How do you choose between SQL and NoSQL databases?

Choose SQL (PostgreSQL, MySQL) for structured data requiring ACID transactions, complex JOINs, and strict schema enforcement. Choose NoSQL (MongoDB, DynamoDB, Cassandra) for flexible schemas, massive horizontal scale, high write throughput, and eventual consistency workloads.

When should you choose Vertical Scaling vs Horizontal Scaling?

Start with vertical scaling (adding CPU/RAM to one server) for simplicity. Transition to horizontal scaling (adding more servers) when hardware limits are reached, cost curves become prohibitive, or high availability is required. Horizontal scaling introduces distributed system complexity.

What is the difference between Sharding, Partitioning, and Indexing?

Indexing speeds up read queries within a single table using B-Tree or Hash structures. Partitioning splits a single table into logical physical chunks within one database instance. Sharding distributes rows across multiple distinct database servers using a shard key, scaling write throughput and storage.

How do you scale WebSockets to millions of concurrent connections?

Increase Linux file descriptor limits, use Layer 4 load balancing with sticky sessions, and implement a Redis Pub/Sub backplane so any WebSocket server can relay messages to clients connected to other servers. Pattern: Client → HAProxy → WS Servers ↔ Redis Pub/Sub.

What is the difference between LSI and GSI in DynamoDB?

A Local Secondary Index (LSI) shares the same partition key as the base table but uses a different sort key. It is limited to 10GB per item collection and supports strong consistency. A Global Secondary Index (GSI) uses a completely different partition key, spans all partitions, supports only eventual consistency, and has its own dedicated throughput capacity.

Which database should I use for transactions, user profiles, logs, and analytics?

Use PostgreSQL for financial transactions (ACID compliance). Use MongoDB for user profiles (flexible JSON). Use Cassandra for logs and high-throughput writes (LSM tree). Use Apache Druid for real-time analytics (columnar storage). Use Elasticsearch for search and catalog (inverted index). Use Redis for sessions and caching (in-memory).

How does Consistent Hashing work?

Consistent hashing maps both servers and keys onto a circular ring. A key routes clockwise to the nearest server. When a node is added or removed, only 1/N keys need to migrate (vs nearly 100% in modulo hashing). Virtual nodes (100-200 per server) ensure even distribution and prevent hotspots.

How do you design a distributed rate limiter?

Use a Token Bucket algorithm for burst-friendly limiting. For distributed enforcement across multiple API gateway instances, use Redis with Lua scripts to atomically evaluate and increment counters, preventing race conditions (dirty reads) where concurrent requests could bypass the limit.

How does Distributed Tracing (Jaeger / Zipkin) trace requests across microservices? — System Design Interview Answer & Trade-offs

Architectural Breakdown & AnalysisFAANG Aligned

In microservice architectures, a single user click triggers calls across dozens of services. Distributed tracing tracks request execution paths across boundaries.

Context Propagation Headers (W3C Trace Context)

Services inject HTTP headers into downstream RPC requests:

traceparent: 00-4bf92f3577b34da6a3ce929d0e0e4736-00f067aa0ba902b7-01
Trace ID (`4bf9...`): Unique identifier shared across the entire call graph.
Span ID (`00f0...`): Unique identifier for an individual operation within a service.

Head-Based vs Tail-Based Sampling

Head-Based Sampling: Decides to sample a trace at the initial API Gateway. Efficient, but might miss rare errors.
Tail-Based Sampling: Collects all spans first, sampling 100% of traces containing HTTP 5xx errors or latency anomalies.

NOTE — Standard Framework

Standardize telemetry instrumentation across languages using OpenTelemetry (OTel).

Key Architectural Comparison

Tracing Primitive	Scope	Information Contained	Propagation Header
Trace ID	Entire end-to-end request lifecycle across all microservices.	Globally unique 64/128-bit identifier.	`traceparent` (W3C standard)
Span ID	Single unit of contiguous work within a specific microservice.	Start time, duration, service name, tags, error flags.	`traceparent`
Baggage	Cross-cutting user/tenant context passed across RPC boundaries.	User ID, tenant tier, feature flags.	`baggage`

How does Distributed Tracing (Jaeger / Zipkin) trace requests across microservices?

Context Propagation Headers (W3C Trace Context)

Head-Based vs Tail-Based Sampling

Key Architectural Comparison

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