In a nutshell, a microservice is a software component that is decoupled and deployed from your legacy/primary system to run an individual business process or single service. It typically integrates seamlessly with well-known interfaces, just like APIs, and possesses its own data storage.
This architecture enables you to leave your old systems intact, while allowing you to work on development and deployment of new features.
Microservices break down business processes to their most basic level by creating small, separate processes that eventually replace large, single applications. Breaking a system into smaller parts allows each microservice to focus on a single business capability for best results.
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Organizations such as Amazon, Twitter, Uber, eBay, and Netflix have already adopted the microservices architecture pattern.
Implementing microservices today comes with a wide range of IT and business benefits. A few of them include the following:
- Eliminate the need for legacy migration, while freeing up more time and resources for your immediate development initiatives.
- Unlike traditional methods, you can make independent releases.
- Improve modularity and reduce interdependencies to make application development easier to understand and test.
- Empower smaller teams to develop and deploy new services.
- Respond faster to changing market needs and build new products with language and platform freedom (less restrictions).
Additionally, microservices also fully support development methodologies such as DevOps, Agile, Continuous Delivery and Deployment. Also, microservices are deployed independently and are resilient to outages. An issue in one area doesn’t shut down all your processes.
As you can see in the illustration above, every microservice is a semi-independent entity with its own database, allowing separate releases.
As mentioned earlier, the main benefit of the microservice architecture is that it dramatically improves agility and velocity. When you correctly decompose a system into microservices, you can develop and deploy each one independently, in tandem with the other services.
There are 3 main parameters that need to be taken into consideration and taken care of while developing and deploying microservices:
Commonly used metrics for read performance are either the number of operations per second or a combination of how fast you can run queries and how fast you can retrieve results. The speed of retrieving results is dependent upon how well you can organize and index data.
With Write performance, the easy metric is to determine the number of Write operations your microservice performs per second. Microservices that collect and process transient data need databases that can perform thousands or even millions of Write operations per second.
Delivering instant user experiences require a low-latency database, something that can be done by deploying the microservice close to its database. For Read and Write ops, the typical numbers for low latency are lower than 1 ms. For high latency, the number is typically higher than 10 ms.
Efficiency also comes into play here.
The microservices’ database footprint must be minimal while retaining the ability to scale on-demand. Their components also need to be available for rapid development and deployment, requiring any database service to support the on-demand creation of hundreds of instances per second.
It must be noted that each microservice’s persistent data must be private and only accessible via it’s own API . If multiple microservices were to share persistent data, then you need to carefully coordinate changes to the data’s schema, which significantly slows down development.
Additionally, more often than not, your services need to share data.
For example, if multiple services need access to user profile data, you can possibly encapsulate the user profile data with a service, which is then called upon by other services. You can also possibly use some kind of event-driven mechanism to replicate data to each service that needs it.
Furthermore, it’s important to understand that microservices process or manage data at different stages in its lifecycle. You should ideally classify all data involved. For example, the User Activity Tracker microservice will involve Key-value, hash, JSON, and sorted set data values.
The aforementioned challenges can become overwhelming when a large number of microservices dwell in your ecosystem. Not only do microservices require databases that are light-weight, but they also need to have relational characteristics, which are typically of the slow type.
Either way you choose to achieve high performance with low latency, you will need a comprehensive database management solution that will allow you to stay on top of things at all times and respond to events as the happen. There is very little margin for error in today’s dynamic market.
Only monitoring your ops and responding to events quickly can help you optimize your offering/s. Failure detection is an often overlooked aspect that is taking down business due to brand reputation damage and the frustration of disappointed customers who eventually decide to leave.
You can now flag potential issues in real-time and get actionable insights for quicker mitigation to create seamless database delivery pipelines.
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