Any concept.
clear in 60 seconds.

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

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

Distributed caching spreads data across multiple servers for better performance and reliability.

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

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

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

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

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

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

Jitter helps spread out client retries to avoid traffic spikes.

Stateless servers are easier to scale because they don't store user session data locally.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Kafka uses replication and in-sync replicas to ensure data is always available.

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

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

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

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

Classifying failures and using controlled retries in Kafka prevents outages.

Elasticsearch has different node roles that help manage data and scale clusters effectively.

FAANG engineers use high-level design principles to create scalable systems.

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

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

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

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