Event-Driven

Building resilient event-driven architectures with Apache Kafka

Building resilient event-driven architectures with Apache Kafka

Even though cloud-native computing has been around for some time—the Cloud Native Computing Foundation was started in 2015; an eon in computer time—not every developer has experienced the, uh, “joy” of dealing with distributed systems. The old patterns of thinking and architecting systems have given way to new ideas and new problems. For example, it’s not always possible (or advisable) to connect to a database and run transactions. Databases themselves are giving way to events and Command Query Responsibility Segregation (CQRS) and eventual consistency. Two-phase commits are being replaced with queues and database sagas, while monoliths are replaced with microservices, containers, and Kubernetes. “Small and local” thinking rules the day.

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Custom policies in Red Hat 3scale API Management, Part 2: Securing the API with rate limit policies

Custom policies in Red Hat 3scale API Management, Part 2: Securing the API with rate limit policies

In Part 1 of this series, we discussed the policy framework in Red Hat 3scale API Management—adding policies to the APIcast gateway to customize API request and response behavior. In this article, we will look at adding rate limiting, backend URL protection, and edge limiting policies to the APIcast gateway. We’ll also review which policies are appropriate to use for different use cases.

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Event-driven APIs and schema governance for Apache Kafka: Get ready for Kafka Summit Europe 2021

Event-driven APIs and schema governance for Apache Kafka: Get ready for Kafka Summit Europe 2021

As a developer, I’m always excited to attend the Kafka Summit, happening this year from May 11 to 12. There are so many great sessions addressing critical challenges in the Apache Kafka ecosystem. One example is how changes to event-driven APIs are leading developers to focus on contract-first development for Kafka.

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Deploying the Mosquitto MQTT message broker on Red Hat OpenShift, Part 2

Deploying the Mosquitto MQTT message broker on Red Hat OpenShift, Part 2

The first half of this article introduced the Mosquitto  Message Queuing Telemetry Transport  (MQTT) message broker and showed how to build Mosquitto into an image suitable for use in a container. In this second half of the article, you will configure and deploy the Mosquitto image into an application that runs on Red Hat OpenShift. You can obtain the files for the example from my GitHub repository.

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Deploying the Mosquitto MQTT message broker on Red Hat OpenShift, Part 1

Deploying the Mosquitto MQTT message broker on Red Hat OpenShift, Part 1

Mosquitto is a lightweight message broker that supports the Message Queuing Telemetry Transport  (MQTT) protocol. Mosquitto is widely used in Internet of Things (IoT) and telemetry applications, where a fully-featured message broker like Red Hat AMQ would be unnecessarily burdensome. Mosquitto also finds a role as a message bus for interprocess communication in distributed systems. Because it avoids complex features, Mosquitto is easy to tune and handles substantial application workloads with relatively modest memory and CPU resources.

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Securely connect Red Hat Integration Service Registry with Red Hat AMQ Streams

Securely connect Red Hat Integration Service Registry with Red Hat AMQ Streams

Red Hat Integration Service Registry is a datastore based on the Apicurio open source project. In my previous article, I showed you how to integrate Spring Boot with Service Registry. In this article, you’ll learn how to connect Service Registry to a secure Red Hat AMQ Streams cluster.

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Design event-driven integrations with Kamelets and Camel K

Design event-driven integrations with Kamelets and Camel K

The Kamelet is a concept introduced near the end of 2020 by Camel K to simplify the design of complex system integrations. If you’re familiar with the Camel ecosystem, you know that Camel offers many components for integrating existing systems. An integration’s granularity is related to its low-level components, however. With Kamelets you can reason at a higher level of abstraction than with Camel alone.

A Kamelet is a document specifying an integration flow. A Kamelet uses Camel components and enterprise integration patterns (EIPs) to describe a system’s behavior. You can reuse a Kamelet abstraction in any integration on top of a Kubernetes cluster. You can use any of Camel’s domain-specific languages (DSLs) to write a Kamelet, but the most natural choice is the YAML DSL. The YAML DSL is designed to specify input and output formats, so any integration developer knows beforehand what kind of data to expect.

A Kamelet also serves as a source or sink of events, making Kamelets the building blocks of an event-driven architecture. Later in the article, I will introduce the concept of source and sink events and how to use them in an event-driven architecture with Kamelets.

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Deploy integration components easily with the Red Hat Integration Operator

Deploy integration components easily with the Red Hat Integration Operator

Any developer knows that when we talk about integration, we can mean many different concepts and architecture components. Integration can start with the API gateway and extend to events, data transfer, data transformation, and so on. It is easy to lose sight of what technologies are available to help you solve various business problems. Red Hat Integration‘s Q1 release introduces a new feature that targets this challenge: the Red Hat Integration Operator.

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