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

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

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Jitter helps spread out client retries to avoid traffic spikes.

Database per Service ensures each microservice owns its data, reducing dependencies.

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

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

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

Alerting helps notify the right people about production issues quickly.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

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

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

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

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

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

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Monolithic and microservices architectures each have unique advantages and challenges for software development.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

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

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

Choosing the right boundaries for microservices is crucial for effective architecture.

Fallbacks allow systems to handle failures safely and maintain user experience.

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

Elasticsearch Data Streams simplify time-series data management with one name for many indices.