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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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.

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

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

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.

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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.

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

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

Kafka uses retries and Dead Letter Queues to manage message processing failures.

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.

Kafka uses retention to keep historical data and compaction to keep the latest state.

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.

In distributed systems, timestamps don't always reflect the true order of events.

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

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

Distributed tracing helps track requests across microservices to find delays.