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PostHeaderIcon [PyConUS 2024] How Python Harnesses Rust through PyO3

David Hewitt, a key contributor to the PyO3 library, delivered a comprehensive session at PyConUS 2024, unraveling the mechanics of integrating Rust with Python. As a Python developer for over a decade and a lead maintainer of PyO3, David provided a detailed exploration of how Rust’s power enhances Python’s ecosystem, focusing on PyO3’s role in bridging the two languages. His talk traced the journey of a Python function call to Rust code, offering insights into performance, security, and concurrency, while remaining accessible to those unfamiliar with Rust.

Why Rust in Python?

David began by outlining the motivations for combining Rust with Python, emphasizing Rust’s reliability, performance, and security. Unlike Python, where exceptions can arise unexpectedly, Rust’s structured error handling via pattern matching ensures predictable behavior, reducing debugging challenges. Performance-wise, Rust’s compiled nature offers significant speedups, as seen in libraries like Pydantic, Polars, and Ruff. David highlighted Rust’s security advantages, noting its memory safety features prevent common vulnerabilities found in C or C++, making it a preferred choice for companies like Microsoft and Google. Additionally, Rust’s concurrency model avoids data races, aligning well with Python’s evolving threading capabilities, such as sub-interpreters and free-threading in Python 3.13.

PyO3: Bridging Python and Rust

Central to David’s talk was PyO3, a Rust library that facilitates seamless integration with Python. PyO3 allows developers to write Rust code that runs within a Python program or vice versa, using procedural macros to generate Python-compatible modules. David explained how tools like Maturin and setup-tools-rust simplify project setup, enabling developers to compile Rust code into native libraries that Python imports like standard modules. He emphasized PyO3’s goal of maintaining a low barrier to entry, with comprehensive documentation and a developer guide to assist Python programmers venturing into Rust, ensuring a smooth transition across languages.

Tracing a Function Call

David took the audience on a technical journey, tracing a Python function call through PyO3 to Rust code. Using a simple word-counting function as an example, he showed how a Rust implementation, marked with PyO3’s @pyfunction attribute, mirrors Python’s structure while offering performance gains of 2–4x. He dissected the Python interpreter’s bytecode, revealing how the CALL instruction invokes PyObject_Vectorcall, which resolves to a Rust function pointer via PyO3’s generated code. This “trampoline” handles critical safety measures, such as preventing Rust panics from crashing the Python interpreter and managing the Global Interpreter Lock (GIL) for safe concurrency. David’s step-by-step breakdown clarified how arguments are passed and converted, ensuring seamless execution.

Future of Rust in Python’s Ecosystem

Concluding, David reflected on Rust’s growing adoption in Python, citing over 350 projects monthly uploading Rust code to PyPI, with downloads exceeding 3 billion annually. He predicted that Rust could rival C/C++ in the Python ecosystem within 2–4 years, driven by its reliability and performance. Addressing concurrency, David discussed how PyO3 could adapt to Python’s sub-interpreters and free-threading, potentially enforcing immutability to simplify multithreaded interactions. His vision for PyO3 is to enhance Python’s strengths without replacing it, fostering a symbiotic relationship that empowers developers to leverage Rust’s precision where needed.

Hashtags: #Rust #PyO3 #Python #Performance #Security #PyConUS2024 #DavidHewitt #Pydantic #Polars #Ruff

PostHeaderIcon Understanding Dependency Management and Resolution: A Look at Java, Python, and Node.js

Understanding Dependency Management and Resolution: A Look at Java, Python, and Node.js

Mastering how dependencies are handled can define your project’s success or failure. Let’s explore the nuances across today’s major development ecosystems.

Introduction

Every modern application relies heavily on external libraries. These libraries accelerate development, improve security, and enable integration with third-party services. However, unmanaged dependencies can lead to catastrophic issues — from version conflicts to severe security vulnerabilities. That’s why understanding dependency management and resolution is absolutely essential, particularly across different programming ecosystems.

What is Dependency Management?

Dependency management involves declaring external components your project needs, installing them properly, ensuring their correct versions, and resolving conflicts when multiple components depend on different versions of the same library. It also includes updating libraries responsibly and securely over time. In short, good dependency management prevents issues like broken builds, “dependency hell”, or serious security holes.

Java: Maven and Gradle

In the Java ecosystem, dependency management is an integrated and structured part of the build lifecycle, using tools like Maven and Gradle.

Maven and Dependency Scopes

Maven uses a declarative pom.xml file to list dependencies. A particularly important notion in Maven is the dependency scope.

Scopes control where and how dependencies are used. Examples include:

  • compile (default): Needed at both compile time and runtime.
  • provided: Needed for compile, but provided at runtime by the environment (e.g., Servlet API in a container).
  • runtime: Needed only at runtime, not at compile time.
  • test: Used exclusively for testing (JUnit, Mockito, etc.).
  • system: Provided by the system explicitly (deprecated practice).

<dependency>
  <groupId>junit</groupId>
  <artifactId>junit</artifactId>
  <version>4.13.2</version>
  <scope>test</scope>
</dependency>
    

This nuanced control allows Java developers to avoid bloating production artifacts with unnecessary libraries, and to fine-tune build behaviors. This is a major feature missing from simpler systems like pip or npm.

Gradle

Gradle, offering both Groovy and Kotlin DSLs, also supports scopes through configurations like implementation, runtimeOnly, testImplementation, which have similar meanings to Maven scopes but are even more flexible.


dependencies {
    implementation 'org.springframework.boot:spring-boot-starter'
    testImplementation 'org.springframework.boot:spring-boot-starter-test'
}
    

Python: pip and Poetry

Python dependency management is simpler, but also less structured compared to Java. With pip, there is no formal concept of scopes.

pip

Developers typically separate main dependencies and development dependencies manually using different files:

  • requirements.txt – Main project dependencies.
  • requirements-dev.txt – Development and test dependencies (pytest, tox, etc.).

This manual split is prone to human error and lacks the rigorous environment control that Maven or Gradle enforce.

Poetry

Poetry improves the situation by introducing a structured division:


[tool.poetry.dependencies]
requests = "^2.31"

[tool.poetry.dev-dependencies]
pytest = "^7.1"
    

Poetry brings concepts closer to Maven scopes, but they are still less fine-grained (no runtime/compile distinction, for instance).

Node.js: npm and Yarn

JavaScript dependency managers like npm and yarn allow a simple distinction between regular and development dependencies.

npm

Dependencies are declared in package.json under different sections:

  • dependencies – Needed in production.
  • devDependencies – Needed only for development (e.g., testing libraries, linters).

{
  "dependencies": {
    "express": "^4.18.2"
  },
  "devDependencies": {
    "mocha": "^10.2.0"
  }
}
    

While convenient, npm’s dependency management lacks Maven’s level of strictness around dependency resolution, often leading to version mismatches or “node_modules bloat.”

Key Differences Between Ecosystems

When switching between Java, Python, and Node.js environments, developers must be aware of the following fundamental differences:

1. Formality of Scopes

Java’s Maven/Gradle ecosystem defines scopes formally at the dependency level. Python (pip) and JavaScript (npm) ecosystems use looser, file- or section-based categorization.

2. Handling of Transitive Dependencies

Maven and Gradle resolve and include transitive dependencies automatically with sophisticated conflict resolution strategies (e.g., nearest version wins). pip historically had weak transitive dependency handling, leading to issues unless careful pinning is done. npm introduced better nested module flattening with npm v7+ but conflicts still occur in complex trees.

3. Lockfiles

npm/yarn and Python Poetry use lockfiles (package-lock.json, yarn.lock, poetry.lock) to ensure consistent dependency installations across machines. Maven and Gradle historically did not need lockfiles because they strictly followed declared versions and scopes. However, Gradle introduced lockfile support with dependency locking in newer versions.

4. Dependency Updating Strategy

Java developers often manually manage dependency versions inside pom.xml or use dependencyManagement blocks for centralized control. pip requires updating requirements.txt or regenerating them via pip freeze. npm/yarn allows semver rules (“^”, “~”) but auto-updating can lead to subtle breakages if not careful.

Best Practices Across All Languages

  • Pin exact versions wherever possible to avoid surprise updates.
  • Use lockfiles and commit them to version control (Git).
  • Separate production and development/test dependencies explicitly.
  • Use dependency scanners (e.g., OWASP Dependency-Check, Snyk, npm audit) regularly to detect vulnerabilities.
  • Prefer stable, maintained libraries with good community support and recent commits.

Conclusion

Dependency management, while often overlooked early in projects, becomes critical as applications scale. Maven and Gradle offer the most fine-grained controls via dependency scopes and conflict resolution. Python and JavaScript ecosystems are evolving rapidly, but require developers to be much more careful manually. Understanding these differences, and applying best practices accordingly, will ensure smoother builds, faster delivery, and safer production systems.

Interested in deeper dives into dependency vulnerability scanning, SBOM generation, or automatic dependency update pipelines? Subscribe to our blog for more in-depth content!

PostHeaderIcon [PyConUS 2023] Fixing Legacy Code, One Pull Request at a Time

At PyCon US 2023, Guillaume Dequenne from Sonar presented a compelling workshop on modernizing legacy codebases through incremental improvements. Sponsored by Sonar, this session focused on integrating code quality tools into development workflows to enhance maintainability and sustainability, using a Flask application as a practical example. Guillaume’s approach, dubbed “Clean as You Code,” offers a scalable strategy for tackling technical debt without overwhelming developers.

The Legacy Code Conundrum

Legacy codebases often pose significant challenges, accumulating technical debt that hinders development efficiency and developer morale. Guillaume illustrated this with a vivid metaphor: analyzing a legacy project for the first time can feel like drowning in a sea of issues. Traditional approaches to fixing all issues at once are unscalable, risking functional regressions and requiring substantial resources. Instead, Sonar advocates for a pragmatic methodology that focuses on ensuring new code adheres to high-quality standards, gradually reducing technical debt over time.

Clean as You Code Methodology

The “Clean as You Code” approach hinges on two principles: ownership of new code and incremental improvement. Guillaume explained that developers naturally understand and take responsibility for code they write today, making it easier to enforce quality standards. By ensuring that each pull request introduces clean code, teams can progressively refurbish their codebase. Over time, as new code replaces outdated sections, the overall quality improves without requiring a massive upfront investment. This method aligns with continuous integration and delivery (CI/CD) practices, allowing teams to maintain high standards while delivering features systematically.

Leveraging SonarCloud for Quality Assurance

Guillaume demonstrated the practical application of this methodology using SonarCloud, a cloud-based static analysis tool. By integrating SonarCloud into a Flask application’s CI/CD pipeline, developers can automatically analyze pull requests for issues like bugs, security vulnerabilities, and code smells. He showcased how SonarCloud’s quality gates enforce standards on new code, ensuring that only clean contributions are merged. For instance, Guillaume highlighted a detected SQL injection vulnerability due to unsanitized user input, emphasizing the tool’s ability to provide contextual data flow analysis to pinpoint and resolve issues efficiently.

Enhancing Developer Workflow with SonarLint

To catch issues early, Guillaume introduced SonarLint, an IDE extension for PyCharm and VSCode that performs real-time static analysis. This tool allows developers to address issues before committing code, streamlining the review process. He demonstrated how SonarLint highlights issues like unraised exceptions and offers quick fixes, enhancing productivity. Additionally, the connected mode between SonarLint and SonarCloud synchronizes issue statuses, ensuring consistency across development and review stages. This integration empowers developers to maintain high-quality code from the outset, reducing the burden of post-commit fixes.

Sustaining Codebase Health

The workshop underscored the long-term benefits of the “Clean as You Code” approach, illustrated by a real-world project where issue counts decreased over time as new rules were introduced. By focusing on new code and leveraging tools like SonarCloud and SonarLint, teams can achieve sustainable codebases that are maintainable, reliable, and secure. Guillaume’s presentation offered a roadmap for developers to modernize legacy systems incrementally, fostering a culture of continuous improvement.

Hashtags: #LegacyCode #CleanCode #StaticAnalysis #SonarCloud #SonarLint #Python #Flask #GuillaumeDequenne #PyConUS2023

PostHeaderIcon [DevoxxFR2014] Apply to dataset

features = full_dataset.apply(advanced_feature_extraction, axis=1)
enhanced_dataset = pd.concat([full_dataset, features], axis=1)


To verify feature efficacy, correlation matrices and PCA are employed, confirming strong discriminatory power.

## Model Selection, Implementation, and Optimization

The binary classification problem—human versus random—lends itself to supervised learning algorithms. Christophe Bourguignat systematically evaluates candidates from linear models to ensembles.

Support Vector Machines provide a strong baseline due to their effectiveness in high-dimensional spaces:

from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

svm_model = SVC(kernel=’rbf’, C=10.0, gamma=0.1, probability=True, random_state=42)
cross_val_scores = cross_val_score(svm_model, X_train, y_train, cv=5, scoring=’roc_auc’)
print(“SVM Cross-Validation AUC Mean:”, cross_val_scores.mean())

svm_model.fit(X_train, y_train)
svm_preds = svm_model.predict(X_test)
print(classification_report(y_test, svm_preds))


Random Forests offer interpretability through feature importance:

rf_model = RandomForestClassifier(n_estimators=500, max_depth=15, random_state=42)
rf_model.fit(X_train, y_train)
rf_importances = pd.DataFrame({
‘feature’: X.columns,
‘importance’: rf_model.feature_importances_
}).sort_values(‘importance’, ascending=False)
print(“Top Features:\n”, rf_importances.head(5))


Gradient Boosting (XGBoost) for superior performance:

from xgboost import XGBClassifier

xgb_model = XGBClassifier(n_estimators=300, learning_rate=0.05, max_depth=8, random_state=42)
xgb_model.fit(X_train, y_train)
xgb_preds = xgb_model.predict(X_test)
print(“XGBoost Accuracy:”, (xgb_preds == y_test).mean())


Optimization uses Bayesian methods via scikit-optimize for efficiency.

## Evaluation and Interpretation

Comprehensive evaluation includes ROC curves, precision-recall plots, and calibration:

from sklearn.metrics import roc_curve, precision_recall_curve

fpr, tpr, _ = roc_curve(y_test, rf_model.predict_proba(X_test)[:,1])
plt.plot(fpr, tpr)
plt.title(‘ROC Curve’)
plt.show()


SHAP values interpret predictions:

import shap

explainer = shap.TreeExplainer(rf_model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)
“`

Practical Deployment for Geek Use Cases

The model deploys as a Flask API for generating verified random combinations.

Conclusion: Democratizing ML for Everyday Insights

This extended demonstration shows how Python and open data enable geeks to build meaningful ML applications, revealing human biases while providing practical tools.

Links:

PostHeaderIcon No handlers could be found for logger…

Case

You run a Python script, and you get an error similar to:
[python]No handlers could be found for logger &quot;stomp.py&quot;[/python]

Fix

Edit the script, and add the following block:
[python]import logging
logging.basicConfig()[/python]
This will produce explicit log that will allow you to identity the source of the actual issue.