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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.

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

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

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

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

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

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

Backpressure prevents system overload by managing message processing rates in Kafka.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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

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

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

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

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

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

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

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

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

Graceful degradation allows apps to function partially during service failures.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

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

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

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

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

Kafka's assign() method allows manual control of partition assignments for consumers.

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