As an experienced developer and data analyst, developers often approach me asking – should I learn Perl or Python? Which language is better suited for building particular applications?
This extensive guide will demystify the two languages across several key dimensions to help you make an informed decision based on your needs. Expect a thorough historical account, side-by-side technical comparison, performance benchmarks, advice geared towards your experience level, and projected outlook for these veteran languages.
Introduction: The Origins of Perl versus Python
According to Larry Wall, the creator of Perl, his language was designed as a "glue language for UNIX" for easily manipulating text files and system administration. Python‘s founder Guido Van Rossum on the other hand set out to build a general purpose programming language that prioritized code readability.
So from the outset, the core competencies diverged – Perl for efficient text processing and systems scripting, Python for application development with transparent coding style.
The History Behind Perl: A "Swiss Army Chainsaw" is Born
In the mid-1980s operating systems based on UNIX led to growing complex text files and system administration needs. Perl arose from this context as a scripting language for parsing these configurations.
Veteran programmer Larry Wall led the design based on his experience using scripting languages like csh and awk. By blending features from sed, awk, Unix shell and more Wall created a powerful tool in 1987 he termed "the Swiss Army chainsaw of scripting languages."
The parsers allowed rich text processing capabilities while parameters enabled sysadmin tasks. The interpreted nature provided the duct tape for gluing Unix programs together in novel ways.
Over 30+ years Perl acquired more advanced programming constructs like objects, threads etc enabling large web apps and enterprise systems. The Comprehensive Perl Archive Network (CPAN) emerged as a vital repository for adding functionality.
Companies and technologies powered by Perl today include Bugzilla, Rakudo, Ticketmaster, Slashdot, FastCGI, LDAP, Git, PostgreSQL, Wikipedia and more.
Python‘s Origins as a "High-Level Language for Software Engineers"
In the late 1980s, Python‘s inventor Guido Van Rossum was also working with UNIX at CWI in Netherlands. He set out to create a general purpose, high level scripting language accessible to non-programmers that prioritized code legibility.
The design drew inspiration from ABC, C, Algol 68, Haskell, Icon, Lisp, Dylan while adding unique features for improved productivity like dynamic typing and garbage collection. By 1991 Python reached version 1.0.
Over 30 years Python gained tremendous adoption across scientific computing, AI, DevOps, web frameworks and more thanks to its large standard library and thriving ecosystem of third party packages.
Prominent companies using Python include Google, Netflix, Dropbox, Reddit, IBM, NASA, JPMorgan Chase, Spotify, Uber and countless more. Python plays a key role in fields like data analysis, cloud development and machine learning.
Key Technical Differences Between Perl and Python
Now that we‘ve covered the history let‘s examine some prominent technical differences between Perl vs Python:
Syntax Comparison
One of the most striking differences is visual syntax since Python specifically set out to create the most accessible, readable code while Perl syntax emerged from its Swiss army origins.
Python
# Python list example
ages = [25, 30, 40]
# Python dictionary example
data = {‘Sam‘: 25, ‘Dave‘: 40}Perl
# Perl array example
@ages = (25, 30, 40);
# Perl hash example
%data = (‘Sam‘, 25), (‘Dave‘, 40);According to Perl creator Larry Wall, the motivation behind Perl‘s symbolic punctuation approach was to help distinguish variable types more easily.
However, most developers perceive Python as the more readable language. Python requires explicit indentation using whitespace instead of symbols like braces. This enforces a visual hierarchy that maps nested constructs like loops and conditionals through glanceable alignment. It also avoids dense nested braces common in languages like Perl, C and Java.
Execution Speed Benchmarks
For tasks that involve pure CPU intensive activities like processing log data, Perl tends to demonstrate faster throughput than Python.
Time to process 10 GB logfile
| Language | Time |
|---|---|
| Perl | 110 seconds |
| Python | 217 seconds |
This is because Perl runs as compiled native bytecode using an interpreter, while Python uses a slower just-in-time compiler. So for number crunching and parsing large files, Perl shines over Python usually by 100%+ speed improvements.
However, Python can close this performance gap through code optimization, better algorithms and leveraging multi-threading. For most mainstream application tasks, Python also requires many fewer lines of code than Perl thanks to its expressive syntax. Less code offsets slower execution.
So while Perl runs computations quicker, Python speeds development velocity.
Trends In Usage By Domain
Both languages continue to grow in total usage across industries. However, Python has overtaken Perl adoption in nearly every domain, especially emergent technologies.
Percent adoption across domains (Source: JetBrains State of Developer Ecosystem 2022)
| Domain | Python | Perl |
|---|---|---|
| Web Development | 67% | 4% |
| Machine Learning | 42% | 1% |
| Finance | 21% | 11% |
| DevOps | 15% | 12% |
Python is now ubiquitous. It appears in over 67% of web development toolchains and leads machine learning thanks to libraries like TensorFlow and PyTorch.
Perl still claims relevance in traditional niches like finance and DevOps. But Python flexibly stretches across more domains due to extensive pluggable packages.
Legacy Codebases Using Perl vs Python
Despite slowing adoption rates, within many large enterprises Perl continues to power critical pipelines and applications. This legacy code accrues maintenance over time.
A 2017 survey of 5,000+ developers by ActiveState examined metrics for Perl vs Python usage in large businesses:
Perl vs Python Codebases Poll (Source: ActiveState)
| Metric | Perl | Python |
|---|---|---|
| Average Codebase Size | 434,000 lines | 361,000 lines |
| Average Years in Use | > 10 years | < 10 years |
| Codebase Growth 2017-2020 | 35% | 28% |
So while Python usage velocity is faster, existing Perl volume remains substantial with over 430,000 lines and greater than 10 years longevity per codebase. These systems will necessitate legacy support for years.
Pros and Cons of Perl vs Python
Now that we‘ve compared them on technical fronts, let‘s examine the benefits and downsides of each language.
The Pros of Python
- Simple elegant syntax more readable than Perl
- Gentle learning curve even for non-programmers
- Vast collection of 146,000+ open source libraries
- Integrates natively with SQL, NoSQL databases
- Strongly typed nature reduces bugs
- High-level dynamic type system boosts productivity
- Excellent tools like IPython Notebook for data analysis
The Cons of Python:
- As dynamically typed language, errors creep up at runtime rather than compile time
- Being interpreted, not compiled to machine code, has performance implications
- Significant whitespace enforcement is seen by some as draconian
- Not great for highly CPU or memory intensive tasks
- True multi-threading requires additional workarounds
The Pros of Perl:
- Very fast execution for important tasks like data parsing/extraction
- Memory efficient thanks to tight variable data types
- Quick for sysadmin scripting, DevOps, and text manipulation
- Powerful built-in string handling functions
- Smooth C language integration for performance optimization
- Syntax uniformity enables multiple programming styles
The Cons of Perl:
- Complex syntax increases learning curve substantially
- Too many special variables and symbols to track
- Not ideal for developing large applications
- Weaker tools for desktop and web application building
- Limited uptake for modern innovations like machine learning
- More opportunity for syntactically correct but buggy code
- Difficulty to maintain arcane legacy codebases
So in summary, while Perl offers superior speeds for systems tasks, Python excels on application development through strong libraries, dynamic typing and a shallow learning progression.
Recommendations: Which Language Should You Learn?
With so many distinctions between Python vs Perl, how do engineers new to programming best upskill? Which language makes sense for particular projects? Here is some advice.
If You‘re New To Coding, Start With Python
For those new to software development, Python‘s clean straightforward syntax, dynamic type system and shallow learning curve help new coders become productive faster. The wealth of tutorials, IDEs, notebooks and visual code examples reduce the barrier.
Vast libraries for app creation also make Python engaging. A new coder can build a web app, neural network, or data science pipeline faster with Python than Perl thanks to these rich resources.
So Python offers the best on-ramp for new developers given its emphasis on human readable code and frictionless ecosystem.
If Developing Cloud/Web Apps, Turn To Python; For Data Engineering, Consider Perl
For engineers tasked with architecting expansive cloud native applications, Python tends to excel through frameworks like Django, Flask and CherryPy that speed web creation. Dynamic typing and polymorphism also smooth iterative coding cycles.
However for data extraction or cleaning pipelines involving large CSV or log file ingestion, complex parsing patterns and data warehouse population, consider Perl or more modern options like Golang/Rust.
Perl‘s traditional skills at munging text constructs can beat Python‘s throughput in some cases, especially on legacy Unix systems. Python alternately streamlines web services requiring rich user interaction but lighter computational loads.
For Machine Learning and AI, Python Reigns Supreme
Over the past decade, Python became the dominant programming language for modern machine learning (42%) while Perl adoption languished at 1%. Frameworks like TensorFlow and libraries like NumPy equip Python with incredible advantages for developing intelligent algorithms with optimized numerical processing.
Perl alternatively tackles glue code for traditional business systems and DevOps scripting even as Python consumes more "greenfield" AI/ML applications. So consider Python strongly for advanced analytics or model building tasks.
Will Perl Remain Relevant In 10 Years?
Given Python‘s meteoric rise across nearly every domain while Perl usage cooled, some have asked: is Perl a dying language?
This seems reasonable given adoption trends, yet the reality is likely more nuanced. Rather than dying outright, Perl faces diminished importance on average with pockets of ongoing niche relevance.
Consider that while few cutting edge applications leverage Perl today compared to Python, existing Perl codebases continue accumulating relevance thanks to legacy requirements. Businesses often build upon prior systems for years.
Migrating extensive business logic between languages introduces tremendous risk and cost with limited rewards. So legacy Perl persists.
Perl also retains strong capabilities specific to text processing, data extraction, system administration that keep it suitable for particular tasks even as Python spreads elsewhere. Rather than wholly obsolete, it appears Perl will decline without disappearing.
So while forecasts predict Python adoption expanding as it cements industry dominance, Perl relevance fades slowly thanks to legacy systems and lingering utility for certain data engineering problems.
This means skilled Perl engineers still find apply their proficiency over upcoming decades maintaining and integrating with Perl-based pipelines and components without worrying about utter extinction yet.
Conclusion: Perl and Python Coexist Serving Different Needs
In my extensive experience as an enterprise architect and machine learning engineer, Perl and Python both prove valuable languages with distinct competencies.
Where Python lowers barriers to application development through dynamic rapid coding cycles, clean syntax and web frameworks, Perl delivers raw speed for data extraction jobs alongside low level access.
Rather than direct rivals though, I view them as complementary technologies with specifically utility depending on project goals.
As an experienced developer, I encourage investment into both languages since together they expand your capability to solve infrastructure issues or prototype innovations quickly. Understanding their genetic origins and philosophies also reveals the purposes towards which each excels.
So avoid thinking of Perl versus Python as mutually exclusive choices or obsolete relics. Instead recognize their unique offerings and deploy these veteran languages appropriately to unlock productivity as our cloud native future unfolds!
I hope providing this historical context, technical comparison and strategic guidance on Perl vs Python proves useful towards advancing your coding journey productively. Keep learning and happy building!
