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Python vs. Scala: A Deep Dive Comparison

Python vs. Scala: A Deep Dive Comparison 

Even if you’re relatively new to programming, you’ve most likely come across both the Python and Scala programming languages. Python and Scala are two of the most widely used languages in today’s programming ecosystem.

In this piece, we’re not looking to stir the proverbial pot about which programming language is better. There are enough heated debates that address those arguments in great detail. And at the end of the day, either programming language will work fine with StreamSets. 

Instead, we’re going to compare how Python and Scala stack up against each other in performance, cost, scalability, and ease of use. Let’s dig in to the comparison between Python and Scala.

What is Python? 

If you do a quick search of the most popular coding languages with programmers today, you’ll almost surely see Python at the top of the list. The reason is that the Python language is known to be a very intuitive, easy-to-use, general-purpose coding language. Developed as an open-source programming language in the late 1980s, Python works for a wide variety of software development applications.

Because it’s a general-purpose programming language, Python has become a go-to for data science, machine learning, data processing, data analytics, software application development, and web development applications. It’s also a go-to coding framework for scripting a quick application or managing a SQL database. This makes Python a favorite in the data analysis and data engineering communities — and with data scientists too.

Further, this general-purpose approach paired with its intuitive, easy-to-understand code syntax has made Python one of the most popular coding languages for beginners to programming. 

And although we emphasized Python’s fit for small-scale apps, scripting, and data processing, don’t write off Python as a language fit only for small-scale data science applications. Python fuels high-performance systems, including many of today’s favorite services such as Netflix, Twitter, and other large-scale operations.

Python at a Glance 

Interpreted vs. Compiled Language: Python is an interpreted language, which means implementations execute instructions without first compiling a program into machine-language instructions
Dynamically vs. Statically Typed: The Python language is dynamically typed
Best Suited For: Python code is best suited for scientific and numeric computing along with data science, big data, and small-scale projects. Python is also a major back-end web development language.
Object-Oriented vs Functional: Python is an object-oriented programming language

What is Scala?

Scala – short for “scalable language” – is a general-purpose language. 

Like Python, it is intuitive and easy to use. Scala is quite a unique programming language. At first glance, Scala appears to be a quick-and-dirty scripting language much like Python. However, there’s a lot more to Scala than meets the eye. 

If we look “under the hood,” Scala is an object-oriented programming language that also incorporates a lot of the functional programming attributes a language like Python is loved for. However, Scala is a distinctly different language than Python.

Building off the momentum of Java, Scala was built to use the Java Virtual Machine (JVM). This allows Scala developers to interchange Java code on the fly and use a vast ecosystem of standard libraries compatible with the JVM. 

Further, Scala is statically typed, helping programmers avoid bugs, leading to more error-free, concise, and readable code.

Scala At a Glance 

Interpreted vs. Compiled Language: Scala may appear to be interpreted but it’s actually compiled via the JVM
Dynamically vs. Statically Typed: Scala is a statically typed language
Best Suited For: Scala code is best suited for data science and big data use cases 
Object-Oriented vs Functional:  Scala is an object-oriented programming language like C# and C++.

Python vs. Scala Comparison

Category
Python
Scala
Performance
Python simply can’t keep up with Scala when it comes to performance at scale. As an interpreted scripting language, Python’s performance simply doesn’t scale like Scala because it needs to be translated to run on distributed systems.

Scala blows Python out of the water when it comes to performance. Running on the JVM, statically typed, access to Java libraries, and multi-threading all play into Scala’s superior performance.

Cost

As a dynamically typed language, Python introduces the opportunity for errors, bugs, and vulnerabilities over the statically typed Scala. This leads to higher costs down the road. 

Python, however, is perfect for scripting on the fly or piecing together a rapid prototype, leading to a very lean, cost-optimized approach that saves time and resources.

As a statically typed language, Scala supports a higher degree of type-safety compared to Python, leading to fewer errors, bugs, and vulnerabilities in code. In this regard, Scala has a lower TCO. 

Scala, however, is known to require larger coding teams to accomplish the same task written in Python. This leads to a higher upfront cost in time, resources, and employee salaries.

Scalability
Python requires the compiler to interpret plain text at runtime as a scripted language, taking up time and resources. Also, as a dynamically typed language, Python is more error-prone. Both of these factors are significant drawbacks for Python regarding scalability.

Even though Python may be a more scalable language for quick and easy application and script development, as a statically typed language with integration to JVM and Java libraries, Scala blows Python out of the water when it comes to performance at scale.

Security
As a dynamically typed language, Python does not follow the tenets of type-safety, allowing users to change variables, thus leading to more errors and less secure code.

Scala follows the tenets of type-safety guarding against changing variables as a statically typed language, thus leading to fewer errors and more secure code.

Ease of Use

When it comes to ease of use, Python is the big winner. It’s very easy to learn with a highly readable syntax, making it an excellent programming language for beginners, quick applications, and scripts.

Scala is not a difficult language to get started with, but it is considered a complicated programming language to master. The static-typing makes Scala more challenging to use compared to Python.

Performance 

When it comes to performance, Scala is the clear winner over Python. One reason Scala wins on performance is that it is a statically typed programming language and Python is a dynamically typed programming language. With statically typed languages, the compiler knows each variable or expression at runtime. 

With a dynamically typed language, variables are interpreted during runtime and don’t follow a predefined structure for defining variables. This less formal approach to variable definition can be great for writing up a quick application or script. However, this flexibility opens up the door to more coding errors and requires more resources from the compiler during runtime. 

Further, Scala uses the JVM (Java Virtual Machine) and leverages a huge ecosystem of Java libraries. This gives Scala performance attributes similar to Java. Lastly, Scala supports multithreading, where tasks within an application run simultaneously. 

These three characteristics of the Scala programming language allow Scala to run up to 10 times faster during runtime over Python!

Cost 

Assessing cost when analyzing Python and Scala isn’t as straightforward as you might imagine since coding in both Python and Scala IDEs is, of course, free. To determine costs, we need to look at the resource toll and security costs.

Python is a quick-and-dirty programming language that supports dynamic typing, perfect for scripting on the fly and piecing together a rapid prototype. Resource cost and time allocation are going to be very low. 

But if we look at the bigger picture, Scala is designed to be less prone to bugs as it is statically typed. Why is this important when discussing cost? Because application errors and compile-time errors equal time and money. If an application has a bug, it may introduce a security vulnerability putting the application or greater organization at risk in the worst-case scenario. In a best-case scenario, buggy applications or errors mean unhappy customers and frequent patching.

When you consider the cost of Python and Scala regarding both team resources as well as the cost of potential bugs and security vulnerabilities, we believe Scala and Python are tied. 

Scalability 

Scalability should be an easy guess, right? Scala is short for scalable language after all!  As a statically typed language, it is more scalable than the dynamically typed Python because statically typed languages are less error-prone and compile faster at runtime. 

Scala is known to be more scalable again comes back to some of the basic principles of Scala – static typing, object-oriented, and functional. Statically typed languages will always be more scalable than dynamically typed ones because they are less error-prone and compile faster at runtime. 

To this point, Python, as a scripting language, has to be interpreted from plain text at runtime by the compiler. This takes computing resources at runtime – a significant drawback of Python in scalability. So, all and all, Python being more prone to errors and requiring more computing resources at runtime all point to the fact that Scala is superior in the scalability quadrant. However, the conversation around scalability doesn’t end here.

When addressing scalability through another lens, the human element, Python may be the clear winner. Since Python removes the hard-coding rules associated with static typing and is a very easy-to-use intuitive scripting language, it acts as a better solution for quick application or script. What does this mean regarding scalability? For teams looking to optimize around human resources (i.e., coding time), Python can be a great go-to option.

At the end of the day, when addressing scalability, it comes down to determining if your team is looking to optimize around resource overhead or staffing. 

Security

One way to assess the security of a coding language is through what’s known as type safety. Type safety is a computer science term that is defined by the extent to which a programming language guards against changing variables to limit errors. 

Here, statically typed languages such as Scala follow the tenets of type safety because Scala limits variables being rewritten. In contrast, the dynamically typed Python does not follow the tenets of type safety.

Why is the flexibility of Python a security concern, you might ask? This flexibility can unintentionally introduce mistakes, bugs, loopholes, and vulnerabilities in the code that may put the application or organization at risk while still compiling during runtime.  

At the end of the day, Scala, following the tenets of type safety, is simply a more secure language leading to fewer bugs and application vulnerabilities.

Ease of Use

Python is hands-down the easier to use coding language. In fact, Python is well known as one of the easiest programming languages around, with the lowest learning curve for beginners. What you lose in performance at runtime, scalability, and type-safety with Python, you make up for in the easy-to-read nature of this plain text scripting language. Those new to programming may want to give Python a shot. 

On the other hand, Scala isn’t considered a hard programming language per se. It’s relatively straightforward to get started. However, Scala is regarded as a hard programming language to master. 

Moving Data 

Build, run, monitor, and manage smart data pipelines with the StreamSets DataOps platform’s graphical user interface or programmatically through the StreamSets SDK via Python. You get powerful extensibility via the support of custom processors for Scala and Pyspark which helps you better operationalize your code.

It’s never been easier to get started. 

 

The post Python vs. Scala: A Deep Dive Comparison appeared first on StreamSets.

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