Comparing Service Fabric and Rabbit MQ Cloud Microservices
When asked to define cloud microservices, most people, from analysts to developers do not know how to explain it in simple English that the larger audience can decipher.
Cloud microservices are a cloud-native technique of developing software applications, which comprise of modular and independent services. Each cloud native application runs a one-of-a-kind process and it communicates via a well-defined mechanism like a container.
What does the Microservices Architecture comprise of?
To understand the microservices architecture, it is important to understand the genesis of the cloud microservices. A white paper “Taking the cloud Native Approach with Microservices by the Google Cloud Platform,” explains at length the intricacies of the cloud environment in the backend and the frontend.
Initially, each monolithic application, which resided on a single server, consisted of three layers:
- Application or business logic
The three DevOps layers were built in one intertwined stack located on a data server on one monolithic server. Generally, an entire application consists of several modules of code that serve particular functions such as different types of logic, logging, graphics rendering code, or a database.
In a monolithic architecture, the users interacted with one layer, which communicated with the application layer and the database layer. The data then traveled back to the stack to the user. This was an effective and efficient way of organizing an application, but it created numerous single failure points, which resulted in outages in case of a code bug or hardware failure.
The structure did not have self-repair and if the system was partly damaged, it would require human intervention as a software or hardware fix. If you decided to scale or refactor any of these layers, you would have to buy a new server. You would need to buy a monolithic application running on one server and divide a portion of your users to the new system.
SOA (Service Oriented Architecture)
With time, the architectures changed, and different layers existed independently outside one server. Applications were created for the integration of these services via an enterprise service bus for communicating. This approach enabled engineers to focus on one part of the application structure. Decoupling services and independent development needed the use of an API gateway, a set of rules used by services to communicate.
Cloud microservices further break down the Service Oriented Architecture strategy as a group of granular functional services. Small services are combined to create large macro services, which provide a greater ability to update the source code of one function in a large end-user application. A microservice addresses one concern such as logging function, web applications, or data search. This improves flexibility because the failure points are independent, which creates a stable and reliable application architecture.
This approach creates opportunities for microservices to self-repair. For example, if a microservice in a single cluster has three sub-functions and one fails, tools for orchestration like Kubernetes help in self-repair without human intervention. The self-repair is transparent for the end-user and automatic.
Kubernetes is described as an open-source orchestration for Docker containers. It handles node scheduling incomplete clusters and as a load balancer, ensuring their state in the microservice lifecycle is a match with the users’ intentions.
How Cloud Microservices Interconnect
Cloud microservices are decoupled or independent of each other, but still manage to communicate.
For a microservice architecture, services are cross dependent, and the different services that act as a connection between the microservices include:
- Remote Procedure Calls (RPC)
- Brokers such as RabbitMQ
- REST APIS
There are different types of cloud service providers some of the major ones being Azure, which uses Service fabric, and Amazon Web Services (AWS) which uses RabbitMQ.
Azure Service Fabric
Azure is a cloud platform by Microsoft, formerly known as Windows Azure, and a Platform as a Service (PaaS). Service Fabric came about when Microsoft moved from the delivery of monolithic boxed products to service delivery. Over time, the Service Fabric platform evolved as other services began adopting it. Service Fabric ran in Azure and standalone deployments in Windows Servers.
Azure is a popular platform with several cloud services from analytics to computing, storage, and network. Azure Service Fabric is a distributed systems platform that eases the process of packaging, deploying, and managing reliable and scalable microservices.
The main purpose of Service Fabric is to solve the tough problems that come with building and maintaining the service. It also aims to use infrastructural resources as efficiently as possible to enable teams to solve business issues via the cloud microservices route.
Service Fabric helps to create applications that go the microservices route by providing them with:
- A platform for system services to discover services, manage state or route messages.
- APIs for productive programming to help you in building apps as microservices such as Reliable Services or ASP.NET Core
- The ability for application deployment, running either as processes or in containers. Service Fabric is an orchestrator and a container.
Benefits of Service Fabric
Service Fabric, though an Azure platform, works well on all types of cloud infrastructure. You can deploy the platform on-premises infrastructure or public clouds. Its advantages include:
· Deployments, availability, and versioning
One of the major benefits of using Service Fabric is the ease of container or service deployment across a distributed cluster of different machines. Service Fabric was created for small as well as large deployments. It scales seamlessly depending on the demand, and it bundles each deployment as a package, making it easy to replicate across environments.
Service fabric allows individual services versions to be hosted side-by-side, enabling each instance to be individually updated. When services are hosted in a cluster environment, it allows for maintenance and upgrades with zero outages or downtime, because it redistributes the requests to nodes while an upgrade is ongoing. When rolling back from flopped deployments, similar logic is applied.
· Application Monitoring
Service Fabric can monitor services, applications, and infrastructure. The first application monitoring is via the application code instrumentation. Since this logging type is made possible by the app business logic, it is the onus of the developers to select which tools to use, and how to integrate the tools into the code.
· Managing APIs
Service Fabric integrates with API management and organizations can use it to log events, for rate limiting, defining routing rules, and add access control without modification of the app code.
Amazon Web Services (AWS) RabbitMQ
Amazon Web Services (AWS) is a cloud service platform by Amazon and provides services in various domains such as storage, delivery, computing, and other domains that help your business capabilities to grow and scale. These domains can be used as services, used for creating and deploying various applications in the cloud.
Cloud microservices structures as a not-so-new approach to software developing, but a combination of different proven and successful concepts such as:
- Service-oriented software (SOA)
- Agile software development
- API- first design
- Continuous Integration /Continuous Delivery (CI/CD)
Various aspects of scalable microservices architecture (data store, user interface, and microservices) are recommended, as well as using Cloud services for maintaining a serverless architecture for the reduction of operational complexities.
Serverless is an operational module by these principles:
- Zero infrastructure to manage or provision
- Scaling automatically by consumption unit
- Pay for value used model
- In-built fault tolerance and availability
AWS runs on RabbitMQ, which is open-source software that acts as a message broker and receives and forwards messages. It acts as a middleman and helps to reduce loading and delivery times that are taken by web servers.
The RabbitMQ handles many different languages, the most common being Java. The latest version of Rabbit MQ is Apache Kafka, released in 2011. While Rabbit MQ is a general-purpose and supports such protocols as STOMP, AMQP, and MQTT, it can handle high-throughput cases like processing online payment. It can act as the message broker between microservices and take care of background jobs.
Apache Kafka is a message bus that can handle high-ingress information streams and replay. Kafka is a message broker that allows apps to process and reprocess data that has been streamed. Kafka uses straightforward routing techniques that utilize a routing key to send messages to a single topic.
Benefits of RabbitMQ
RabbitMQ makes asynchronous processing possible, which means you can queue a message without immediate processing. RabbitMQ is suitable for blocking tasks or long-running tasks, which allows a web server to respond fast to requests instead of having to perform intensive tasks on demand. RabbitMQ stores messages and sends them to the end-user when ready.
RabbitMQ implements the AMQP (Advanced Message Queuing Protocol), which is language-neutral and you can use any language, just as you would if using HTTP or TCP. RabbitMQ runs on all the major OS (Operating System) and supports carious developer platforms such as PHP, Python, Java, etc.
Benefits of RabbitMQ include:
A third-party system can easily interact with the messages. The messages can be processed by anyone with no problems,
RabbitMQ is an open-source message broker that is reliable, having been in the market for over 10 years and is continually improved and updated. RabbitMQ has an active development team that adds plugins, new features, and improvements.
· Supports standard protocols
RabbitMQ supports numerous standardized protocols such as MQTT, STOMP, AMQP, etc. where AMMQP 0.9.1 is natively implemented. RabbitMQ can support various standardized message protocols, meaning it can be used in various instances. It allows for the replacement of the RabbitMQ broker with other AMPQ bases brokers.
· Used by various companies
Numerous companies in different industries, such as Netflix, all rely on microservice architecture and use RabbitMQ.
· User friendly
RabbitMQ is a user-friendly service and it is easy to alter its configurations to suit its intended purpose per use case. RabbitMQ is the most deployed message broker and written in Erlang, making it a tried and tested message broker.
· Flexible and Scalable
The RabbitMQ is both flexible and scalable. Your team requires maintaining the message senders and receivers to and from the message queue. If the message queue grows larger, the standard action is to add more users and parallelize the load, which is a simple, yet effective technique of scaling.
Key Differences between Azure and AWS
Some top differences between Azure and AWS include:
· PaaS Capabilities
Both Azure and AWS are similar in their provision of PaaS capabilities for storage, machines, and virtual networking. Azure has faster and stronger PaaS capabilities, which is more important in cloud services. Microsoft Azure PaaS provides DevOps with the tools and environment to build new cloud services. With Azure, Microsoft sorts most of the infrastructural management behind the scenes, ensuring you have 100% concentration on innovation.
In cloud computing, compute services are a core service, and with the significant data generated, the need for faster processing in real-time is constant. Computing ability means you can spawn and scale-up instances on demand. Both Azure and AWS cater to these computing needs.
AWS provides services like AWS Lambda, Elastic Beanstalk, and EC2, etc., while Azure provides services on the same lines such as App Service, Azure Virtual Machine, Container Service, Azure Functions, etc. The difference comes in the cost, with Azure instances getting more expensive as their size goes up. For example, a 256GB RAM instance will cost $6.76 an hour on Azure while it will only cost $3.20 an hour on AWS.
Both Azure and AWS pricing models are pay as you go. AWS charges by the hour while Azure charges by the minute. For short-term subscriptions, Azure provides more flexibility, and in some services, Azure is more expensive than AWS, especially when the architecture begins scaling up.
Both Azure and AWS offer reliable and long-running storage services. Azure offers services like Standard Archive, Blob, and Disk Storage. AWS uses EBS, Glacier, and AWS S3, which ensures automatic replication and availability across various regions.
For AWS temporary storage, it starts to function every time the instance starts or stops. Upon termination, it provides you with block storage, which equals a hard disk. The block storage can be kept separate or attached to an EC2 instance.
Azure utilizes page blobs and temporary storage for VM volume. Azure uses the Block Storage option and has cold and hot storage as well.
· Networking Services
Under the Cloud Umbrella, Amazon Virtual Private Cloud (VPC) makes it possible to create isolated networks, which enables consumers to create route tables, network gateways, private IP addresses, ranges, and subnets.
Microsoft Azure Virtual Network allows you to do all that VPC can do. Both vendors have solutions for the extension of the on-premise data center to the cloud, and firewalls.
· Database services
Data comes in various formats, and the databases keep evolving. Azure and AWS provide varying database services for handling structured and unstructured data. If you want durability, Azure offers Azure SQL Server Database.
AWS offers Amazon RDS which supports various database engines like MySQL, Amazon Aurora, PostgreSQL, Oracle, and MariaDB. Azure’s interface is smoother and friendlier while AWS has better provisions for more instances. AWS has a mature environment for Big Data in comparison to Azure.
· Orchestration and Container Support
As mentioned above, AWS provides more mature analytics and Big Data offerings. It has different services in areas like mobile applications, IoT, and developing computing environments depending on needs. The platform runs on Linux or Windows containers.
Amazon has a long-standing relationship with government agencies, which ensures better cloud offerings by the government. Amazon also provides excellent security measures, which ensure access to users when accessing the cloud. This aspect is especially crucial for companies, which deal with very sensitive data.
Is Azure better than AWS?
It is difficult to say which of the two services is better. In some circumstances, Azure is better or vice versa, depending on the size and needs of the organization. On pricing, AWS is better than Azure, and most people generally feel AWS is better than Azure in the range of services Amazon offers. AWS has been around for longer than Azure has, and has more servers.
AWS and Azure have two different approaches to cloud microservices, but a similar goal. The end game of the microservice-computing goal is to offer a platform, which is low maintenance and automated.
Both of these Cloud platforms have deep capabilities, and it is quite a feat picking one clear winner. Azure is excellent for Hybrid Cloud and the integration of Microsoft’s product stacks, while AWS has immense flexibility and additional features. It boils down to your organizational needs before you make a choice.
Adopting cloud microservices has many benefits such as helping you in service discovery, releasing and updating software faster and more frequently, as well as adds extra features. Microservices improve your development team’s innovation and creative ability and frequent releases.
With the advent of the 5G networks, cloud architectures are being thrust into the limelight. The trend of cloud services will become more enhanced, and it will evolve into more advanced processes. Service developers need to decide what best fits their organizations by weighing administration, performance, service deployment, provider support, and ease of use.