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

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

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

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

Latency, throughput, and response time are key metrics in system performance.

Jitter helps spread out client retries to avoid traffic spikes.

Latency is the time for one request, while throughput is how many requests are processed.

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

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

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Availability is about access; reliability is about correct performance.

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

Graceful degradation allows apps to function partially during service failures.

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

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

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

Cache penetration occurs when requests for non-existent data overload the database.

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

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

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

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

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

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

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

Load shedding helps systems reject excess requests to maintain performance during high demand.

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

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

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

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.

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

Alerting helps notify the right people about production issues quickly.

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

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

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

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

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

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

Keep-Alive allows reusing HTTP connections to improve efficiency and reduce overhead.

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

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

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

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

Caching helps applications respond faster by serving data from memory instead of hitting the database.

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

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

Consistent hashing helps distribute data across servers with minimal movement.

Request-Response asks for a result, while Events notify that something has happened.

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

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

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

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

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

Retry storms occur when too many retries overload a failing service.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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