Distributed Systems — Production Playbook (50M Users Scale)

Context: Tum ek staff engineer ho. Tumhare system pe 50 million monthly active users hain. Peak pe 200K+ requests/second aate hain. Yeh notes woh sab cover karte hain jo Phase 3 (databases, Kafka, Raft, CAP) ke baad padna chahiye — production-level distributed systems design, operations, aur failure handling.

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Phase 3 mein kya cover ho chuka hai (skip karo)

• PostgreSQL internals (MVCC, WAL, indexes, vacuum)
• Query optimization
• ACID transactions, isolation levels
• Redis internals
• CAP theorem, PACELC, consistency models
• Sharding strategies, consistent hashing
• Kafka deep dive
• Event-driven architecture (outbox, saga, CQRS, event sourcing)
• Raft consensus
• Distributed database survey (CockroachDB, DynamoDB, Cassandra, Spanner)

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Kya NAHI cover hua — yeh folder iske liye hai

01 — Load Balancing at Scale

L4 vs L7 load balancing, health checks, draining, global load balancing (GeoDNS, Anycast), consistent hashing in LB, hot partition handling, connection pooling at LB layer.

02 — Caching Strategies (Multi-Layer)

Cache-aside, read-through, write-through, write-behind. CDN layer. Application cache (Redis). Local in-process cache. Cache stampede/thundering herd. Cache invalidation strategies. Distributed cache topology.

03 — Service Discovery & Service Mesh

DNS-based vs registry-based discovery. Consul, etcd for service registry. Envoy sidecar proxy. Istio/Linkerd service mesh. mTLS between services. Traffic shaping, canary routing at mesh level.

04 — Observability in Production

Structured logging at scale. Distributed tracing (OpenTelemetry, Jaeger). Metrics (Prometheus, Grafana). SLOs/SLIs/SLAs. Error budgets. Alerting that doesn't suck. On-call playbooks.

05 — Failure Handling & Resilience

Circuit breakers. Bulkheads. Retry with exponential backoff + jitter. Timeout budgets. Graceful degradation. Chaos engineering. Blast radius containment. Dependency isolation.

06 — Distributed Locking & Coordination

Redlock algorithm (and its controversies). ZooKeeper recipes. Fencing tokens. Leader election patterns. Distributed scheduling (cron at scale).

07 — Data Pipelines & Stream Processing

CDC (Change Data Capture). Debezium. Stream processing (Flink concepts). Materialized views at scale. Data lake vs data warehouse. ETL vs ELT. Schema registry (Avro, Protobuf schema evolution).

08 — Deployment & Infrastructure at Scale

Blue-green, canary, rolling deployments. Feature flags. Database migrations at scale (zero-downtime). Kubernetes fundamentals for backend engineers. Auto-scaling policies. Cost optimization.

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System Assumptions (50M Users)

Monthly Active Users:     50,000,000
Daily Active Users:       15,000,000
Peak concurrent users:    1,500,000
Peak requests/sec:        200,000+
Data growth:              ~500GB/month
Total stored data:        ~20TB

Infrastructure:
  - Multi-region (at least 2: primary + DR)
  - Kubernetes clusters (3+ per region)
  - PostgreSQL (sharded, 3 shards minimum)
  - Redis cluster (6+ nodes)
  - Kafka cluster (5+ brokers, 3x replication)
  - CDN (Cloudflare/CloudFront)
  - Object storage (S3) for media
  
Team size: 15-30 backend engineers across 4-6 teams

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Reading Order

Ek file per topic hai. Order mein padho ya jo problem face kar rahe ho woh pehle padho:

1. 01-load-balancing-at-scale.md — Traffic kaise distribute hota hai
2. 02-caching-strategies.md — DB load 10x kaise kam karein
3. 03-service-discovery-mesh.md — Services ek dusre ko kaise dhundhti hain
4. 04-observability-production.md — Production mein kya ho raha hai kaise jaanein
5. 05-failure-handling-resilience.md — Jab cheezein tootein toh kya karein
6. 06-distributed-locking-coordination.md — Concurrent operations coordinate kaise karein
7. 07-data-pipeline-stream-processing.md — Data flow at scale
8. 08-deployment-infra-at-scale.md — Ship kaise karein bina downtime ke