Python? Java? Rust? Qual a Diferença? | Discutindo Linguagens

New Section

In this section, Fabio Akita introduces the topic of discussing popular programming languages, emphasizing a different perspective on viewers' favorite languages.

Introduction to Popular Programming Languages

• Fabio shares technical insights and personal opinions about various programming languages, aiming to provide a fresh perspective rather than comparing them.

• Emphasizes the intention to help viewers see their favorite language from a new angle without engaging in debates about which language is superior.

• Mentions causing anxiety in beginners by showcasing unfamiliar concepts but encourages learning and revisiting the content for better understanding.

Python's Rise in Popularity

This section delves into Python's surge in popularity due to its applications in data science and machine learning projects.

Python's Dominance in Data Science

• Attributes Python's popularity growth to projects like Jupyter Notebooks, Pandas, Tensorflow, and PyTorch within data science and machine learning domains.

• Compares Python's rise to Perl's prominence in the 90s for scripting Linux systems and web applications due to its regex capabilities.

Perl's Influence on Scripting Languages

Exploring Perl's impact on subsequent scripting languages like Ruby, Python, PHP, and others.

Legacy of Perl

• Discusses Perl's significant role in text processing through regular expressions for tasks such as pattern matching DNA sequences.

• Highlights Perl's stagnation with the development of Perl 6 leading users towards more modern languages like Python due to uncertainty about Perl's future evolution.

Regular Expressions Across Languages

Examining how various programming languages implement regular expressions influenced by Perl.

Implementation of Regular Expressions

Understanding Syntax and Language Configurations

In this section, the speaker discusses how different programming languages configure syntax for regular expressions. They explore examples like "verbatim string literal" in C# and the use of ICU syntax in languages such as Java, C++, Swift, and PHP.

Exploring Syntax Variations

• The speaker mentions that each language can configure the details of syntax differently. For instance, C# offers the option of using "verbatim string literal" for writing regex without escaping special characters.

• Some languages opt for ICU syntax over Perl style for regular expressions. Examples include Java's java.util.regex, C++'s icu::RegexPattern, Swift's NSRegularExpression, and PHP's "preg_*" functions.

Challenges with Syntax Compatibility

This part delves into personal preferences regarding syntax styles, highlighting challenges faced when switching between different syntax formats like Perl and ICU in programming languages such as Objective-C and Swift.

Personal Preferences and Challenges

• The speaker expresses a preference for Perl syntax but acknowledges stumbling over ICU syntax while working on iOS development in Swift or Objective-C.

• An anecdote is shared about experimenting with importing Oniguruma to add Perl regex syntax to an Objective-C project 11 years ago, showcasing the challenges faced due to compatibility issues with libraries like Time from Linux.

Divergence in Date Formatting Standards

Here, the discussion shifts towards date and time formatting standards across various programming languages, contrasting strftime function usage in C with ICU format adoption by languages like Objective-C and Swift.

Contrasting Date Formatting Approaches

• The speaker highlights differences in date formatting standards between languages. While some like C++, Python, Ruby still adhere to strftime format from C, others such as Swift, Java, C#, utilize the ICU format.

• Despite ICU being considered an international Unicode standard for date formatting, Unix/Linux-derived formats continue to be prevalent due to historical usage patterns.

Influence of Underlying Languages on Scripting Languages

This segment emphasizes how scripting languages inherit functionalities from underlying platforms like C through examples from JavaScript or Python calling libraries written in C.

Impact of Underlying Platforms

• The speaker illustrates that many features in scripting languages are implemented using libraries written in lower-level languages like C.

• Highlighting examples from JavaScript or Python where functionalities are derived from libc library functions showcases how scripting languages leverage underlying platform capabilities.

Fortran, C++, and Python in Data Science

In this section, the speaker discusses the importance of Fortran, C++, and Python in data science projects, emphasizing their roles in key libraries like NumPy and SciPy.

Fortran, C++, and Python Dependencies

• NumPy is essential for using SciPy, a scientific module for statistics, optimization, linear algebra, signal processing, and more. SciPy heavily relies on Fortran (one-fifth) and C/C++ (another one-fifth), showcasing the significance of non-Python languages in data science projects.

• Projects like TensorFlow predominantly utilize C++ for performance-critical components. Even popular tools like PyTorch rely significantly on C++. Understanding these dependencies highlights that proficiency in Python alone may not suffice for comprehensive comprehension of machine learning frameworks.

Performance Considerations Beyond Python

• Exploring TensorFlow reveals that over half of its codebase is written in C++, with only a fifth attributed to Python. This distribution underscores the necessity of understanding languages like C++ for deep comprehension of neural networks and GPU acceleration.

• JavaScript's Tensorflow.js leverages TypeScript and JavaScript but incorporates performance-critical sections in C++. This multi-language approach ensures efficient execution across browsers by utilizing WebGL and Web Assembly support.

Underlying System Dependencies

The discussion shifts towards system dependencies impacting programming languages such as Python and JavaScript due to underlying libraries like LibC.

System Dependencies Impacting Programming Languages

• System dependencies like LibC can vary across operating systems, affecting language functionality. For instance, running Python on Windows necessitates distinct code adaptations compared to Linux or Mac environments due to platform-specific requirements.

• Delving into the GitHub repository of CPython unveils platform-specific code differentiations through conditional compilation directives. These variations underscore how programming languages adapt to diverse system environments for optimal performance.

Node.js Architecture: libuv Library

The speaker delves into Node.js architecture focusing on libuv library's pivotal role in enabling event-driven functionalities within Node.js applications.

Node.js Architecture Insights

Understanding System Operations and Programming Languages

In this section, the speaker delves into the importance of system operations and programming languages in enabling various functionalities across different platforms.

The Role of libuv in Node.js Development

• Different operating systems implement asynchronous I/O differently: Linux uses epoll, UNIX BSD employs Kqueue, and Windows utilizes IOCP.

• The libuv library acts as a shell in C++ to abstract diverse asynchronous I/O methods, facilitating Node.js operation on any OS.

• Deno, a Node.js competitor, incorporates over 60% Rust code but still relies on libuv functions similar to those used by Node.js for event loops.

Insights into Programming Language Dependencies

This segment explores how various programming languages rely on C and C++ for critical components despite being developed in other languages.

Programming Language Dependencies

• Java, known for its "Write Once, Run Anywhere" motto, contains 13% C++ code within the OpenJDK project.

• Many programming languages like Ruby, PHP, DotNet depend significantly on C and C++, utilizing libraries such as LibC and OpenSSL for essential functionalities.

Exploring Database Systems Architecture

The discussion shifts towards database systems architecture and the predominant use of C and C++ in popular databases.

Database System Composition

• Postgres database comprises over 85% C code while MySQL (MariaDB) consists of approximately half C++ and 40% C.

• MongoDB is primarily written in C++, emphasizing the prevalence of these traditional languages even in newer technologies like databases.

Diverse Language Applications in Web Services

The speaker highlights the usage of varied programming languages in web services development to underscore their strengths and applications.

Application Diversity

• Java and Scala dominate systems like Apache Kafka (97% Java), showcasing their efficacy for distributed systems compared to older counterparts like Erlang.

• Go emerges as a potential contender for future database development due to its efficiency but currently lags behind established languages like Java and Erlang.

Kernel Functionality & Interactions with Hardware

This part elucidates the critical role of kernels written in C/C++ that facilitate interactions between software programs running at different privilege levels within an operating system.

Kernel Operations

• Kernels serve as intermediaries between userland programs (Ring 3) and hardware devices by managing privileged operations at Ring Zero level.

Understanding Low-Level Programming Languages

In this section, the speaker discusses the necessity of low-level programming languages for kernel-level operations and device driver development.

The Importance of Low-Level Languages

• Low-level languages like C are essential for interacting directly with the kernel without unnecessary abstractions.

• To create drivers that interact closely with the kernel, a compiled language generating binaries compatible with C's ABI is required.

• Distinction between compiled languages (e.g., C, Rust) and technically interpreted languages (e.g., Python, JavaScript) in terms of binary compatibility and memory management.

Binary Compatibility and Memory Management

This section delves into the significance of binary compatibility for seamless communication between different programming languages.

Binary Compatibility Explained

• Only binaries sharing the same ABI can communicate directly via pointers without requiring data conversion processes.

• Discussion on serialization, deserialization, marshalling, and unmarshalling as methods to convert data formats between different languages.

Memory Duplication Challenges in Programming

The speaker explores memory duplication challenges in programming when dealing with incompatible data structures.

Memory Duplication Issues

• Illustration of memory duplication through XML to JSON conversion example highlighting increased memory usage due to format incompatibility.

• Real-world scenarios like web servers duplicating HTML content for client browsers exemplify common memory duplication challenges.

Integration Challenges with External Libraries

This part focuses on integration challenges when working with external libraries written in different languages.

Integration Complexity

• Integration complexities arise when converting data structures between languages like Python and C, leading to duplicated memory usage.

Understanding Python, C, and Rust Interactions

In this section, the speaker delves into examples in Python, C, and Rust to illustrate how these languages interact and handle data structures like strings differently.

Python Interaction with C

• : Demonstrates the need for an intermediary code in Cython to facilitate communication between Python and C.

• : Explains the concept of marshalling for converting Python string objects to arrays of characters in C to prevent memory conflicts.

Rust Interaction with C

• : Showcases how Rust interacts with a C library by directly working with C strings through conversions using CString.

• : Compares handling strings in Rust and C, highlighting the manual iteration required for manipulating C strings compared to Rust's convenience methods.

Language Selection Insights

• : Discusses the trade-offs between writing code in Rust or directly in C when compatibility with existing structures is crucial.

• : Offers opinions on language selection based on system programming needs, advocating for languages like C++, Rust, Nim, or D for low-level tasks.

Impact of Go and Rust on Software Development

This segment explores how languages like Go and Rust have influenced software development practices compared to traditional languages such as Python or Perl.

Evolution of Tool Building

• : Highlights how Go and Rust have streamlined tool creation processes previously done using a combination of languages like C and Python.

Efficiency Comparison

Understanding Systems Distributed and Programming Languages

In this section, the speaker discusses systems distributed and programming languages, focusing on the evolution of technologies like Apache Hadoop, Apache Solr, and Java in handling large volumes of data.

Evolution of Distributed Systems

• The speaker mentions technologies like "ps" and "fd" as replacements for "find," highlighting the evolution in system tools.

• Apache's efforts led to an ecosystem supporting distributed systems with tools like ZooKeeper facilitating consensus among nodes.

• Systems like Cassandra handle petabytes of data efficiently through coordination and fault tolerance mechanisms.

Language Choices in Development

• Java was a predominant choice due to familiarity and productivity despite alternatives like Erlang or C++.

• Erlang's advanced features for distributed services were ahead of its time, influencing modern solutions like ZooKeeper.

Modern Language Considerations

• The speaker speculates on using Elixir or Go for modern systems but acknowledges Java's established infrastructure.

• Transitioning to newer languages may offer productivity benefits but is impractical given existing ecosystems' maturity.

Challenges in Application Development

This segment delves into the considerations between developing commercial applications versus foundational software components, emphasizing language choices based on productivity needs.

Commercial Application Development

• For commercial applications such as web development, various language frameworks are discussed (Ruby on Rails, Django, Laravel).

Stability vs. Innovation

• Foundational software components require stability with incremental improvements for performance and security.

Productivity in Web Applications

• Web applications demand rapid feature deployment to compete effectively while ensuring quick bug fixes.

Language Selection Criteria

• Balancing efficiency with ease of training drives language choices towards Python or JavaScript over more specialized options.

Mobile App Development Framework Considerations

The discussion shifts towards mobile app development frameworks such as React Native and Flutter for efficient application creation without deep low-level knowledge requirements.

Mobile App Framework Selection

Prioritizing Efficiency in App Development

In this segment, the speaker emphasizes the importance of optimizing app development processes for efficiency and differentiation.

Creating Efficient Components

• Smartphones with over 2 gigabytes of RAM are common, highlighting the need to avoid wastage.

• To differentiate in app development, creating low-level components in Swift for iOS or Kotlin for Android is crucial.

Leveraging Hardware for Advanced Applications

This part delves into maximizing hardware capabilities for advanced applications like data science, machine learning, and artificial intelligence.

Utilizing Low-Level Programming

• Accessing instruction sets like AVX, SSE, CUDA on NVIDIA GPUs, or OpenCL on AMD GPUs is vital for optimal performance.

Importance of Parallel Computing Components

The discussion shifts to the significance of parallel computing components over programming languages in fields like deep learning.

Focus on Hardware Components

• Emphasizes that access to parallel computing components like AVX and CUDA is more critical than the programming language itself.

Practicality Over Complexity in Machine Learning

This part highlights the practical applications of machine learning beyond complex models like GPT3.

Accessibility of Useful Models

• While creating advanced models may be costly and exclusive, simpler machine learning models like TensorFlow can still be highly beneficial for everyday tasks.

Utilizing Simple Algorithms Effectively

The focus here is on how basic algorithms such as statistical classification can significantly enhance systems like e-commerce recommendations.

Practical Algorithm Applications

• Basic algorithms play a crucial role in various systems from e-commerce to social media platforms by enabling features like product recommendations and fuzzy search functionalities.

Bridging Knowledge Gaps in Development

The speaker addresses common misconceptions among developers regarding advanced technologies and emphasizes continuous learning.

Encouraging Continuous Learning

• Acknowledges that understanding concepts may require multiple revisits for beginners while providing insights for experienced professionals.

(https://www.youtube.com/watch?v=<video_id>&feature=youtu.be&t=<timestamp>) Fostering Maturity in Technological Choices

Wrapping up with a reflection on technology choices based on maturity rather than novelty.

Prioritizing Maturity Over Novelty