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

Vector clocks help track the order and concurrency of events in distributed systems.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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

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

Synchronous communication waits for a response, while asynchronous continues without waiting.

Latency is the time for one request, while throughput is how many requests are processed.

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

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

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

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

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

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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

Backpressure helps manage message flow in Kafka during traffic spikes.

Crash failures stop a service, while network failures disrupt communication.

Backpressure prevents system overload by managing message processing rates in Kafka.

Message delivery semantics define how messages are sent in distributed systems and handle failures.

Read-after-write consistency ensures users see their latest updates immediately.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Transactions ensure database operations are reliable using ACID properties.

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

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

Request-Response asks for a result, while Events notify that something has happened.

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

Availability is about access; reliability is about correct performance.

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

Failover ensures systems remain operational by managing leader changes during failures.

Kafka consumer offsets help track message processing to avoid loss and duplication.

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

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

Latency, throughput, and response time are key metrics in system performance.

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