💻 IT & Coding concepts,
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.

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.

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

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

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

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.

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

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

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.

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

Kafka rebalances consumer groups to manage partition assignments effectively.

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

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.

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

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

Consistent hashing helps distribute data across servers with minimal movement.

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

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

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

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

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.

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

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

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

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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

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

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

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

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

Read-after-write consistency ensures users see their latest updates immediately.

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

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

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

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

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

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.