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Distributed caching spreads data across multiple servers for better performance and reliability.

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

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

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

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

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

Database replication enhances data availability and read performance but has challenges like replication lag.

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

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

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

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

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

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Network partitions occur when servers are operational but can't communicate, affecting system performance.

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

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

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

Elasticsearch uses master election and quorum to prevent split brain scenarios.

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

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

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

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

Failover ensures systems remain operational by managing leader changes during failures.

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

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

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

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

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

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

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

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

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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

Correlation IDs help track requests in microservices for easier debugging.

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

An API Gateway simplifies client access to backend services in microservices.

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

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

Database replication helps keep data available and allows systems to handle more read requests.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Consistent hashing helps distribute data across servers with minimal movement.

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

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

Crash failures stop a service, while network failures disrupt communication.

Graceful degradation allows apps to function partially during service failures.

Connection pooling reuses database connections but can cause issues if misconfigured.

Distributed tracing helps track requests across microservices to find delays.

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