Anatomy of Sequential Data Processing With Java Streams

Functional Programming History

The history of functional programming can be traced back to Lambda-calculus. It is a mathematical language invented by Alonzo in 1930. In some ways, lambda-calculus is the first programming language. Some principles of lambda calculus can be found in modern functional languages and even in Java also. The first principle is that the main object in this kind of language is functions and functions in the mathematical sense of these words. something that takes input and returns the output.

It always returns the same output if based on the same input. Hence, this property goes by the name of being stateless. In other words, the function has no memory of previous inputs.

This property is so important that it’s been given different names such as purity or pure functions. It’s also been called referential transparency. The consequence of this property is that there are no variables because variables are used to preserve the memory of past events. Therefore, there’s also no assignments and no traditional loops because loops are based on some variable repeatedly changing its value.

How Can We Program With No Loops?

Let’s see a simple example. Consider two java web development fragments here, these two methods as below:

On the left-hand side, there is a method called arraySum(). It takes an array of integers as a parameter, and very simply sums all the values and returns the sum. Now, how can we do the same thing in java recursion? We can use recursion for this.

In recursion, the method is going to call itself with slightly different parameters. In the Right-hand site method, I have used an extra parameter called ‘start’. This method will return the sum of the elements of the array whose index is greater than or equal to start. In the else block, it will return the sum of the first element plus the result of calling the same method with an increased value of start. This obtains the same effect as the more natural method on the left side. It does so without any variables.

Just because we can replace the iteration with the recursion, it does not mean that we should do it. In particular, we should not do it in Java as Java is not optimized for recursion. It means that the example on the left-hand side is going to only require constant space to perform the sum, and this constant space is taken by two variables “sum” and “i”.

On the other hand, the method on the right-hand side requires linear extra space. In other words, an amount of space or memory which is proportional to the size of the input array and this amount of memory is used by recursion because a very recursive call to the method will take some space in stack. It means that if the input array is too large, the iterated versions on the left will work fine, while the recursive version on the right will run in the stack overflow issue.

#java #data processing #loops #java streams #anatomy #no loops

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Anatomy of Sequential Data Processing With Java Streams

Anatomy of Sequential Data Processing With Java Streams

Functional Programming History

The history of functional programming can be traced back to Lambda-calculus. It is a mathematical language invented by Alonzo in 1930. In some ways, lambda-calculus is the first programming language. Some principles of lambda calculus can be found in modern functional languages and even in Java also. The first principle is that the main object in this kind of language is functions and functions in the mathematical sense of these words. something that takes input and returns the output.

It always returns the same output if based on the same input. Hence, this property goes by the name of being stateless. In other words, the function has no memory of previous inputs.

This property is so important that it’s been given different names such as purity or pure functions. It’s also been called referential transparency. The consequence of this property is that there are no variables because variables are used to preserve the memory of past events. Therefore, there’s also no assignments and no traditional loops because loops are based on some variable repeatedly changing its value.

How Can We Program With No Loops?

Let’s see a simple example. Consider two java web development fragments here, these two methods as below:

On the left-hand side, there is a method called arraySum(). It takes an array of integers as a parameter, and very simply sums all the values and returns the sum. Now, how can we do the same thing in java recursion? We can use recursion for this.

In recursion, the method is going to call itself with slightly different parameters. In the Right-hand site method, I have used an extra parameter called ‘start’. This method will return the sum of the elements of the array whose index is greater than or equal to start. In the else block, it will return the sum of the first element plus the result of calling the same method with an increased value of start. This obtains the same effect as the more natural method on the left side. It does so without any variables.

Just because we can replace the iteration with the recursion, it does not mean that we should do it. In particular, we should not do it in Java as Java is not optimized for recursion. It means that the example on the left-hand side is going to only require constant space to perform the sum, and this constant space is taken by two variables “sum” and “i”.

On the other hand, the method on the right-hand side requires linear extra space. In other words, an amount of space or memory which is proportional to the size of the input array and this amount of memory is used by recursion because a very recursive call to the method will take some space in stack. It means that if the input array is too large, the iterated versions on the left will work fine, while the recursive version on the right will run in the stack overflow issue.

#java #data processing #loops #java streams #anatomy #no loops

Tyrique  Littel

Tyrique Littel

1600135200

How to Install OpenJDK 11 on CentOS 8

What is OpenJDK?

OpenJDk or Open Java Development Kit is a free, open-source framework of the Java Platform, Standard Edition (or Java SE). It contains the virtual machine, the Java Class Library, and the Java compiler. The difference between the Oracle OpenJDK and Oracle JDK is that OpenJDK is a source code reference point for the open-source model. Simultaneously, the Oracle JDK is a continuation or advanced model of the OpenJDK, which is not open source and requires a license to use.

In this article, we will be installing OpenJDK on Centos 8.

#tutorials #alternatives #centos #centos 8 #configuration #dnf #frameworks #java #java development kit #java ee #java environment variables #java framework #java jdk #java jre #java platform #java sdk #java se #jdk #jre #open java development kit #open source #openjdk #openjdk 11 #openjdk 8 #openjdk runtime environment

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Siphiwe  Nair

Siphiwe Nair

1622608260

Making Sense of Unbounded Data & Real-Time Processing Systems

Unbounded data refers to continuous, never-ending data streams with no beginning or end. They are made available over time. Anyone who wishes to act upon them can do without downloading them first.

As Martin Kleppmann stated in his famous book, unbounded data will never “complete” in any meaningful way.

“In reality, a lot of data is unbounded because it arrives gradually over time: your users produced data yesterday and today, and they will continue to produce more data tomorrow. Unless you go out of business, this process never ends, and so the dataset is never “complete” in any meaningful way.”

— Martin Kleppmann, Designing Data-Intensive Applications

Processing unbounded data requires an entirely different approach than its counterpart, batch processing. This article summarises the value of unbounded data and how you can build systems to harness the power of real-time data.

#stream-processing #software-architecture #event-driven-architecture #data-processing #data-analysis #big-data-processing #real-time-processing #data-storage

Gerhard  Brink

Gerhard Brink

1622108520

Stateful stream processing with Apache Flink(part 1): An introduction

Apache Flink, a 4th generation Big Data processing framework provides robust **stateful stream processing capabilitie**s. So, in a few parts of the blogs, we will learn what is Stateful stream processing. And how we can use Flink to write a stateful streaming application.

What is stateful stream processing?

In general, stateful stream processing is an application design pattern for processing an unbounded stream of events. Stateful stream processing means a** “State”** is shared between events(stream entities). And therefore past events can influence the way the current events are processed.

Let’s try to understand it with a real-world scenario. Suppose we have a system that is responsible for generating a report. It comprising the total number of vehicles passed from a toll Plaza per hour/day. To achieve it, we will save the count of the vehicles passed from the toll plaza within one hour. That count will be used to accumulate it with the further next hour’s count to find the total number of vehicles passed from toll Plaza within 24 hours. Here we are saving or storing a count and it is nothing but the “State” of the application.

Might be it seems very simple, but in a distributed system it is very hard to achieve stateful stream processing. Stateful stream processing is much more difficult to scale up because we need different workers to share the state. Flink does provide ease of use, high efficiency, and high reliability for the**_ state management_** in a distributed environment.

#apache flink #big data and fast data #flink #streaming #streaming solutions ##apache flink #big data analytics #fast data analytics #flink streaming #stateful streaming #streaming analytics