Microservice Architecture - Explanation, pros and cons

As most Software Developers out there, most of my experience is with (huge) monolithic apps. So when I first read or heard about microservices I was a little confused about how they work and how to implement this kind of architecture, and most important.. does it work? any pros and cons? is it the next step in the development process?

I don't believe microservices can be considered "the best aproach" in every single situation. However, it looks like a good option. But, as everything in life, all is relative...

I'll show you some facts I found on the internet about microservices architecture so you can have a better understanding about it. It you are confused (as I was), hope it helps to clarify some concepts....


1. What is Architecture (Software)?

Architecture is the fundamental organization of a system embodied in its components (i.e. Web Server, Application Server, Databases,Storage, Communication layer, etc…), their relationships to each other, and to the environment (i.e. deployment environment shared server, dedicated server, cloud deployment, etc..), and the principles guiding its design and evolution.

2. What is microservice architecture ?

Microservice means developing a single, small, meaningful functional feature as single service, each service has it’s own process and communicate with lightweight mechanism, deployed in single or multiple servers.

3. Advantages of microservice architecture ?

Each micro service is small and focused on a specific feature / business requirement.
Microservice can be developed independently by small team of developers (normally 2 to 5 developers).
Microservice is loosely coupled, means services are independent, in terms of development and deployment both.
Microservice can be developed using different programming language (Personally I don’t suggest to do it).
Microservice allows easy and flexible way to integrate automatic deployment with Continuous Integration tools (for e.g: Jenkins, Hudson, bamboo etc..).
The productivity of a new team member will be quick enough.
Microservice is easy to understand, modify and maintain for a developer because separation of code,small team and focused work.
Microservice allows you to take advantage of emerging and latest technologies (framework, programming language , programming practice, etc.).
Microservice has code for business logic only, No mixup with HTML,CSS or other UI component.
Microservice is easy to scale based on demand.
Microservice can deploy on commodity hardware or low / medium configuration servers.
Easy to integrate 3rd party service.
Every microservice has it’s own storage capability but it depends on the project’s requirement, you can have common database like MySQL or Oracle for all services.

4. Disadvantages of microservice architecture ?

Microservice architecture brings a lot of operations overhead.
DevOps Skill required (http://en.wikipedia.org/wiki/DevOps).
Duplication of Effort.
Distributed System is complicated to manage .
Default to trace problem because of distributed deployment.
Complicated to manage whole products when number of services increases.

5. In which case / requirement microservice architecture best fit ?

When you need to support Desktop, web , mobile, Smart TVs, Wearable, etc… or you don’t know in future which kind of devices you need to support.

6. Which products / companies are using Microservie architecture?

Most large scale web sites including Twitter, Netflix, Amazon and eBay have evolved from a monolithic architecture to a microservices architecture.

7. How independent micro services communicate with each other?

It’s depend upon requirement, normally developers use HTTP/REST with JSON or Protobuf (Binary protocol) but are free to use any communication protocol.

8. Why is it that everyone are talking about microservices now?

It’s been nearly 15 years since the concept of Service Oriented Architecture really took hold. With the improvement of RESTful web service and JSON as a data interchange format has made it easier than ever to build easily interconnectable services simply and quickly.

9. Presentation

http://www.infoq.com/presentations/Micro-Services



10. Video





11. Picture: Monolithic vs Modular vs Service oriented architecture

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Programming thought of the day:

• Are you an exception? I bet I can catch you. =)

The Top 10 AI And Machine Learning Use Cases Everyone Should Know About

This is an interesting article I found at Forbes.com . This might help to understand and visualize how important and critical is working with Machine Learning technology. Possibilities are endless, but for now, we are going to focus on 10 examples.... enjoy!


by: Bernard Marr

Machine learning is a buzzword in the technology world right now, and for good reason: It represents a major step forward in how computers can learn.

Very basically, a machine learning algorithm is given a “teaching set” of data, then asked to use that data to answer a question. For example, you might provide a computer a teaching set of photographs, some of which say, “this is a cat” and some of which say, “this is not a cat.” Then you could show the computer a series of new photos and it would begin to identify which photos were of cats.

Machine learning then continues to add to its teaching set. Every photo that it identifies — correctly or incorrectly — gets added to the teaching set, and the program effectively gets “smarter” and better at completing its task over time.

It is, in effect, learning.

1. Data Security

Malware is a huge — and growing — problem. In 2014, Kaspersky Lab said it had detected 325,000 new malware files every day. But, institutional intelligence company Deep Instinct says that each piece of new malware tends to have almost the same code as previous versions — only between 2 and 10% of the files change from iteration to iteration. Their learning model has no problem with the 2–10% variations, and can predict which files are malware with great accuracy. In other situations, machine learning algorithms can look for patterns in how data in the cloud is accessed, and report anomalies that could predict security breaches.

2. Personal Security

If you’ve flown on an airplane or attended a big public event lately, you almost certainly had to wait in long security screening lines. But machine learning is proving that it can be an asset to help eliminate false alarms and spot things human screeners might miss in security screenings at airports, stadiums, concerts, and other venues. That can speed up the process significantly and ensure safer events.

3. Financial Trading

Many people are eager to be able to predict what the stock markets will do on any given day — for obvious reasons. But machine learning algorithms are getting closer all the time. Many prestigious trading firms use proprietary systems to predict and execute trades at high speeds and high volume. Many of these rely on probabilities, but even a trade with a relatively low probability, at a high enough volume or speed, can turn huge profits for the firms. And humans can’t possibly compete with machines when it comes to consuming vast quantities of data or the speed with which they can execute a trade.


4. Healthcare

Machine learning algorithms can process more information and spot more patterns than their human counterparts. One study used computer assisted diagnosis (CAD) when to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed. Additionally, machine learning can be used to understand risk factors for disease in large populations. The company Medecision developed an algorithm that was able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

5. Marketing Personalization

The more you can understand about your customers, the better you can serve them, and the more you will sell. That’s the foundation behind marketing personalisation. Perhaps you’ve had the experience in which you visit an online store and look at a product but don’t buy it — and then see digital ads across the web for that exact product for days afterward. That kind of marketing personalization is just the tip of the iceberg. Companies can personalize which emails a customer receives, which direct mailings or coupons, which offers they see, which products show up as “recommended” and so on, all designed to lead the consumer more reliably towards a sale.

6. Fraud Detection

Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

7. Recommendations

You’re probably familiar with this use if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next. These recommendations are getting smarter all the time, recognizing, for example, that you might purchase certain things as gifts (and not want the item yourself) or that there might be different family members who have different TV preferences.

8. Online Search

Perhaps the most famous use of machine learning, Google and its competitors are constantly improving what the search engine understands. Every time you execute a search on Google, the program watches how you respond to the results. If you click the top result and stay on that web page, we can assume you got the information you were looking for and the search was a success. If, on the other hand, you click to the second page of results, or type in a new search string without clicking any of the results, we can surmise that the search engine didn’t serve up the results you wanted — and the program can learn from that mistake to deliver a better result in the future.

9. Natural Language Processing (NLP)

NLP is being used in all sorts of exciting applications across disciplines. Machine learning algorithms with natural language can stand in for customer service agents and more quickly route customers to the information they need. It’s being used to translate obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case.

10. Smart Cars

IBM recently surveyed top auto executives, and 74% expected that we would see smart cars on the road by 2025. A smart car would not only integrate into the Internet of Things, but also learn about its owner and its environment. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.

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Programming thought of the day:

• Maybe if we start telling people the brain is an app they will start using it.
•