What are Microservices?
Microservices are a set of software applications designed for a limited scope that work with each other to form a bigger solution. Each service has minimal capabilities for the sake of creating a very modularized overall architecture. Microservices architecture is analogous to a manufacturing assembly line, where each service is like a station in the assembly line. Just as each station is responsible for one specific task, the same holds true for microservices.
Each station is an “expert” in its given responsibilities, thus promoting efficiency, consistency, and quality in the workflow and the outputs. Contrast that to a manufacturing environment in which each station is responsible for building the entire product itself. This is analogous to a monolithic software application that performs all tasks within the same process.
How Do Microservices Work?
Microservices represent a design pattern in which each service is just one small piece of a bigger overall system. Each microsystem performs a specific and limited scope task that contributes to the end result. Each task could be as simple as “calculate the standard deviation of the input data set” or “count the number of words in the text.” The key behind building microservices is planning the system to identify the distinct subtask, then writing applications that address each subtask. As each service needs to deliver output data to the next, a microservices architecture often uses a lightweight messaging system for that data handoff.
In general, there are no specific requirements around building microservices, which gives application developers a lot of flexibility in terms of programming language, technology environment, and deployment options. Specific requirements often drive the choice of technology, and when high performance and low latency are required, an in-memory processing engine is typically used. Engines like Hazelcast Platform leverage the power of in-memory processing to build and run extremely fast application services in environments that need to react quickly to huge volumes of data.
Monolithic vs. Microservices Architecture
Microservices are advantageous over monolithic applications for several reasons. They are easier to build (because of their smaller size), to deploy, to scale (as more services can be added to any step to run in parallel), to maintain, etc. Microservices are not new, as the concept has roots in older design patterns such as service-oriented architectures (SOA), modularity, and separation of concerns. They represent a new architectural style for large-scale systems that could otherwise be more difficult to manage in a traditional monolithic architecture.
The key difference between a monolithic and a microservice architecture is that a monolithic program is a single, unified unit, while a microservice architecture is a group of smaller, independently deployable services.
Each service in a microservices design is in charge of a certain element or function. Since only the service that needs to be changed needs to be changed, this makes it easier to change or update the code base for individual services. Microservices designs are also easy to scale because each service can grow or shrink on its own without affecting the other services.
In a monolithic program, all of the code, data, and settings are closely linked. This makes it hard to change or update the app because any changes have to be made to all of its parts. Also, it can be hard to grow monolithic apps because any changes to the app have to be made to all of its instances.
Microservice Benefits
Microservices architecture offers several advantages over monolithic architectures, including scalability, resilience, agility, cost-effectiveness, and portability.
Scalability
The inherent scalability of microservices systems is a significant benefit. Microservices provide autonomous scaling of each service, in contrast to monolithic systems where scaling frequently necessitates scaling the entire system. For instance, it is simple to scale up a service if it receives a lot of traffic without affecting the performance of other services. Microservices designs are better equipped to respond to changing workloads thanks to this scaling flexibility, which also facilitates effective resource management.
Resilience
Microservices architectures also perform exceptionally well in this area. A single component failure in a monolithic architecture has the potential to bring the whole thing down. The failure of one service, however, has no impact on the others when using microservices. Users can continue access other services even if one goes down, guaranteeing continuity and minimizing the effects of outages. This fault isolation improves system robustness and improves user experiences.
Agility
Microservices architectures also have the benefit of being agile. The architecture allows for the independent modification, updating, or replacement of individual services without affecting other services. Faster development cycles are made possible by this modular approach because modifications may be made more quickly. By limiting the scope of modifications to certain services rather than the entire system, it also makes the process of introducing new features and correcting faults simpler. Microservices architectures’ increased agility enables businesses to respond more quickly to changing customer and market demands.
Cost-effectiveness
When compared to monolithic structures, microservices architectures might result in cost savings. Costs associated with development can be minimized because each service can be created and maintained separately. Teams that concentrate on particular services develop more quickly and allocate resources more effectively. Additionally, maintenance expenses can be decreased because just the necessary services must receive updates or repairs rather than the complete system. Microservices architectures are a desirable option for companies looking to optimize their development and maintenance costs due to their cost-effectiveness.
Portability
The portability of microservices architectures is excellent. Because each service may be launched independently, moving services between platforms or settings is simpler. Organizations can take use of many infrastructure options, such as cloud services or on-premises configurations. They can switch between them easily thanks to this flexibility. Microservices designs’ portability makes deployment and migration plans easier, giving businesses the opportunity to select the best platforms for their requirements.
Challenges
Microservices architectures, despite their many benefits, are not without challenges. These include testing complexity, security issues, communication problems, development and management complexities, and could lead to increased costs.
Complexity
Compared to monolithic designs, developing and managing microservices architectures might be more complex. The reason for this complexity is that there are more interdependencies across services because each one operates independently. Development teams must therefore carefully manage and deal with these dependencies during the development and maintenance lifecycles.
Security
Compared to monolithic infrastructures, securing microservices architectures can present more difficulties. As a result of each service being accessible via the internet, there are more potential attack vectors that may be utilized by bad actors. It becomes essential to have strong security measures in place to protect both the individual services and the entire system from flaws and intrusions.
Communication
Microservice designs can make it more difficult for services to communicate effectively. This problem arises because each service has a different technology stack, which could make communication more difficult. To guarantee seamless coordination and information flow between services, teams must navigate the complexities of integrating various technologies and protocols and deal with any communication problems that may develop.
Costs
Compared to monolithic systems, developing and sustaining microservices architectures might be more expensive. Because each service requires its own infrastructure setup, this higher cost is a result of infrastructure costs. As teams struggle to manage the interconnections between services and guarantee their proper operation, the presence of several services also adds communication and coordination expenses.
Testing
In comparison to monolithic designs, testing microservices architectures might be more challenging. Separate testing efforts for each component are necessary due to the unique nature of each service. This requires extensively testing each service independently and addressing any integration issues that might develop as a result of interactions between various services. To ensure the overall stability and reliability of the architecture, thorough testing of the entire system becomes crucial.
Example Use Cases
Data Pipelines
A microservices-based system that utilizes an extensive data pipeline would be ideal for a reporting system on a company’s retail store sales (for example). Each step in the data preparation process would be handled by a microservice: data collection, cleansing, normalization, enrichment, aggregation, reporting, etc. The workflow naturally creates a trackable lineage. If any problems are encountered in the data, it is relatively easy to trace back which service might need to be updated.
Machine Learning
Another use case entails machine learning (ML). A microservices-based ML environment collects, aggregates, and analyzes a data flow so that the ML framework can determine an outcome. In such an environment, the data runs through a workflow that has many steps, and each step is handled by one of the services. One advantage of using a microservices architecture for machine learning is that multiple machine learning frameworks can be included in the workflow so that multiple models can be created on the same data flow. This is useful when the machine learning frameworks are targeting a predicted outcome. Having multiple frameworks running simultaneously provides a comparison around which model is providing the best results.