What is Julia language used for?

Some of the usages of Julia programming language are given below:

§ Data Science – this process is meant to extract data from diverse sources, attentive to finding out information from a significant and heavy amount of data. For this, one of the critical packages in Julia is OnlineStats.jl.

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§ Machine Learning – a field of ​​computer science that is intended for analyzing the data that systematizes the construction of analytical models. For this drive, the Julia programming language has influential packages like Knet.jl and Flux.jl.

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§ Scientific Computing – is the study related to the structure of mathematical models and numerical solutions, which use computers to observe and solve scientific and engineering problems. The central packages in using scientific computing with Julia are DifferentialEquations.jl, AbstractFFTs.jl, Yao.jl, IterativeSolvers.jl, Optim.jl, and JuMP.jl.

§ Data Visualization – is the process of varying a large amount of data into a graphical format, aiding decision-makers in a company or organization. The main package for this drive-in Julia is Plots.jl.

§ The Julia REPL (Read/Evaluate/Print/Loop) gives easy access to special characters, like Greek alphabetic characters, subscripts, and special math symbols. Just sort a backslash, then type a string to insert the parallel character. It allows developers to raise some equation and directly type it in. For example,

julia>\sqrt<TAB>

Julia trades the \sqrt with a square root symbol:

julia>√

It can be used for writing software in the diversity of application domains because Julia does not include language constructs planned to be used within a specific application domain. Julia lets you write UIs, statically compile the code, or even fixed up it on a webserver. It provides Lisp-like macros and other meta-programming facilities. The standard library also provides asynchronous I/O, process control, cataloging, profiling, and more.

What language is Julia written in?

Julia can be written in the following languages:

· C# for the runtime and a few major data types. Julia has a humble and efficient way to call functions written in C. Though most code can be written in Julia, there are many high-quality, advanced libraries for numerical computing already registered in C#. So, to make easy use of this existing code, Julia makes it simple and effective to call C# functions. Furthermore, Julia has a “no boilerplate” philosophy: functions can be termed directly from Julia without any “glue” code, code generation, or compilation, even from the communicating prompt. This is proficient just by making an appropriate call with ccall syntax, which looks like an ordinary function call.

• Scheme (specifically femtolisp) for the parser and the early lowering/optimization.

• C++ as the border to LLVM.Julia influences LLVM to achieve the levels of performance comparable to C++ language. It is a powerfully typed but dynamic language with a strong focus on generics. Similar to C++ programming, function congestion is supported, resulting in a “multiple dispatch” system that chooses the correct function to call on the basis of the types of the arguments, and compiling dedicated versions of the function as needed. However, unlike C++, the dispatch decision is delayed until runtime, eliminating the difference between compile-time and runtime types. Julia is an excellent high-level companion to a C++ library. Its combination of high performance and almost zero-overhead FFI to C++ allows interfacing at any level of thought, even in inner loops without a vital performance penalty.

A lot of the semantics for Julia’s composition are also applied directly in Julia as functions and macros. Most of the data types in the standard library, besides arrays, strings, possibly a few others, are also applied directly in Julia.

Is Julia a functional programming language?

Functional programming is a programming standard where programs are built by applying and combining functions. Julia uses some features present in functional programming languages and can be used to some extent for coding in a functional programming style, but Julia is not a functional programming language. Here are some of the belongings that most functional programming languages do, but Julia doesn’t:

Julia has great order functions, but it’s lost two very beneficial concepts: function composition and partial function application. The functional programming languages, by evasion, work with immutable data structures. Julia’s data structures are variable. (It gives immutable types and a few mechanisms to improve the variable data structures). 

A significance of the above is that you can’t use a mutable variable to stock the state of a loop. So recursion is castoff instead of looping. To aid with recursion (defining the base case and the recursive case), pure FP languages bid pattern matching, which Julia doesn’t. Also, here it’s the instance of tail call optimization. Most FP languages use it due to their reliance on recursion, while Julia does not. It’s a handy feature that many procedures rely on recursion. (You’ll have to alter them to use while loops in Julia). Another significance is that you can’t have global variables for dealing with the app’s state. Julia allows them, although it strongly advises against using them, due to poor performance. Due to immutability, you won’t have operators and operations like “==”, “+=”, “++”, etc.

So if you favor FP, Julia is not a good choice. Because you would miss some very beneficial concepts in FP, like partial function application, function configuration, and pattern matching, it’s better to go for a wholesome functional programming language.

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Julia programming language examples

Code examples are given as under:

Creating table from Julia Vectors

x=[false,true,false,true]

y=[‘B’,’B’,’A’,’A’]

z=[.1,.3,.2,.4]

julia>x=[false,true,false,true]

4–elementArray{Bool,1}:

false

true

false

true

julia>y=[‘B’,’B’,’A’,’A’]

4–elementArray{Char,1}:

‘B’

‘B’

 ‘A’

‘A’

julia>z=[.1,.3,.2,.4]

4–elementArray{Float64,1}:

0.1

0.3

0.2

0.4

Use table to create a table, create a primary key, and sort by the primary key

t1=table(x,y,z)

Tablewith4rows,3columns:

123

───────────────

false‘B’0.1

true‘B’0.3

false‘A’0.2

true‘A’0.4

t2=table(x,y,z,names=[:x,:y,:z])

Tablewith4rows,3columns:

xyz

───────────────

false‘B’0.1

true‘B’0.3

false‘A’0.2

true‘A’0.4

Sort by index variable

In addition, we can sort the table by the primary key, which can be set using keyword parameters

t3=table(x,y,z,names=[:x,:y,:z],pkey=(:x,:y))

Tablewith4rows,3columns:

xyz

───────────────

false‘A’0.2

false‘B’0.1

true‘A’0.4

true‘B’0.3

NDSparse: N-dimensional sparse array with arbitrary index.

ndsparse((x,y),z)

2–dNDSparsewith4values(Float64):

12│

───────────┼────

false‘A’│0.2

false‘B’│0.1

true‘A’│0.4

true‘B’│0.3

Access data from Table and NDSparse

Created a t3table previously

t3

Tablewith4rows,3columns:

xyz

───────────────

false‘A’0.2

false‘B’0.1

true‘A’0.4

true‘B’0.3

The data in the table can be accessed through the index

julia>t3[1]

(x=false,y=‘A’,z=0.2)

julia>t3[1].z

0.2

Julia databases                 

The Julia Programming Language funds all the common databases through its ODBC.jl and JDBC.jl packages, with built-in drivers for MySQL, SQLite, and PostgreSQL. These can be used for dismissal queries and obtaining the required output.MySQL is an open-source social database. Precisely, MySQL has had a status as a high-speed database for read-heavy workloads, sometimes at the price of concurrency when mixed with write operations. PostgreSQL, also known as Postgres, advertised as the most progressive open-source relational database in the world. It was made to be feature-rich, extendable, and standards-submissive. In the past, Postgres’ performance was more stable – reads were usually slower than MySQL. Still, it was able to write large amounts of data more proficiently, and it handled concurrency better.

The differences in performance between MySQL and Postgres have been spread out in recent versions. MySQL is still swift at reading data, only if one is using the old MyISAM engine. If using InnoDB (which allows dealings, key constraints, and other significant features), differences are minor. These features are absolutely critical to an initiative or consumer-scale applications, so using the old engine is not a choice. On the other hand, MySQL has also been improved to lessen the gap when it comes to heavy data writes.

Postgres is an object-relational database, while MySQL is a virtuously relational database. It means that Postgres contains features like table inheritance and function overloading, which can be crucial to specific applications.

When selecting between MySQL and PostgreSQL, performance should not be a factor for most run estimated future growth. In addition, both platforms are perfectly accomplished of replication, and many cloud providers offer managed scalable versions of both databases. Therefore, it’s good to consider the benefits of Postgres over MySQL before you start your next project with the default database setting.

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Julia programming language examples

Code examples are given as under:

Creating table from Julia Vectors

x=[false,true,false,true]

y=[‘B’,’B’,’A’,’A’]

z=[.1,.3,.2,.4]

julia>x=[false,true,false,true]

4–elementArray{Bool,1}:

false

true

false

true

julia>y=[‘B’,’B’,’A’,’A’]

4–elementArray{Char,1}:

‘B’

‘B’

 ‘A’

‘A’

julia>z=[.1,.3,.2,.4]

4–elementArray{Float64,1}:

0.1

0.3

0.2

0.4

Use table to create a table, create a primary key, and sort by the primary key

t1=table(x,y,z)

Tablewith4rows,3columns:

123

───────────────

false‘B’0.1

true‘B’0.3

false‘A’0.2

true‘A’0.4

t2=table(x,y,z,names=[:x,:y,:z])

Tablewith4rows,3columns:

xyz

───────────────

false‘B’0.1

true‘B’0.3

false‘A’0.2

true‘A’0.4

Sort by index variable

In addition, we can sort the table by the primary key, which can be set using keyword parameters

t3=table(x,y,z,names=[:x,:y,:z],pkey=(:x,:y))

Tablewith4rows,3columns:

xyz

───────────────

false‘A’0.2

false‘B’0.1

true‘A’0.4

true‘B’0.3

NDSparse: N-dimensional sparse array with arbitrary index.

ndsparse((x,y),z)

2–dNDSparsewith4values(Float64):

12│

───────────┼────

false‘A’│0.2

false‘B’│0.1

true‘A’│0.4

true‘B’│0.3

Access data from Table and NDSparse

Created a t3table previously

t3

Tablewith4rows,3columns:

xyz

───────────────

false‘A’0.2

false‘B’0.1

true‘A’0.4

true‘B’0.3

The data in the table can be accessed through the index

julia>t3[1]

(x=false,y=‘A’,z=0.2)

julia>t3[1].z

0.2

Julia vs. Python

Python may not be satisfactory enough for the developers behind Julia, indeed focused on scientific computing, machine learning, data mining, large-scale linear algebra, and parallel and distributed computing. A brief comparison between Julia and Python is given as under:

· Julia is compiled. For faster runtime performance, Julia JIT is based on LLVM.

· Julia is collaborating. Includes a REPL or an interactive command line, comparable to what Python proposed.

· Julia has direct syntax, similar to Python, with short syntax, expressive and powerful.

· Julia associates the advantages of dynamic and static typing.

· Julia can openly interact with external libraries that are written in C and Fortran. It can also be interfaced with Python code over the PyCall library and even share data.

· Julia funds meta-programming and has a complete debugging package.

· Julia is rapid. The JIT compiler and its features allow Julia to overtake un-optimized Python. Whereas Python can be made faster through external libraries, third-party JIT compilers (PyPy), and optimized tools like Cython.

· Julia has math syntax. Julia’s primary mark audience comprises scientific computing languages ​​such as Matlab, R, Mathematica, and Octave.

· Julia has computerized memory management. Unlike Python, Julia doesn’t load the user with the particulars of allocating and freeing memory.

· Julia suggests superior parallelism. Whereas Python’s ways for parallelizing operations generally require data to be serialized and desired across threads or nodes.

Although Julia was developed especially for data science, Python offers some benefits for professionals in the field.

· Python is qualified as compared to Julia.

· Python has more third-party packages, which are one of the language’s biggest fascinations. 

· Python has millions of users as compared to Julia.

· Python is getting faster along with refining processing and parallelism.

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Julia vs. R

Julia and R can be chiefly classified as “Languages” tools. Developers define Julia as “A high-level, high-performance dynamic programming language for technical computing.” It has a syntax that is familiar to users of other technical computing backgrounds. In addition, it offers a sophisticated compiler, distributed parallel execution, numerical precision, and an extensive mathematical function library. 

While R is a statistical research tool, it is still used by statisticians and students to make numerous statistical computations and examines. Statistical techniques like linear and non-linear modeling, time-series analysis, classification, classical statistic tests, clustering, and others are all implemented by R and its libraries. R is also used for machine learning research and deep learning as well. R is one of the most commonly used languages for machine learning, with libraries that facilitate monitored and unmonitored learning. Other research involving large data sets like big data, finding genetic anomalies and patterns, various drug compositions all use R to sift through an extensive collection of related data and to draw significant deductions from it. R is also used in finance, manufacturing, social media, and the IT sector.

Data scientists would first use a tool like Python or R to develop an algorithm because it was easy to search the data and make maps in those languages. Then, when they were happy with the algorithm, they would rewrite the program in C++ or Java to get fast computer processing performance. Julia is more rapid than R because it is specially designed to quickly implement the basic mathematics that underlies most data science, like matrix expressions and linear algebra.

Julia is now commonly used, with over 2 million people have downloaded it; it hopes that Julia will exceed Python and R as the central language for data science and chiefly for machine learning.

Hence, from all the discussion in this article, we have concluded that Julia is a free and open-source, dynamic, high-level, and high-performance programming language. The core language is minimal, and the standard library is written in the Julia language itself. In Julia, you can call many other advanced, high-performance basic codes, like linear algebra, string processing, random number generation, and fast Fourier transform. It has a rich type of syntax for generating or describing objects. It has high performance, close to statically compiled language, designed for parallel and distributed computing. Julia Programming Language supports all the common databases through its ODBC.jl and JDBC.jl packages, with built-in drivers for MySQL, SQLite, and PostgreSQL. 

Julia is now broadly used. Its practices are in the fields of machine learning, data visualization, scientific computing, and data science. It can also be used for writing software in the diversity of application domains because Julia does not contain language constructs intended to be used within a specific application domain. Julia lets you write UIs, statically compile your code, or even set up it on a webserver. It provides Lisp-like macros and other meta-programming facilities. The standard library also offers asynchronous I/O, process control, logging, profiling, and more.

Julia is faster than Python. The JIT compiler and its features permit Julia to beat un-optimized Python. Julia is also quicker than R because it is specially designed to swiftly implement the basic mathematics that underlies most data science, like matrix expressions and linear algebra.

On the other hand, Julia has high-order functions, but it’s missing two very beneficial concepts: function composition and partial function application. The functional programming languages, by default, work with immutable data structures. Julia’s data structures are mutable. Therefore, Julia cannot be taken as an available programming language.

References:

https://en.wikipedia.org/wiki/Julia_(programming_language)

https://developer.okta.com/blog/2019/07/19/mysql-vs-postgres

https://www.infoworld.com/article/3241107/julia-vs-python-which-is-best-for-data-science.html#:~:text=Julia%20can%20interface%20directly%20with,Julia%20supports%20metaprogramming.

https://www.asapdevelopers.com/python-backend-language/