Shutterstock 246839701

How Anti-Patterns can constrain Microservices Adoption

Microservices create lots of small, distributed single-purpose services, with each service owning its own data. This service and data coupling support the notion of a bounded context and a shared-nothing architecture. Each service and its corresponding data compartmentalize and are completely independent from all other services. The data-driven migration antipattern occurs when you are migrating from a monolithic application to a Microservices architecture. Anti-pattern because of the migration for both the service functionality and the corresponding data together at the start while creating Microservices.

There are two primary goals during any Microservices conversion effort. The first is to split the functionality of the monolithic application into small, single-purpose services. The second is to migrate the monolithic data into small databases owned by each service.

Communicating Between Microservices:

The important aspects of developing Microservices rather than a monolithic application is inter-service communication. There are two communication styles i.e. synchronous vs asynchronous, one-to-one vs one-to-many mechanisms.

Communication can be through HTTP protocols like Thrift, and gRPC depending on suitability. Use of HTTP for inter-service communication is the best choice for Microservices when services communicate in a synchronous manner. Message queues like Kafka or RabbitMQ are an ideal choice if retry mechanism and stability are important.

Tracking and Controlling Microservice Dependencies:

Dependency management is an important part of system and software design. Dependency cycles are hazardous when they involve the mechanisms to access and modify a service. It can also disrupt recovery from two simultaneous outages. Two systems evolve to depend upon each other cannot be restarted while neither is available. A job-scheduling system may depend on writing to a data-storage system, but that data storage system may depend on the job-scheduling system to assign resources to it.

A source-control system outage left both the source-code repository and documentation server unavailable. The only way to get to the documentation or source code of the source control system is to recover the same system. Without this information about the system’s internals, the engineer’s response is significantly obstructed.

Serverless vs Kubernetes:

Serverless platforms like AWS Lambda and Google Cloud Functions are excellent options for Microservices. It is highly scalable and reduces the complexity of infrastructure maintenance and deployment. The code has to adhere to platform-specific guidelines. So, it is important to consider the consequences before developing Microservices.

Kubernetes is another important tool in terms of scalability. It is an open source orchestration platform for automating deployment, scaling, and operations of application containers across clusters of hosts. This drastically reduces the complexity of maintaining and scaling Microservices.

Monitoring and Performance Testing:

Microservices monitoring is a very important aspect involved in its design. It is important to identify if each log gets associated with a request and its corresponding service. We should have a linking ID in all logs that uniquely identifies the request. Performance testing should be a part of the development process. Benchmarks have to be clearly identified for taking any architectural decision.

Controlling Versioning Strategy:

Versioning strategy leads to unmanageable code and dependencies. An efficient versioning strategy should be in place for the Microservices architecture. One of the easiest strategies is to have an API version and include the version in the route URL.

Design of Microservices workload data access patterns:

The design of Microservices depends on the database of an organization. Data access patterns should be clearly separated across Microservices. Sometimes, it is fine to use one database through multiple service instances, as long as data is in clearly separate collections.

Shared Libraries:

Shared functionality does not have any direct business value that need not be a Microservice on its own. Common code functionalities like logging, DB access, and service communications are not directly related to a business goal, can go to common shared libraries. This can later be accessed by all Microservices.

Many companies are looking towards Microservices architecture style to leverage the benefits of performance testing, inter-service communication, fine-grained scalability, and overall agility. However, with the help of Techcello framework, companies developing Microservices can address the solution of the challenges like service granularity, data migration, organizational change, and distributed processing.

Print Friendly

Jothi Rengarajan

Jothi serves as the Principal Architect of Techcello. She is responsible for the architecture and technical roadmap of Techcello; she plays consultative role for various customers of Techcello in architecting their products on top of Techcello. Prior to Techcello, Jothi worked with Aspire Systems as Technical architect, where she has architected and developed over 30+ SaaS/Multi-tenant products for global ISVs. Jothi is a prominent speaker on Cloud Technologies in various technical forums. She has 16+ years of software development experience. Jothi has bachelors in electronics and communication engineering.

More Posts - Website