Using Apache Avro for passing Java objects through a message broker, with a schema registry


Avro logo This article follows directly from my previous example on using Avro with a message broker, and uses essentially the same demonstration code and set-up. However, in the previous article, the communicating clients used a local copy of the Avro schema on disk. In this article, we'll store the Avro schema in the Apicurio schema registry.

I don't normally presume to tell readers the order or manner in which they should read things. However, it's only fair for me to warn the reader that, without a fairly detailed reading of my previous article, this one will be incomprehensible.

I will show source code that demonstrates the salient new features but, in fact, there are few changes in the source from the previous example. The few changes are in the build process, particularly in the Maven pom.xml. Full source code of this example is available in my GitHub repository

To run the example you'll need an AMQP (1.x)-compatible message broker, like Apache Artemis, or the Red Hat product based on it, AMQ 7. You'll also need a running instance of Apicurio registry.

Apicurio schema registry is one of a set of Apicurio tools, which includes a graphical schema designer and data modeler. When I use the term "Apicurio" in this article, I'll always mean the schema registry.


In my previous example, I showed how to set up two Java clients that passed structured data through an AMQP message broker. The data was serialized and deserialized using Apache Avro, which uses a compact binary representation. Part of the reason for the compactness is that Avro depends on a schema, which indicates the composition of the structured object. The schema must be available to all clients that use the same structured data.

In the previous example, the structured data was a list of cartoon bears -- Yogi, Paddington, etc. The sending client created a list of these objects, serialized them using Avro, and placed them on the message broker. The receiving client read the binary data from the broker, and deserialized it using Avro. Both the sender and receiver used a representation of the Avro schema in a local file. This is an impractical arrangement in a large, complex installation, and it's more common to use a schema registry like Apicurio.

About Apicurio schema registry

Apicurio registry is a store for schema artefacts, with an HTTP REST interface. Clients make REST requests to add, modify, query, and retrieve schema artefacts. Apicurio registry does not store the artefacts itself -- it delegates this to a storage backend -- a relational database or a Kafka installation. In this example, we'll use the default, in-memory database.

Kafka is a message-handling or event-handling platform, and it doesn't seem a natural thing to use as a storage back-end for a schema registry. However, Apicurio is widely used with the combination of Avro and Kafka. Since Kafka can store long-lived data -- even though this is not its usual mode of operation -- it makes sense to store schema artefacts in Kafka topics, rather than providing an additional means of storage. It's a pragmatic arrangement, rather than a logical one.

In this simple application, we'll be using Apicurio as little more than a webserver. We'll use the web-based user interface to upload and name the schema, and the build process for the clients will obtain the schema using this same name. However, Apicurio offers much more than this. In particular, it provides a way to manage schema updates, using user-defined rules that prevent breaking changes being made to public APIs.

Apicurio registry is a Java application implemented using the Quarkus framework. It has some built-in understanding of Avro schema artefacts, but it isn't limited to this kind of data.

Setting up Apicurio

Apicurio is available pre-built in a number of different formats but, for this simple application, we'll use the source code so we can run in "dev" (development) mode.

I'm mostly focusing on using Apicurio with simple, command-line operations. The command-line examples that follow are for Linux, but I imagine that similar approaches will work with other platforms. However, this isn't something I've tested.

Download the source from GitHub either by cloning the repository, or simply downloading and unpacking the source bundle.

You should be able to build Apicurio using Maven:

$ ./mvnw clean package -DskipTests

and run it in dev mode like this:

$ ./mvnw quarkus:dev 

However, bear in mind that Apicurio requires Java version 11 or later, which can be a bit of a nuisance for people who use multiple Java versions on the same machine. You'll need to set the $PATH to indicate the required Java runtime, as well as setting JAVA_HOME. For example:

$ PATH=/usr/jdk-15/bin/:$PATH JAVA_HOME=/usr/jdk-15/ ./mvnw package -DskipTests 

When it's running, Apicurio listens on HTTP port 8080. In "dev" mode it does no authentication, and stores schemas in an in-memory (non-persistent) database. If you restart Apicurio you'll need to upload the schema again, but uploading a single schema doesn't take more than a few seconds.

Uploading the AVRO schema to Apicurio

The schema is in the source code bundle as the file schema/bear.schema. I have purposely not used the conventional ".avsc" as the filename extension, because the Avro Maven plugin is too clever -- it will search the filesystem for .avsc files and, if it finds one, will use it in preference to the version in the Apicurio registry. It's difficult to prove that the registry is really workin, if the same information can easily be found in local storage.

Point a web browser to the Apicurio server, typically localhost:8080. Select "Upload artifact". Fill in the "name" field as "Bear", but leave the group name blank. Use the "Browse" button to locate the file `bear.schema`. The interface should look like this:

Apicurio interface when uploading schema

Note that Apicurio is case-sensitive in naming; the clients will have to use the specific name "Bear" to download the schema, not "bear".

With the schema in place, the Apicurio interface should look this this:

Apicurio interface after uploading schema

Testing that the schema can be retrieved

The simplest thing that a client can do is to retrieve a specific schema. In fact, we can test that using a simple HTTP client like curl:

$ curl http://localhost:8080/api/artifacts/Bear

If everything is OK, we should get a copy of the schema that we originally uploaded.

Clients can also search for schema artefacts by name, and by other characteristics. A simple search might be:

$ curl http://localhost:8080/api/search/artifacts?search=Bear

Without a search term, this request will simply list all schema IDs on the server. Search results are in JSON format, but actual schema elements are returned in whatever format they were originally provided. With Avro, schema definitions are also in JSON format, which is why the Apicurio server always appears to respond with JSON -- that's just a coincidence.

Integrating Apicurio registry into the build process

Most of the build process is exactly the same as in my previous example. The only difference is that there is a new step that retrieves the schema from the registry. There is a Maven plug-in for this, as there is for building the necessary Java classes from the schema. The addition to the Maven pom.xml looks like this:



There are a few points to note about this definition.

In short, when we run mvn package, the Maven Apicurio plug-in will retrieve the schema by its ID, and store it in the application's target/ directory. The Avro plug-in will then find that schema, and convert it to one or more Java classes, which it will insert into the application's source tree, and which will then get compiled.

Running the example

Everything that is new about this example, compared to the previous one, takes place at build time. Therefore running the code is exactly the same as before: with the message broker running, run the sender application from the sender/ directory, and the receiver application from the receiver/ directory. You should see the structured data being displayed by the receiver as it arrives.

Note that, with this specific implementation, the registry is not required, and need not be running, during the client's runtime. All of the registry-related work has already been done before any client runs.

Closing remarks

This article present the simplest, practical use of Avro with Apicurio that I could think of. Since we're using a message broker for passing data, it would be nice if Apicurio could use the broker as a storage back-end, as it can with Kafka. That would allow for a tidy installation. Sadly, however, there is not such implementation. If you want to store schema artefacts for use with a message broker, you'll need to set up some kind of storage back-end -- most likely a relational database.

I've only scratched the surface of what the schema registry can do, and the ways it can be integrated into a software build workflow. I've also not even touched on authentication, version control, containerization, or the use of the registry for schema types other than Avro. These are, perhaps, subjects for later articles.