System Design Fundamentals
System design turns product requirements into a workable plan for components, data, communication, capacity, and failure handling. It helps you explain why a design fits a workload and which trade-offs it makes.
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Intro
System Design Fundamentals
System design is the work of turning a problem into a technical plan that can be built, operated, and changed. You identify requirements, shape the major components, trace data and requests, and make trade-offs explicit.
A good system design is not the diagram with the most boxes. It is a defensible answer to a specific workload. The same feature can need a different design when its traffic, latency, durability, security, cost, or team constraints change.
The mental model: requirements become flows and evidence
Use this sequence:
requirements -> estimates -> interfaces -> data -> components -> failure controls -> evidence
Requirements tell you what success means. Estimates expose likely pressure points. Interfaces define the system boundary. Data and flows reveal where work and state move. Components implement those flows. Failure controls keep one problem from spreading. Evidence tests whether your assumptions hold.
Keep the sequence iterative. A data choice can change an interface. A failure scenario can change the component layout. A load test can disprove a capacity estimate.
Start with requirements
Separate functional requirements from quality requirements.
Functional requirements describe user-visible outcomes. Examples include creating an order, reading a timeline, or receiving a notification.
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Sources
- https://learn.microsoft.com/en-us/azure/architecture/guide/design-principles/build-for-business
Supports
- Requirements and business needs before architecture choices
- Different flows having different availability, scalability, consistency, and recovery needs
- Measurable objectives informing architecture decisions
- Quiz answer about requirement-first design
- https://learn.microsoft.com/en-us/azure/architecture/guide/design-principles/scale-out
Supports
- Horizontal scaling with interchangeable instances
- Session affinity and local state as scaling constraints
- Bottleneck identification before adding capacity
- Asynchronous messaging, flow control, queue buffering, and independent scaling
- Scaling in and transient-fault handling
- Quiz answers about estimates and horizontal scaling
- https://learn.microsoft.com/en-us/azure/architecture/guide/architecture-styles/
Supports
- Architecture styles selected from business and quality requirements
- Trade-offs of synchronous communication and asynchronous messaging
- Eventual consistency and duplicate-message concerns
- Communication, latency, complexity, and manageability costs
- https://learn.microsoft.com/en-us/azure/architecture/guide/design-principles/make-all-things-redundant
Supports
- Redundancy to avoid single points of failure
- Replicas and multiple instances as redundancy mechanisms
- Redundancy choices driven by requirements and cost
- Quiz answer about replication and freshness decisions
- https://docs.aws.amazon.com/wellarchitected/latest/framework/rel-05.html
Supports
- Network latency and loss in distributed interactions
- Graceful degradation, throttling, controlled retries, limited queues, and timeouts
- Stateless design where practical
- Quiz answers about bounded queues and retry amplification
- https://sre.google/sre-book/service-level-objectives/
Supports
- SLI and SLO definitions
- Latency, error rate, throughput, availability, durability, and correctness indicators
- User-relevant measurement
- Percentiles exposing tail latency hidden by averages
- Measurement windows and workload classes
- Quiz answer about latency percentiles
- https://sre.google/sre-book/service-best-practices/
Supports
- User-focused service objectives
- Monitoring and error budgets
- Production readiness and evidence
- Quiz answer about revising assumptions from test evidence
- https://www.rfc-editor.org/rfc/rfc9110
Supports
- Safe and idempotent HTTP method semantics
- Automatic retry constraints for non-idempotent requests
- Repeated operations and quiz answer about idempotency
- https://www.rfc-editor.org/rfc/rfc9111
Supports
- HTTP cache storage and reuse
- Freshness, staleness, validation, and invalidation semantics
- Quiz answer about complete cache design
- https://github.com/sindresorhus/awesome
Supports
- Discovery of the Awesome Testing list and listed load-testing tools
- Discovery decision for k6, Apache JMeter, and Gatling
- https://github.com/TheJambo/awesome-testing
Supports
- Inclusion of k6, Apache JMeter, and Gatling as performance or load-testing tools
- https://grafana.com/docs/k6/latest/
Supports
- k6 as a scriptable load and performance testing tool
- Test authoring, execution, results, and performance-testing concepts
- Awesome Links rationale for k6
- https://jmeter.apache.org/usermanual/
Supports
- Test plans, thread groups, samplers, assertions, and CLI execution
- Load-test reporting and broad protocol support
- Awesome Links rationale for Apache JMeter
- https://docs.gatling.io/
Supports
- Code-driven load testing and supported SDK languages
- Virtual users, load models, protocols, metrics, and automation
- Awesome Links rationale for Gatling