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

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

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.

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

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

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

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

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

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

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

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

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

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

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

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

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

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

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.

Kafka uses state stores to remember previous events in stateful stream processing.

Jitter helps spread out client retries to avoid traffic spikes.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

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.

Physical time tells when events happen, while logical time tells their order.

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

Consensus ensures that distributed systems can agree on decisions even during failures.

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.

Poison messages in Kafka can cause infinite retries, risking system stability.

ZooKeeper uses ephemeral and sequential nodes for managing distributed services effectively.

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

Availability is about access; reliability is about correct performance.

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

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.

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

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.

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

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

Kafka commit strategies determine how offsets are managed during message processing.

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

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