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Kafka uses different strategies to assign partitions to consumers in a group efficiently.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Microservices can communicate using five key patterns based on their needs.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Graceful degradation allows apps to function partially during service failures.

Load shedding helps systems reject excess requests to maintain performance during high demand.

L4 and L7 load balancers differ in how they route network traffic based on layers of the OSI model.

Fallbacks allow systems to handle failures safely and maintain user experience.

Choosing between a shared database and a database per service affects system design significantly.

Microservices and modular monoliths differ mainly in deployment and communication methods.

REST API design focuses on creating clean, efficient, and scalable web services.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Choosing the right boundaries for microservices is crucial for effective architecture.

API Gateway, Proxy, and Load Balancer serve different roles in managing requests and traffic in systems.

Elasticsearch mapping defines how fields are indexed and searched, with dynamic and explicit options.

Choosing the right distributed lock depends on your system's needs and existing tools.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

The choice between SQL and NoSQL databases depends on your application's specific needs.

Database per Service ensures each microservice owns its data, reducing dependencies.

HLD provides an overview of system architecture, while LLD details implementation specifics.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

FAANG interviewers evaluate problem understanding, architecture, scalability, and communication skills.