Microservices

Recently I attended an RCA meeting for an incident that happened in prod few weeks back. The issue was somewhat of a 2nd degree failure. To put it in simple terms, lets say a workflow composed of two services - A and B. When user performed an action from the UI, it made a call to Service A which then internally called service B with some parameters based on user preferences. In this case, due to some unusual combination of preferences, Service-A ended making call to Service-B with parameters that Service-B didn’t fully understood. Solution - Write an integration test to cover this scenario. Reasoning - Since this involved 3 different components (User Preferences, Service A and Service B), it should be covered by an integration test. More Reasoning: This is sort of the usual way of thinking about the integration tests - meaning whenever there are multiple services involved, we immediately think about writing an integration test. But is this the right approach specially in microserv...

Microservice architecture paradigm fits well within the Unix’s first principle/philosophy: “Make one program do one thing well”.  We love creating small services which has identity of its own (within a bounded context). These services have their own CI/CD pipelines and provides loose coupling b/w components which significantly improves our ability to deliver code to production. In reality, a workflow can span multiple services. Its very common to find scenarios like below: In the above example, a workflow spans 3 services (Service-1, 2 and 3). Service1 receives the original request. In order to process this request, it GET(s) some data from its dependent services(d1/d2/d3). It further runs some business logic and produces an intermediate response #A. It then makes a state mutable call (POST/PUT/DELETE/PATCH) to Service-B using #A as input and waits for its response. Service-B follows the same paradigm and the workflow ends when all services returns.  The scena...

Microservice architecture comes with a high cost of service dependencies which impacts the overall performance of the system. In a typical setup, when a request hits the service, it first needs to grab data from its dependent apis and after which it will perform more operations and return the final result back to the caller. Dependent apis may further call other apis or talk to a DB in order the return the requested data and hence creating a complex graph of dependencies.  Caching is an effective way to improve the performance of such system. In many cases, you might be able to cache the response of the dependent apis locally and avoid making those extra calls specially if the data is somewhat static in nature. TTL is a great feature which allows you to cache a data for certain duration after which it expires. The pattern looks something like this: First check if the data is cached. If not, get the data from the original source and cache it for future calls. Recently...

Thank you folks for raising some great questions. I am consolidating them here for everyone to benefit! How is your experience using Kubernetes?  >> Kubernetes seems very powerful and some experts would say its the modern day kernel for container orchestration. Internally, we use Rancher which I believe has Kuberneter support but I don’t think we are using it yet. We have an entire SRE team that brings fun delivery tools into the pipeline and they are actively looking into it. What are the preferred languages for writing microservices? Any recommended frameworks? >> It really depends on the type of work. If you are writing heavy ETL pipelines then python is pretty awesome! If its a CPU intensive use compiled languages - golang/.net core/java. If its orchestration style or simple get/put/delete DB operation - use nodejs. All the services should be deployed as container so it doesn’t matter which language is used. In terms of frameworks, I have used aiohttp, flask...

One of the most important selling point of moving towards microservice architecture is the speed of delivery! Today customers can research products, compare reviews and ultimately switch to better products online with unprecedented ease. To survive in this high customer-focused and fast moving industry, we need our teams to deliver software at an accelerated rate and possibly multiple times in a day. For us over the time, our delivery pipeline has also evolved: During the Enterprise days, the release cycle was 6 weeks long. If you missed anything - wait for another 6 weeks. We used home grown scripts to coordinate all the deployments and overall experience was just awful. During the early days of Cloud, we moved to a daily deployments but these were at the end of the day with significant downtime and required lots of coordination with different teams. Today we have deployments on demand. Teams are deploying multiple time in a day with zero downtime! We love Slack at...

Adoption of microservices is not easy and I have had my own share of painful experiences during the initial phase of the transition. In this blog, I will go over some of factors that can be critical to determine the outcome of such transition. Lack of Engineering Culture - What’s an Engineering Culture anyway? Its a culture that practices lean-principles and further enables osmosis style learning for everyone. There is recognition for great work and people are willing to learn from each other. Its a culture where everyone is empowered and feels responsible to participate/evolve/improve all aspects of the SDLC irrespective of their primary role. You can have specialized teams for the different parts but everyones feedback is welcomed. Processes are not followed like a religion but evolved throughout the lifecycle. Adoption of microservices is a steep learning curve for everyone. If an organization doesn’t have the right Engineering mindset, avoid doing microservices. You have ev...

Dark-launches and Feature toggles are two great practices to deploy code faster to production and get early feedback. Combined with microservices architecture, it becomes a very powerful tool and once adopted, there is no turning back! You will be thrilled by the speed with which you can build and ship software in a safe manner! In this blog, I will try to explain what these terms means. Dark Launch Dark launch is a practice where you can test/validate your new functionality in production without having to worrying about disturbing the original workflow. This technique is very useful when writing a new version of the functionality or even adding more functionality that can be triggered based of any existing workflow. The steps to achieve it are very simple: Deploy the new functionality in prod.  Incase if it is new version of an existing functionality and you want to test correctness:  clone the request going into v1-logic —> then call the v1-logic —> clone the...