Programming News and Topics: Latest News on Computer Programming from Techminis

A naukri.com initiative

New

Home

Programming News

Medium

346

Image Credit: Medium

7 Expert Tips to Make Your Java Lambdas Soar

Java lambdas introduced in Java 8 simplify functional programming, but can lead to performance pitfalls and unreadable code if not handled properly.
Method references can replace verbose lambda expressions, improving readability.
Minimize capturing variables in lambdas to avoid memory overhead.
Carefully use parallel streams with lambdas to boost performance for CPU-bound tasks.
Short, focused lambdas enhance readability and reduce debugging complexity.
Lambdas don't handle checked exceptions directly; wrap them in try-catch blocks.
Define custom functional interfaces for reusable lambda patterns.
Keep lambdas pure by avoiding side effects for predictability.
These tips aim to make writing clean, efficient code with Java lambdas.
Implementing these tips can enhance microservices readability and robustness.
Share your experience with applying these lambda tips in the comments.

Read Full Article

20 Likes

Self-Learning-Java

128

Image Credit: Self-Learning-Java

Stop False Alerts: How the for Clause Works in Prometheus

Prometheus allows for the creation of alerts that don't trigger immediately to prevent false alarms from temporary issues.
The for clause in Prometheus alerting rules enables alerts to fire only if a condition persists for a specific duration.
An example alert rule using the for clause is provided with a demonstration involving the node_exporter service.
The example rule sets a 2-minute wait time before firing the 'NodeExporterDown' alert if the node_exporter service remains down.
Prometheus needs to continuously detect the specified condition for the set duration before sending a notification.
The process for configuring and implementing alert rules in Prometheus, as well as monitoring their states, is detailed in the example.
Users can observe alerts transitioning from inactive to pending, and eventually to firing states based on the specified conditions and time.

Read Full Article

7 Likes

Dev

312

Image Credit: Dev

AI-Driven ESG and Compliance: How ServiceNow Can Anchor Sustainable Enterprise Governance

ESG (Environmental, Social, and Governance) is crucial for companies, moving from a buzzword to a necessity with tightening regulations and increasing investor and consumer expectations.
AI-driven automation, along with platforms like ServiceNow, can help modernize ESG workflows and enhance governance structures.
Key business benefits of ESG adoption include meeting regulatory requirements, meeting stakeholder expectations, reducing risks associated with manual reporting, and creating operational value.
AI makes ESG reporting scalable by automating data ingestion, providing real-time dashboards, extracting stories from data, and detecting anomalies early.
ServiceNow offers ESG and GRC modules for centralized data collection, compliance tracking, risk assessments, incident tracking, and AI governance on a single platform.
Real impact in enterprise sustainability is seen through environmental monitoring, supply chain compliance, diversity, equity & inclusion initiatives, and compliance reporting automation.
The architectural approach involves data integration, AI fabric, ServiceNow platform utilization, workflow automation, and analytics for reporting progress.
Business outcomes include reduced manual reporting effort, faster audit cycles, improved risk responsiveness, and enhanced stakeholder trust and brand value.
MJB Technologies offers ESG enablement services such as strategy workshops, technical integration, GenAI narrative automation, governance workflow setup, and KPI tracking.
AI-driven ESG with ServiceNow integration offers a future-proofed, transparent, and impactful governance approach for enterprises.
ESG empowered by AI and ServiceNow streamlines processes, reduces risks, and enhances stakeholder trust, paving the way for smart and automated governance.

Read Full Article

18 Likes

Self-Learning-Java

Image Credit: Self-Learning-Java

Materialized Views in BigQuery

Materialized views in BigQuery store precomputed query results on disk to improve query performance and reduce costs.
Compared to standard views, materialized views do not need to execute the query each time, making them faster and more cost-effective.
Benefits include improved performance, cost savings, and ideal usage for scenarios where data does not change frequently.
Key points to remember: Materialized view and base table must be in the same dataset, can only use one base table without joins, and are mainly used for aggregation or filtering.
Limitations include inability to use materialized views in copy, import, or export jobs and breakage if the base table is deleted.
Steps for creating materialized views include defining the base table, inserting sample data, and creating materialized views for different aggregations.
Examples of materialized views created include total sales per salesperson, daily sales total, total sales by region, and top salesperson daily summary.
Materialized views are useful for speeding up repeated queries with aggregations and filtering in BigQuery but require monitoring freshness and caution with base table deletions.
Query rerouting can automatically utilize existing materialized views for better query efficiency in BigQuery.

Read Full Article

5 Likes

Dev

196

Image Credit: Dev

Setup SSL/TLS for PostgreSQL with Docker

Setting up transport layer security (TLS) for PostgreSQL in production is crucial to prevent man-in-the-middle attacks.
The guide focuses on connecting securely to the PostgreSQL database with a local certificate authority and enforcing encrypted connections with strict settings.
Prerequisites include having Docker installed and running on the machine.
Steps involve generating a certificate authority (CA), creating server and client certificate files, setting up TLS in PostgreSQL with an initialization script, building a Docker image with TLS enabled, and testing the connection.
Deploying PostgreSQL with TLS on Sliplane involves creating a GitHub repository, logging in to Sliplane, deploying a server, choosing repository as deploy source, creating certificates, and adding environment variables for successful deployment.
The tutorial concludes with accessing the PostgreSQL instance via a secure connection using psql client.
Environmental variables required include POSTGRES_PASSWORD, SERVER_CERT, SERVER_KEY, and ROOT_CA_CRT.
The tutorial provides detailed steps and scripts to enable SSL/TLS for PostgreSQL within a Docker environment.
Ensure all generated certificate files are treated as secrets and not added to version control or Docker images.
Overall, the tutorial offers a comprehensive guide for implementing SSL/TLS for PostgreSQL with Docker and ensuring secure database connections.

Read Full Article

11 Likes

Self-Learning-Java

Understanding Limitations of BigQuery Views

BigQuery views are powerful tools for abstracting complex queries but have specific limitations developers must be aware of.
Views and base tables must be in the same region to avoid errors.
Data from views cannot be exported directly, materializing results into a table is necessary.
Standard SQL queries cannot reference Legacy SQL views; recreate them using Standard SQL.
Query parameters cannot be used in views; apply them outside the view.
Views cannot be used in wildcard table queries, as they don't conform to BigQuery's wildcard patterns.
Maximum nesting of views is limited to 16 levels; beyond that, a 'Resources exceeded' error occurs.
Each dataset can have a maximum of 2,500 views; consider reorganizing or consolidating views if needed.
Views are recomputed at query time, impacting performance for complex logic or large tables.
Consider using Materialized Views for frequent access to complex joins or aggregations to improve performance.
Design data models with awareness of view limitations to ensure scalability and performance in BigQuery.

Read Full Article

2 Likes

Medium

235

Image Credit: Medium

Choosing the Right CMS: How We Help Our Clients Decide

When a potential client arrives with a strong CMS preference, understanding the reasons behind it is crucial, such as familiarity, content migration concerns, or lack of exposure to other platforms.
Evaluation of CMS options starts early, considering both client's editorial and end users' needs to recommend the right strategy.
Factors like design requirements, editorial workflows, organizational structures, and technical considerations play significant roles in CMS evaluation.
Technical aspects like performance, scalability, developer community, and tech stack are also key considerations during CMS evaluation.
The evaluation process involves collaboration and a deep understanding of project needs and constraints to find a CMS that fits well.
Commonly used CMS platforms include Wagtail, Statamic, and Payload, each offering unique strengths in terms of flexibility and content management.
Wagtail is suitable for complex editorial sites, Statamic excels in admin experience for static content, while Payload offers high customization potential.
Each CMS platform has its pros and cons, with considerations like customizability, scalability, content modeling, and ease of use guiding their selection.
Choosing the right CMS can significantly impact editorial team efficiency and content quality, aligning with the client's vision and goals.
The goal is to implement a CMS that enhances the editorial experience, allowing teams to focus on creating exceptional content.

Read Full Article

14 Likes

Logrocket

132

Image Credit: Logrocket

When to use OpenAI vs. open source LLMs in production

Choosing between OpenAI and open source Language Model Models (LLMs) involves considering factors like performance, customization, and cost.
OpenAI, with models like GPT-4 and GPT-3.5, offers state-of-the-art performance, while open source alternatives like Llama 3 and Mistral provide more control and customization options.
Factors affecting the choice include setup time, integration complexity, latency, cost structure, customization, developer experience, data privacy, and offline capability.
For frontend developers, OpenAI offers simplicity with API calls, quick prototyping, and predictable latency, whereas open source models require more infrastructure setup but allow for greater flexibility and customization.
Performance and user experience, cost implications, and customization differ between OpenAI and open source LLMs, impacting how AI features are integrated into frontend applications.
OpenAI is suitable for rapid prototyping and general-purpose AI features, while open source models excel in privacy-focused applications and specialized workflows.
Security considerations vary, with OpenAI requiring proper token handling and API security, while self-hosted open source models offer better data control and compliance.
Implementing LLM integration involves backend proxies for OpenAI and tools like Ollama for open source models, with libraries and frameworks simplifying integration complexities.
The future of LLM integration includes hybrid approaches, improved tooling for open source deployment, and browser-based inference for lightweight models directly in browsers.
The choice between OpenAI and open source LLMs depends on factors like cost, control, compliance, and specific project requirements, with potential for a hybrid approach based on evolving needs.

Read Full Article

7 Likes

Logrocket

274

Image Credit: Logrocket

UX designers vs. developers: Who really owns accessibility (A11y) in UX design?

Accessibility in UX design is not solely the responsibility of developers; designers play a crucial role from the beginning.
Designers must ensure that designs are accessible for all users, including those with disabilities like keyboard-only users.
Factors such as technical perception, lack of training, and poor team collaboration lead to accessibility issues in products.
Effective collaboration between designers and developers is essential to create accessible products.
Designers need to start integrating accessibility from the discovery and ideation stages throughout the product lifecycle.
Strategies include using tools like Stark and Figma plugins, implementing accessibility checklists, and prioritizing team education.
Shared tools for testing like Axe Devtools, Google Lighthouse, Silktide, and WAVE API help ensure ongoing accessibility compliance.
Fostering a culture of accessibility requires team education, collaborative workflows, and shared tools for testing at every stage of development.
Web accessibility is a necessity to ensure inclusive digital products that cater to diverse user needs.

Read Full Article

16 Likes

Javacodegeeks

290

Image Credit: Javacodegeeks

JSONObject Extract Keys Example

JSON (JavaScript Object Notation) is a lightweight data-interchange format commonly used for data transmission.
It can have flat key-value pairs or nested structures, affecting key access using dot or bracket notation.
Understanding both flat and nested keys is crucial for JSON data parsing and transformation in applications.
A Java example showcases extracting keys from JSON using JSONObject and recursive traversal.
The code traverses flat and nested keys, adding them to a list for subsequent processing.
Best practices include using notation for nested keys and handling JSONObjects and JSONArrays recursively.
Validating JSON structures before key extraction is advised for robustness, especially with dynamic data.
Extracting keys in Java involves thoughtful handling of JSON structures using recursion for effective data extraction.
The code output presents all keys, top-level and nested, in a readable dot and bracket notation format.
Developing skills in JSON processing is crucial, and subscribing to educational resources can aid in becoming proficient.

Read Full Article

17 Likes

Medium

325

Day 7: When Phone Detox Actually Works (And You Surprise Yourself)

The author discussed considering participating in a web development competition despite feeling rusty in frontend development skills.
The author acknowledged the need to refresh their JavaScript and CSS knowledge and catch up on new frameworks in web development.
Joining the competition could serve as motivation to dust off old skills and learn new ones, seen as 'networking' rather than distraction.
The author reflected on distinguishing intentional vs. compulsive phone use, noticing a difference during designated breaks compared to mindless scrolling.
Although not groundbreaking, these changes represent small steps toward personal progress and moving forward in the right direction.
The author acknowledged that these changes may not solve all productivity issues but are positive steps forward.

Read Full Article

19 Likes

Medium

235

Keycloak: The Modern Way to Secure Your Apps

Keycloak is an open-source IAM solution developed by Red Hat, offering authentication, authorization, SSO, social login, and user federation.
It is self-hosted, flexible, and powerful, similar to Auth0 or Firebase Auth.
Keycloak simplifies user, permissions, and roles management for web apps, mobile apps, or APIs.
Setting up Keycloak can be done using Docker or manual installation.
After installation, one can log in to the Keycloak Admin Console at http://localhost:8080 with admin/admin credentials.
Keycloak revolves around concepts like realms, clients, users, roles, and groups for configuration and security.
It supports OAuth2 and OpenID Connect by default.
Keycloak can be integrated with frontend applications for user authentication using various libraries like keycloak-js, react-keycloak, or passport-keycloak.
For apps requiring login and role-based access control, Keycloak is a valuable addition to the tech stack saving development time and supporting modern protocols.
Keycloak is an open-source tool that scales with the needs of developers.

Read Full Article

14 Likes

Medium

277

Image Credit: Medium

Why 90% of Developer SaaS Tools Fail in Year Two (Analysis of 50+ Failed Startups)

A developer tools startup recently announced its closure despite positive feedback from users and significant funding.
Many developer tools startups fail to sustain their businesses despite having products loved by engineers.
An analysis of 50+ failed developer tool startups revealed that technical quality did not guarantee business success.
The main reasons for failure were identified as business model and go-to-market challenges.
These companies typically faced challenges around the 18–24 month mark, hindering their growth and sustainability.

Read Full Article

14 Likes

Dev

320

Image Credit: Dev

Next.js Forgot to Design APIs. Tirne Didn’t.

Tirne aims to improve side effect tracing and performance in API development compared to Next.js.
Tirne offers declarative routes, explicit side effects, edge-native speed, and type-aligned logic.
Quick start with 'npx create-tirne-app' and performance benchmarks show Tirne's speed superiority.
Performance metrics like cold start, request latency, and requests/sec demonstrate Tirne's efficiency.
Tirne outperforms Next.js API Routes significantly in terms of speed and performance.
Examples of structured Tirne code for handling APIs are provided for easy understanding.
Tirne simplifies authentication handling and error management with clear structures and testability.
Error handling is built-in with Tirne, offering structured error responses and clean error boundaries.
Tirne encourages building APIs that are structured, testable, and explicit for better code understanding.

Read Full Article

19 Likes

PlanetPython

303

Python Insider: Python 3.14.0 beta 3 is here!

Python 3.14.0 beta 3 is now available, as part of the Python 3.14 development cycle.
Beta releases like 3.14.0b3 allow the community to test new features and bug fixes before the final release.
Maintainers of third-party Python projects are encouraged to test with 3.14 and report any issues.
Python 3.14 is feature-complete in the beta phase, with minimal changes expected after the first release candidate.
Key features of Python 3.14 include support for free-threaded Python, deferred type annotation evaluation, and template string literals.
Other additions in Python 3.14 are multiple interpreters in the standard library, Zstandard compression support, and improved UUID module.
Python 3.14 also introduces enhancements in debugging, error messages, HMAC implementation, and remote attaching in the pdb module.
Build changes include using Sigstore for verifying release artifacts and providing an experimental JIT compiler in macOS and Windows binaries.
Incompatible changes, removals, and deprecations are documented for Python 3.14.
The Python installer for Windows is replaced by a new install manager, available from the Windows Store or FTP page.

Read Full Article

18 Likes

For uninterrupted reading, download the app

Programming News

Discover more