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

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

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

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

Secure API design requires multiple strategies to protect sensitive data and access.

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

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

Fault domains help prevent multiple servers from failing together in an architecture.

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

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

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

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

HLD provides an overview of system architecture, while LLD details implementation specifics.

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

Choosing between single and multi-region architecture affects system resilience and cost.

Graceful degradation allows apps to function partially during service failures.

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

OLTP handles transactions while OLAP is for data analysis and reporting.

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

Availability is about access; reliability is about correct performance.

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

Data residency is crucial for compliance with laws about where data can be stored.

High Availability ensures your application remains operational even during server failures.

Active-passive architecture ensures a standby system takes over if the main system fails.

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

Active-Active Architecture uses multiple regions to serve traffic simultaneously for better resilience.

Authentication verifies who you are, while authorization determines what you can do.

Functional requirements define what a system does, while non-functional requirements define how it performs.

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

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

A single component's failure can take down your entire application, even with redundancy.

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

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

CQRS separates how we change data from how we read data, improving scalability.

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

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

Containers share the host OS kernel, while VMs virtualize hardware with separate OS.

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

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

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

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

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

Transactions ensure database operations are reliable using ACID properties.

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

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

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