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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.

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

Lamport Timestamps help order events in distributed systems while preserving causality.

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.

Cache eviction policies determine which data to remove when caches are full.

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

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.

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

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

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.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

Elasticsearch ILM automates data management through defined lifecycle phases.

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.

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.

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

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

Poison messages in Kafka can cause infinite retries, risking system stability.

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

Kafka's `seek()` lets you replay only specific messages, saving time.

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

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

Kafka uses heartbeats and timeouts to check if consumers are alive.

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.

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

Kafka uses state stores to remember previous events in stateful stream processing.