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Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

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

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

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

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

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

Cache stampede occurs when many requests hit the database due to a cache miss.

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

Kafka's parallel processing depends on partitions, not just consumers or threads.

Kafka can help ensure messages are processed once, but requires careful design.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Lamport Timestamps help order events in distributed systems while preserving causality.

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

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

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

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Database isolation levels determine how transactions interact and affect data consistency.

Jitter helps spread out client retries to avoid traffic spikes.

Consistency models determine what data reads in distributed systems can see.

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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

Paxos is a protocol that helps distributed systems agree on one value despite failures.

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

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

Kafka consumers resume processing from the last committed offset after a crash.

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

Transactions ensure database operations are reliable using ACID properties.

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

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.