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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.

You can replay Kafka events safely by separating live and replay processes.

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Kafka uses state stores to remember previous events in stateful stream processing.

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

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

gRPC allows efficient communication between services using a structured request-response model.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

Backpressure helps manage message flow in Kafka during traffic spikes.

Graceful degradation allows apps to function partially during service failures.

Lamport Timestamps help order events in distributed systems while preserving causality.

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

Jitter helps spread out client retries to avoid traffic spikes.

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

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.

Kafka commit strategies determine how offsets are managed during message processing.

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

Kafka can help ensure messages are processed once, but requires careful design.

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.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Apache Kafka is a system for managing large volumes of events efficiently.

Alerting helps notify the right people about production issues quickly.

Cache stampede occurs when many requests hit the database due to a cache miss.

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

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

FAANG engineers use high-level design principles to create scalable systems.

In distributed systems, timestamps don't always reflect the true order of events.

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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

Stateless servers are easier to scale because they don't store user session data locally.

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.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

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.

Kafka consumers resume processing from the last committed offset after a crash.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Raft and Paxos are protocols that help computers agree on values in distributed systems.

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

Transactions ensure database operations are reliable using ACID properties.

Kafka uses brokers for data handling and KRaft controllers for metadata management.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

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

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

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

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.

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