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About Romi Awasthy

Romi Awasthy
Romi is a software architect with a large enterprise. She is passionate about bringing opensource, lean and modern technologies to enterprise

Akka Java for large-scale event processing

We are designing a large scale distributed event-driven system for real-time data replication across transactional databases. The data(messages) from the source system undergoes a series of transformations and routing-logic before reaching its destination. These transformations are multi-process and multi-threaded operations, comprising of smaller stateless steps and tasks that can be performed concurrently. There is no shared state across processes instead, the state transformations are persisted in the database, and each process pulls its work-queue directly from the database.

Based on this, we needed a technology that supported distributed event processing, routing and concurrency on the  Java + Spring platform, the three options considered were, MessageBroker (RabbitMQ), Spring Integration and Akka.

RabitMQ: MQ was the first choice because it is the traditional and proven solution for messaging/event-processing. RabbitMQ, because it is popular light-weight open source option with commercial support from a vendor we already use. I was pretty  impressed with RabbitMQ, it was easy to use, lean, yet supported advance distribution and messaging features. The only thing that it lacked for us, was the ability to persist messages in Oracle.

Even though RabbitMQ is Open Source (free), for enterprise use, there is a substantial cost factor to it. As MQ is an additional component in the middleware stack, it requires dedicated staff for administration and maintenance, and  a commercial support for the product. Also, setup and configuration of MesageBroker has its own complexity and involves cross-team coordination.

MQs are primarily EAI products and provide cross-platform (multi-language, multi-protocol) support. They might be too bulky and expensive when used just as asynchronous concurrency and parallelism solution.

Spring Integration:Spring has a few modules that provide scalable asynchronous execution. Spring TaskExecutor  provides asynchronous processing with lightweight thread pool options. Spring Batch  allows distributed asynchronous processing via the Job Launcher and Job Repository. Spring Integration extends it further by providing EAI features, messaging, routing and mediation capabilities.

While all three Spring modules have some of the required feature, it was difficult to get everything together. Like this user, I was expecting Spring Integration would have RMI-like remoting capability.

Akka Java:  Akka is a toolkit and runtime for building highly concurrent, distributed, and fault tolerant event-driven applications on the JVM. It has a Java API and I decided to give it a try.

Akka  was easy to get started, I found Activator quite helpful. Akka is based on Actor Model, which is  a message-passing paradigm of achieving concurrency without shared-objects and blocking. In Akka, rather than invoking an object directly, a message is constructed and send it to the object (called an actor) by way of an actor reference. This design greatly simplifies
concurrency management.

However, the simplicity does not mean that a traditional lock-based concurrent program (thread/synchronization) can be  converted into Akka with few code changes. One needs to design their Actor System by defining smaller tasks, messages and communication between the them.  There is a learning curve for Akka’s concepts and Actor Model paradigm. It is comparatively small, given the complexity of concurrency and parallelism that it abstracts.

Akka offers the right level of abstraction, where you do not have to worry about thread and synchronization of shared-state, yet you get full flexibility and control to write your custom concurrency solution.

Besides  simplicity, I thought the real power of Akka is, remoting and its ability to  distribute actors across multiple nodes for high scalability. Akka’s Location Transparency and Fault Tolerance make it easy to scale and distribute  application without code changes.

I was able to build a PoC for my multi-process and multi-threading use-case, fairly easily.  I still need to work out Spring injection in Actors.

A few words of caution, Akka’s Java code has a lot of typecasting due to Scala’s type system and achieving object mutability could be tricky. I am tempted to reuse my existing JPA entities (mutable) as messages for reduced database calls.

Also, Akka community, is geared towards Scala and there is less material on Akka Java.

In spite of all this, Akka Java seems cheaper, faster and efficient option out of the three.


Reference: Akka Java for large-scale event processing from our JCG partner Romi Awasthy at the Tried and Tested blog.

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2 Comments on "Akka Java for large-scale event processing"

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Alonso Isidoro Roman

Sounds interesting, could you share code to download and learn? github?


Nick E

sounds good, just implemented a similar solution using the Akka Java API with Spring dependency injection, the GitHub typesafe example was useful for the Spring side of things. One change I made though was to abstract the use of the Spring extension into a generic Props service to reduce the coupling of my actor creation with Spring – this proved especially useful for testing purposes