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The talk presented the advantages of moving beyond transactional architectures in favor of eventual consistency.
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By leveraging event meshes with technologies like Knative, developers can achieve decoupled, reliable microservices without extensive re-engineering.
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This solution addresses inefficiencies and aligns distributed systems with real-world business processes.
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The team had figured out they could leverage the <em>CQRS</em> pattern together with <em>Knative’s Event Mesh</em>
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The team had figured out they could leverage the <ahref="https://martinfowler.com/bliki/CQRS.html"><em>CQRS</em></a> pattern together with <em>Knative’s Event Mesh</em>
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to modernize their application in a non-extrusive way.</p>
<p>The core of this solution is an event mesh—a dynamic, flexible layer that routes, retries, and processes asynchronous events across distributed components.
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<p>The core of this solution is an event mesh — a dynamic, flexible infrastructure layer that routes, retries, and processes asynchronous events across distributed components.
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Using <em>Knative Eventing</em> and <em>CloudEvents</em>, this pattern enables:</p>
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Based on <ahref="https://knative.dev">Knative</a> project.</p>
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<p><ahref="https://swc.saas.ibm.com/en-us/redhat-marketplace/products/red-hat-amq">Red Hat AMQ Streams</a>
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<p><ahref="https://developers.redhat.com/products/streams-for-apache-kafka">Streams for Apache Kafka</a>
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— (Optional) Provides a persistence for <em>Event Mesh</em>, likely needed in production.
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Based on <ahref="https://strimzi.io/">Strimzi</a> project.</p>
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<p><ahref="https://cloudevents.io/">CloudEvents</a> — Provides a standard for event metadata</p>
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<p>Rust and <ahref="https://access.redhat.com/products/quarkus">Quarkus</a> — Implementation examples.</p>
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<p><ahref="https://opentelemetry.io/">OpenTelemetry</a> — (Optional) Facilitates tracing for observability.</p>
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<p><ahref="https://opentelemetry.io/">OpenTelemetry</a>— (Optional) Facilitates tracing for observability.</p>
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<p>Rust and Java — Implementation examples.</p>
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<p><em>Kubernetes</em>, <em>Knative</em>, <em>Strimzi</em>, <em>CloudEvents</em>, and <em>OpenTelemetry</em> are <ahref="https://landscape.cncf.io/">CNCF projects</a>.</p>
The capture should be performed regardless of any potential operational disruptions affecting dependent services (e.g., invoicing).</p>
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<p>In such scenarios, transactional applicationstypically return an error, which prevents any data from being changed, and causes the loss of real-world intent of end-users.
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This results in an adverse user experience and a deviation from the genuine business process, potentially leading to customer dissatisfaction.</p>
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<p>In such scenarios, transactional applications, which typically encompass a number of steps in a user workflow, make all the steps undone when an error is returned.
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This prevents any data from being changed, and causes the loss of real-world intent of end-users, and results in an adverse user experience and a deviation from the genuine business process, potentially leading to customer dissatisfaction.</p>
Secondly, it routes these events to their respective endpoints, ensuring that the appropriate microservices are notified and can subsequently update their internal states based on the event data.</p>
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<p>The mesh’s inherent resilience is further bolstered by its exponential backoff strategy, which it employs when encountering operational failures.
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<p>The mesh’s inherent resilience is further bolstered by its built-in retry strategies (linear or exponential backoff), which it employs when encountering operational failures.
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This mechanism ensures that the system retries the operation until it succeeds, thus mitigating the risk of data loss or system disruption due to transient issues.</p>
<p><strong>Decomposition of the application into independently functioning services</strong>:
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This approach facilitates a division of labor, with each service handling specific responsibilities.
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This approach facilitates a division of labor, with each service handling specific responsibilities — the <ahref="https://en.wikipedia.org/wiki/Domain-driven_design">Domain-driven design</a> approach fits here quite well.
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This not only enhances maintainability but also fosters scalability, as services can be independently scaled based on their demands.</p>
<p>An end-user application sends an <em>HTTP</em> request to the <em>Event Mesh</em>. Such message can be understood as a <em>Command</em> type event.</p>
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<p>The <em>Event Mesh</em> (Broker) persists the event in a queue (e.g. Kafka).
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<p>The <em>Event Mesh</em> (Broker) persists the event in a queue (like an Apache Kafka topic, but the implementation is hidden from the user).
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After <em>Event Mesh</em> persists safely the data, it returns a successful <em>HTTP</em> response with the <code>202 Accepted</code> return code.
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At this point, the operation could already be considered successful, from the end-user point of view.
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It will eventually settle correctly in all downstream systems.</p>
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<p>The <em>Event Mesh</em> routes the event to the appropriate endpoint based on the event’s metadata and configured triggering rules.</p>
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<p>The <em>Event Mesh</em> routes the event to the appropriate endpoint based on the CloudEvent’s metadata and configured triggering rules.</p>
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<p>The endpoints receive the events and process them, updating their internal states and potentially emitting new events for downstream consumers.
<h3id="_improving_resilience_in_applications"><aclass="anchor" href="#_improving_resilience_in_applications"></a><aclass="link" href="#_improving_resilience_in_applications">2.8. Improving Resilience in Applications</a></h3>
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<p>Traditional systems often rely on synchronous calls and transactions, which can cascade failures across components.
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Replacing these with asynchronous event-driven communication reduces dependencies and makes the system <em>Eventually Consistent</em>.</p>
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Replacing these with asynchronous event-driven communication reduces dependencies and makes the system <ahref="https://en.wikipedia.org/wiki/Eventual_consistency"><em>Eventually Consistent</em></a>.</p>
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<p>For example, invoicing and notification services in a ride-sharing platform can process events independently, ensuring that downtime in one service does not block the entire workflow.</p>
The talk presented the advantages of moving beyond transactional architectures in favor of eventual consistency.
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By leveraging event meshes with technologies like Knative, developers can achieve decoupled, reliable microservices without extensive re-engineering.
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This solution addresses inefficiencies and aligns distributed systems with real-world business processes.
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The team had figured out they could leverage the <em>CQRS</em> pattern together with <em>Knative’s Event Mesh</em>
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+
The team had figured out they could leverage the <ahref="https://martinfowler.com/bliki/CQRS.html"><em>CQRS</em></a> pattern together with <em>Knative’s Event Mesh</em>
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to modernize their application in a non-extrusive way.</p>
<h2id="_the_solution"><aclass="anchor" href="#_the_solution"></a><aclass="link" href="#_the_solution">3. The Solution</a></h2>
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<p>The core of this solution is an event mesh—a dynamic, flexible layer that routes, retries, and processes asynchronous events across distributed components.
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<p>The core of this solution is an event mesh — a dynamic, flexible infrastructure layer that routes, retries, and processes asynchronous events across distributed components.
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Using <em>Knative Eventing</em> and <em>CloudEvents</em>, this pattern enables:</p>
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