From Monolithic to Microservices
Microservices are designed to replace the design of legacy, monolithic applications where all the functionality is developed in a single codebase. With a monolithic application, you could introduce bugs into another part of your code base when you update or add a feature. You must rebuild, deploy, and test the entire application when you update or add a feature, which results in slower development, a steeper learning curve for developers trying to understand the application, and the danger of merge conflicts. You also can’t scale individual components that might be under a heavy load, like a reporting module. You’d have to scale the entire application, which can result in inefficient use of your resources and higher resource costs in general.
Java Development: With monolithic architectures, Java developers will often face slow startup and deployment times, often referred to as a “cold start.” They’ll also find it more difficult to upgrade their JDK and frameworks, due to tangled dependencies. Because updating a major framework is a high-risk undertaking in a monolithic application, this results in framework version jumps, where a Java engineering team might have to maintain legacy code for a lot longer, until they reach the boiling point where they can’t further delay the update. Then the upgrade to a new version is incredibly challenging and time-consuming. Development teams will also experience a high memory footprint and a very slow scale-out, such as scaling up during traffic spikes.
The Benefits of Microservices
Monolithic applications bundle together the user interface, business logic, and data. They are simpler to build, but they are harder to change or scale. Microservices break up the application into independent services that communicate with each other. You can build, add, update, scale, or replace each service independently, without affecting the other services.
In the previous diagram, the monolithic architecture tightly couples the application’s user interface, business logic, and data access layer, which then sends data to and from the database. The microservices architecture is where the user interface calls individual, decoupled microservices, many with their own databases.
With microservices, you benefit from faster development and deployments, efficient scalability and resource management (where you’re able to scale individual components), reliability (with decreased risk), fault isolation (quick identification of the faulty component), productive maintenance (from decoupled services), and the removal of technical debt (unnecessary code that needs to be maintained or that might cause bugs).
Java Development: Microservices allow Java developers to move away from being tied to massive monolithic application servers (such as JBoss, WebLogic, or older Spring installations) with lengthy startup times. Java developers also will be able to more easily integrate additional types of databases, as well as services written in other languages. They’ll also have increased database flexibility (such as running SQL and NoSQL databases), access to faster reloading development tools, the benefits of containerization, and enhanced observability in the Java ecosystem.
What are the Challenges of Microservices?
First, when you adopt the microservices architecture, you are faced with the challenges of managing a complex, distributed system. Instead of one application, you could have dozens of independent services, each with its own CI/CD pipeline, monitoring, logging, and deployment configuration. This will require skilled DevOps and site reliability engineering (SRE). It could also increase your operational overhead.
Second, data transactions can be difficult, as each microservice typically owns its own database, to ensure independence. Managing that data can become complicated. You must use distributed data transactions, like the saga design pattern, to create compensating transactions to undo a step that fails. You must maintain eventual consistency across your services, and querying data across multiple services can become slow and complex, which often requires you to build a separate reporting data store. You also need to avoid data duplication from siloed data, such as storing the same customer name in both the order processing and notification components, to mitigate the risk of synchronization failures and data inconsistency.
Third, you’ll need to manage your cost and resource consumption. Each service is often an isolated unit running its own container or process, which means that your application could generate more memory and compute resources than a typical monolithic architecture would. Similarly, if you need multiple deployment pipelines, databases, service mesh, or monitoring tools, then it can increase your infrastructure and licensing costs. You might require more technical maturity and specialized skillsets on your engineering team, which could increase your staffing costs.
Finally, you need to monitor for potential organizational and project mismatches. For smaller, simpler applications or startups that prioritize rapid iteration, a modular monolithic architecture is often preferable over the benefits of microservices. This approach allows you to avoid the initial overhead of building a microservices architecture.
Microservices Challenges for Java Development:
- Java is great for performance, but you should be aware of how microservices challenges can impact your Java development and architecture.
- Memory Overhead: A standard JVM process requires a lot of memory to load the JVM and its classes. If you replace a large Java monolith with 50 smaller Java microservices, then you are starting 50 separate JVM processes, each with its own base memory overhead. This can lead to a higher total memory footprint across the cluster.
- Higher Infrastructure Costs for Inefficient Designs: If you can’t efficiently pack your Java microservices into a single physical server or Kubernetes node, then you might raise your infrastructure costs and cancel out any benefit of containerization.
- Reliance on traditional frameworks or JVM configurations can reduce the benefit of rapid scaling.
- Version Drift: Watch out for version drift, where multiple teams are working on multiple microservices, but they end up using different versions of tools, due to poor communication and collaboration.
Comparing the Advantages and Disadvantages of Microservices
The following table compares the advantages and disadvantages of a monolithic architecture versus a microservices architecture:
| Monolithic Architecture (single unit) | Microservices Architecture (distributed) | |
|---|---|---|
| Initial Development | Easy and fast for new, small projects, such as a prototype or minimum viable product (MVP). | Slow due to initial architecture overhead in setting up the infrastructure. |
| Technology Stack | Low flexibility due to single language and framework. | High flexibility (polyglot) by optimizing the language and database for each task. |
| Complexity | Lower, but it grows rapidly as the application scales. | Higher from managing distributed systems and API gateways. |
| Technical Debt | Higher risk, due to dead code that’s no longer being used. | Lower risk, due to code that’s optimized per task. |
| Component Communication | Fast function calls with no network latency. | Slower network calls with potential network latency and complex distributed transactions. |
| Scalability | Inefficient, requiring you to scale the entire application if only one module is under heavy load. | Efficient, where individual services can be scaled independently. |
| Reliability | Low fault isolation, where any bug can crash the entire application. | High fault isolation, where the failure of one service is less likely to affect the rest of the system. |
| Deployment | Simple, with one executable or package, but slow, where a small change requires redeploying the entire application. | Complex, with many independent deployments, but fast, where you can update individual services. |
| Debugging and Monitoring | Easier, with centralized logs and a single process flow. | Difficult, with specialized distributed tracing and logging systems. |
Is Adopting a Microservices Architecture Worth it?
Monolithic architecture can be preferrable for small projects, early prototypes or minimum viable products (MVPs), small development teams, or when simplicity and speed to market are the priorities. For large, complex, and rapidly evolving applications, an independent team can find more value in working together on individual services in the microservices architecture. Microservices are also better when high scalability and fault tolerance are application requirements.
Azul Prime: Improve Java-Based Microservices
Azul Prime solves two of the biggest disadvantages that developers face when building Java microservices: slow startup and warmup time and garbage collection (GC) pauses.
Eliminating the Java warmup problem: Traditional JVMs need time to profile and just-in-time (JIT) compile the code before reaching peak performance. Because microservices frequently scale up or restart, the delay can ruin the application experience. Azul Prime helps in two ways:
- First, Prime features ReadyNow technology that records the JIT compilation and optimization data to save a profile that can quickly load. This allows your code to compile at an optimal level, where your microservices reach full performance immediately upon launch.
- Second, the Cloud Native Compiler (CNC) centralizes JIT compilation outside of the individual microservice JVMs. Instead of each microservice consuming CPU resources for compilation, they offload the compilation to the centralized CNC. This results in smaller microservice containers that use less memory, which helps you save significantly on cloud resources(often saving 20% or more).
Eliminating garbage collection (GC) pauses: Microservices can have inconsistent response times due to how the JVM’s GC pauses the application threads to clean up memory issues. Azul Prime helps with this problem:
- Continuously Concurrent Compacting Collector (C4) is pauseless and independent of the heap size. It performs the work concurrently alongside the application threads. This eliminates the latency spikes caused by GC, resulting in predictably low latency.
Java developers building microservices can use Azul Prime to solve major performance and consistency issues that are inherent when running a distributed Java application. To learn more about how AzulPrime can provide you with a high-performance JVM, see Azul Prime: High Performance JVM.
