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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.

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

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

Availability is about access; reliability is about correct performance.

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

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

N+1 redundancy means having one extra server to maintain capacity during a failure.

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

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

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

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

Batch processing collects data to process later, while stream processing handles data in real-time.

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

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

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

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

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

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

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.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

Jitter helps spread out client retries to avoid traffic spikes.

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

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

Alerting helps notify the right people about production issues quickly.

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

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

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

An error budget shows how much downtime is acceptable while meeting reliability goals.

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

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

Late-arriving events can be dropped, updated, or routed based on system needs.

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

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

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

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

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

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