My older post from 2001 - The MOM Player: JAVA MESSAGING SERVICE

This is an older post  publishedd by  http://www.networkmagazineindia.com

Link - The MOM Player: JAVA MESSAGING SERVICE

Microservices Training | Microservices Docker Example | Microservices Tu...

Micro-services are self-contained, independent application units that each fulfill only one specific business function, so they can be considered small applications in their own right. What will happen if you decide to build several micro-service with different technology stacks? Your team will soon be in trouble as developers have to manage even more environments than they would with a traditional monolithic application.
The solution is: using micro-service and containers to encapsulate each micro-service. Docker is a tool that helps you manage those containers.

I am sharing a free youtube tutorial that we used to train our team developing micro-service.

https://youtu.be/UWl7X2fUWTM?t=21

Another tutorial on Microservices Docker, Kubernetes  

Database design for Microservices

History of Microservices - 

A workshop of software architects held near Venice in May 2011 used the term "microservice" to describe what the participants saw as a common architectural style that many of them had been recently exploring.In May 2012, the same group decided on "microservices" as the most appropriate name. James Lewis presented some of those ideas as a case study in March 2012 at 33rd Degree in Kraków in Micro services - Java, the Unix Way as did Fred George about the same time. Adrian Cockcroft, former director for the Cloud Systems at Netflix, described this approach as "fine grained SOA", pioneered the style at web scale, as did many of the others mentioned in this article - Joe Walnes, Dan North, Evan Bottcher and Graham Tackley. <Source : Wikipedia>

Accepted Definition of Microservices - 

Microservices are a software development technique - a variant of the service-oriented architecture (SOA) structural style— that arranges an application as a collection of loosely coupled services. In a microservices architecture, services are fine-grained and the protocols are lightweight.

 Database design for Microservices

The main idea behind microservices architecture is that some types of applications become easier to build and maintain when they are broken down into smaller, composable pieces which work together. The main benefit of the microservices architecture is that it improves agility and reduced development time.When you  correctly decompose a system into microservices, you can develop and deploy each microservice independently and in parallel with the other services.

In order to be able to independently develop microservices , they must be loosely coupled. Each microservice’s persistent data must be private to that service and only accessible via it’s API . If two or more microservices were to share persistent data then you need to carefully coordinate changes to the data’s schema, which would slow down development.
There are a few different ways to keep a service’s persistent data private. You do not need to provision a database server for each service. For example,  if you are using a relational database then the options are:

• Private-tables-per-service – each service owns a set of tables that must only be accessed by that service
• Schema-per-service – each service has a database schema that’s private to that service
• Database-server-per-service – each service has it’s own database server.

Private-tables-per-service and schema-per-service have the lowest overhead.  Using a schema per service is ideal since it makes ownership clearer. For some applications, it might make sense for database intensive services to have their own database server.
It is a good idea to create barriers that enforce this modularity. You could, for example, assign a different database user id to each service and use a database access control mechanism. Without some kind of barrier to enforce encapsulation, developers will always be tempted to bypass a service’s API and access it’s data directly.It might also make sense to have a polyglot persistence architecture. For each service you choose the type of database that is best suited to that service’s requirements. For example, a service that does text searches could use ElasticSearch. A service that manipulates a social graph could use Neo4j. It might not make sense to use a relational database for every service.
There are some downsides to keeping a service’s persistent data private. Most notably, it can be challenging to implement business transactions that update data owned by multiple services. Rather than using distributed transaction, you typically must use an eventually consistent, event-driven approach to maintain database consistency.
Another problem, is that it is difficult to implement some queries because you can’t do database joins across the data owned by multiple services. Sometimes, you can join the data within a service. In other situations, you will need to use Command Query Responsibility Segregation (CQRS) and maintain denormalizes views.
Another challenge is that  services sometimes need to share data. For example, let’s imagine that several services need access to user profile data. One option is to encapsulate the user profile data with a service, that’s then called by other services. Another option is to use an event-driven mechanism to replicate data to each service that needs it.
In summary,  it is important that each service’s persistent data is private. There are, however,  a few different ways to accomplish this such as a schema-per-service. Some applications benefit from a polyglot persistence architecture that uses a mixture of database types.  A downside of not sharing databases is that maintaining data consistency and implementing queries is more challenging.

How is AWS Lambda used in Localytics?

How is AWS Lambda used in Localytics?

Localytics is a Boston-based, web and mobile app analytics and engagement company. Its marketing and analytics tools are being extensively used by some major brands, such as ESPN, eBay, Fox, SalesForce and The New York Times, to understand and evaluate the performance of their apps and to engage with the existing as well as the new customers.

The software developed by Localytics is employed in more than 37,000 apps on more than 3 billion devices all around the world.

Regardless of how popular Localytics is now, Localytics had faced some serious challenges before they started using Lambda.

Let’s see what the challenges were before we discuss how Lambda came to the rescue and helped Localytics overcome these challenges.

Challenges

• Billions of data points uploaded every day from different mobile applications running Localytics analytics software are fed to the pipeline that they support.
• Additional capacity planning, utilization monitoring, and infrastructure management were required since the engineering team had to access subsets of data in order to create new services.
• The platform team was more inclined toward enabling self-service for engineering teams.
• Every time a microservice was added, the main analytics processing service for Localytics had to be updated.

   

• Localytics now uses AWS to send about 100 billion data points monthly through Elastic Load Balancing where ELB helps in distributing the incoming application traffic across multiple targets.
• Afterward, it goes to Amazon Simple Queue Service where it enables us to decouple and scale microservices, distributed systems, and serverless applications.
• Then, it reaches Amazon Elastic Compute Cloud and, finally, into an Amazon Kinesis stream that makes it easy to collect, process, and analyze real-time, streaming data so that we can get timely insights and can react quickly to new information.
• With the help of AWS Lambda, a new microservice is created for each new feature of marketing software to access Amazon Kinesis. Microservices can access data in parallel.

 

Walking In The Cloud

I have been discussing Cloud Strategy with a group of architects and once again it became evident that most people still seem to think Cloud is just another product that you can adopt without really need for a enterprise wide Cloud Strategy. It is not as simple as selecting one cloud service provider from Amazon Web Services (AWS) , Google, Microsoft Azure, Salesforce, and IBM.. Enterprises should take help of Cloud Architect  or else take help from the Cloud service provider to define it's Cloud Strategy. For example if you are considering AWS then AWS architect will provide architecture guidance to the enterprise to define its cloud strategy. Each vendor has multiple products offerings for it's Cloud and it is important to understand how Cloud Service Provider will replace your hardware infrastructure and how it will provide a robust solution.

Key points to decide the cloud strategy

• How data is going to be stored on the cloud and how you will save data center expenses?
• How cloud will enable application to scale on demand ?
• How Cloud Capacity planning is different from owned infrastructure capacity planning? 
• How enterprise will save capital expenses by way of Pay-As-You-Use variable expense
• How cloud will increase speed of delivery by way of using API and reusable services
• How business will become more agile by moving to cloud based architecture
• What are the additional features of individual cloud service provider that differentiate it from others

Many enterprises still lack clarity about their cloud strategy because they are not familiar with the Cloud offerings. An enterprise that wants to adopt the Cloud across all it's business units must have a mature and well-formed understanding of its Enterprise Architecture and a clear view of it's components. Enterprise Architecture Planning enables an enterprises to build structural foundations to support proposed business strategies. It captures the vision of an enterprise by integrating its dimensions to contextualize transformation strategies, organizational structures, business capabilities, data pools, IT applications, and all technology objects. Every business unit of an enterprise is subject to change, and each change may have significant consequences throughout organizational domains.

Cloud Computing is a paradigm to decentralize data centers, by visualizing both infrastructure and platform, and enable services using the internet. It gives access to platforms, services, and tools from browsers deployed across millions of terminals. As well, it reduces the management and maintenance of all the resources associated to technology and infrastructure while providing dynamism, independence, portability, usability, and salability of platform tools.
 

Android Error Solution / Solved : Unable to detect adb version, exit value 0xc0000135

I have Windows 10 license but I prefer using Windows 8.1 on my development laptop. Recently I refreshed my Windows 8.1 and reinstalled Android Studio 3.5.2 and I started getting the following error as soon as I open the Android Studio 

The error basically happens because I have a fresh Windows 8.1 with all required updates but Windows requires  'Windows Universal C Runtime' which gets installed as part of Widows update and because of reinstall of Windows the update is missing. As mentioned in error text 'Android Studio' recommended solution is to download the C run-time package from Microsoft support website (URL is in the image below) or else you can download the 3 ADB specific files from internet and add them to your platform-tools folder on your Windows machine.

Android Studio Error : Unable to detect adb version, exit value 0xc0000135

 

 

Solution:

1. Before you begin implementing changes backup the  platform-tools folder on your machine

2. Add the platform-tools directory path into the system path environment variable.
3. Replace the following 3 files in your platform_tools directory in the path
   (C:\Users\{YourAccount}\AppData\Local\Android\Sdk\platform-tools) As you might know the (Your Account) has to be replaced with your Windows Machine name. If you machine name is DataScience, YourAccount should be replaced by DataScience in the path. 
4. And this is image of the 3 files that I downloaded and replaced in my platform-tools folder
5. Restart Android Studio and the error should be fixed

Digital Medicine - Future of Research & Medical Care

The modern practice of scientific medicine depends on the existence of the written and printed information to store medical information. New digital tools can't just record clinical data, they can also generate medical intelligence by analyzing historical data. This leap of industry into "digital medicine" is potentially precise, effective, widely distributed & available to more people than the current medical practice. Critical steps in the creation of Digital Medicine are  analysis of the impact of new technologies & coordinated efforts to direct technological development towards creating a new paradigm of medical care. So Digital Technology can be used in two areas in medicine, to aid research and for medical care.

3D modelling is used to produce precise representations of anatomies in patients. This enables medical teams to plan and visualize complex surgeries & to produce life-saving implants and prostheses cuastomized for individual patients. It’s a remarkable evolution that is  having a tremendous impact on patient’s lives.However, this is only the tip of the iceberg. The true revolution in medicine and medical care in the 21st century will not come from such physical models, but from virtual ones. Looking into the future, these virtual models will be able to simulate the true physiology and Pathophysiology of human beings in coming years,  changing forever the way we research, diagnose and treat injuries and disease.

While your virtual twin may seem like a distant dream, progress in bringing this dream to life is actually already well underway in the nascent field of Bio-intelligence. Bio-Intelligence uses computer technologies to model, simulate, visualize and experience biological medical processes in a virtual environment. While drug makers have for some time modeled and screened virtual proteins and compounds against medical databases, drug development and production remain largely rooted in the real world, and collaboration between disciplines and organizations has been limited.

Every day, drug makers work to produce real drugs that they test on real animals, and then on real patients in real clinical trials. And the time and money they expend is staggering. According to studies companies can expect to spend $3 billion over a period of ten years to bring a single new drug to market

Add to this challenge the dynamism and complexity of living systems, and it becomes clear that a collaborative approach to research and development, along with the use of virtual modelling and simulation, could bring enormous benefits to life science and healthcare industries. Collaboration between scientific disciplines and between pharmaceutical companies, research labs, health service providers and computer companies would allow sharing of knowledge and experience to foster insight and innovation.

And, the collaborative use of computer models and simulation would enable researchers to better understand complex systems and more accurately predict the biological effects of various medicines and treatments, enabling drug makers in turn to fine tune real-world assays and eliminate ineffective treatments from trials before the drugs are even produced.

The changing landscape of research today is forcing the bioinformatics community to seek a new level of data sharing and collaboration only made possible with new platforms.Such approaches could also open the door to truly personalized healthcare medicine as collaboratively produced models and simulations are combined with real world data from individual patients. These changes could produce significant innovation and gains in efficiency, effectiveness and safety, bringing better heath treatment outcomes to everyone.

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