In this Java vs Python complete comparison guide, you will learn:
- What the two languages are, when they were born, and how popular they are
- Their pros and cons
- How they differ in terms of syntax, performance, and scalability
- Which is best for web scraping
Let’s dive in!
Java vs Python: Introducing to the Two Languages
Java and Python are among the most popular programming languages in the IT industry. They both have a large community of developers, but they differ when it comes to syntax, type handling, and execution models.
Java is a complex language that is suitable for applications where performance and reliability are paramount. Instead, Python is easier to approach and shines in data science and machine learning tasks. The choice between the two depends on your project’s specific needs and requirements.
If you are eager to see their main aspects side by side, take a look at the Java vs Python table below:
|Syntax||Verbose, boilerplate, with strict rules||Concise, readable, simple|
|Performance||Fast execution||Slow execution|
|Ecosystem||Extensive, with millions of libraries available||Vast, with thousands of libraries available|
|Community||Millions of users||Millions of users|
|Scalability||Very high, especially in the enterprise field||Good|
|Web Scraping||Possible, but not the main focus||Supported by many libraries and tutorials|
Java: Characteristics, Features, Libraries
Java, whose 1.0 version was released by Sun Microsystems in 1996, is an object-oriented programming language. According to Statista, as of 2023, more than 30% of global developers use it. This is because of its renowned portability, robustness, and vast ecosystem. Maven alone, the most popular Java project management tool, counts more than 10 million libraries. In addition, Spring Boot, the most popular Java-based framework, is the 12th most widely used in the world.
When you think of Java, this is what should come to your mind:
- Object-oriented programming: Java is an object-oriented language that promotes modularity and reusability through classes and objects. It supports inheritance, interfaces, and abstract classes.
- Platform independence: Write once, run anywhere. Java applications can work on any platform with a compatible JVM (Java Virtual Machine).
- High-level: The Java syntax is similar to that of C and C++, but it has fewer low-level utilities because it operates at a higher level of abstraction.
- Strongly typed: It enforces strict type checking at compile-time, reducing runtime errors.
- Exception handling: Robust error handling via try … catch statements to deal with exceptions.
- Rich standard library: Java boasts an extensive standard library that provides I/O, networking, and data management functionality.
- Automatic memory management: A built-in garbage collection simplifies resource handling.
- Multi-threading support: Native support for multi-threading and multi-tasking for simplified concurrent programming.
- Large community and ecosystem: A vast and active community contributes daily to a wealth of open-source libraries and frameworks.
Python: Characteristics, Features, Libraries
Python, with its initial release in 1991 by Guido van Rossum, is a dynamically typed, interpreted, open-source programming language. As of 2023, almost 50% of worldwide developers use Python, making it one of the most popular languages in the industry. Its appeal lies in its simplicity, readability, and extensive library ecosystem. The PyPI (Python Package Index) alone hosts more than 450,000 packages.
In general, Python is well-known for these aspects:
- Versatility and readability: Python’s concise and intuitive syntax is well-suited for inexperienced developers and beginners.
- Dynamic typing: Variable types can change during runtime for extreme flexibility.
- Interpreted language: Python code is interpreted. This can affect performance but also provides agility and portability.
- Rich standard library: The Python Standard Library is one of the richest with a broad spectrum of modules for tasks like file I/O, regular expressions, and data manipulation.
- Garbage collection: Developers do not have to worry about memory allocation and deallocation.
- Concurrency support: Python provides tools for asynchronous programming.
- Open-source: The language is developed under an open source license, making it freely usable and distributable. Its GitHub repository has over 50k stars.
- Data-science capabilities: Python is the language of choice when it comes to data-science, thanks to a large set of math libraries.
- Vast community: Millions of developers around the world know Python and contribute to libraries and frameworks.
Python and Java: Pros
Explore the main advantages that come with both programming languages.
- Can run on various operating systems thanks to the JVM.
- Widely used in large-scale enterprise applications and systems because of its robustness.
- Supports scalable applications and systems.
- Works in a wide range of applications, from web and mobile development to embedded systems.
- More libraries available when compared to Python.
- Strongly typed.
- Old programming language, which means there is a knowledgeable community.
- Object-oriented, but also supports functional concepts.
- Open source.
- Easy syntax.
- Great for scripting, ideal for fast development.
- Cross-platform compatibility, running seamlessly on diverse operating systems.
- A favorite for data science, AI, and machine learning due to its simplicity and rich libraries.
- Requires little code, especially compared to Java.
- A welcoming and vibrant community.
- Supports object-oriented programming, with functional capabilities.
Python and Java: Cons
Dig into the Java vs Python comparison by taking a look at the disadvantages related to the two languages.
- Verbose and less intuitive syntax compared to languages like Python, which makes it prone to boilerplate code.
- Higher memory consumption than most other languages (but not Python).
- Requires compilation, which can slow down development and testing.
- Challenging for beginners due to its strict type system and complex setup.
- Not the best choice for small, lightweight projects.
- Slow and CPU-intensive, especially compared to Java and many other languages.
- Limited mobile app development capabilities compared to Java.
- Hard to make it scale.
- Issues in the migration from Python 2 to Python 3.
- Dynamic typing can lead to bugs.
Java vs Python: Which Is Better?
As with any comparison between programming languages, it is difficult to identify a winner in general terms. To better analyze the Java vs Python issue, you need to explore how the two languages behave in specific aspects and scenarios. Time to do that!
Generally, Python’s learning curve is considered to be more gentle and beginner-friendly compared to Java. Why? Because of its idiomatic simplicity and readability, often referred to as “Pythonic” code. In particular, that makes it an excellent choice for newcomers to programming as Python code looks like pseudocode.
Its concise syntax and dynamic typing reduce cognitive load, enabling faster code comprehension. On top of that, Python comes with a rich standard library with pre-built modules that offer with intuitive functions for various tasks, simplifying many common programming challenges.
Java, on the other hand, has a steeper learning curve, primarily due to its stricter rules and more complex syntax. Its verbosity can be overwhelming, especially at the beginning. Also, it requires explicit type declarations, which can be intimidating for beginners. However, Java’s strong typing can lead to fewer errors in the long run.
Who wins the Java vs Python performance comparison? Java, according to several benchmarks.
Nevertheless, the same benchmarks show that Java needs many more resources than Python. There is therefore a trade-off.
Java typically outperforms Python because compiled languages are usually faster than interpreted languages. The main factor that favors Java’s execution speed and efficiency is that it is first translated into bytecode and then executed by the JVM. The compilation process allows for various optimizations, resulting in faster code execution.
Python does not convert the code into machine-level instructions before execution, as it happens with compilation. Instead, it reads the source code line by line and translates it to machine code on the fly during runtime. That introduces a significant overhead that makes it run slower than Java. Keep in mind, though, that Python’s performance can be improved with the external libraries written in C or C++.
Python’s well-known ease of use makes it more suitable for small-scale applications. This does not mean that you cannot make it scale, but it may not be so easy either. In detail, Python’s GIL (Global Interpreter Lock) limits its ability to scale in CPU-bound applications, particularly on multi-core processors.
Java is often preferred for scalability. The reason is its strong support for multi-threading and a more mature ecosystem for large-scale enterprise applications. Those aspects have made it the go-to language for creating high-performance, concurrent, and distributed systems. Java’s scalability is well demonstrated in enterprise-grade solutions, as its architecture makes it well ideal for handling mission-critical tasks.
For Web Scraping
To better understand who wins the head-to-head, let’s compare them in a specific use case. Web scraping is about automatically retrieving data from the Internet. The idea is to build an automated script that can connect to a target site and extract data of interest from it.
Python is the preferred choice for web scraping due to its extensive library ecosystem and simplicity. Specifically, there are many Python libraries and frameworks, including:
- BeautifulSoup: A Python library for parsing and navigating HTML and XML documents.
- Scrapy: A powerful web crawling framework that facilitates efficient and structured web scraping.
- Requests: A Python module for making HTTP requests, making it easier to send and receive data.
These are well-suited for data scraping tasks. Plus, Python’s simplicity and expressiveness make it an ideal choice for writing and maintaining scraping scripts. Check out our guide to Python web scraping.
Java can also be used effectively in this domain. The main online data extraction library is Jsoup, a lightweight HTML parsing library. Another popular one is HtmlUnit, a browser automation library. Read our Java web scraping guide.
Java is faster, but the performance advantages are more pronounced in CPU-intensive scraping tasks, which is not the case for web scraping. In general, Python’s concise syntax and extensive libraries, together with community support and tutorials, make it a best solution for scraping the Web. However, companies that use Java as their primary language, may find it a logical choice for web scraping to maintain consistency in their technology stack.
In this Java vs Python article, you learned what the two languages are and what they have to offer. In detail, you saw a comparison involving different aspects and how Java and Python behave when it comes to scraping data from Web pages.
Talk to one of our data experts about our scraping solutions.