MESSAGE DELIVERY SEMANTICS IN EVENT-DRIVEN ARCHITECTURE

MESSAGE DELIVERY SEMANTICS IN EVENT-DRIVEN ARCHITECTURE
In event-driven architecture, message delivery semantics define how messages are transmitted between producers and consumers. These semantics play a crucial role in ensuring the reliability and consistency of event-driven systems, which are widely adopted for their scalability and responsiveness. There are three primary delivery semantics:
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1. At-Most-Once Delivery
This guarantees that a message is delivered to the consumer at most one time. If the message is lost during transmission or processing, it is not retried. While this approach is simple and fast, it risks message loss, making it suitable for scenarios where occasional data loss is tolerable, such as non-critical system logs or metrics.
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2. At-Least-Once Delivery
Here, messages are delivered to consumers one or more times, ensuring that no message is lost. To achieve this, the system retries delivery until an acknowledgment is received. However, this can lead to duplicate messages, which must be handled by the consumer to ensure idempotency. This approach is ideal for critical systems where data integrity is essential, such as financial transactions or order processing systems.
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3. Exactly-Once Delivery
This is the most stringent guarantee, ensuring each message is delivered only once, with no duplication or loss. It requires sophisticated mechanisms like distributed transactions or idempotent consumer design to maintain state consistency. While resource-intensive, exactly-once semantics are crucial in applications like payment gateways or distributed ledger systems, where accuracy is non-negotiable.
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Key Considerations
Network Reliability
ntermittent failures can affect message transmission. Systems must handle retries and acknowledgments effectively.
Message Ordering
Maintaining the correct order of events is crucial for systems relying on sequential data processing.
Idempotency
Designing consumers to process duplicate messages without side effects ensures robustness, especially for at-least-once delivery.
Trade-offs
Higher guarantees (like exactly-once) increase complexity and resource consumption.
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Real-World Examples:
At-Most-Once: Streaming telemetry data from IoT devices.
At-Least-Once: Event-driven inventory management systems.
Exactly-Once: Financial systems where a single event (e.g., fund transfer) must never be duplicated or lost.
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By selecting the appropriate delivery semantic based on the application’s needs and trade-offs, developers can design resilient and efficient event-driven architectures that align with business requirements.
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Mas Irsyad, seorang Software Engineer dengan pengalaman kerja di perusahaan Super, SuperApp (YC W18), bakal berbagi insight berharga dari pengalaman nya di kursus dan lapangan.
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