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

Request-Response asks for a result, while Events notify that something has happened.

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

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Backpressure helps manage message flow in Kafka during traffic spikes.

Graceful degradation allows apps to function partially during service failures.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Backpressure prevents system overload by managing message processing rates in Kafka.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Synchronous communication waits for a response, while asynchronous continues without waiting.

Kafka consumer offsets help track message processing to avoid loss and duplication.

Vector clocks help track the order and concurrency of events in distributed systems.

Alerting helps notify the right people about production issues quickly.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

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

Monolithic and microservices architectures each have unique advantages and challenges for software development.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Distributed backpressure helps manage the flow of work between services to prevent overload.

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

Transactions ensure database operations are reliable using ACID properties.

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

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

Distributed locks ensure only one server performs a task, avoiding duplication.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.