Having fun with Jolt transformations

Jolt is a Java library which can be used to transform JSON to JSON (here). A Jolt transformation itself is also a JSON file. You can use it in products such as Apache NiFi and Apache Camel. In this blog post I'll describe my first experiences with Jolt transformations. 

For me personally Jolt transformations are not intuitive and not that powerful when for example compared to the capabilities of XSLT for transforming XML. It is available in Apache NiFi though and can be used without the 'execute code' permission, that is why I decided to use it to transform JSON files. I might misunderstand basic Jolt concepts which can cause suggested solutions to be overly complex or in other ways suboptimal.

Apache NiFi: JSON to SOAP

Apache NiFi is a powerful open source integration product. A challenge you might encounter when integrating systems is that one system can produce JSON messages and the other has a SOAP API available. In this blog post I'll show how you can use NiFi to convert JSON input to a SOAP service call. This involves abstracting an AVRO schema for the JSON, converting it to XML and transforming the XML to a SOAP message. 

In this example I'm using several publicly available websites. You should of course be careful. Do not copy/paste sensitive XML or JSON on these sites!

Apache NiFi: Automating tasks using NiPyAPI

Apache NiFi has a powerful web-based interface which provides a seamless experience between design, control, feedback, and monitoring. Sometimes however, you want to automate tasks instead of doing them manually using the UI. This does not only allow you to perform the tasks a lot quicker but it also helps make them more reproducible. It allows you to incorporate tasks in for example a CI/CD system without requiring human intervention. A NiFi feature to help you automate tasks is its powerful API. In order to more easily use this API from Python, NiPyAPI is available. In this blog post I'll describe some things you can do with NiPyAPI, some challenges I encountered and how I fixed them. You can find my sample code here.

Apache NiFi: Avoid these common pitfalls

Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It has a powerful UI which can be used for both development and operations. In addition, the NiFi Registry is available to make promoting software from one environment to the next, easier. In order to use NiFi efficiently, I'd like to point out some common pitfalls when using NiFi.

Vagrant and Hyper-V: Don't do it!

I've used Vagrant since 2015 in combination with Virtualbox for creating development machines. Recently however I'm experiencing more issues with Virtualbox. For example CPUs getting stuck when assigning multiple CPUs to a VM and issues with auto adjusting the guest resolution when I resize the VM window. These annoyances drove me to try out Vagrant with Hyper-V (running an Ubuntu 21.04 guest on a Windows 11 host). In this blog post I'll describe my experiences. In summary, it did not make me happy. A lot of things which work out of the box with Vagrant and VirtualBox require effort to get working in Hyper-V. Not only that but several alternative solutions are required outside of Hyper-V because of lack of features. I think I should try VMWare next to see if it will provide a better experience. You can download my Vagrantfile and provisioning script here.

Merge AVRO schema and generate random data or Java classes

Previously I wrote about generating random data which conforms to an AVRO schema (here). In a recent use-case, I encountered the situation where there were several separate schema files containing different AVRO types. The message used types from those different files. For the generation of random data, I first needed to merge the different files into a single schema. In addition, I wanted to generate Java classes for the complete message which required importing dependent types in the pom.xml. In this blog post I'll describe how I did that.

Java: Validating JSON against an AVRO schema

AVRO schema are often used to serialize JSON data into a compact binary format in order to for example transport it efficiently over Kafka. When you want to validate your JSON against an AVRO schema in Java, you will encounter a challenge. The JSON which is required to allow validation against an AVRO schema from the Apache AVRO libraries is not standard JSON. It requires explicit typing of fields. Also when the validation fails, you will get errors like: "Expected start-union. Got VALUE_STRING" or "Expected start-union. Got VALUE_NUMBER_INT" without a specific object, line number or indication  of what is expected. Especially during development, this is insufficient.

In this blog post I'll describe a method (inspired by this) on how you can check your JSON against an AVRO schema and get usable validation results. First you generate Java classes of your AVRO schema using the Apache AVRO Maven plugin (which is configured differently than documented). Next you serialize a JSON object against these classes using libraries from the Jackson project. During serialization, you will get clear exceptions. See my sample code here.