Top Microservice Release Management Concerns
Organizations looking to implement microservice architecture today are looking at a wide range of challenges. The main ones include:
1 – Multiple Development Methodologies
What is a blessing for devs can turn into a curse for Project Managers (PMs) and Release Managers (RMs). For example, using both Scrum and Kanban can cause huge problems with frequent releases. A plan needs to be made for the entire delivery process, which can become challenging.
2 – Visibility and Transparency
With releases for today’s microservices-driven apps becoming more and more complex, you need a centralized solution to track the real-time status of the entire application release process pipeline. That’s the only way you can ensure you’re on schedule with malfunctions kept to a minimum.
3 – Governance and Compliance
The list of compliance and regulatory guidelines is constantly on the rise (i.e – GDPR). This means that organizations need to be on top of things. They are now required to pinpoint non-compliant services in real-time and implement sustainable release approval processes to stay on the safe side.
4 – Service Endpoints
Endpoints used to be configured manually for each service when their number was small and they were static in nature. But with the number of services growing exponentially and the service endpoints becoming more and more dynamic, things are becoming complicated.
Release engineering teams are encountering bigger loads and environment stability is suffering due to escalating misconfigurations.
5 – Recovery from Failures
Dynamic scalability is the need of the hour. However, this also introduces a wide range of malfunctions and issues that can even lead to downtimes. Restarting VMs and applications used to get the job done in the past, but this is a problematic solution in today’s complex and dynamic space.
IMPORTANT
It is highly recommended to decompose your data before you
decompose your code, as it has a direct impact on
overhead and cost of ownership.
The Microservice Release Process Checklist
Do you want to embrace microservices as a means to scale and release updates more frequently? Firstly, you need to realize microservices are fragmented, making them difficult to track and manage. Secondly, your goal should be to automate the releases of microservices-driven apps.
Here are 10 important things you’ll need to implement while creating a robust and sound microservices release pipeline for smooth delivery.
1 – Use One Repository per Service – This will allow you to create isolution to help avoid cross-population of code into different services.
2 – Create Independent CI and Deployment for Each Service – This will enable faster setups, while requiring lesser tool integration and testing.
3 – Ensure Flexibility – Make sure your solution can support any workflow, from front-end development to complex embedded software processes.
4 – Scalability – Make sure your solution is tool and environment agnostic with scalability capabilities to cope with all services and pipelines.
5 – Auditability – This will make sure you record everything: who checked-in the code, tests and their results, configurations used, and stakeholders.
6 – Testing – You will need to improve quality by enforcing full integration with your service virtualization and all of your test automation tools.
7 – Compliance – Each and every pipeline should have fully integrated compliance, security, and acceptance tests with proper documentation.
8 – Promote Pipeline and Process Reusability – This is a key thing to do if you want to scale up and save valuable time and resources.
9 – Monitoring – Make sure you have the right tools in place to monitor your microservices at all times, not just after the release.
10 – Orchestration – Plug in the tool chain into your DevOps to orchestrate CI, testing, configuration, provisioning, deployments, and releases.
Continuous Delivery and Release Automation
The bottom line is clear. Microservices introduce a wide range of architectural complexities and management overhead, particularly in the testing and maintenance spaces. You will need a seamless and streamlined automation pipeline for continuous delivery of microservices.
Your automation platform can now enable you to normalize your pipelines to use parameters and modeling of the applications and pipeline processes. You can then reuse pipeline models and processes between services and teams, saving you valuable time and resources.
Automation will also allow you to consistently log and monitor all services to create a feedback loop to your pipeline. All you need to do is to make sure that your pipeline automation is connected to your monitoring tools so that alerts can trigger automatic processes (rolling back, scaling, etc.)
To sum it up, automation is the best way to minimize bottlenecks, mitigate risks, improve quality, and boost up time-to-market speeds.
Dealing with Legacy Databases in a Modern Microservices World
But what about your database/s? It’s already been established that every microservice should ideally have its own database. However, this is not always possible as decomposing monolithic databases is quite a challenging task due to the extra work and network overload.
This is where automation comes into play, even if you have decided to temporarily proceed with shared databases for your microservices or can’t break them down due to legacy apps that will continue using them.
You can now plan, visualize, test and make sure all releases are going smoothly with centralized database automation tools. These solutions help you track, monitor and respond to issues in real-time with actionable insights, making life easier for your developers, release managers and DevOps professionals.
All common issues such as configuration drifts, conflicts between shared repositories of different microservices, and similar problems are immediately flagged and examined before and after every release, significantly reducing the need for manual intervention. It also eventually becomes easier to enforce company policies and regulatory protocols.
The fact is that todays scaled up and dynamic ecosystems cannot be handled manually. Only proper automation can get the job done.
