Any concept.
clear in 60 seconds.

Kafka consumer lag shows how far behind consumers are from new messages.

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Latency, throughput, and response time are key metrics in system performance.

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

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

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

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

Cache invalidation is about knowing when to refresh cached data.

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

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

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

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

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

Cache penetration occurs when requests for non-existent data overload the database.

Elasticsearch ILM automates data management through defined lifecycle phases.

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

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

Alerting helps notify the right people about production issues quickly.

Vector clocks help track the order and concurrency of events in distributed systems.

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

SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

Graceful degradation allows apps to function partially during service failures.

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

A timeout indicates a deadline was exceeded, not necessarily a failure.

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.

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

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

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

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

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

Fetch limits control data size from Kafka, while poll records limit processed records.