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

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

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

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

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.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Jitter helps spread out client retries to avoid traffic spikes.

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

Connection pooling reuses database connections but can cause issues if misconfigured.

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

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

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

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

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

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

Database replication helps keep data available and allows systems to handle more read requests.

Partial failure means some services fail while others keep running, impacting system reliability.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

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

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

Choosing the right distributed lock depends on your system's needs and existing tools.

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

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.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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