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Design Scenarios

Open-ended, senior-level system design problems.

Learn the reason behind each component

14 independent guides. Each includes local concept explanations, a correctness contract, a worked failure trace, and a runnable Python learning model.

Read in this order: requirements → concepts → data flow → concurrency and failure behavior → sizing → worked example. Explain what can fail before choosing a technology.

01·Highly Reliable ServiceDefine reliability through user-visible outcomes, independent capacity, and bounded request-path behavior. 02·Unified Observability PlatformUnify metrics, logs, and traces through shared context while budgeting storage, coverage, and query isolation. 03·High-Volume Telemetry Ingestion PipelineAccept heterogeneous signals into bounded durable pipelines with clear per-signal delivery and privacy policies. 04·Distributed Log Search EngineBuild a searchable retained log with explicit visibility, durability, query completeness, and authorization boundaries. 05·Distributed Tracing SystemConnect distributed operations through propagated identities and interpret incomplete, sampled traces honestly. 06·Logs-to-Metrics Real-Time AlertingExtract stable metrics from events and alert on user-relevant budget consumption without hiding missing data. 07·Multi-Datacenter Routing & FailoverRecover from a lost or isolated region without promoting a stale or unauthorized writer. 08·Scaling from 10× to 100×Scale the measured bottleneck while preserving data and failure semantics as traffic grows. 09·Capacity Forecasting SystemForecast unconstrained demand and purchase enough surviving capacity before lead time runs out. 10·Petabyte-Scale Data PipelineReduce analytical bytes scanned and publish reproducible dataset versions despite task retries and late data. 11·Zero Data Loss During a Network PartitionPreserve acknowledged critical commits under network partitions while separating ordering, durability, and availability. 12·Testability in Distributed MicroservicesMake distributed logic reproducible in fast tests while recognizing what mocks and toy stores cannot prove. 13·Zero-Downtime Deployment & RollbackChange code and data in compatible stages while preserving a real rollback horizon and measuring user impact. 14·Consensus: Raft & PaxosUnderstand the state and protocol rules that let crash-recovering nodes preserve one committed history.

Find a guide or concept

14 guides

Designing a Capacity Forecasting System — A System Design Interview Guide

Forecast unconstrained demand and purchase enough surviving capacity before lead time runs out.

Forecast percentiles and headroom · Failure domains and redundant capacity · SLIs, SLOs, and error budgets · Cardinality · Backpressure and load shedding

Consensus: Raft & Paxos — A System Design Interview Guide

Understand the state and protocol rules that let crash-recovering nodes preserve one committed history.

Raft terms and commit rules · Quorum · Write-ahead log and fsync · Linearizability · CAP and consistency models · Fencing tokens

Designing a Distributed Tracing System — A System Design Interview Guide

Connect distributed operations through propagated identities and interpret incomplete, sampled traces honestly.

Spans and trace context · Head and tail sampling · Transactional outbox · Cardinality · SLIs, SLOs, and error budgets

Designing a Distributed Log Search Engine — A System Design Interview Guide

Build a searchable retained log with explicit visibility, durability, query completeness, and authorization boundaries.

Inverted indexes and columnar storage · Partition pruning and predicate pushdown · Write-ahead log and fsync · Offsets and checkpoints · Cardinality

Designing a Logs-to-Metrics Real-Time Alerting Pipeline — A System Design Interview Guide

Extract stable metrics from events and alert on user-relevant budget consumption without hiding missing data.

Event time and watermarks · Rollups and weighted aggregation · Cardinality · Burn-rate alerting · SLIs, SLOs, and error budgets · Offsets and checkpoints

Designing Global Multi-Datacenter Routing & Failover — A System Design Interview Guide

Recover from a lost or isolated region without promoting a stale or unauthorized writer.

RPO and RTO · Failure domains and redundant capacity · Quorum · Fencing tokens · Replication · CAP and consistency models

Designing a Unified Observability Platform — A System Design Interview Guide

Unify metrics, logs, and traces through shared context while budgeting storage, coverage, and query isolation.

Spans and trace context · Head and tail sampling · Cardinality · Inverted indexes and columnar storage · Rollups and weighted aggregation · Backpressure and load shedding · SLIs, SLOs, and error budgets

Zero Data Loss During a Network Partition — A System Design Interview Guide

Preserve acknowledged critical commits under network partitions while separating ordering, durability, and availability.

CAP and consistency models · Quorum · Raft terms and commit rules · Write-ahead log and fsync · Linearizability · Failure domains and redundant capacity · Fencing tokens

Designing a Petabyte-Scale Data Pipeline with Strong SLAs — A System Design Interview Guide

Reduce analytical bytes scanned and publish reproducible dataset versions despite task retries and late data.

Partition pruning and predicate pushdown · Inverted indexes and columnar storage · Offsets and checkpoints · Event time and watermarks · DAG scheduling · Transactional outbox

Designing a Highly Reliable Service — A System Design Interview Guide

Define reliability through user-visible outcomes, independent capacity, and bounded request-path behavior.

SLIs, SLOs, and error budgets · Burn-rate alerting · Failure domains and redundant capacity · Backpressure and load shedding · Idempotency keys · Wall and monotonic clocks

Scaling a System from 10× to 100× — A System Design Interview Guide

Scale the measured bottleneck while preserving data and failure semantics as traffic grows.

Cache-aside and invalidation · Consistent hashing · Replication · Backpressure and load shedding · Failure domains and redundant capacity · Transactional outbox

Designing a High-Volume Telemetry Ingestion Pipeline — A System Design Interview Guide

Accept heterogeneous signals into bounded durable pipelines with clear per-signal delivery and privacy policies.

Offsets and checkpoints · Backpressure and load shedding · Pub/sub and durable queues · Cardinality · Transactional outbox · Spans and trace context

Designing for Testability in Distributed Microservices — A System Design Interview Guide

Make distributed logic reproducible in fast tests while recognizing what mocks and toy stores cannot prove.

Dependency injection and test doubles · Optimistic concurrency and compare-and-set · Wall and monotonic clocks · Canaries, shadow traffic, and rollout gates · SLIs, SLOs, and error budgets · Idempotency keys

Zero-Downtime Deployment & Rollback — A System Design Interview Guide

Change code and data in compatible stages while preserving a real rollback horizon and measuring user impact.

Expand-contract migrations · Canaries, shadow traffic, and rollout gates · Optimistic concurrency and compare-and-set · Transactional outbox · SLIs, SLOs, and error budgets · Failure domains and redundant capacity

Download the Python concept models · Python 3.10+, no packages required.

Student clarity and correctness review · 19 October 2026