“If you are working in an organization that places lots of restrictions on how developers can do their work, then microservices may not be for you.” ― Sam Newman
Microservice architecture, or simply microservices, is a distinctive method of developing software systems that tries to focus on building single-function modules with well-defined interfaces and operations. The trend has grown popular in recent years as Enterprises look to become more Agile and move towards a DevOps and continuous testing. Microservices can help create scalable, testable software that can be delivered weekly, not yearly.
Microservices architecture (courtesy: Cloud Application Architecture Guide and smartbear).
Sam Newman : “Microservices are small, autonomous services that work together.”
Frye:The idea with microservices is to focus on building individual services that do one thing and one thing well.
Nic Grange, CTO at Retriever Communications: “Microservices are an approach to designing software systems that are made up of small independent services that each have a specific purpose.”
Ali Hussain, CTO at Flux7: “Microservices are an approach to addressing a large or complex business problem using a set of smaller, simpler services that work together; a microservice runs its own unique process that contributes to an overarching business goal.”
Dr. Ratinder Ahuja, Founder and CEO of ShieldX Networks: “Microservices are an approach to application development in which a large application is built as a suite of modular services. Each module supports a specific business goal and uses a simple, well-defined interface to communicate with other sets of services.”
Dustin Horning, Solutions Engineer at Zesty.io: “Microservices are to software what factories are to manufacturing. Instead of having one person [or] machine build a whole car, each area is specialized in its task: This one hammers rivets, this one paints.” AND “Microservices is breaking down one large objective into its parts, and having those parts be accomplished independently.” (OK, that was two explanations, but we’ll let it slide.)
Justin Bingham, CTO at Janeiro Digital: “Microservices are components of an application or broader ecosystem architected to operate independently – each responsible for a specific business or technical domain.”
Michael Ducy, Director of Product Marketing at Chef: “It's breaking down the development and release of applications into smaller pieces of work.”
Kong Yang, Head Geek at SolarWinds: “Microservices are a method of developing software applications which are made up of independently deployable, modular services. Each microservice runs a unique process and communicates through a well-defined, lightweight mechanism, such as a container, to serve a business goal.”
Microservices allow an organization to reduce dependencies, develop faster, and scale.—Aviran Mordo
Microservices Definition by Lewis/Fowler:
“Microservices are important simply because they add unique value in a way of simplification of complexity in systems. By breaking apart your system or application into many smaller parts, you show ways of reducing duplication, increasing cohesion and lowering your coupling between parts, thus making your overall system parts easier to understand, more scalable, and easier to change. The downside of a distributed system is that it is always more complex from a systems standpoint. The overhead of many small services to manage is another factor to consider. ” ― Lucas Krause,
The Microservices approach is about breaking your system ("pile of code") into many small services, each typically has its own:
Consider this architecture style for:
Large applications that require a high release velocity.
Complex applications that need to be highly scalable.
Applications with rich domains or many subdomains.
An organization that consists of small development teams.
Sam Newman in Building Microservices, enumerates the key benefits of Microservices as following:
With a system composed of multiple, collaborating services, we can decide to use different technologies inside each one. This allows us to pick the right tool for each job, rather than having to select a more standardized, one-size-fits-all approach that often ends up being the lowest common denominator.
A key concept in resilience engineering is the bulkhead. If one component of a system fails, but that failure doesn’t cascade, you can isolate the problem and the rest of the system can carry on working. Service boundaries become your obvious bulkheads. In a monolithic service, if the service fails, everything stops working. With a monolithic system, we can run on multiple machines to reduce our chance of failure, but with microservices, we can build systems that handle the total failure of services and degrade functionality accordingly.
With a large, monolithic service, we have to scale everything together. One small part of our overall system is constrained in performance, but if that behavior is locked up in a giant monolithic application, we have to handle scaling everything as a piece. With smaller services, we can just scale those services that need scaling, allowing us to run other parts of the system on smaller, less powerful hardware.
A one-line change to a million-line-long monolithic application requires the whole application to be deployed in order to release the change. That could be a large-impact, high-risk deployment. In practice, large-impact, high-risk deployments end up happening infrequently due to understandable fear.
With microservices, we can make a change to a single service and deploy it independently of the rest of the system. This allows us to get our code deployed faster. If a problem does occur, it can be isolated quickly to an individual service, making fast rollback easy to achieve.
Microservices allow us to better align our architecture to our organization, helping us minimize the number of people working on any one codebase to hit the sweet spot of team size and productivity. We can also shift ownership of services between teams to try to keep people working on one service collocated.
One of the key promises of distributed systems and service-oriented architectures is that we open up opportunities for reuse of functionality. With microservices, we allow for our functionality to be consumed in different ways for different purposes. This can be especially important when we think about how our consumers use our software.
If you work at a medium-size or bigger organization, chances are you are aware of some big, nasty legacy system sitting in the corner. The one no one wants to touch. The one that is vital to how your company runs, but that happens to be written in some odd Fortran variant and runs only on hardware that reached end of life 25 years ago. Why hasn’t it been replaced? You know why: it’s too big and risky a job.
With our individual services being small in size, the cost to replace them with a better implementation, or even delete them altogether, is much easier to manage.
Microservice architecture reduces the team management complexity, but it is not able to diminish the need of team communication. They need to make sure an update in one’s service is not breaking some other functionality. We can find this problem in monolith architecture applications too.
We can choose a different technology stack for a different component (polyglot). It leads to the problem of non uniform application design and architecture. It may can increase maintenance cost in the long run.
We need to have a mature Dev Ops team to handle the complexity involved in maintaining Microservice based application. Due to several moving parts of the application, it becomes complex and requires a high level of expertise. Increased Resource use - Initial investment to run these applications are high because all the independently running components need their own runtime containers with more memory and CPU.
Independently running components interact with each other over a network. Such systems require reliable and fast network connections. Marshalling and Un marshalling - When one component needs data from another component, the sender marshals the data in some standard from its internal representation, while the receiver un-marshalls data into its own representation before use. This definitely requires more processing compared to conventional application architecture.
Inter Service Communication needs to be secured to avoid any inter communication security breach. Due to several moving parts, these applications are more prone to security vulnerabilities.
Testing of such application is definitely harder compared to a monolith application.
Unavailability of the right tools are also an issue to be considered.
Need log analysis tool for log analysis ,Splunk or ELK stack
(Image courtesy:pivotal)
Microservices - Not A Free Lunch! http://highscalability.com/blog/2014/4/8/microservices-not-a-free-lunch.html
It is very difficult to maintain multiple Microservices as increased Complexity.
It is extremely difficult to find good architects for creating Microservice architecture in right way.
Guidelines and examples for White House Web APIs, encouraging consistency, maintainability, and best practices across applications. White House APIs aim to balance a truly RESTful API interface with a positive developer experience (DX).
This document borrows heavily from:
After you identify the microservices in your application, validate your design against the following criteria:
Reuse continues to be a principle of microservice design. However, the scope of reuse has been reduced to specific domains within the business. The effort of designing for this reuse, which in the early days of SOA included wasted efforts in designing enterprise-wide canonical models, was fruitless because it was too ambitious.
However, it must be noted that the canonical model in its restricted scope can be of benefit. In line with the reuse it facilitates, its scope has been reduced. With the ‘merit based reuse’ approach, an emerging model is preferred over a predetermined one. Teams can agree on communication models for deciding how microservices must be adapted for use outside the contexts in which they were designed.
If an existing microservice API does not suit your domain or ‘business group’, you might be better off building another microservice that does it.
-Alison Jarris
Scale Cube, defined by Martin L. Abbott and Michael T. Fisher. This model explains how infinite scaling can be achieved by implementing a three-dimensional approach.
The Art of Scalability book describes three dimension scalability model: the scale cube. The microservice architecture is an application of Y-axis scaling on the scale cube.
• Horizontal Duplication and Cloning (X-Axis )
• Functional Decomposition and Segmentation - Microservices (Y-Axis)
• Horizontal Data Partitioning - Shards (Z-Axis)
Scale Cube (Image courtesy:microservices.io)
APIs are standardized wrappers that create an interface through which microservices can be packaged and surfaced. This makes APIs the logical enforcement point for key concerns of a microservices architecture such as security, governance, and reuse. Because APIs are able to house these concerns, they are considered a foundational component of a microservices architecture(mulesoft).
Microservices are design to use internally, while APIs are used to expose functionality to the outside world.
Miniservices have been called pragmatic microservices. You can get started with them faster and pick and choose the pieces that make sense for your team.
“[A miniservice is] like a group of microservices come together in a little pattern.” — Ross Garrett
“Each microservice must handle its own data, miniservices may share data.” — Arnaud Lauret
Don’t confuse architectural perfection with business value.” — Ross Garrett
Leveraging the pieces of that practice that makes sense for me and getting most of the functionality benefits," says Ross Garrett.
Nanoservice is an antipattern where a service is too fine-grained. A nanoservice is a service whose overhead (communications, maintenance, and so on) outweighs its utility.
DDD deals with large models by dividing them into different Bounded Contexts and being explicit about their interrelationships. Martin fowler
How big a Microservice should be is: it should have a well defined bounded context that will enable us to work without having to consider, or swap, between contexts.
The relationship between all the participating services are described by a single endpoint
Service choreography is a global description of the participating services, which is defined by exchange of messages, rules of interaction and agreements between two or more endpoints. Choreography employs a decentralized approach for service composition.
andrei from stack
"We came across the decision to orchestrate our microservices by using a "god" like service that controls the business logic or a choreographed approach where the microservices basically pass messages, In microservice architecture choreography is preferred over orchestration."
You are an orchestrator, a coordinator of data and functions. You are not a transformer. Stay out of the business of messing with other people’s schema.
Recommended ones include the Saga pattern, routing slips, and stateful workflows. Each pattern works with a certain level of complexity. Study up and match the right patterns to your orchestration.
https://medium.com/capital-one-tech/microservices-when-to-react-vs-orchestrate-c6b18308a14c
No | about | url |
---|---|---|
1 | What are the Advantages of Microservices? - Sam Newman | - https://www.youtube.com/watch?v=KV3j3MZTXgk |
2 | Design Microservice Architectures the Right Way | - https://www.youtube.com/watch?v=j6ow-UemzBc |
3 | Mastering Chaos - A Netflix Guide to Microservices | - https://www.youtube.com/watch?v=CZ3wIuvmHeM |
4 | API Academy Microservices Boot Camp @ CA World: Designing a Microservices Architecture | -https://www.youtube.com/watch?v=iZNSPKxAd5w |
5 | Data Strategies for Microservice Architectures | - https://www.youtube.com/watch?v=n_V8hBRoshY |
6 | Refactor your Java EE application using Microservices and Containers by Arun Gupta | -https://www.youtube.com/watch?v=iJVW7v8O9BU |
7 | Principles Of Microservices by Sam Newman s | -https://www.youtube.com/watch?v=PFQnNFe27kU |
8 | PGOTO 2016 • Appsec and Microservices • Sam Newman | - https://www.youtube.com/watch?v=wlt7nCRWx_w |
9 | Avoiding Microservice Megadisasters - Jimmy Bogard | - https://www.youtube.com/watch?v=gfh-VCTwMw8 |
10 | 10 Tips for failing badly at Microservices by David Schmitz | - https://www.youtube.com/watch?v=X0tjziAQfNQ |
11 | Lessons from the Birth of Microservices at Google | - https://www.youtube.com/watch?v=Fz1PoXqxAZc |
12 | Event Sourcing You are doing it wrong by David Schmitz | - https://www.youtube.com/watch?v=GzrZworHpIk |
13 | The hardest part of microservices is your data | - https://www.youtube.com/watch?v=MrV0DqTqpFU |
14 | Data Design and Modeling for Microservices | - https://www.youtube.com/watch?v=KPtLbSEFe6c |
15 | The Art of Discovering Bounded Contexts by Nick Tune | - https://www.youtube.com/watch?v=ez9GWESKG4I |
16 | The challenges of migrating 150+ microservices to Kubernetes -Sarah Wells (Financial Times) | - https://www.youtube.com/watch?v=fgI3cOdv87I&feature=youtu.be |
17 | Revitalizing Aging Architectures with Microservices | - https://www.youtube.com/watch?v=SPGCdziXlHU |
No | about | url |
---|---|---|
1 | Developing Microservices with Aggregates Chris Richardson | - https://www.infoq.com/presentations/aggregates-modular-microservices |
2 | Top 5+ Microservices Architecture and Design Best Practices Ajitesh Kumar | - https://dzone.com/articles/top-5-microservices-architecture-and-design-best-p |
3 | Microservices: Patterns and Practices Panel C. Richardson, R. Shoup, L. Ryan, R. Tangirala, and R. Schloming participate in a discussion on microservices and the challenges faced at scale, the strategies to use and more. | -https://www.infoq.com/presentations/microservices-patterns-practices-panel |
4 | Microservices Patterns Red Hat Videos | - https://www.youtube.com/watch?v=_YzzxrSIQGw |
5 | 7 Microservice Patterns Explained (Ivar Grimstad) | - https://www.youtube.com/watch?v=4IFVBfLBl1Y |
6 | Three Microservice Patterns to Tear Down Your Monoliths | - https://www.youtube.com/watch?v=84W9iY3CwdQ |
7 | 14 Architectural patterns for microservice development | - https://www.youtube.com/watch?v=yVZS1HZrlEw |
8 | Reducing Microservices Architecture Complexity with Istio and Kubernetes | - https://www.youtube.com/watch?v=k42jqkjtYKY |
9 | Developing Microservices with Aggregates | - https://www.infoq.com/presentations/aggregates-modular-microservices |
10 | The Seven Deadly Sins of Microservices by Daniel Bryant | - https://www.youtube.com/watch?v=Jw6TYEb1Opw |
11 | Microservices Anti-Patterns | - https://www.youtube.com/watch?v=I56HzTKvZKc |
No | about | url |
---|---|---|
1 | Don’t Build a Distributed Monolith | - https://www.microservices.com/talks/dont-build-a-distributed-monolith/ |
2 | In this talk from the API360 Microservices Summit in New York, June 2016, Vijay Alagarasan of Asurion explores lessons learned and anti-patterns to avoid when implementing microservices. | - https://www.apiacademy.co/resources/videos/api360-microservices-summit-microservices-anti-patterns |
3 | Microservices Anti-Patterns | - https://vimeo.com/198927025 |
4 | Microservices Anti-Patterns | - https://vimeo.com/118020043 |
5 | API360 Microservices Summit – Microservices Antipatterns – Vijay Alagarasan, Asurion | - https://www.youtube.com/watch?v=uTGIrzzmcv8 |
6 | Stefan Tilkov - Microservices Patterns and Anti-patterns | - https://www.youtube.com/watch?v=VaYmRe104HU |
7 | 10 Tips for failing badly at Microservices by David Schmitz | - https://www.youtube.com/watch?v=X0tjziAQfNQ |
8 | 10 Tips for failing badly at Microservices by David Schmitz | -https://www.oreilly.com/library/view/microservices-antipatterns-and/9781491963937/video255789.html |
No | about | url |
---|---|---|
1 | Seven Microservices Anti-patterns | - https://www.infoq.com/articles/seven-uservices-antipatterns |
2 | Microservices Anti-patterns: It’s All About the People | - https://opencredo.com/blogs/microservices-anti-patterns-its-all-about-the-people/ |
3 | The 7 Deadly Sins of Microservices | - https://opencredo.com/blogs/7-deadly-sins-of-microservices/ |
4 | Microservices? Please, Don't | - https://dzone.com/articles/microservices-please-dont |
5 | How Anti-Patterns Can Stifle Microservices Adoption in the Enterprise | - https://blog.appdynamics.com/engineering/how-to-avoid-antipatterns-with-microservices/ |
A compiled list to public failure/horror stories related to microservice infrastructure.
TBD
Build the API with consumers in mind--as a product in its own right.
Use the Collection Metaphor.
Use nouns as resource names (e.g. don’t use verbs in URLs).
Make resource representations meaningful.
Support filtering, sorting, and pagination on collections.
Support link expansion of relationships. Allow clients to expand the data contained in the response by including additional representations instead of, or in addition to, links.
Support field projections on resources. Allow clients to reduce the number of fields that come back in the response.
Use the HTTP method names to mean something:
Use HTTP status codes to be meaningful.
Use ISO 8601 timepoint formats for dates in representations.
Consider connectedness by utilizing a linking strategy. Some popular examples are:
Use OAuth2 to secure your API.
Use Content-Type negotiation to describe incoming request payloads.
For example, let's say you're doing ratings, including a thumbs-up/thumbs-down and five-star rating. You have one route to create a rating: POST /ratings
How do you distinguish the incoming data to the service so it can determine which rating type it is: thumbs-up or five star?
The temptation is to create one route for each rating type: POST /ratings/five_star and POST /ratings/thumbs_up
However, by using Content-Type negotiation we can use our same POST /ratings route for both types. By setting the Content-Type header on the request to something like Content-Type: application/vnd.company.rating.thumbsup or Content-Type: application/vnd.company.rating.fivestar the server can determine how to process the incoming rating data.
Evolution over versioning. However, if versioning, use the Accept header instead of versioning in the URL.
Consider Cache-ability. At a minimum, use the following response headers:
Ensure that your GET, PUT, and DELETE operations are all idempotent. There should be no adverse side affects from operations.
These guidelines aim to support a truly RESTful API. Here are a few exceptions:
HTTP verbs, or methods, should be used in compliance with their definitions under the HTTP/1.1 standard. The action taken on the representation will be contextual to the media type being worked on and its current state. Here's an example of how HTTP verbs map to create, read, update, delete operations in a particular context:
HTTP METHOD | POST | GET | PUT | DELETE |
---|---|---|---|---|
CRUD OP | CREATE | READ | UPDATE | DELETE |
/dogs | Create new dogs | List dogs | Bulk update | Delete all dogs |
/dogs/1234 | Error | Show Bo | If exists, update Bo; If not, error | Delete Bo |
(Example from Web API Design, by Brian Mulloy, Apigee.)
No values in keys:
"tags": [
{"id": "125", "name": "Environment"},
{"id": "834", "name": "Water Quality"}
],
Values in keys:
"tags": [
{"125": "Environment"},
{"834": "Water Quality"}
],
Error responses should include a common HTTP status code, message for the developer, message for the end-user (when appropriate), internal error code (corresponding to some specific internally determined ID), links where developers can find more info. For example:
{
"status" : 400,
"developerMessage" : "Verbose, plain language description of the problem. Provide developers
suggestions about how to solve their problems here",
"userMessage" : "This is a message that can be passed along to end-users, if needed.",
"errorCode" : "444444",
"moreInfo" : "http://www.example.gov/developer/path/to/help/for/444444,
http://drupal.org/node/444444",
}
Use three simple, common response codes indicating (1) success, (2) failure due to client-side problem, (3) failure due to server-side problem:
Information about record limits and total available count should also be included in the response. Example:
{
"metadata": {
"resultset": {
"count": 227,
"offset": 25,
"limit": 25
}
},
"results": []
}
Example: http://example.gov/api/v1/magazines.json
Response body:
{
"metadata": {
"resultset": {
"count": 123,
"offset": 0,
"limit": 10
}
},
"results": [
{
"id": "1234",
"type": "magazine",
"title": "Public Water Systems",
"tags": [
{"id": "125", "name": "Environment"},
{"id": "834", "name": "Water Quality"}
],
"created": "1231621302"
},
{
"id": 2351,
"type": "magazine",
"title": "Public Schools",
"tags": [
{"id": "125", "name": "Elementary"},
{"id": "834", "name": "Charter Schools"}
],
"created": "126251302"
}
{
"id": 2351,
"type": "magazine",
"title": "Public Schools",
"tags": [
{"id": "125", "name": "Pre-school"},
],
"created": "126251302"
}
]
}
Example: http://example.gov/api/v1/magazines/[id].json
Response body:
{
"id": "1234",
"type": "magazine",
"title": "Public Water Systems",
"tags": [
{"id": "125", "name": "Environment"},
{"id": "834", "name": "Water Quality"}
],
"created": "1231621302"
}
Example: Create – POST http://example.gov/api/v1/magazines/[id]/articles
Request body:
[
{
"title": "Raising Revenue",
"author_first_name": "Jane",
"author_last_name": "Smith",
"author_email": "[email protected]",
"year": "2012",
"month": "August",
"day": "18",
"text": "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam eget ante ut augue scelerisque ornare. Aliquam tempus rhoncus quam vel luctus. Sed scelerisque fermentum fringilla. Suspendisse tincidunt nisl a metus feugiat vitae vestibulum enim vulputate. Quisque vehicula dictum elit, vitae cursus libero auctor sed. Vestibulum fermentum elementum nunc. Proin aliquam erat in turpis vehicula sit amet tristique lorem blandit. Nam augue est, bibendum et ultrices non, interdum in est. Quisque gravida orci lobortis... "
}
]
TBD
Spring Cloud provides tools for developers to quickly build some of the common patterns in distributed systems such as configuration management, service discovery, circuit breakers, routing, etc. It is build on top of Netflix OSS libraries, written in Java, for Java developers.
The unified programing model offered by the Spring Platform itself and rapid application creation abilities of Spring Boot give developers a great microservice development experience. For example, with few annotations, you can create a Config Server, and with few more annotations, you can get the client libraries to configure your services.
There is a rich selection of libraries covering the majority of runtime concerns. Since all libraries are written in Java, it offers multiple features, greater control, and fine tuning options.
The different Spring Cloud libraries are well-integrated with one another. For example, a Feign client will also use Hystrix for Circuit Breaking, and Ribbon for load balancing requests. Everything is annotation driven, making it easy to develop for Java developers.
One of the major advantages of the Spring Cloud is also its drawback — it is limited to Java only. A strong motivation for the MSA is the ability to interchange technology stacks, libraries, and even languages, when required. That is not possible with Spring Cloud. If you want to consume Spring Cloud/Netflix OSS infrastructure services, such as configuration management, service discovery, or load balancing, the solution is not elegant. The Netflix Prana project implements the sidecar pattern to expose Java-based client libraries over HTTP to make it possible for applications written in non-JVM languages to exist in the NetflixOSS ecosystem, but it is not very elegant.
There is too much responsibility for Java developers to care about and for Java applications to handle. Each microservice needs to run various clients for configuration retrieval, service discovery, and load balancing. It is easy to set those up, but that doesn't hide the buildtime and runtime dependencies to the environment. For example, developers can create a Config Server with @EnableConfigServer, but that is only the happy path. Every time developers want to run a single microservice, they need to have the Config Server up and running. For a controlled environment, developers have to think about making the Config Server highly available, and since it can be backed by Git or Svn, they need a shared file system for it. Similarly, for service discovery, developers need to start a Eureka server first. For a controlled environment, they need to cluster it with multiple instances on each AZ, etc. It feels like as a Java developers have to build and manage a non-trivial microservices platform in addition to implementing all the functional services.
Spring Cloud alone has a shorter scope in the microservices journey, and developers will also need to consider automated deployments, scheduling, resource management, process isolation, self-healing, build pipelines, etc. for a complete microservices experience. For this point, I think it is not fair to compare Spring Cloud alone to Kubernetes, and a more fair comparison would be between Spring Cloud + Cloud Foundry (or Docker Swarm) and Kubernetes. But that also means that for a complete end-to-end microservices experience, Spring Cloud must be supplemented with an application platform like Kubernetes itself.
Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It is polyglot and provides primitives for provisioning, running, scaling and managing distributed systems.
Kubernetes is a polyglot and language agnostic container management platform that is capable of running both cloud-native and traditional containerized applications. The services it provides, such as configuration management, service discovery, load balancing, metrics collection, and log aggregation, are consumable by a variety of languages. This allows having one platform in the organization that can be used by multiple teams (including Java developers using Spring) and serve multiple purposes: application development, testing environments, build environments (to run source control system, build server, artifact repositories), etc.
When compared to Spring Cloud, Kubernetes addresses a wider set of MSA concerns. In addition to providing runtime services, Kubernetes also lets you provision environments, set resource constraints, RBAC, manage application lifecycle, enable autoscaling and self-healing (behaving almost like an antifragile platform).
Kubernetes technology is based on Google's 15 years of R&D and experience of managing containers. In addition, with close to 1000 committers, it is one of the most active Open Source communities on Github.
Kubernetes is polyglot and, as such, its services and primitives are generic and not optimized for different platforms such as Spring Cloud for JVM. For example, configurations are passed to applications as environment variables or a mounted file system. It doesn't have the fancy configuration updating capabilities offered by Spring Cloud Config.
Kubernetes is not a developer-focused platform. It is intended to be used by DevOps-minded IT personnel. As such, Java developers need to learn some new concepts and be open to learning new ways of solving problems. Despite it being super easy to start a developer instance of Kubernetes using MiniKube, there is a significant operation overhead to install a highly available Kubernetes cluster manually.
Kubernetes is still a relatively new platform (2 years old), and it is still actively developed and growing. Therefore there are many new features added with every release that may be difficult to keep up with. The good news is that this has been envisaged, and the API is extensible and backward compatible.
It is suggested that each resource accept a 'mock' parameter on the testing server. Passing this parameter should return a mock data response (bypassing the backend).
Implementing this feature early in development ensures that the API will exhibit consistent behavior, supporting a test driven development methodology.
Note: If the mock parameter is included in a request to the production environment, an error should be raised.
“Organizations which design systems are constrained to produce designs which are copies of the communication structures of these organizations.” — Melvin Conway (1967).
Conway's Law asserts that organizations are constrained to produce application designs which are copies of their communication structures. This often leads to unintended friction points.
The 'Inverse Conway Maneuver' recommends evolving your team and organizational structure to promote your desired architecture. Ideally your technology architecture will display isomorphism with your business architecture.
“Microservice,” :- “Microservice we are following mostly the domain-driven approach, the idea is to have a cross-functional team.”
Inspired by the coming soon....