Phase 13 Interactive Simulators(13)
Browse all 13 labs →Scale-Up vs Scale-Out Physics Lab
Watch Amdahl's Law flatten vertical throughput while cloud price curves turn exponential.
- Scale-Up vs Scale-Out PhysicsFREE
- Sticky Session Survival LabFREE
- 100k Seconds/Day EstimatorFREE
- 500M DAU Capacity PipelineFREE
- B+ Tree vs LSM Matcher
- Celebrity Fan-Out Defender
- Little's Law Pool Tuner
- Batch Amortization Lab
- Compression Bill Calculator
- Speed-of-Light Latency Lab
- Regional Failover Drill
- GeoDNS vs Anycast Race
- FinOps Cost Lever Board
Performance, Scaling & Capacity
Phase 13 establishes the quantitative foundation and high-performance scaling principles required to design resilient global distributed systems.
All Topics in Phase 13
0 of 13 completedExplore the fundamental engineering trade-offs of scaling: Vertical (Scale-Up) vs Horizontal (Scale-Out), hardware limits, NUMA architecture, Amdahl's Law, and cloud cost inflection curves.
Design highly elastic cloud architectures: Externalizing session state to distributed caches, overcoming sticky session traps, Kubernetes StatefulSets vs Deployments, and scaling stateful WebSocket connections.
Master rapid capacity sizing and mental arithmetic: Powers of two vs powers of ten, the 100,000 seconds/day rule, Jeff Dean's latency numbers, and converting business scale into CPU, RAM, disk, and bandwidth requirements.
Work through a comprehensive, rigorous end-to-end capacity planning model: Sizing a 500M DAU global social platform from QPS and 5-year multi-tier storage to network egress bandwidth and Redis cluster RAM sizing.
Architect for divergent workload profiles: Deep comparison of storage engines (B+ Trees vs LSM Trees), CQRS read denormalization vs write-buffering queues, and fan-out-on-write vs fan-out-on-read.
Solve extreme traffic skew and partition key saturation: Hybrid Fan-Out architecture for viral accounts, write-side salted distributed counters, read-side cache key replication, and local in-memory L1 micro-caching.
Maximize throughput and eliminate OS thread thrashing: PostgreSQL connection pool sizing formulas, Little's Law ($L = \lambda W$), PgBouncer transaction pooling modes, and tuning CPU vs I/O worker thread pools.
Multiply system throughput by 10x–100x: Amortizing system call and network packet overhead, SQL multi-row bulk inserts vs COPY protocols, Kafka producer batch tuning (linger.ms & batch.size), and micro-batching buffer patterns.
Slash cloud bandwidth egress bills and storage footprints: Algorithm benchmarks (Brotli vs Zstandard vs Snappy vs Gzip), binary serialization with Protocol Buffers, and avoiding compression traps.
Overcome the physical speed of light in fiber: Global latency boundaries (~5ms per 1,000 km), edge compute with localized read replicas (LiteFS / Aurora Global), edge TLS termination, and GDPR data residency compliance.
Architect for catastrophic disaster recovery: RTO and RPO modeling, Active-Passive automated failover and split-brain fencing, Multi-Region Active-Active with CRDTs and Spanner TrueTime, and automated regional evacuation.
Steer global user traffic across worldwide datacenters: GeoDNS and EDNS Client Subnet (ECS), DNS TTL caching traps, BGP Anycast Layer-3 routing mechanics, and sub-second disaster recovery failover.