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

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

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

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

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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

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

You can replay Kafka events safely by separating live and replay processes.

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

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

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

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

Apache Kafka is a system for managing large volumes of events efficiently.

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

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

Kafka's `seek()` lets you replay only specific messages, saving time.

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

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

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

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.

Logs, metrics, and traces help engineers debug issues in software systems.

Elasticsearch index templates automatically apply settings to new indices based on patterns.

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