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Why Golang for Big data and Parallel Processing Applications?

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Golang, or commonly known as Go language, is one of the fastest-growing programming languages. Developed in 2007 and released publically in 2009, it is a compiled and statically typed language that was created at google due to the issue of developer and application scalability with existing languages. Golang for Big Data also had the problem of integrating new developers with the complex and heavy C++ codebase.

What is Big Data?

Big data platform can be described as an extensive collection of related data, from which useful information can be fetched and used for various projects. It does not specify the amount of data, but the amount can be huge. It is useful as many companies use big data to fetch information about their customers and improve customer experience. It is also handy in the medical field as information through big data can help predict the area and cause infectious diseases and, thus, help find the cure to them.

What is Parallel Processing?

Parallel processing is a method/process in which we split a process into different parts and execute them simultaneously using different processors on the same system. Big Data Processing reduces the time taken by the program to run. Golang is considered to be better than most programming languages for Big Data and Parallel Processing Applications. There are so many reasons to consider Big Data using Golang for these tasks:

Golang for Big data Applications

Go is getting popular these days, and many infrastructure projects as Docker, Kubernetes, etc. are powered by it. Also, Golang Big Data Processing is the right choice if you want to build reliable and efficient software. Big Data consists of data, which means it will include lots of storage capability and databases. Also, Golang for Big Data is used for this task should be suitable for databases and storage and should be able to handle them reliably.
Go is the programming language with the highest labor demand for 2020 according to the Hired survey.
Golang uses different database methodologies and techniques such as cockraochDB and influxDB, etc., which allow it to handle massive amounts of od data. The Golang Big Data Processing and execution speed of go language is a lot faster than most of the other present programming languages. Hence, data gathering and organization become faster, and so more data can be handled in less time. Some of the significant data aspects in which Golang for Big Data is better than other languages:

1. Data Collection

Big Data application has to collect and organize data. Data organization and gathering is an excellent feature of golang. Golang can organize and store data very efficiently as it can use various datastores. Golang Big Data Processing can parse JSON quickly and reliably, so data organization becomes easy.

2. Visualization

Golang is compatible with Web development and custom APIs, which gives a visual analysis of the final result.

3. Machine learning

Golang for Big Data can also be used for machine learning purposes. It has golearn, goml, hector etc., which help in general-purpose machine learning. It also has some other packages like Sajari/regression, Bayesian, etc. which enhance the machine learning capabilities of golang. There are different machine learning frameworks(e.g., H2O, etc.), which can be integrated with golang.

Why Golang is a better option for Parallel Processing applications?

Apart from high computation speed and reliability, Big Data using Golang has many other features that help it to become a better language for many operations, including parallel processing. Usually, many programming use threads for parallel processing applications, as threads are like parallelly running sub-tasks or sub-processes. But, there are a lot of limitations or drawbacks of using multithreading for this purpose. Follow the link to know more about Golang Unit Testing and Testing Best Practices

Drawbacks of threads

  1. One drawback of Golang for Big Data is the size of the threads, the threads have large stack size, and thus consume a lot of memory. They have a capacity of more than 1 MB.
  2. Threads slow down the processing as they need to get all resources from the OS, which may take a bit of time if there are a lot of threads running simultaneously.
  3. Due to the high memory consumption, the threads are a lot heavier; we can only a few thousand or tens of thousands at a time.
  4. Multithreading not only increases the size of the codebase, but it also increases the complexity of the codebase. The risk of deadlocks is increased, and Golang for Big Data becomes difficult to debug the code.
  5. On the other hand, golang has goroutines, which are like threads, but a lot lighter. Unlike threads, goroutines consume very little memory. Some of the benefits of goroutines over threads are:

Advantages of Goroutines

  1. The initial stack size of a goroutine is only 2 KB compared to threads that are more than 1 MB in size.
  2. The scheduling in case of goroutines is done by go runtime and not by the OS. Go’s segmentation stack grows according to our needs, so that.
  3. We can run millions of goroutines at once, as they consume comparatively less memory.
  4. In goroutines, the sharing of data is done safely using channels. The deadlock is a sporadic case for goroutines as the synchronization is handled by going itself.
Due to these advantages of goroutines, golang can handle Parallel Processing in Golang more reliably than most other programming languages. Golang is a better option for big data and Golang Big Data Processing applications due to many reasons, as explained above. Furthermore, Golang for Big Data has other benefits that make golang superior to many other present programming languages in many cases. It is growing so fastly due to its impressive capabilities and performance.

Conclusion

In the end, golang should be preferred in the areas of big data and parallel processing applications as it has the advantages that help us choose a better option. Moreover, even if the data storage capabilities have increased to a considerable amount, there is still a need for saving time and cost. As a newer technology, many people are still afraid to upgrade to Golang for Big Data, even in the areas where it can perform better than the older technologies and languages. But it can become one of the most powerful programming languages.

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