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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.

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.

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

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.

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.

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

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.

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.

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.

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

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.

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.