Program languages performance comparison

Mar 8, Initial commit. Feb 17, Add cli support. May 14, Add plotting a graph via R. Add termcolor to reqs.

View code. Speed comparison of programming languages This projects tries to compare the speed of different programming languages. About A repo which compares the speed of different programming languages.

Even though some programming languages are slow they are easy to use and they drastically reduce the development and labor cost and also reduces time. The speed of the programming language depends upon various factors like, Compiler Cross-Platform Security Statically typed and dynamically typed Compiler: The compiled languages are always going to be faster than interpreted languages. The compiler compiles all of the code into executable machine code at once, whereas the interpreter scans the program line by line and converts it into machine code.

So it delays the execution time for interpreters. The compiler while compiling converts the high-level language into intermediary code which saves up most of the time during execution. The computer executes the intermediary code much faster than interpreters. Cross-Platform: In some object-oriented languages like Java and C , the source code would undergo both compilation and interpretation. Because of cross-platform usage and conversion into byte code, speed will be compromised here, which makes the cross-platform languages slower than other languages.

Furthermore, to provide the most long-term relevant comparison, the newest versions of each language runtime environment at the time was used. These calls can be parameterized with a target URL, a specified amount of calls to execute and a delay to wait between the calls. The targets for this service were newly created Lambda functions. The functions consist of six hello world example functions, written in the six programming languages under scope, respectively. When invoked, the function code does only return a fixed string response.

Each function has a concurrency restriction attached, which limits the number of instances that may exist of the function to 1. This ensures that no new instances with additional cold starts are created, because another execution did not yet finish. The latency to the client is not an optimal choice as a metric, since the client was using an unstable private connection.

Hence the latency benchmarks inside of AWS were taken as a basis for the experiments results. X-Ray logs every step of the execution in detailed metrics. Relevant metrics for the experiment were the reaction time of the request and the reaction time of the function. Further relevant for identifying the cold starts, was the internal information about the function execution, containing an initialization metric and the invocation time of the code.

Further note that for Go not all metrics were available through X-Ray, this might be caused by the ambiguity of the environment version. The only specification naturally available is 1.

The table above shows the mean documented times for the API response, function response, invocation and initialization of 10 cold starts in six different container runtimes. The percentage values state the metrics percentage participation in the APIs response time.

The API response times range from ms in Python, making it the fastest function to react overall on a cold start, to ms in. Net Core, making it the slowest. The overall mean of a function returning in Lambda is ms. The second fastest environment is Ruby with ms and the third is Node. The initialization times, which are the metric representing the time it took to initialize a new container with runtime, reflect these rankings.

Noticeably slowest is Java, with an initialization time of ms. Also, relatively slow is Go with ms and. Net Core with ms. Rust has a strong type system, and its syntax is pretty easy to learn.

This language compiles fast too. This makes it one of the fastest programming languages today. Dart was developed by Google in It is an object-oriented programming language. It compiles fast too. Julia is a high-level, high-performance dynamic programming language that is designed for numerical computing tasks.

By exploring the use of many advanced programming techniques, this programming language is specifically intended to address the requirements of scientific computing.

One of its main features includes multiple dispatches, which allow more flexible code and generality. It offers built-in types for numerous statistics and machine learning applications, which makes it the fastest-growing programming language. Julia compiles slower than other languages but runs faster than them because of its blazing fast startup time. It was released back in to support the development of web applications but has since evolved into a powerful general-purpose, highly-scalable programming language.

It is used by companies like Yahoo and LinkedIn to build their applications and for other use cases like IoT Internet of Things , robotics, etc.

It supports high concurrency and processes requests in parallel. This makes Node. Thus, allowing you to build your applications more quickly. This is a general-purpose Object-Oriented Programming OOP language that is concurrent, class-based, and object-oriented in nature and thus supports the preceding programming styles like imperative, generic, and reflective. Java can be used in a wide range of applications and is supported by a large number of IDEs and libraries.

Java compiles slower than other languages but runs faster because of its high-performing standard library. One of the reasons that Java is slower than some other languages is because it uses a Java virtual machine.

The Java virtual machine is software that allows Java programs to run on different types of hardware and operating systems. This machine is not as fast as the hardware that it is running on, which makes Java programs run a little bit slower than they would if they were running on the native machine.

However, the Java virtual machine offers more portability than other programming languages. C pronounced as C Sharp is a multi-paradigm language that can be used to create both multi-threaded and non-multi-threaded programs. NET framework stack. Its startup time is even faster compared to other languages, which makes it a popular language in the industry today.