Modularity is a cornerstone of good application design. As systems become more distributed, we’re faced with unique challenges to achieving effective modularity. How do you organize, encapsulate, and version loosely-coupled services?
In this series of posts, I will cover how modular architectures were built for two diverse Java-based applications: a highly reliable SOA tax processing platform that interfaces with legacy systems; and a low-latency, event-based system for FX currency trading. Modularity was achieved using OSGi, Service Component Architecture (SCA), and Fabric3 as the runtime stack.
This post will start with a brief overview of the technologies involved in creating these modular systems and proceed to a detailed discussion of how they were used to build the SOA tax processing platform for a European government. In a subsequent post, we will cover how the same modularity techniques were applied to successfully deliver the low-latency FX trading architecture to a major bank.
From OSGi to Service Composition
There is no one technology that offers a complete modularity solution. That’s because modularity provides a number of features and exists at a number of levels in an application.
In terms of features, modularity:
- Reduces complexity by segmenting code into discrete units
- Provides a mechanism for change by allowing application components to be versioned
- Promotes reuse by defining contracts between subsystems
Modularity is also present at different levels of an application:
Figure 1: Application Modularity
While much of the above diagram will be familiar to Java developers, it’s worth defining what we mean by service composition and architectural modularity. Service-Orientation (organizing application logic into contract-based units) and Dependency Injection (popularized by frameworks Such as Spring and Guice, among others) are the foundation of modern architectural modularity. In a nutshell, both help to decouple subsystems, thereby making an application more modular.
What’s missing is architectural encapsulation and composition. For example, applications often need to expose coarse-grained services that are themselves composed of multiple finder-grained services. Traditional integration platforms, ESBs, and Java EE lack facilities for doing this in a simple, effective manner. You may have seen this with the proliferation of services exposed via an Enterprise Service Bus (ESB) or Spring application contexts that contain hundreds or even thousands of beans.
Like Object-Orientation, what’s needed is a way to group collections of services together for better management and mechanism to encapsulate the implementation details of particular services:
Fabric3 provides such a composition mechanism that works well for both SOA as well as event-driven designs. I’ll now turn to how this was achieved in a tax-processing system and a FX Trading platform.
I’ve deliberately chosen these two examples because each application has a different set of requirements. The tax system is what many would label a SOA integration platform: it receives asynchronous requests for tax data, interfaces with a number of legacy systems, process the results, and sends a response to the requesting party. The FX system, in contrast, is concerned with extreme (microsecond) latencies: it receives streams of market data, processes them, and in turn provides derived foreign exchange pricing feeds to client systems.
The tax system architecture looks like this:
Figure 3: Tax System Architecture
Tax information requests are received via a custom reliable messaging layer to a gateway service, which transactionally persists the message request and initiates processing. Processing takes place in a number of steps using a series of complex rules and interactions with multiple legacy systems. When data has been received and processed, a response is sent via the messaging layer to the requestor.
The principal modularity challenge faced when designing the system was to separate the core processing (a state machine that transitions a request through various stages) from the rules evaluation and logic that connects to the legacy systems.
A key goal of modularizing the various subsystems was to provide a straightforward versioning mechanism. For example, tax rules typically change every tax year. Consequently, existing rules had to be preserved (to handle requests for data involving previous tax years) alongside the current year rules. Modularizing the rules allowed for them to be updated without affecting other parts of the system.
The parts of the application that interfaced with the legacy systems to retrieve tax data were also isolated in a module. Similar to the rules, this allows changes to the way external interfaces are made to be altered without impacting the rest of the system. Modularity served an additional practical purpose: the code to interface with the legacy systems was complex and tedious. By segmenting that complexity, the overall system was made easier to understand and maintain.
What did this modularity translate to in practice? The development environment was setup as a Maven multi-module build. The base API modules contain Java interfaces for various services. Individual modules for core processing, rules, and integration depends on relevant API modules:
The multi-module build enforces development-time modularity. For example, the rules module cannot reference classes in the integration module. OSGI is used for runtime code modularity. The API modules export packages containing the service interfaces while each dependent module imports the API interfaces it requires.
The tax system uses service composition to enforce modularity at the service level. The core processing, rules, and integration subsystems are all composed of multiple fine-grained services. The integration subsystem in particular exposes a single interface for receiving requests from the core processing module. This request is then passed through a series of services that invoke legacy systems using Web Services (WS-*):
Figure 6: Tax System Integration Module
Service composition is handled in Fabric3 by using SCA composites. Similar to a Spring application context, a composite specifies a set of components and their wiring. In this example, we use XML to define the composite (The next version of Fabric3 will also support a Java-based DSL):
<composite name=”IntegrationComposite” …> <service name=”TaxSystem” promote=”TaxSystem”/> <component name=”TaxSystem> <implementation.java class=”…”/> <reference name=”idprocessor” target=”IdProcessor”/> <component/> <component name="IdProcessor"> <implementation.java class=”…”/> <reference name=”locationProcessor” target=”LocationProcessor”/> <reference name=”legacySystem”> <binding.ws uri=”….”/> </reference> </component> <component name=”LocationProcessor > <implementation.java class=”…”/> <reference name=”dataProcessor” target=”DataProcessor”/> <reference name=”legacySystem”> <binding.ws uri=”….”/> </reference> </component> … other components … </composite>
Figure 7: The Integration Module Composite
As its name implies, a Composite provides a way to compose coarser-grained services from private, finer-grained ones. In the above example, the service element promotes, or exposes, the TaxSystem service as the public interface of the composite.
Figure 8: Service Promotion
When this is done, client services in the core processing module can reference the TaxSystem integration composite as a single service:
<component name=”MessageProcessor”> <implementation.java class=”..”/> <reference name=”taxSystem” target=”IntegrationComposite”/> <component>
With composites in place, the tax system successfully delivered a consistent modular design from the code layer to its service architecture:
Figure 9: Service Architecture Modularity
After more than a year in production, the investment in this modular design paid off. The integration module was re-written to take advantage of new, significantly different legacy system interfaces without the need to refactor the other subsystems.
In the next post, we will cover how service composition was used to modularize a low-latency, event-based FX trading platform. In this case, service composition was employed to simplify the system architecture and provide a mechanism for writing custom plugins while maintaining sub-millisecond performance.