Any concept.
clear in 60 seconds.

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

Cache invalidation is about knowing when to refresh cached data.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Distributed locks ensure only one server performs a task, avoiding duplication.

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

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

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

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