In this article, I will take you through the entire process of compiling a Python application into a static binary. When we compile the Python application, it encapsulates all the requirements in one place. We’ll be able to see how this is possible.
Using Static Binaries: The Benefits
The use of static binaries in the context of Python has many interesting benefits. Making it possible to distribute without the need for separate Python installations and guaranteeing consistent performance across many systems improves portability.
Simplifying maintenance and reducing compatibility problems, isolation from outside dependencies, if offered by static binaries through optimized execution and memory usage, can boost performance while enhancing security by reducing dependency on dynamic library loading. Requiring fewer steps and reducing dependence on outside variables, static binaries simplify the deployment process.
Offering independence and capability in situations like embedded systems or isolated networks, these binaries prove invaluable in restricted or offline settings. After getting briefed about the article, let us begin this journey wherein we will take a deep dive into various topics, including some basic information about static binary.
Covering some advantages and use cases, we will then move towards understanding Python’s nature towards these binary files. Then, coming towards the most interesting part, we will actually try to code and implement this in practice, but before that, we might have to learn about some tools that are used for this compilation.
Then, lastly, we would see some use cases and wrap up this article.
Understanding Static Binary and Python
Let us understand the two major components of this article as two different individuals, and then use the knowledge gained to put these two together.
What is Static Binary?
An executable file that already has all the required code and libraries to run a program is known as a static binary. It is also referred to as a statically generated executable or a statically linked binary. Therefore, there is no need for external shared libraries or dynamic loading during runtime because the binary already has all the requirements required for the program to run.
Functions and libraries from outside sources are linked and immediately built into the executable at compilation time in a static binary. Dynamic binaries, in contrast, rely on shared libraries that are kept separately on the system and loaded during runtime.
Python’s Dynamic Nature
In contrast to a fully compiled language, where all code is converted into machine-executable instructions before starting, Python’s dynamic nature refers to its distinctive behavior of executing code on-the-fly, frequently during runtime. Although Python’s dynamic behavior makes it immensely adaptive and flexible, it also poses some difficulties, particularly when it comes to producing static binaries.
Some of the advantages of its dynamic nature are dynamic data structures, interactive programming, and rapid development. When it comes to producing static binaries, certain difficulties are introduced.
The creation of a fully static binary that contains all dependencies is challenging since Python code is interpretable at runtime and depends on dynamic linking to outside libraries. The need to include the interpreter itself, the standard library, and any other libraries the program requires to be stored in the binary may be the reason for this.
Challenges in creating Static Binary
Due to Python’s dynamic nature, producing static binaries for Python programs presents major issues. Bundling the interpreter within the binary is challenging due to the runtime interpretation of Python code.
All required modules must be identified and included in the binary in order for dynamic module loading, in which modules are loaded during execution, to work. Given the variations in architectures and systems, it is challenging to incorporate shared libraries and dependencies into the binary.
The management of memory resources must be precise in order to address dynamic memory allocation and garbage collection. Due to different system setups, it can be difficult to guarantee consistent behavior across platforms. Additionally, the packaging of the full Python runtime and libraries in order to get static binaries can lead to larger file sizes.
Why Compile Python Applications into a Static Binary?
For Python programs, using static binaries has several clear benefits. First off, by including all dependencies within the executable itself, these binaries improve portability. Getting rid of compatibility issues and providing distribution across many platforms is done by eliminating the need for separate Python installations.
Additionally, distribution is expedited because just one self-contained binary is required for deployment, which eliminates the need for users to maintain external dependencies or install Python interpreters. Reducing the attack surface and preventing potentially shared library attacks is another advantage of static binaries, which improves security and reduces the need for dynamic library loading.
For protecting sensitive apps and data, this is a very important security improvement. Performance advantages result from reduced runtime interpretation and dynamic linking. By speeding up the application, code execution can be optimized, and memory overhead may be reduced.
Because static binaries contain all necessary components, version management and dependency tracking are made simpler, which is another benefit. Static binaries excel in remote contexts with restricted internet connectivity, such as air-gapped computers, by delivering self-sufficient and dependable performance without external dependencies.Stability and predictability are provided, and concerns about dependencies are reduced by this encapsulation.A smooth installation experience is provided to end-users by static binaries, removing the need to set up Python environments or maintain libraries, and developers are given the freedom to concentrate on writing rather than complexities in deployment.
In many different fields, static binaries have practical advantages.Cross-platform compatibility is guaranteed by them, as a variety of architectures and operating systems can run the programs without any interruption.Consistent performance across all equipment is assured by static binaries in industrial automation, and in contexts with limited resources, there are computing benefits from their effectiveness.
Data analysis, healthcare, the financial sector, cyber security, and education are some of the industries that benefit from simplified distribution. Gaming, remote environments, and embedded systems, demonstrating their broad impact on improving dependability, lowering compatibility problems, and streamlining deployment, are some of the applications and sectors where static binaries give consistent performance.
Several tools are available that make it simpler to compile Python programs into static binaries due to the dynamic nature of the language.
Some well-known tools are mentioned below:
A popular tool called PyInstaller is used to package Python programs into standalone executables. It builds a single binary that contains the interpreter and necessary libraries after scanning the application’s code to find dependencies. Numerous platforms are supported by PyInstaller, which also provides options for modifying the compilation procedure.
If you feel keen to know more about PyInstaller, please check out the linked resource.
Python code is converted into optimized C code by Nuitka, which then compiles it into native machine code. The executables produced by this procedure are quick and effective. With the ‘–standalone’ option, Nuitka can create entirely static binaries without the requirement for external dependencies.
The utility cx_Freeze “freezes” Python code into executables by bundling it with the necessary modules and interpreter. System compatibility is guaranteed, and standalone binaries that are suitable for distribution are created by it.
PyOxidizer is made to build a standalone Python program that may be shared as separate executables. It enables programmers to package assets like graphics and data files in addition to the Python interpreter and dependencies.
Compiling a Python Application into Static Binary
I’ll be using PyCharm as my IDE for the purposes of this demonstration.
If you decide to continue using PyCharm IDE, I’ll provide a link to a guide that will walk you through all the fundamentals of the programme so you may follow along with this course and get a better understanding of it.
Now that we have all the necessary components, we can start compiling our Python application into a static Binary. The PyInstaller package, which will serve as our primary tool for this task, is what we will need to get going.
Use the terminal command below to install PyInstaller.
pip install pyinstaller
For this demonstration, I’ll create a brand-new “.py” file in which I’ve written a straightforward function that prints the Fibonacci sequence. You can apply it on your own using the code supplied below.
a = 0
b = 1
res = 
for i in range(2, n):
a = b
b = res[-1]
n = int(input("Please Enter Number of Element to Print: "))
res = fibonacci(n)
print("Fibonacci Series: ", *res)
We will consider the above file as our Python application, a very basic one indeed. Now that we have the application ready, let’s compile it into a static binary. We must first use our terminal to access the directory where our program is located in order to compile it. I’ll be using the PyCharm terminal in this example, but you could also use the machine’s built-in default terminal or command prompt, depending on your operating system.
As you can see, I used my terminal to navigate to the current directory:
Once the desired location in our terminal is reached, we need to run the below-given code in our terminal.
pyinstaller --onefile fibonacci.py
Running the above line of code in your terminal will compile your application into a static binary.
Once this is done successfully, you can see a dist folder created inside the current folder, inside that folder, you can find you static binary file that you just created.
So this is basically how you can create a static binary file for your Python Application.
Challenges and Limitations
Since we have discussed almost everything about the process, it is also important to know the challenges and limitations of compiling a Python application, as every coin has two sides, and we must be aware of both of them.
Challenges come along with the various advantages offered by the compilation of Python applications into static binaries. File sizes tend to be larger due to the inclusion of the interpreter and libraries. Although dependency-related issues are reduced, some external dependencies may remain. Static binaries limit dynamic behavior as dependencies are fixed at compile time, potentially limiting flexibility. Compatibility issues can arise with changes in system libraries or configurations.
Developing static binaries requires careful resource inclusion and tool selection. Memory-constrained environments can be impacted due to the increase in memory overheads. Updating and maintaining static binaries across platforms can be complex. Additional efforts are demanded by different architectures for creating platform-specific binaries. While security can be enhanced, vulnerabilities in bundled components still pose a risk.
Best Practices and Tips
Follow best practices for generating static binaries from Python apps for the best outcomes. To avoid excessive bloating and control file size, give priority to the selective inclusion of modules and libraries. External dependencies should be clearly documented, even though static binaries tend to solve problems. Include configuration files in the binary or give the user placement instructions.
To ensure security and compatibility, thoroughly test the binaries across platforms and update dependencies. Utilize tool options and flags to alter the compilation process, optimizing memory use to offset any overhead. Package resources, like photos, provide thorough documentation in a variety of test settings. To improve the performance and dependability of the static binary, seek community cooperation, keep version control, and welcome user feedback.
Compiling Python applications into static binaries has two major benefits: improved portability and simplified deployment. Reduced dependency problems, more security, and easier distribution are benefits. Despite issues like large file sizes and limited dynamic behavior, best practices and careful testing ease worries and provide effective, standalone executables for a variety of applications.