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clear in 60 seconds.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

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

Jitter helps spread out client retries to avoid traffic spikes.

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.

Partial failure means some services fail while others keep running, impacting system reliability.

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

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.

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

Retry storms occur when too many retries overload a failing service.

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

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

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

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