | Use |
|---|---|
| async-profiler | JVM CPU + alloc flame graphs |
| perf / pprof | Linux / Go profiling |
| JFR + JMC | Low-overhead JVM profiling |
| eBPF / bcc / bpftrace | Kernel-level observability |
| Pyroscope / Parca | Continuous profiling |
| Distributed tracing | Jaeger, Tempo, OTel |