Any concept.
clear in 60 seconds.

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.

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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.

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

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

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

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

Bully, Raft, and ZooKeeper are distinct algorithms for leader election in distributed systems.

Kafka rebalances consumer groups to manage partition assignments effectively.

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

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

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

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

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

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

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.

Kafka Rebalance Listeners help manage state during partition changes in consumer groups.

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

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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.

Kafka lag recovery time is how fast consumers can process backlogged messages.

Distributed tracing helps track requests across microservices to find delays.

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

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

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.