Java *

If you utilize Apache Spark, you probably have a few applications that consume some data from external sources and produce some intermediate result, that is about to be consumed by some applications further down the processing chain, and so on until you get a final result.

We suspect that because we have a similar pipeline with lots of processes like this one:

Click here for a bit larger version

Each rectangle is a Spark application with a set of their own execution parameters, and each arrow is an equally parametrized dataset (externally stored highlighted with a color; note the number of intermediate ones). This example is not the most complex of our processes, it’s fairly a simple one. And we don’t assemble such workflows manually, we generate them from Process Templates (outlined as groups on this flowchart).

So here comes the One Ring, a Spark pipelining framework with very robust configuration abilities, which makes it easier to compose and execute a most complex Process as a single large Spark job.

And we just made it open source. Perhaps, you’re interested in the details.

Who doesn’t know about Java today? You can hear its mention in every nook and corner of the world. A significant majority of IT companies provide Java-based applications, while businesses are still driven to secure java applications due to robustness and stringent security facilities. However, Java frameworks are a matter of chaos for a variety of business owners that want their application to be standout.

The fact is that there is no dearth of java frameworks that makes application development simple and efficient. However, a number of people have a query regarding the selection of java framework on the basis of features, ease-of-use, cost, and many more. Businesses hire programmers in India, though they stay confused on which platform they want their app to be created.

The beginning

<dependency>
     <groupId>com.github.michaelgantman</groupId>
     <artifactId>MgntUtils</artifactId>
     <version>1.5.1.2</version>
</dependency>

 <dependency>
     <groupId>com.github.michaelgantman</groupId>
     <artifactId>MgntUtils</artifactId>
     <version>1.5.1.2</version>
     <classifier>javadoc</classifier>
</dependency>

<dependency>
     <groupId>com.github.michaelgantman</groupId>
     <artifactId>MgntUtils</artifactId>
     <version>1.5.1.2</version>
     <classifier>sources</classifier>
</dependency>

• Map

Parameters used

Working

How to implement?

const map = (transformFunction, array1) =>
  array1.reduce((newArray1, xyz) => 
{
	newArray1.push(transformFunction(xyz));

	return newArray1;
  }, 
[]
);

Use case:

const double = (x) => x * 2;
const reverseString = (string) =>
  string
	.split('')
	.reverse()
	.join('');

map(double, [200, 300, 400]);

Output: [400, 600, 800]

map(reverseString, ['Hello Alka', 'I love cooking']);
// ['alkA olleH', ‘gnikooc evol I']

What is Swoft?

Swoft is a PHP high performance microservice coroutine framework. It has been published for many years and has become the best choice for php. It can be like Go, built-in coroutine web server and common coroutine client and is resident in memory, independent of traditional PHP-FPM. There are similar Go language operations, similar to the Spring Cloud framework flexible annotations.

Through three years of accumulation and direction exploration, Swoft has made Swoft the Spring Cloud in the PHP world, which is the best choice for PHP's high-performance framework and microservices management.

Github

https://github.com/swoft-cloud/swoft

Future Software Development Is Here!

Let’s Try Out With Some Code

X=np.array([[0,1,1,0],[0,1,1,1],[1,0,0,1]])
y=np.array([[0],[1],[1]])

def sigmoid ():
   return 1/(1 + np.exp(-x))
def derivatives_sigmoid ():
   return x * (1-x)

epoch=10000
lr=0.1
inputlayer_neurons = X.shape[1]
hiddenlayer_neurons = 3
output_neurons = 1

wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bh=np.random.uniform(size=(1,hiddenlayer_neurons))
wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))
bout=np.random.uniform(size=(1,output_neurons))

Working with SOAP often gets tricky, and dealing with WSDL might be a huge contribution to the complexity of this task. Really, it could be the least expected thing to face when you are into a modern & fancy language like for example, Scala, that is well known for its reactiveness and asynchronous way of dealing with requests. In fact, many of the software developers that have made their way into industry quite recently, might not even know about SOAP and WSDL protocols, and get quickly annoyed or even enraged when first trying to connect to such a legacy service. So, should we deprecate this altogether in favour of modern technology stack, or maybe there is a less painful solution?

Introduction

Code search and navigation are important features of any IDE. In Java, one of the commonly used search options is searching for all implementations of an interface. This feature is often called Type Hierarchy, and it looks just like the image on the right.

It's inefficient to iterate over all project classes when this feature is invoked. One option is to save the complete class hierarchy in the index during compilation since the compiler builds it anyway. We do this when the compilation is run by the IDE and not delegated, for example, to Gradle. But this works only if nothing has been changed in the module after the compilation. In general, the source code is the most up-to-date information provider, and indexes are based on the source code.

Finding immediate children is a simple task if we are not dealing with a functional interface. When searching for implementations of the Foo interface, we need to find all the classes that have implements Foo and interfaces that have extends Foo, as well as new Foo(...) {...} anonymous classes. To do this, it is enough to build a syntax tree of each project file in advance, find the corresponding constructs, and add them to an index.