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

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

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

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

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

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

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

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

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

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

A reverse proxy routes client requests to different backend services efficiently.

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

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

The TCP 3-way handshake ensures reliable connection establishment between a client and server.

REST, RPC, and gRPC are different methods for communication in software systems.

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

RPC allows one service to call a function in another service over the network.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

DNS helps microservices find each other, but it has limitations that may require a service registry.

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

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

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

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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

Redis is a fast in-memory data store used for caching and low-latency applications.

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

DNS resolution translates a URL into an IP address for web access.

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

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

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

Consensus ensures that distributed systems can agree on decisions even during failures.

Leader election helps distributed systems select one coordinator from many nodes.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

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

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

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

HTTP methods tell the server what to do, and status codes tell us the result.

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

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

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

The Token Bucket algorithm helps manage how many requests a server can handle.

A CDN speeds up web applications by caching content closer to users.

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