What is Obfuscation in Java?

 Obfuscation in Java is the process of making the code difficult to understand or reverse-engineer by obscuring its purpose, logic, and structure. It involves renaming classes, methods, and variables with meaningless names, removing comments and unnecessary white spaces, and restructuring the code to make it hard to follow.

The main purpose of obfuscation is to protect the intellectual property of the code, making it more difficult for others to copy, modify or steal it. It is particularly important for commercial software where source code protection is critical to prevent reverse engineering, hacking or piracy.

How to mock static object inside an Interface by using JUnit5 in Java?

 Mocking static objects inside an interface using JUnit5 requires the use of a mocking framework. One popular mocking framework in Java is Mockito. Here are the steps to mock a static object inside an interface using JUnit5 and Mockito:

Add the Mockito library to your project. You can do this by adding the following dependency to your build file:

Java code obfuscation using Proguard

 Java code obfuscation is a technique used to protect the intellectual property of software developers by making the source code of their applications harder to understand and reverse-engineer. One popular tool for obfuscation is Proguard, which is a free, open-source tool that can be used to shrink, optimize, and obfuscate Java code.

Here are the steps to obfuscate your Java code using Proguard:

How to solve Proguard NotSerializableException in Java Application?

ProGuard is a tool used in Java development to help optimize and obfuscate code. It can sometimes cause issues with serialization if it strips out classes or fields that are necessary for serialization.

A NotSerializableException is typically thrown when an object is not serializable. In other words, when an object cannot be written to a stream and then reconstructed from that stream later.

To resolve this issue with ProGuard, you can do the following:

Trading View Pine Script for Moving Average + EMA + Previous Day High and Low

Trading View free version chart only allows max 3 indicators. So I hereby adding an Pine Script for the following indicators

1) Moving Average - 5 (Period)

2) Moving Average - 20 (Period)

3) Exponential Moving Average - 200 (Period)

4) Previous Day High and Low

How to print elements of an Spark RDD?

By using rdd.foreach(println) or rdd.map(println). On a single machine, this will generate the expected output and print all the RDD’s elements. 

 However, in cluster mode, the output to stdout being called by the executors is now writing to the executor’s stdout instead, not the one on the driver, so stdout on the driver won’t show these! To print all elements on the driver, one can use the collect() method to first bring the RDD to the driver node thus: rdd.collect().foreach(println). This can cause the driver to run out of memory, though, because collect() fetches the entire RDD to a single machine; if you only need to print a few elements of the RDD, a safer approach is to use the take(): rdd.take(100).foreach(println).

What is Spark Parallelize?

Parallelize is a method to create an RDD from an existing collection. Parallelized collections are created by calling JavaSparkContext’s parallelize method on an existing Collection in your driver program. The elements of the collection are copied to form a distributed dataset that can be operated on in parallel.

List<Integer> data = Arrays.asList(1, 2, 3, 4, 5);

JavaRDD<Integer> distData = sc.parallelize(data);