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

A naukri.com initiative

New

Home

Programming News

Dev

398

Image Credit: Dev

25 Must-Check Clojure Resources for Developers: Tutorials, Tools, and Tips

The 25 resources laid out here are sure to provide something for any Clojure developer, beginner or seasoned.
Code kata and similar are always strong practice methods, and those looking for Clojure-specific training will do well to check out Clojure practice challenges from Codewars.
For a fantastic library of Clojure resources, look no further than ClojureVids, a collection of high-quality videos for programmers at any level.
The must-have GitHub projetcs for anyone working integrally with Clojure are taoensso/carmine, antoniogarrote/clj-ml and clojure][core.async, which will have developers covered for redis client messages, machine learning libraries and facilities for async programming.
It is worth exploring additional resources like razum2um/aprint, seancorfield/honeysql and fulcrologic/guardrails, which aim to offer users better libraries for data structure printing, database applications (SQL) and function call validation respectively.
Other exciting tools available to those working with Clojure include rinuboney/clatern, a GitHub project for machine learning in Clojure, and juxt/bolt, an integrated security system for applications built on component.
On top of this, there are libraries like aria42/infer, providing inference and machine learning capabilities in Clojure coding, and theleoborges/bouncer, offering a validation DSL for Clojure and Clojurescript applications.
Finally, those looking for platform-specific applications that are also compatible with Clojure will want to check out alekseysotnikov/buran, a bidirectional, data-driven RSS/Atom feed consumer, producer and feeds aggregator, and avli/clojureVSCode, a project offering Clojure support for Microsoft's Visual Studio Code.
Kezban is an interesting open-source library for Clojure and ClojureScript that provides a number of useful functional utilities.
All in all, the resources highlighted here offer a full spectrum of valuable tools for anyone looking to up their Clojure coding game.

Read Full Article

23 Likes

Javacodegeeks

Image Credit: Javacodegeeks

Implementing Distributed Tracing in Java with OpenTelemetry and Jaeger

Distributed tracing provides insights into the performance of your microservices architecture, allowing you to monitor, troubleshoot, and optimize the system efficiently.
OpenTelemetry and Jaeger are two leading tools in the observability space to implement distributed tracing in Java applications which helps to gather important performance metrics and identify bottlenecks, errors, and performance issues.
A span represents a single operation within a trace, such as a service call or a database query. Trace refers to a collection of spans that represent the lifecycle of a request as it propagates through various services. Context, on the other hand, is metadata associated with a span, including trace and span identifiers, which help maintain context as a request moves across services.
OpenTelemetry is a vendor-neutral, open-source framework for collecting distributed traces and metrics from applications. It provides libraries, APIs, and instrumentation tools for multiple languages, including Java. On the other hand, Jaeger is an open-source distributed tracing system developed by Uber.
OpenTelemetry collects and exports trace data, while Jaeger serves as the backend for storing and visualizing this data.
To set up OpenTelemetry to capture trace data and export it to Jaeger, we need necessary dependencies, such as opentelemetry-api, opentelemetry-sdk, and opentelemetry-exporter-jaeger. We also need to initialize the OpenTelemetry SDK and configure the Jaeger exporter.
Once OpenTelemetry is set up, we can start instrumenting our code to create spans and send that data to Jaeger instance running in docker using a particular port.
By using OpenTelemetry for collecting trace data and Jaeger for visualization, Java developers can gain deep insights into the performance of microservices architectures.
With OpenTelemetry’s flexible API and Jaeger’s rich visualizations, developers can ensure their applications perform optimally, handle failures gracefully, and scale efficiently.
Distributed tracing is a powerful tool for observing and troubleshooting complex, distributed systems. By following the steps in this guide, you can implement distributed tracing in your Java applications and begin uncovering performance issues that may otherwise go unnoticed.

Read Full Article

2 Likes

Prodevelopertutorial

Image Credit: Prodevelopertutorial

Left view and right view of a Binary Tree

To print the left and right view of a binary tree, we can follow these steps:
1. Perform a level order traversal on the binary tree.
2. For the left view, get the first node at each level.
3. For the right view, get the last node at each level.

Read Full Article

1 Like

Prodevelopertutorial

294

Image Credit: Prodevelopertutorial

Top view of a binary tree

To print the top view of a binary tree, we can apply level order traversal and vertical order traversal.
Level order traversal gives the nodes in the order of their occurrence in each level.
Vertical order traversal helps to calculate the horizontal distance of each node.
To get the top view, we select the first node at each horizontal distance.

Read Full Article

17 Likes

Prodevelopertutorial

203

Image Credit: Prodevelopertutorial

Boundary Traversal of Binary Tree

To solve the boundary traversal of a binary tree, we need to list the left boundary nodes, right boundary nodes, and leaf nodes.
For the given binary tree, the boundary traversal is as follows: a, b, d, h, a, c, g, l, h, i, j, f, k, l.
To get the left boundary nodes, we traverse the left side of the tree from the root until we reach a leaf node.
Similarly, to get the right boundary nodes, we traverse the right side of the tree from the root until we reach a leaf node, but in a bottom-up manner.

Read Full Article

12 Likes

Dev

280

Image Credit: Dev

Why We Need Purpose-Built Platform Engineering Tools for AI/ML

Traditional tools fail to meet the unique demands of AI/ML pipelines, leaving teams struggling with inefficiencies, scalability issues, and compliance challenges.
Purpose-built Platform Engineering tools provide a specialized platform, enabling seamless development, deployment, and maintenance of AI/ML models.
Managing continuous retraining and evolving data with generic tools leads to inefficiencies.
The absence of a robust platform to support AI/ML workflows significantly hinders the efficiency, scalability, and reliability of machine learning projects.
Standardized development environments across teams and systems develops collaboration between data scientists, machine learning engineers, and DevOps teams.
Robust cloud platforms such as AWS, GCP, and Azure optimize resource usage based on model demand.
Jozu provides a specialized platform that enhances the machine learning project lifecycle for AI/ML engineers and is built on open source KitOps.
The trend of Platform Engineering for AI/ML will evolve to address current limitations while opening new frontiers in AI/ML applications.
More platform tools will evolve to comply with laws like the EU AI Act.
Through tools like KitOps and Jozu Hub, it advances MLOps, enabling reproducibility, transparency, and accountability in model lifecycle management.

Read Full Article

16 Likes

Medium

Image Credit: Medium

Earnings money about the cheelee

Cheelee is a cartoon creation and video editing app.
Key features include cartoon creation, video editing, music and sound effects, sharing and discovery, and community interaction.
Monetization options include ad revenue, in-app purchases, and sponsored content.
The target audience includes content creators, social media enthusiasts, and entertainment seekers.
Cheelee is available for download on iOS and Android devices.

Read Full Article

1 Like

Prodevelopertutorial

104

Image Credit: Prodevelopertutorial

Bottom view of binary Tree

To print the bottom view of a binary tree, we need to calculate the horizontal distance for every node.
Then, a hash table is created to list down all the horizontal distances in ascending order.
The nodes with similar horizontal distances are listed in level order traversal.
The last nodes from each horizontal distance are selected to obtain the bottom view.

Read Full Article

6 Likes

Logrocket

190

Image Credit: Logrocket

Should navigation bars be sticky or fixed?

Choosing the best type of navigation for your digital product depends on several factors, including your product, your target users, and your UX goals.
Navigation plays a critical role in both UX and business; well-structured navigation affects a user's cognitive load, engagement rates, conversion rates, and SEO ratings.
Sticky and fixed navigation have become popular choices for user-centered navigation in recent years due to their ability to enhance user engagement, satisfaction, and loyalty.
Sticky and fixed navigation describe two different types of navigation; sticky navigation is dependent on user scroll position and adapt to it while fixed navigation remains ‘locked’ in place regardless of the user scroll.
For content-heavy websites, such as e-commerce product pages or mobile devices, sticky navigation is recommended to keep the critical parts of navigation without detracting attention from the page's content.
Fixed navigation works best when web pages have minimal content, or limited scrolling like single-page sites, or for brand visibility.
Users prefer sticky navigation due to its benefits of optimized visual hierarchy, dynamic navigation, and increased confidence.
Sticky and fixed navigation bars each offer unique strengths and weaknesses, making them ideal for specific scenarios.
It’s essential to test different navigation types to pick one that best supports both users and businesses in meeting their goals.
Overall, choosing the best type of navigation for your digital product is critical to enhance the user experience, increase engagement, and drive conversions.

Read Full Article

11 Likes

PlanetPython

330

Image Credit: PlanetPython

Python Morsels: Python's range() function

The range function in Python can be used for counting upward, counting downward, or performing an operation a number of times.
To count upwards in Python, the range function is a convenient option. It accepts a start and stop integer and generates a sequence of numbers from the start to just before the stop number.
You can also call range with just one argument to start at 0 and stop just before that argument.
The range function can also accept a third argument to specify a step value.

Read Full Article

19 Likes

Medium

440

Image Credit: Medium

YAGNI Design Principle

The YAGNI (You Ain't Gonna Need It) design principle advises against making assumptions and predictions about future product requirements.
Over-engineering a solution can make it more complicated, time-consuming, and difficult for team members to understand.
YAGNI promotes simplicity, addressing known requirements, and avoiding unnecessary waste in software development.
By following the YAGNI principle, maintainability and flexibility of the code are increased, allowing for easier updates and revisions if product requirements change.

Read Full Article

26 Likes

Dev

253

Image Credit: Dev

Why Duplicating Environments for Microservices Backfires

When testing microservices, creating an environment which closely mirrors production conditions is essential for developer productivity and release cadence.
On-demand environments, in which each developer or team has a separate environment in a Kubernetes cluster or namespace, are often used as a solution.
However, these environments can quickly become complicated to manage and lead to a divergence from production, causing reduced reliability in tests.
Maintaining data across several databases in numerous ephemeral environments is also a significant challenge which can lead to outdated data sets and reduced test effectiveness.
The author suggests using shared environments called “sandboxes”, which offer tunable isolation for each test client within a single shared environment.
This approach reduces resource usage and costs, eliminates ‘it works on my machine’ issues, and provides a more reliable testing environment.
However, context propagation, data isolation, and handling message queues are key considerations for implementation.
Microservices testing with on-demand environments is unsustainable as architectures grow in complexity.
The shared environment approach with sandboxes for isolation provides a more efficient, cost-effective, and scalable testing solution.
The approach has already been successfully implemented by companies such as Brex, Earnest, and DoorDash, and improved developer productivity.

Read Full Article

15 Likes

Dev

190

Image Credit: Dev

Simplify Python-Informix Connections with wbjdbc

Managing JDBC connections and JVM setup for Python applications can be tedious, especially when working with databases like Informix. Enter wbjdbc, a Python library designed to simplify these tasks.
wbjdbc is a Python library that streamlines JDBC and JVM setup, making it easy to connect to databases like Informix.
Key features of wbjdbc include simplified JVM initialization, built-in JDBC driver support, precompiled dependencies, lightweight installation, and automation of routine tasks.
wbjdbc eliminates manual setup, reduces boilerplate code, and allows developers to focus on automation, making it ideal for tasks involving frequent database interactions.

Read Full Article

11 Likes

Dev

380

Image Credit: Dev

Introduction to Trie (Prefix Tree)

A Trie is a tree-like data structure used for storing and searching strings efficiently.
Key properties of a Trie include nodes representing characters, a root node serving as the starting point, children representing characters, and an end-of-word marker.
Basic Trie operations include insertion, search, and prefix search.
Advanced Trie operations include deleting a word, counting words with a prefix, and providing autocomplete suggestions.

Read Full Article

22 Likes

Dev

131

Image Credit: Dev

TOAST: PostgreSQL's Secret Sauce for Handling Big Data 🍞

TOAST (The Oversized-Attribute Storage Technique) is PostgreSQL's solution for managing big data.
TOAST organizes large data by using compression and storing chunks in a separate table.
Understanding TOAST helps in designing better schemas and optimizing storage.
TOAST comes with trade-offs like additional I/O and potential performance impact.

Read Full Article

7 Likes

For uninterrupted reading, download the app

Programming News

Discover more