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

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

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

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

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

Microservices can communicate using five key patterns based on their needs.

Replicas keep your system available, while snapshots allow for data recovery.

Alerting helps notify the right people about production issues quickly.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

Tokenization and analyzers help search engines process and match text effectively.