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Apache Kafka vs. Oracle Transactional Event Queues as Microservices Event Mesh

This blog focuses on transactional and message delivery behavior, particularly as it relates to microservice architectures.  There are of course numerous areas to compare MongoDB, PostgresSQL, and Kafka with the converged Oracle DB and Oracle Transactional Event Queues/AQ that are beyond the scope of this blog.

The Oracle database itself was first released in 1979 (PostgresSQL was released in 1997 and MongoDB in 2009).  Oracle Advanced Queuing (AQ) is a messaging system that is part of every Oracle database edition and was first released in 2002 (Kafka was open-sourced by LinkedIn in 2011 and Confluent was founded in 2014).  

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Implementing a Rock-Paper-Scissors Game Using Event Sourcing

Our favorite runtime environment for applications is usually Dropwizard but since many out there prefer Spring Boot I decided to use it instead for this article. Serialized Java client works with any runtime environment or platform you use on the JVM.

Configure the Serialized project

To develop our game we will use Serialized aggregates and projections. The aggregates will store the events for each game and the projections will provide a view of each game as well as a high score list of the top winners (in the case of multiple games being run).

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The Story of a Microservice Transformation in Hepsiburada

We launched the new checkout ecosystem, 'Erebor,' a few months ago in Hepsiburada. In this article, I will talk about what we have experienced during this microservice transformation.

“A software ecosystem is the interaction of a set of actors on top of a common technological platform that results in a number of software solutions or services.” Software Ecosystem: Understanding an Indispensable Technology and Industry by David G. Messerschmitt and Clemens Szyperski

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Running Axon Server: From Local Install to Full-Featured Cluster in the Cloud Part 1

Axon Server is the flagship product of AxonIQ, and companion product to the Open Source Axon Framework. Axon Server itself is available in two editions, Standard and Enterprise, with Axon Server Standard Edition (SE) available under the AxonIQ Open Source license, while Axon Server Enterprise Edition (EE) is licensed as a commercial product with a full range of support options.

In this series, I would like to take you along installing Axon Server for several different scenarios, starting with a local installation of Axon Server Standard Edition as a “normal” application, via Docker and Docker-compose, to Kubernetes, to eventually arrive at a full cluster of Axon Server Enterprise Edition on Cloud VMs. All platform examples can be run using both editions, but I wouldn’t expect you to run a three-node EE cluster on your laptop, just as it doesn’t make sense to go for a large production microservice architecture using a single-node SE install.

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Event Sourcing in .NET Core: A Gentle Introduction

Event sourcing, aka "the great myth". I've been thinking about writing a series of articles about this for a while, and now it's time to put my hands back on the keyboard. 

I thought that with this long period of confinement at least I could have had more time to write some nice articles, but it turns out the reality has been slightly different so far.

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Kafka Administration and Monitoring UI Tools

Kafka itself comes with command line tools that can perform all necessary administrative tasks. But, those tools aren’t very convenient because they are not integrated into one tool, and you need to run a different tool for different tasks. Moreover, it is getting difficult to work with them when your clusters grow large, or when you have several clusters.

So, today, we will cover some GUI alternatives.

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Apache Kafka In Action

Challenges and Limitations in Messaging

Messaging is a fairly simple paradigm for the transfer of data between applications and data stores. However, there are multiple challenges that are associated with it:

  1. Limited scalability due to a broker becoming a bottleneck.
  2. Strained message brokers due to bigger message size.
  3. Consumers being in a position to consume the messages at a reasonable rate.
  4. Consumers exhibiting non-fault tolerance by making sure that messages consumed are not gone forever.

Messaging Limitations Due to:

High Volume

Messaging applications are hosted on a single host or a node. As a result, there is a possibility of the broker becoming the bottleneck due to a single host or local storage.

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Data Modeling With Indexes: Event Sourcing (Part 1)

In this post, I want to take the notion of doing computation inside RavenDB’s indexes to the next stage. So far, we talked only about indexes that work on a single document at a time, but that's just the tip of the iceberg of what you can do with indexes inside RavenDB. What I want to talk about today is the ability to do computations over multiple documents and aggregate them. The obvious example is in the following RQL query:

That is easy to understand — it is simple aggregation of data. But it can get a lot more interesting. To start with, you can add your own aggregation logic in here, which opens up some interesting ideas. Event sourcing, for example, is basically a set of events on a subject that are aggregated into the final model. Probably the classiest example of event sourcing is the shopping cart example. In such a model, we have the following events:

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Parameter Resolvers in Axon

Axon is an open-source Java framework for building systems in CQRS (Command Query Responsibility Segregation), DDD (Domain Driven Design), and Event Sourcing manner. Axon provides a high level of location transparency, which gives the opportunity to easily split the system into several microservices. You can download the fully open-source package here. The Axon Framework consists of message-based commands, events, and queries that are supported types of messages. For each of these messages, we can define a handler (a method or constructor) to handle it. In some cases, the message itself has enough information to be handled, but in many others, we have a dependency on other components and/or variables (message metadata such as correlation information would be a good example). Axon provides a really powerful mechanism to inject these dependencies into message handlers — Parameter Resolvers. This is the story about them. At the end of this article, you can find a cheat sheet of all resolvers provided by the Axon Framework.

Anatomy

There are two important components that support this mechanism: the  ParameterResolver and the  ParameterResolverFactory (see Figure 1).

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CQRS Replay Performance Tuning

Command Query Responsibility Segregation (CQRS) has become a popular pattern to avoid complex and, therefore, slow database queries in applications. With CQRS, queries are served from dedicated read models that are optimized for the query’s specific needs. This ensures that queries are as simple as possible and, therefore, as fast as possible. The read models need to be initialized and kept up to date with the primary storage (the command side). This is done most easily if the primary storage is event-based: the Event Sourcing (ES) concept. Axon offers a mature and popular implementation of the CQRS/ES concepts on the JVM, but several other implementations exist.

On the one hand, CQRS/ES presents a solution to a common performance problem. On the other hand, it introduces a couple of new potential performance challenges. First, the event store may not be able to deal efficiently with the number of events being stored. If the events are kept in a regular relational database table, performance will severely degrade once it grows to the point where the indices can’t be buffered in RAM. This is when built-for-purpose event stores like Axon Server can come to the rescue.