You might not need Secure Socket Layer (SSL)-based communication between microservices in the same cluster, but it’s often a requirement if you want to connect to a remote web service or message broker. In cases where you will expose a web service or other endpoints, you might also have to use a custom keystore in a microservice deployed on Red Hat OpenShift, so that external clients only connect with a specific truststore.
In this article, I show you how to configure a keystore and a truststore for a Java-based microservice built with Spring Boot. I used the Apache Camel and CXF libraries from Red Hat Fuse to develop the microservice. I used a source-to-image (S2I) deployment and tested the examples in Red Hat OpenShift 4.3.
Continue reading “Adding keystores and truststores to microservices in Red Hat OpenShift”
With the rise of social networks and people having more free time due to isolation, it has become popular to see lots of maps and graphs. These are made using big spatial data to explain how COVID-19 is expanding, why it is faster in some countries, and how we can stop it.
Continue reading Working with big spatial data workflows (or, what would John Snow do?)
During the past months, several noticeable new features have been added to improve the developer experience of application based on Apache Camel. These updates are available in the 0.0.20 release of Visual Studio (VS) Code extension.
Before going into the list of updates in detail, I want to note that I mentioned in the title the VS Code Extension release because VS Code extension is covering the broader set of new features. Don’t worry if you are using another IDE, though, most features are also available in all other IDEs that support the Camel Language Server (Eclipse Desktop, Eclipse Che, and more).
Continue reading “VS Code Language support for Apache Camel 0.0.20 release”
In this article, we demonstrate Red Hat OpenShift’s horizontal autoscaling feature with Red Hat Fuse applications. The result is a Spring Boot-based application that uses the Apache Camel component
twitter-search that searches Twitter for tweets based on specific keywords. If traffic or the number of tweets increases, and this application cannot serve all requests, then the application autoscales itself by increasing the number of pods. The ability to serve all requests is monitored by tracking this application’s CPU utilization on a particular pod. Also, as soon as traffic or CPU utilization is back to normal, the number of pods is reduced to the minimum configured value.
There are two types of scaling: horizontal and vertical. Horizontal scaling is where the number of application instances or containers is increased. Vertical scaling is where system resources like CPU and memory are increased at the running application’s or container’s runtime. Horizontal scaling can be used for stateless applications, whereas vertical scaling is more suitable for stateful applications.
Continue reading “Autoscaling Red Hat Fuse applications with OpenShift”
Red Hat Fuse is a leading integration platform, which is capable of solving any given problem with simple enterprise integration patterns (EIP). Over time, Red Hat Fuse has evolved to cater to a wide range of infrastructure needs.
For more information on each of these, check out the Red Hat Fuse documentation. The Fuse on Red Hat OpenShift flavor uses a Fuse image that has runtime components packaged inside a Linux container image. This article will discuss how to reduce the size of the Fuse image. The same principle can be used for other images.
Continue reading “How to reduce Red Hat Fuse image size”
In the following video, I demonstrate how to deploy Red Hat AMQ Streams (based on upstream Apache Kafka) on OpenShift 4.
I will also demonstrate how to use AMQ Streams in a basic way using Red Hat Fuse. There is a Camel route exposing a REST endpoint at
/goodbye, which—when hit—sends a “Goodbye World” message to the topic. There is also a timer sending “Hello World” messages periodically to the topic. A separate Camel route consumes from the topic and logs the messages for our visibility.
Continue reading “Deploy Red Hat AMQ Streams and Fuse on OpenShift Container Platform 4”
Change Data Capture (CDC) is a pattern that enables database changes to be monitored and propagated to downstream systems. It is an effective way of enabling reliable microservices integration and solving typical challenges, such as gradually extracting microservices from existing monoliths.
With the release of Red Hat AMQ Streams 1.2, Red Hat Integration now includes a developer preview of CDC features based on upstream project Debezium.
This article explains how to make use of Red Hat Integration to create a complete CDC pipeline. The idea is to enable applications to respond almost immediately whenever there is a data change. We capture the changes as they occur using Debezium and stream it using Red Hat AMQ Streams. We then filter and transform the data using Red Hat Fuse and send it to Elasticsearch, where the data can be further analyzed or used by downstream systems.
Continue reading “CDC pipeline with Red Hat AMQ Streams and Red Hat Fuse”
In this article, we will discuss how to set up Red Hat AMQ 6.3 on OpenShift. We will also set up an external Camel-based SSL client to connect to AMQ Broker, a pure-Java multiprotocol message broker.
By using the procedures in this article, you can easily set up the broker in your OpenShift environment and also set up a Camel-based client to quickly produce and consume messages. Also, you can change the log level to get verbose logs, thus getting a better understanding of the complete setup.
I recommend using a source-to-image (s2i) approach for deploying Red Hat AMQ 6.x on OpenShift, but if you do not use an s2i approach, this article will help you to configure logging to get verbose logs. Note that the Red Hat AMQ image used here is ephemeral; it doesn’t support persistence.
Continue reading “Red Hat AMQ 6.3 on OpenShift: Set up, connect SSL client, and configure logging”
APIs are the cornerstone of so many recent breakthroughs: from mobile applications, to the Internet of Things, to cloud computing. All those technologies expose, consume, and are built on APIs. And those APIs are a key driver for generating new revenue. Salesforce generates 50% of its revenue through APIs, Expedia generates 90% of its, and eBay generates 60% of its. With APIs becoming so central, it becomes essential to deal with full API lifecycle management. The success of your digital transformation project depends on it!
This article describes a set of full API lifecycle management activities that can guide you from an idea to the realization, from the inception of an API program up to management at scale throughout your whole company.
Continue reading “Full API lifecycle management: A primer”
This article is the second in a series of three articles about Red Hat Integration. The first article described how the new Red Hat Integration bundle allows citizen integrators to quickly provide an API through tools that make creating an API in five simple steps effortless, and we implemented a demo showing the full API lifecycle on Red Hat Integration. The demo was about providing wine labeling and ranking info via APIs.
In this article, I am going to take you further by implementing a real business transaction with Salesforce. We will create an event-driven integration solution with no code on Red Hat Integration.
The idea of this demo is to receive an order from the client web application through a gated, secured API that will then process the order and forward the needed data to the corresponding Salesforce modules. From there, Salesforce will take care of the order content.
Continue reading “Full integration to Salesforce with Red Hat Integration (Part 2)”