Databases power the backbone of modern applications, and PostgreSQL is one of the most powerful open-source relational databases trusted by top companies worldwide. Whether you’re a beginner or a developer looking to sharpen your database skills, this FREE bootcamp will take you from Zero to Hero in PostgreSQL!

What You’ll Learn?

PostgreSQL fundamentals & installation

Postgres Architecture
Writing optimized queries
Indexing & performance tuning
Transactions & locking mechanisms
Advanced joins, CTEs & subqueries
Real-world best practices & hands-on exercises

This intensive hands on bootcamp is designed for developers, DBAs, and tech enthusiasts who want to master PostgreSQL from scratch and apply it in real-world scenarios.

Who Should Attend?

Beginners eager to learn databases
Developers & Engineers working with PostgreSQL
Anyone looking to optimize their SQL skills

Date: March 22, 23
Time: Will be finalized later.
Location: Online
Cost: 100% FREE

RSVP Here

Support us by subscribing on YouTube!

Support us by subscribing on YouTube!

Prerequisite

1. Checkout this playlist of our previous postgres session https://www.youtube.com/playlist?list=PLiutOxBS1Miy3PPwxuvlGRpmNo724mAlt
   

This bootcamp is completely FREE – Learn without any cost!

Spots are limited – RSVP now to reserve your seat!

Introduction

Spike testing is a type of performance testing that evaluates how a system responds to sudden, extreme increases in load. Unlike stress testing, which gradually increases the load, spike testing simulates abrupt surges in traffic to identify system vulnerabilities, such as crashes, slow response times, and resource exhaustion.

In this blog, we will explore spike testing in detail, covering its importance, methodology, and full implementation using K6.

Why Perform Spike Testing?

Spike testing helps you

• Determine system stability under unexpected traffic surges.
• Identify bottlenecks that arise due to rapid load increases.
• Assess auto-scaling capabilities of cloud-based infrastructures.
• Measure response time degradation during high-demand spikes.
• Ensure system recovery after the sudden load disappears.

Setting Up K6 for Spike Testing

Installing K6

# macOS
brew install k6  

# Ubuntu/Debian
sudo apt install k6  

# Using Docker
docker pull grafana/k6  

Choosing the Right Test Scenario

K6 provides different executors to simulate load patterns. For spike testing, we use

• ramping-arrival-rate → Gradually increases the request rate over time.
• constant-arrival-rate → Maintains a fixed number of requests per second after the spike.

Example 1: Basic Spike Test

This test starts with low traffic, spikes suddenly, and then drops back to normal.

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  scenarios: {
    spike_test: {
      executor: 'ramping-arrival-rate',
      startRate: 10, // Start with 10 requests/sec
      timeUnit: '1s',
      preAllocatedVUs: 100,
      maxVUs: 500,
      stages: [
        { duration: '30s', target: 10 },  // Low traffic
        { duration: '10s', target: 500 }, // Sudden spike
        { duration: '30s', target: 10 },  // Traffic drops
      ],
    },
  },
};

export default function () {
  http.get('https://test-api.example.com');
  sleep(1);
}

Explanation

• Starts with 10 requests per second for 30 seconds.
• Spikes to 500 requests per second in 10 seconds.
• Drops back to 10 requests per second.
• Tests the system’s ability to handle and recover from traffic spikes.

Example 2: Spike Test with High User Load

This test simulates a spike in virtual users rather than just requests per second.

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  scenarios: {
    user_spike: {
      executor: 'ramping-vus',
      stages: [
        { duration: '30s', target: 20 },  // Normal traffic
        { duration: '10s', target: 300 }, // Sudden spike in users
        { duration: '30s', target: 20 },  // Drop back to normal
      ],
    },
  },
};

export default function () {
  http.get('https://test-api.example.com');
  sleep(1);
}

Explanation:

• Simulates a sudden increase in concurrent virtual users (VUs).
• Helps test server stability, database handling, and auto-scaling.

Example 3: Spike Test on Multiple Endpoints

In real-world applications, multiple endpoints may experience spikes simultaneously. Here’s how to test different API routes.

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  scenarios: {
    multiple_endpoint_spike: {
      executor: 'ramping-arrival-rate',
      startRate: 5,
      timeUnit: '1s',
      preAllocatedVUs: 200,
      maxVUs: 500,
      stages: [
        { duration: '20s', target: 10 },  // Normal traffic
        { duration: '10s', target: 300 }, // Spike across endpoints
        { duration: '20s', target: 10 },  // Traffic drop
      ],
    },
  },
};

export default function () {
  let urls = [
    'https://test-api.example.com/users',
    'https://test-api.example.com/orders',
    'https://test-api.example.com/products'
  ];
  
  let res = http.get(urls[Math.floor(Math.random() * urls.length)]);
  console.log(`Response time: ${res.timings.duration}ms`);
  sleep(1);
}

Explanation

• Simulates traffic spikes across multiple API endpoints.
• Helps identify which API calls suffer under extreme load.

Analyzing Test Results

After running the tests, K6 provides key performance metrics

http_req_duration......: avg=350ms min=150ms max=3000ms
http_reqs..............: 10,000 requests
vus_max................: 500
errors.................: 2%

Key Metrics

• http_req_duration → Measures response time impact.
• vus_max → Peak virtual users during the spike.
• errors → Percentage of failed requests due to overload.

Best Practices for Spike Testing

• Monitor application logs and database performance during the test.
• Use auto-scaling mechanisms for cloud-based environments.
• Combine spike tests with stress testing for better insights.
• Analyze error rates and recovery time to ensure system stability.

Spike testing is crucial for ensuring application stability under sudden, unpredictable traffic surges. Using K6, we can simulate spikes in both requests per second and concurrent users to identify bottlenecks before they impact real users.

Introduction

Stress testing is a critical aspect of performance testing that evaluates how a system performs under extreme loads. Unlike load testing, which simulates expected user traffic, stress testing pushes a system beyond its limits to identify breaking points and measure recovery capabilities.

In this blog, we will explore stress testing using K6, an open-source load testing tool, with detailed explanations and full examples to help you implement stress testing effectively.

Why Stress Testing?

Stress testing helps you

• Identify the maximum capacity of your system.
• Detect potential failures and bottlenecks.
• Measure system stability and recovery under high loads.
• Ensure infrastructure can handle unexpected spikes in traffic.

Setting Up K6 for Stress Testing

Installing K6

# macOS
brew install k6  

# Ubuntu/Debian
sudo apt install k6  

# Using Docker
docker pull grafana/k6  

Understanding Stress Testing Scenarios

K6 provides various executors to simulate different traffic patterns. For stress testing, we mainly use

1. ramping-vus – Gradually increases virtual users to a high level.
2. constant-vus – Maintains a fixed high number of virtual users.
3. spike – Simulates a sudden surge in traffic.

Example 1: Basic Stress Test with Ramping VUs

This script gradually increases the number of virtual users, holds a peak load, and then reduces it.

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  stages: [
    { duration: '1m', target: 100 }, // Ramp up to 100 users in 1 min
    { duration: '3m', target: 100 }, // Stay at 100 users for 3 min
    { duration: '1m', target: 0 },   // Ramp down to 0 users
  ],
};

export default function () {
  let res = http.get('https://test-api.example.com');
  sleep(1);
}

Explanation

• The test starts with 0 users and ramps up to 100 users in 1 minute.
• Holds 100 users for 3 minutes.
• Gradually reduces load to 0 users.
• The sleep(1) function helps simulate real user behavior between requests.

Example 2: Constant High Load Test

This test maintains a consistently high number of virtual users.

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  vus: 200, // 200 virtual users
  duration: '5m', // Run the test for 5 minutes
};

export default function () {
  http.get('https://test-api.example.com');
  sleep(1);
}

Explanation

• 200 virtual users are constantly hitting the endpoint for 5 minutes.
• Helps evaluate system performance under sustained high traffic.

Example 3: Spike Testing (Sudden Traffic Surge)

This test simulates a sudden spike in traffic, followed by a drop.

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  stages: [
    { duration: '10s', target: 10 },  // Start with 10 users
    { duration: '10s', target: 500 }, // Spike to 500 users
    { duration: '10s', target: 10 },  // Drop back to 10 users
  ],
};

export default function () {
  http.get('https://test-api.example.com');
  sleep(1);
}

Explanation

• Starts with 10 users.
• Spikes suddenly to 500 users in 10 seconds.
• Drops back to 10 users.
• Helps determine how the system handles sudden surges in traffic.

Analyzing Test Results

After running the tests, K6 provides detailed statistics

checks..................: 100.00% ✓ 5000 ✗ 0
http_req_duration......: avg=300ms min=200ms max=2000ms
http_reqs..............: 5000 requests
vus_max................: 500

Key Metrics to Analyze

• http_req_duration → Measures response time.
• vus_max → Maximum concurrent virtual users.
• http_reqs → Total number of requests.
• errors → Number of failed requests.

Stress testing is vital to ensure application stability and scalability. Using K6, we can simulate different stress scenarios like ramping load, constant high load, and spikes to identify system weaknesses before they affect users.

Introduction

Load testing is essential to evaluate how a system behaves under expected and peak loads. Traditionally, we rely on metrics like requests per second (RPS), response time, and error rates. However, an insightful approach called Average Load Testing has been discussed recently. This blog explores that concept in detail, providing practical examples to help you apply it effectively.

Understanding Average Load Testing

Average Load Testing focuses on simulating real-world load patterns rather than traditional peak load tests. Instead of sending a fixed number of requests per second, this approach

• Generates requests based on the average concurrency over time.
• More accurately reflects real-world traffic patterns.
• Helps identify performance bottlenecks in a realistic manner.

Setting Up Load Testing with K6

K6 is an excellent tool for implementing Average Load Testing. Let’s go through practical examples of setting up such tests.

Install K6

brew install k6  # macOS
sudo apt install k6  # Ubuntu/Debian
docker pull grafana/k6  # Using Docker

Example 1: Basic K6 Script for Average Load Testing

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  scenarios: {
    avg_load: {
      executor: 'constant-arrival-rate',
      rate: 10, // 10 requests per second
      timeUnit: '1s',
      duration: '2m',
      preAllocatedVUs: 20,
      maxVUs: 50,
    },
  },
};

export default function () {
  let res = http.get('https://test-api.example.com');
  console.log(`Response time: ${res.timings.duration}ms`);
  sleep(1);
}

Explanation

• The constant-arrival-rate executor ensures a steady request rate.
• rate: 10 sends 10 requests per second.
• duration: '2m' runs the test for 2 minutes.
• preAllocatedVUs: 20 and maxVUs: 50 define virtual users needed to sustain the load.
• The script logs response times to the console.

Example 2: Testing with Varying Load

To better reflect real-world scenarios, we can use ramping arrival rate to simulate gradual increases in traffic

import http from 'k6/http';
import { sleep } from 'k6';

export let options = {
  scenarios: {
    ramping_load: {
      executor: 'ramping-arrival-rate',
      startRate: 5, // Start with 5 requests/sec
      timeUnit: '1s',
      preAllocatedVUs: 50,
      maxVUs: 100,
      stages: [
        { duration: '1m', target: 20 },
        { duration: '2m', target: 50 },
        { duration: '3m', target: 100 },
      ],
    },
  },
};

export default function () {
  let res = http.get('https://test-api.example.com');
  console.log(`Response time: ${res.timings.duration}ms`);
  sleep(1);
}

Explanation

• The ramping-arrival-rate gradually increases requests per second over time.
• The stages array defines a progression from 5 to 100 requests/sec over 6 minutes.
• Logs response times to help analyze system performance.

Example 3: Load Testing with Multiple Endpoints

In real applications, multiple endpoints are often tested simultaneously. Here’s how to test different API routes

import http from 'k6/http';
import { check, sleep } from 'k6';

export let options = {
  scenarios: {
    multiple_endpoints: {
      executor: 'constant-arrival-rate',
      rate: 15, // 15 requests per second
      timeUnit: '1s',
      duration: '2m',
      preAllocatedVUs: 30,
      maxVUs: 60,
    },
  },
};

export default function () {
  let urls = [
    'https://test-api.example.com/users',
    'https://test-api.example.com/orders',
    'https://test-api.example.com/products'
  ];
  
  let res = http.get(urls[Math.floor(Math.random() * urls.length)]);
  check(res, {
    'is status 200': (r) => r.status === 200,
  });
  console.log(`Response time: ${res.timings.duration}ms`);
  sleep(1);
}

Explanation

• The script randomly selects an API endpoint to test different routes.
• Uses check to ensure status codes are 200.
• Logs response times for deeper insights.

Analyzing Results

To analyze test results, you can store logs or metrics in a database or monitoring tool and visualize trends over time. Some popular options include

• Prometheus for time-series data storage.
• InfluxDB for handling large-scale performance metrics.
• ELK Stack (Elasticsearch, Logstash, Kibana) for log-based analysis.

Average Load Testing provides a more realistic way to measure system performance. By leveraging K6, you can create flexible, real-world simulations to optimize your applications effectively.

Introduction

Are you tired of slow, clunky GUI-based file managers? Do you want lightning-fast navigation and total control over your files—right from your terminal? Meet Superfile, the ultimate tool for power users who love efficiency and speed.

In this blog, we’ll take you on a deep dive into Superfile’s features, commands, and shortcuts, transforming you into a file management ninja!

Why Choose Superfile?

Superfile isn’t just another file manager it’s a game-changer.

Here’s why

Blazing Fast – No unnecessary UI lag, just pure efficiency.

Keyboard-Driven – Forget the mouse, master navigation with powerful keybindings.

Multi-Panel Support – Work with multiple directories simultaneously.

Smart Search & Sorting – Instantly locate and organize files.

Built-in File Preview & Metadata Display – See what you need without opening files.

Highly Customizable – Tailor it to fit your workflow perfectly.

Installation

Getting started is easy! Install Superfile using

# For Linux (Debian-based)
wget -qO- https://superfile.netlify.app/install.sh | bash

# For macOS (via Homebrew)
brew install superfile

# For Windows (via Scoop)
scoop install superfile

Once installed, launch it with

spf

Boom! You’re ready to roll.

Essential Commands & Shortcuts

General Operations

• Launch Superfile: spf
• Exit: Press q or Esc
• Help Menu: ?
• Toggle Footer Panel: F

File & Folder Navigation

• New File Panel: n
• Close File Panel: w
• Toggle File Preview: f
• Next Panel: Tab or Shift + l
• Sidebar Panel: s

File & Folder Management

• Create File/Folder: Ctrl + n
• Rename: Ctrl + r
• Copy: Ctrl + c
• Cut: Ctrl + x
• Paste: Ctrl + v
• Delete: Ctrl + d
• Copy Path: Ctrl + p

Search & Selection

• Search: /
• Select Files: v
• Select All: Shift + a

Compression & Extraction

• Extract Zip: Ctrl + e
• Compress to Zip: Ctrl + a

Advanced Power Moves

• Open Terminal Here: Shift + t
• Open in Editor: e
• Toggle Hidden Files: .

Pro Tip: Use Shift + p to pin frequently accessed folders for even quicker access!

Customizing Superfile

Want to make Superfile truly yours? Customize it easily by editing the config file

$EDITOR CONFIG_PATH

To enable the metadata plugin, add

metadata = true

For more customizations, check out the Superfile documentation.

Final Thoughts

Superfile is the Swiss Army knife of terminal-based file managers. Whether you’re a developer, system admin, or just someone who loves a fast, efficient workflow, Superfile will revolutionize the way you manage files.

Ready to supercharge your productivity? Install Superfile today and take control like never before!

For more details, visit the Superfile website.

Hey everyone! Today, we had an exciting Linux installation session at our college. We expected many to do a full Linux installation, but instead, we set up dual boot on 10+ machines!

Topics Covered:
Syed Jafer – FOSS, GLUGs, and open-source communities
Salman – Why FOSS matters & Linux Commands
Dhanasekar – Linux and DevOps
Guhan – GNU and free software

Challenges We Faced

BitLocker Encryption – Had to disable BitLocker on some laptops
BIOS/UEFI Problems – Secure Boot, boot order changes needed
GRUB Issues – Windows not showing up, required boot-repair

Watch the installation video and try it yourself! https://www.youtube.com/watch?v=m7sSqlam2Sk

Linux Mint Installation Guide https://tkdhanasekar.wordpress.com/2025/02/15/installation-of-linux-mint-22-1-cinnamon-edition/

This is just the beginning!

IndexedDB is a powerful client-side database API for storing structured data in browsers. However, its API is complex, requiring transactions, object stores, and cursors to manage data. LocalBase simplifies IndexedDB by providing an intuitive, promise-based API.

In this blog, we’ll explore LocalBase, its features, and how to use it effectively in web applications.

What is LocalBase?

LocalBase is an easy-to-use JavaScript library that simplifies IndexedDB interactions. It provides a syntax similar to Firestore, making it ideal for developers familiar with Firebase.

Key Features

• Promise based API
• Simple CRUD operations
• No need for manual transaction handling
• Works seamlessly in modern browsers

Installation

You can install LocalBase via npm or use it directly in a script tag

Using npm

npm install localbase

Using CDN

https://cdn.jsdelivr.net/npm/localbase/dist/localbase.min.js

Getting Started with LocalBase

First, initialize the database

let db = new Localbase('myDatabase')

Adding Data

You can add records to a collection,

db.collection('users').add({
  id: 1,
  name: 'John Doe',
  age: 30
})

Fetching Data

Retrieve all records from a collection

db.collection('users').get().then(users => {
  console.log(users)
})

Updating Data

To update a record

db.collection('users').doc({ id: 1 }).update({
  age: 31
})

Deleting Data

Delete a specific document

db.collection('users').doc({ id: 1 }).delete()

Or delete the entire collection

db.collection('users').delete()

Advanced LocalBase Functionalities

1. Updating Data in LocalBase

LocalBase allows updating specific fields in a document without overwriting the entire record.

Basic Update Example

db.collection('users').doc({ id: 1 }).update({
  age: 31
})

This updates only the age field while keeping other fields unchanged.

Updating Multiple Fields

db.collection('users').doc({ id: 1 }).update({
  age: 32,
  city: 'New York'
})

The city field is added, and age is updated.

Handling Non-Existing Documents

If the document doesn’t exist, LocalBase won’t create it automatically. You can handle this with .get()

db.collection('users').doc({ id: 2 }).get().then(user => {
  if (user) {
    db.collection('users').doc({ id: 2 }).update({ age: 25 })
  } else {
    db.collection('users').add({ id: 2, name: 'Alice', age: 25 })
  }
})

2. Querying and Filtering Data

You can fetch documents based on conditions.

Get All Documents in a Collection

db.collection('users').get().then(users => {
  console.log(users)
})

Get a Single Document

db.collection('users').doc({ id: 1 }).get().then(user => {
  console.log(user)
})

Filter with Conditions

db.collection('users').get().then(users => {
  let filteredUsers = users.filter(user => user.age > 25)
  console.log(filteredUsers)
})

Since LocalBase doesn’t support native where queries, you need to filter manually.

3. Handling Transactions

LocalBase handles transactions internally, so you don’t need to worry about opening and closing them. However, you should use .then() to ensure operations complete before the next action.

Example: Sequential Updates

db.collection('users').doc({ id: 1 }).update({ age: 32 }).then(() => {
  return db.collection('users').doc({ id: 1 }).update({ city: 'Los Angeles' })
}).then(() => {
  console.log('Update complete')
})

This ensures that the age field is updated before adding the city field.

4. Clearing and Deleting Data

Deleting a Single Document

db.collection('users').doc({ id: 1 }).delete()

Deleting an Entire Collection

db.collection('users').delete()

Clearing All Data

db.delete()

This removes everything from the database!

5. Using LocalBase in Real-World Scenarios

Offline Caching for a To-Do List

db.collection('tasks').add({ id: 1, title: 'Buy groceries', completed: false })

Later, when the app is online, you can sync it with a remote database.

User Preferences Storage

db.collection('settings').doc({ theme: 'dark' }).update({ fontSize: '16px' })

Stores user settings locally, ensuring a smooth UX.

LocalBase makes IndexedDB developer-friendly with

Easy updates without overwriting entire documents
Simple filtering with JavaScript functions
Automatic transaction handling
Efficient storage for offline-first apps

For more details, check out the official repository:
GitHub – LocalBase

Got Inspired from 450dsa.com .

Introduction

The Web storage api is a set of mechanisms that enable browsers to store key-value pairs. Before HTML5, application data had to be sorted in cookies, included in every server request. Its intended to be far more user-friendly than using cookies.

Web storage is more secure, and large amounts of data can be stored locally, without affecting website performance.

There are 2 types of web storage,

1. Local Storage
2. Session Storage

We already have cookies. Why additional objects?

Unlike cookies, web storage objects are not sent to server with each request. Because of that, we can store much more. Most modern browsers allow at least 5 megabytes of data (or more) and have settings to configure that.

Also unlike cookies, the server can’t manipulate storage objects via HTTP headers. Everything’s done in JavaScript.The storage is bound to the origin (domain/protocol/port triplet). That is, different protocols or subdomains infer different storage objects, they can’t access data from each other.

In this guide, you will learn/refresh about LocalStorage.

LocalStorage

The localStorage is property of the window (browser window object) interface allows you to access a Storage object for the Document’s origin; the stored data is saved across browser sessions.

1. Data is kept for a longtime in local storage (with no expiration date.). This could be one day, one week, or even one year as per the developer preference ( Data in local storage maintained even if the browser is closed).
2. Local storage only stores strings. So, if you intend to store objects, lists or arrays, you must convert them into a string using JSON.stringfy()
3. Local storage will be available via the window.localstorage property.
4. What’s interesting about them is that the data survives a page refresh (for sessionStorage) and even a full browser restart (for localStorage).

Functionalities

// setItem normal strings
window.localStorage.setItem("name", "goku");

// getItem 
const name = window.localStorage.getItem("name");
console.log("name from localstorage, "+name);

// Storing an Object without JSON stringify

const data = {
  "commodity":"apple",
  "price":43
};
window.localStorage.setItem('commodity', data);
var result = window.localStorage.getItem('commodity');
console.log("Retrived data without jsonified, "+ result);

// Storing an object after converting to JSON string. 
var jsonifiedString = JSON.stringify(data);
window.localStorage.setItem('commodity', jsonifiedString);
var result = window.localStorage.getItem('commodity');
console.log("Retrived data after jsonified, "+ result);

// remove item 
window.localStorage.removeItem("commodity");
var result = window.localStorage.getItem('commodity');
console.log("Data after removing the key "+ result);

//length
console.log("length of local storage " + window.localStorage.length);

// clear
window.localStorage.clear();
console.log("length of local storage - after clear " + window.localStorage.length);

When to use Local Storage

1. Data stored in Local Storage can be easily accessed by third party individuals.
2. So its important to know that any sensitive data must not sorted in Local Storage.
3. Local Storage can help in storing temporary data before it is pushed to the server.
4. Always clear local storage once the operation is completed.

Where the local storage is saved ?

Windows

• Firefox: C:\Users\\AppData\Roaming\Mozilla\Firefox\Profiles\\webappsstore.sqlite, %APPDATA%\Mozilla\Firefox\Profiles\\webappsstore.sqlite
• Chrome: %LocalAppData%\Google\Chrome\User Data\Default\Local Storage\

Linux

• Firefox: ~/.mozilla/firefox//webappsstore.sqlite
• Chrome: ~/.config/google-chrome/Default/Local Storage/

Mac

• Firefox: ~/Library/Application Support/Firefox/Profiles//webappsstore.sqlite, ~/Library/Mozilla/Firefox/Profiles//webappsstore.sqlite
• Chrome: ~/Library/Application Support/Google/Chrome//Local Storage/, ~/Library/Application Support/Google/Chrome/Default/Local Storage/

Downside of Localstorage

The majority of local storage’s drawbacks aren’t really significant. You may still not use it, but your app will run a little slower and you’ll experience a tiny developer inconvenience. Security, however, is distinct. Knowing and understanding the security model of local storage is crucial since it will have a significant impact on your website in ways you might not have anticipated.

Local storage also has the drawback of being insecure. In no way! Everyone who stores sensitive information in local storage, such as session data, user information, credit card information (even momentarily! ), and anything else you wouldn’t want shared publicly on social media, is doing it incorrectly.

The purpose of local storage in a browser for safe storage was not intended. It was intended to be a straightforward key/value store for strings only that programmers could use to create somewhat more complicated single page apps.

General Preventions

1. For example, if we are using third party JavaScript libraries and they are injected with some scripts which extract the storage objects, our storage data won’t be secure anymore. Therefore it’s not recommended to save sensitive data as
   • Username/Password
   • Credit card info
   • JWT tokens
   • API keys
   • Personal info
   • Session ids
2. Do not use the same origin for multiple web applications. Instead, use subdomains since otherwise, the storage will be shared with all. Reason is, for each subdomain it will have an unique localstorage; and they can’t communicate between subdomain instances.
3. Once some data are stored in Local storage, the developers don’t have any control over it until the user clears it. If you want the data to be removed once the session ends, use SessionStorage.
4. Validate, encode and escape data read from browser storage
5. Encrypt data before saving

Introduction

Git is an essential tool for version control, and one of its underrated but powerful features is git stash. It allows developers to temporarily save their uncommitted changes without committing them, enabling a smooth workflow when switching branches or handling urgent bug fixes.

In this blog, we will explore git stash, its varieties, and some clever hacks to make the most of it.

1. Understanding Git Stash

Git stash allows developers to temporarily save changes made to the working directory, enabling them to switch contexts without having to commit incomplete work. This is particularly useful when you need to switch branches quickly or when you are interrupted by an urgent task.

When you run git stash, Git takes the uncommitted changes in your working directory (both staged and unstaged) and saves them on a stack called “stash stack”. This action reverts your working directory to the last committed state while safely storing the changes for later use.

How It Works

• Git saves the current state of the working directory and the index (staging area) as a stash.
• The stash includes modifications to tracked files, newly created files, and changes in the index.
• Untracked files are not stashed by default unless specified.
• Stashes are stored in a stack, with the most recent stash on top.

Common Use Cases

• Context Switching: When you are working on a feature and need to switch branches for an urgent bug fix.
• Code Review Feedback: If you receive feedback and need to make changes but are in the middle of another task.
• Cleanup Before Commit: To stash temporary debugging changes or print statements before making a clean commit.

Git stash is used to save uncommitted changes in a temporary area, allowing you to switch branches or work on something else without committing incomplete work.

Basic Usage

The basic git stash command saves all modified tracked files and staged changes. This does not include untracked files by default.

git stash

This command performs three main actions

• Saves changes: Takes the current working directory state and index and saves it as a new stash entry.
• Resets working directory: Reverts the working directory to match the last commit.
• Stacks the stash: Stores the saved state on top of the stash stack.

Restoring Changes

To restore the stashed changes, you can use

git stash pop

This does two things

• Applies the stash: Reapplies the changes to your working directory.
• Deletes the stash: Removes the stash entry from the stash stack.

If you want to keep the stash for future use

git stash apply

This reapplies the changes without deleting the stash entry.

Viewing and Managing Stashes

To see a list of all stash entries

git stash list

This shows a list like

stash@{0}: WIP on feature-branch: 1234567 Commit message
stash@{1}: WIP on master: 89abcdef Commit message

Each stash is identified by an index (e.g., stash@{0}) which can be used for other stash commands.

git stash

This command stashes both tracked and untracked changes.

To apply the last stashed changes back

git stash pop

This applies the stash and removes it from the stash list.

To apply the stash without removing it

git stash apply

To see a list of all stashed changes

git stash list

To remove a specific stash

git stash drop stash@{index}

To clear all stashes

git stash clear

2. Varieties of Git Stash

a) Stashing Untracked Files

By default, git stash does not include untracked files. To include them

git stash -u

Or:

git stash --include-untracked

b) Stashing Ignored Files

To stash even ignored files

git stash -a

Or:

git stash --all

c) Stashing with a Message

To add a meaningful message to a stash

git stash push -m "WIP: Refactoring user authentication"

d) Stashing Specific Files

If you only want to stash specific files

git stash push -m "Partial stash" -- path/to/file

e) Stashing and Switching Branches

Instead of running git stash and git checkout separately, do it in one step

git stash push -m "WIP: Bug Fix" && git checkout other-branch

3. Advanced Stash Hacks

a) Viewing Stashed Changes

To see the contents of a stash before applying

git stash show -p stash@{0}

b) Applying a Stash to a Different Branch

You can stash on one branch and apply it to another

git checkout other-branch
git stash apply stash@{0}

c) Creating a New Branch from a Stash

If you realize your stash should have been a separate branch

git stash branch new-branch stash@{0}

This will create a new branch and apply the stashed changes.

d) Keeping Index Changes

If you want to keep staged files untouched while stashing

git stash push --keep-index

e) Recovering a Dropped Stash

If you accidentally dropped a stash, it may still be in the reflog

git fsck --lost-found

Or, check stash history with:

git reflog stash

f) Using Stash for Conflict Resolution

If you’re rebasing and hit conflicts, stash helps in saving progress

git stash
# Fix conflicts
# Continue rebase
git stash pop

4. When Not to Use Git Stash

• If your work is significant, commit it instead of stashing.
• Avoid excessive stashing as it can lead to forgotten changes.
• Stashing doesn’t track renamed or deleted files effectively.

Git stash is an essential tool for developers to manage temporary changes efficiently. With the different stash varieties and hacks, you can enhance your workflow and avoid unnecessary commits. Mastering these techniques will save you time and improve your productivity in version control.

Happy coding!

Introduction

Binary insertion sort is a sorting algorithm similar to insertion sort, but instead of using linear search to find the position where the element should be inserted, we use binary search.

Thus, we reduce the number of comparisons for inserting one element from O(N) (Time complexity in Insertion Sort) to O(log N).

Best of two worlds

Binary insertion sort is a combination of insertion sort and binary search.

Insertion sort is sorting technique that works by finding the correct position of the element in the array and then inserting it into its correct position. Binary search is searching technique that works by finding the middle of the array for finding the element.

As the complexity of binary search is of logarithmic order, the searching algorithm’s time complexity will also decrease to of logarithmic order. Implementation of binary Insertion sort. this program is a simple Insertion sort program but instead of the standard searching technique binary search is used.

How Binary Insertion Sort works ?

Process flow

In binary insertion sort, we divide the array into two subarrays — sorted and unsorted. The first element of the array is in the sorted subarray, and the rest of the elements are in the unsorted one.

We then iterate from the second element to the last element. For the i-th iteration, we make the current element our “key.” This key is the element that we have to add to our existing sorted subarray.

Example

Consider the array 29, 10, 14, 37, 14

First Pass

Key = 1

Since we consider the first element is in the sorted array, we will be starting from the second element. Then we apply the binary search on the sorted array.

In this scenario, we can see that the middle element in sorted array (29) is greater than the key element 10. So the position of the key element is 0. Then we can shift the remaining elements by 1 position.

Increment the value of key.

Second Pass

Key = 2

Now the key element is 14. We will apply binary search in the sorted array to find the position of the key element.

In this scenario, by applying binary search, we can see key element to be placed at index 1 (between 10 and 29). Then we can shift the remaining elements by 1 position.

Third Pass

Key = 3

Now the key element is 37. We will apply binary search in the sorted array to find the position of the key element.

In this scenario, by applying binary search, we can see key element is placed in its correct position.

Fourth Pass

Key = 4

Now the key element is 14. We will apply binary search in the sorted array to find the position of the key element.

In this scenario, by applying binary search, we can see key element to be placed at index 2 (between 14 and 29). Then we can shift the remaining elements by 1 position.

Now we can see all the elements are sorted.

def binary_search(arr, key, start, end):
    if start == end:
        if arr[start] > key:
            return start
        else:
            return start+1
 
    if start > end:
        return start
 
    mid = (start+end)//2
    if arr[mid] < key:
        return binary_search(arr, key, mid+1, end)
    elif arr[mid] > key:
        return binary_search(arr, key, start, mid-1)
    else:
        return mid
 
def insertion_sort(arr):
    total_num = len(arr)
    for i in range(1, total_num):
        key = arr[i]
        j = binary_search(arr, key, 0, i-1)
        arr = arr[:j] + [key] + arr[j:i] + arr[i+1:]
    return arr
 

sorted_array = insertion_sort([29, 10, 14, 37, 14])
print("Sorted Array : ", sorted_array)

Psuedocode

Consider the array Arr,

1. Iterate the array from the second element to the last element.
2. Store the current element Arr[i] in a variable key.
3. Find the position of the element just greater than Arr[i] in the subarray from Arr[0] to Arr[i-1] using binary search. Say this element is at index pos.
4. Shift all the elements from index pos to i-1 towards the right.
5. Arr[pos] = key.

Complexity Analysis

Worst Case

For inserting the i-th element in its correct position in the sorted, finding the position (pos) will take O(log i) steps. However, to insert the element, we need to shift all the elements from pos to i-1. This will take i steps in the worst case (when we have to insert at the starting position).

We make a total of N insertions. so, the worst-case time complexity of binary insertion sort is O(N^2).

This occurs when the array is initially sorted in descending order.

Best Case

The best case will be when the element is already in its sorted position. In this case, we don’t have to shift any of the elements; we can insert the element in O(1).

But we are using binary search to find the position where we need to insert. If the element is already in its sorted position, binary search will take (log i) steps. Thus, for the i-th element, we make (log i) operations, so its best-case time complexity is O(N log N).

This occurs when the array is initially sorted in ascending order.

Average Case

For average-case time complexity, we assume that the elements of the array are jumbled. Thus, on average, we will need O(i /2) steps for inserting the i-th element, so the average time complexity of binary insertion sort is O(N^2).

Space Complexity Analysis

Binary insertion sort is an in-place sorting algorithm. This means that it only requires a constant amount of additional space. We sort the given array by shifting and inserting the elements.

Therefore, the space complexity of this algorithm is O(1) if we use iterative binary search. It will be O(logN) if we use recursive binary search because of O(log N) recursive calls.

Is Binary Insertion Sort a stable algorithm

It is a stable sorting algorithm, the elements with the same values appear in the same order in the final array as they were in the initial array.

Cons and Pros

1. Binary insertion sort works efficiently for smaller arrays.
2. This algorithm also works well for almost-sorted arrays, where the elements are near their position in the sorted array.
3. However, when the size of the array is large, the binary insertion sort doesn’t perform well. We can use other sorting algorithms like merge sort or quicksort in such cases.
4. Making fewer comparisons is also one of the strengths of this sorting algorithm; therefore, it is efficient to use it when the cost of comparison is high.
5. Its efficient when the cost of comparison between keys is sufficiently high. For example, if we want to sort an array of strings, the comparison operation of two strings will be high.

Bonus Section

Binary Insertion Sort has a quadratic time complexity just as Insertion Sort. Still, it is usually faster than Insertion Sort in practice, which is apparent when comparison takes significantly more time than swapping two elements.

Introduction

What do Reddit, Discord, Medium, and LinkedIn have in common? They use what’s called a skeleton loading screen for their applications. A skeleton screen is essentially a wireframe of the application. The wireframe is a placeholder until the application finally loads.

Rise of skeleton loader.

The term “skeleton screen” was introduced in 2013 by product designer Luke Wroblewski in a blog post about reducing perceived wait time. In this lukew.com/ff/entry.asp?1797 post, he explains how gradually revealing page content turns user attention to the content being loaded, and off of the loading time itself.

Skeleton Loader

Skeleton loading screens will improve your application’s user experience and make it feel more performant. The skeleton loading screen essentially impersonates the original layout.

This lets the user know what’s happening on the screen. The user interprets this as the application is booting up and the content is loading.

In simplest terms, Skeleton Loader is a static / animated placeholder for the information that is still loading. It mimic the structure and look of the entire view.

Why not just a loading spinner ?

Instead of showing a loading spinner, we could show a skeleton screen that makes the user see that there is progress happening when launching and navigating the application.

They let the user know that some content is loading and, more importantly, provide an indication of what is loading, whether it’s an image, text, card, and so on.

This gives the user the impression that the website is faster because they already know what type of content is loading before it appears. This is referred to as perceived performance.

Skeleton screens don’t really make pages load faster. Instead, they are designed to make it feel like pages are loading faster.

When to use ?

1. Use on high-traffic pages where resources takes a bit long to load like account dashboard.
2. When the component contains good amount of information, such as list or card.
3. Could be replaced by spin in any situation, but can provide a better user experience.
4. Use when there’s more than 1 element loading at the same time that requires an indicator.
5. Use when you need to load multiple images at once, a skeleton screen might make a good placeholder. For these pages, consider implementing lazy loading first, which is a similar technique for decreasing perceived load time.

When not to use ?

1. Not to use for a long-running process, e.g. importing data, manipulation of data etc. (Operations on data intensive applications)
2. Not to use for fast processes that that take less than half a second.
3. Users still associate video buffering with spinners. Avoid skeleton screens any time a video is loading on your page.
4. For longer processes (uploads, download, file manipulation ) can use progress bar instead of skeleton loading.
5. As a replacement for poor performance: If you can further optimize your website to actually load content more quickly, always pursue that first.

Let’s design a simple skeleton loading

index.html

<!DOCTYPE html>
<html>
<head>
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width, initial-scale=1">
	<title>Skeleton Loading</title>

	<style>
		.card {
			width:  250px;
			height:  150px;
			background-color: #fff;
			padding: 10px;
			border-radius: 5px;
			border:  1px solid gray;
		}

		.card-skeleton {
			background-image: linear-gradient(90deg, #ccc, 0px, rgb(229 229 229 / 90%) 40px, #ccc 80px);
			background-size: 300%;
			background-position: 100% 0;
			border-radius: inherit;
			animation: shimmer 1.5s infinite;
		}

		.title {
			height: 15px;
			margin-bottom: 15px;
		}

		.description {
			height: 100px;
		}

		@keyframes shimmer{
			to {
				background-position: -100% 0;
			}
		}
	</style>

</head>
<body>
	<div class="card">
		<div class="card-skeleton title"></div>
		<div class="card-skeleton description"></div>
	</div>

</body>
</html>

Suggestions to keep in mind

1. The goal is to design for a perception of decreased waiting time.
2. Contents should replace skeleton exactly by position and size immediately after they are loaded.
3. Using motion that moves from left to right (wave) gives a better perception of decreased waiting time than fading in and out (pulse).
4. Using motion that is slow and steady gives a perception of decreased waiting time.

To my surprise, i came to know about that there is a stuff called as global ignore in git. In this blog i will jot down notes on setting up global .gitignore file.

Git allows you to ignore certain files and directories using a .gitignore file. However, if you frequently work on multiple projects, you might want to configure a global .gitignore file to avoid repeating the same ignore rules across repositories.

Why Use a Global .gitignore?

A global .gitignore file is beneficial when you want to exclude common files across all Git repositories on your system. Some examples include,

• OS-specific files (e.g., macOS .DS_Store, Windows Thumbs.db)
• Editor/IDE-specific files (e.g., .vscode/, .idea/)
• Common compiled files (e.g., *.pyc, node_modules/, dist/)
• Personal configurations that should not be committed (e.g., .env files)
• Or you can just define a format like *.ignore.* , which you can use to ignore any file needed.

Setting Up a Global .gitignore File

1. Create the Global .gitignore File

Run the following command to create a global .gitignore file in your home directory:

 touch ~/.gitignore_global

You can use any path, but ~/.gitignore_global is commonly used.

2. Configure Git to Use the Global .gitignore

Tell Git to use this file globally by running

 git config --global core.excludesFile ~/.gitignore_global

3. Add Rules to the Global .gitignore

Edit the ~/.gitignore_global file using a text editor

 nano ~/.gitignore_global

Example content

# Ignore system files
.DS_Store
Thumbs.db

# Ignore editor/IDE settings
.vscode/
.idea/
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?

# Ignore compiled files
*.o
*.pyc
*.class
node_modules/
dist/

# Ignore environment files
.env
.env.local

Save the file and exit the editor.

4. Verify the Global Ignore Configuration

To check if Git recognizes your global .gitignore file, run

 git config --global core.excludesFile

It should return the path to your global ignore file, e.g., ~/.gitignore_global.

Managing the Global .gitignore

• To update the file, edit ~/.gitignore_global and add/remove rules as needed.
• If you ever want to remove the global .gitignore setting, run:git config --global --unset core.excludesFile
• To list all global configurations, use:git config --global --list

Setting up a global .gitignore file is a simple yet powerful way to maintain cleaner repositories and avoid committing unnecessary files. By following these steps, you can streamline your workflow across multiple Git projects efficiently.

Managing dependencies for small Python scripts has always been a bit of a hassle.

Traditionally, we either install packages globally (not recommended) or create a virtual environment, activate it, and install dependencies manually.

But what if we could run Python scripts like standalone binaries ?

Introducing PEP 723 – Inline Script Metadata

PEP 723 (https://peps.python.org/pep-0723/) introduces a new way to specify dependencies directly within a script, making it easier to execute standalone scripts without dealing with external dependency files.

This is particularly useful for quick automation scripts or one-off tasks.

Consider a script that interacts with an API requiring a specific package,

# /// script
# requires-python = ">=3.11"
# dependencies = [
#   "requests",
# ]
# ///

import requests
response = requests.get("https://api.example.com/data")
print(response.json())

Here, instead of manually creating a requirements.txt or setting up a virtual environment, the dependencies are defined inline. When using uv, it automatically installs the required packages and runs the script just like a binary.

Running the Script as a Third-Party Tool

With uv, executing the script feels like running a compiled binary,

$ uv run fetch-data.py
Reading inline script metadata from: fetch-data.py
Installed dependencies in milliseconds

ehind the scenes, uv creates an isolated environment, ensuring a clean dependency setup without affecting the global Python environment. This allows Python scripts to function as independent tools without any manual dependency management.

Why This Matters

This approach makes Python an even more attractive choice for quick automation tasks, replacing the need for complex setups. It allows scripts to be shared and executed effortlessly, much like compiled executables in other programming environments.

By leveraging uv, we can streamline our workflow and use Python scripts as powerful, self-contained tools without the usual dependency headaches.

The top command in Linux is a powerful utility that provides realtime information about system performance, including CPU usage, memory usage, running processes, and more.

It is an essential tool for system administrators to monitor system health and manage resources effectively.

1. Basic Usage

Simply running top without any arguments displays an interactive screen showing system statistics and a list of running processes:

$ top

2. Understanding the top Output

The top interface is divided into multiple sections

Header Section

This section provides an overview of the system status, including uptime, load averages, and system resource usage.

• Uptime and Load Average – Displays how long the system has been running and the average system load over the last 1, 5, and 15 minutes.
• Task Summary – Shows the number of processes in various states:
  • Running – Processes actively executing on the CPU.
  • Sleeping – Processes waiting for an event or resource.
  • Stopped – Processes that have been paused.
  • Zombie – Processes that have completed execution but still have an entry in the process table. These occur when the parent process has not yet read the exit status of the child process. Zombie processes do not consume system resources but can clutter the process table if not handled properly.
• CPU Usage – Breaks down CPU utilization into different categories:
  • us (User Space) – CPU time spent on user processes.
  • sy (System Space) – CPU time spent on kernel operations.
  • id (Idle) – Time when the CPU is not being used.
  • wa (I/O Wait) – Time spent waiting for I/O operations to complete.
  • st (Steal Time) – CPU cycles stolen by a hypervisor in a virtualized environment.
• Memory Usage – Shows the total, used, free, and available RAM.
• Swap Usage – Displays total, used, and free swap memory, which is used when RAM is full.

Process Table

The table below the header lists active processes with details such as:

• PID – Process ID, a unique identifier for each process.
• USER – The owner of the process.
• PR – Priority of the process, affecting its scheduling.
• NI – Nice value, which determines how favorable the process scheduling is.
• VIRT – The total virtual memory used by the process.
• RES – The actual RAM used by the process.
• SHR – The shared memory portion.
• S – Process state:
  • R – Running
  • S – Sleeping
  • Z – Zombie
  • T – Stopped
• %CPU – The percentage of CPU time used.
• %MEM – The percentage of RAM used.
• TIME+ – The total CPU time consumed by the process.
• COMMAND – The command that started the process.

3. Interactive Commands

While running top, various keyboard shortcuts allow dynamic interaction:

• q – Quit top.
• h – Display help.
• k – Kill a process by entering its PID.
• r – Renice a process (change priority).
• z – Toggle color/monochrome mode.
• M – Sort by memory usage.
• P – Sort by CPU usage.
• T – Sort by process runtime.
• 1 – Toggle CPU usage breakdown for multi-core systems.
• u – Filter processes by a specific user.
• s – Change update interval.

4. Command-Line Options

The top command supports various options for customization:

• -b (Batch mode): Used for scripting to display output in a non-interactive mode.$ top -b -n 1-n specifies the number of iterations before exit.
• -o FIELD (Sort by a specific field):$ top -o %CPUSorts by CPU usage.
• -d SECONDS (Refresh interval):$ top -d 3Updates the display every 3 seconds.
• -u USERNAME (Show processes for a specific user):$ top -u john
• -p PID (Monitor a specific process):$ top -p 1234

5. Customizing top Display

Persistent Customization

To save custom settings, press W while running top. This saves the configuration to ~/.toprc.

Changing Column Layout

• Press f to toggle the fields displayed.
• Press o to change sorting order.
• Press X to highlight sorted columns.

6. Alternative to top: htop, btop

For a more user-friendly experience, htop is an alternative:

$ sudo apt install htop  # Debian-based
$ sudo yum install htop  # RHEL-based
$ htop

It provides a visually rich interface with color coding and easy navigation.

Caching is an essential technique for improving application performance and reducing the load on databases. However, improper caching strategies can lead to serious issues.

I got inspired from ByteByteGo https://www.linkedin.com/posts/bytebytego_systemdesign-coding-interviewtips-activity-7296767687978827776-Dizz

In this blog, we will discuss four common cache problems: Thundering Herd Problem, Cache Penetration, Cache Breakdown, and Cache Crash, along with their causes, consequences, and solutions.

1. Thundering Herd Problem
   1. What is it?
   2. Example Scenario
   3. Solutions
2. Cache Penetration
   1. What is it?
   2. Example Scenario
   3. Solutions
3. Cache Breakdown
   1. What is it?
   2. Example Scenario
   3. Solutions
4. Cache Crash
   1. What is it?
   2. Example Scenario
   3. Solutions

Thundering Herd Problem

What is it?

The Thundering Herd Problem occurs when a large number of keys in the cache expire at the same time. When this happens, all requests bypass the cache and hit the database simultaneously, overwhelming it and causing performance degradation or even a system crash.

Example Scenario

Imagine an e-commerce website where product details are cached for 10 minutes. If all the products’ cache expires at the same time, thousands of users sending requests will cause an overwhelming load on the database.

Solutions

1. Staggered Expiration: Instead of setting a fixed expiration time for all keys, introduce a random expiry variation.
2. Allow Only Core Business Queries: Limit direct database access only to core business data, while returning stale data or temporary placeholders for less critical data.
3. Lazy Rebuild Strategy: Instead of all requests querying the database, the first request fetches data and updates the cache while others wait.
4. Batch Processing: Queue multiple requests and process them in batches to reduce database load.

Cache Penetration

What is it?

Cache Penetration occurs when requests are made for keys that neither exist in the cache nor in the database. Since these requests always hit the database, they put excessive pressure on the system.

Example Scenario

A malicious user could attempt to query random user IDs that do not exist, forcing the system to repeatedly query the database and skip the cache.

Solutions

1. Cache Null Values: If a key does not exist in the database, store a null value in the cache to prevent unnecessary database queries.
2. Use a Bloom Filter: A Bloom filter helps check whether a key exists before querying the database. If the Bloom filter does not contain the key, the request is discarded immediately.
3. Rate Limiting: Implement request throttling to prevent excessive access to non-existent keys.
4. Data Prefetching: Predict and load commonly accessed data into the cache before it is needed.

Cache Breakdown

What is it?

Cache Breakdown is similar to the Thundering Herd Problem, but it occurs specifically when a single hot key (a frequently accessed key) expires. This results in a surge of database queries as all users try to retrieve the same data.

Example Scenario

A social media platform caches trending hashtags. If the cache expires, millions of users will query the same hashtag at once, hitting the database hard.

Solutions

1. Never Expire Hot Keys: Keep hot keys permanently in the cache unless an update is required.
2. Preload the Cache: Refresh the cache asynchronously before expiration by setting a background task to update the cache regularly.
3. Mutex Locking: Ensure only one request updates the cache, while others wait for the update to complete.
4. Double Buffering: Maintain a secondary cache layer to serve requests while the primary cache is being refreshed.

Cache Crash

What is it?

A Cache Crash occurs when the cache service itself goes down. When this happens, all requests fall back to the database, overloading it and causing severe performance issues.

Example Scenario

If a Redis instance storing session data for a web application crashes, all authentication requests will be forced to hit the database, leading to a potential outage.

Solutions

1. Cache Clustering: Use a cluster of cache nodes instead of a single instance to ensure high availability.
2. Persistent Storage for Cache: Enable persistence modes like Redis RDB or AOF to recover data quickly after a crash.
3. Automatic Failover: Configure automated failover with tools like Redis Sentinel to ensure availability even if a node fails.
4. Circuit Breaker Mechanism: Prevent the application from directly accessing the database if the cache is unavailable, reducing the impact of a crash.

class CircuitBreaker:
    def __init__(self, failure_threshold=5):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
    
    def call(self, func, *args, **kwargs):
        if self.failure_count >= self.failure_threshold:
            return "Service unavailable"
        try:
            return func(*args, **kwargs)
        except Exception:
            self.failure_count += 1
            return "Error"

Caching is a powerful mechanism to improve application performance, but improper strategies can lead to severe bottlenecks. Problems like Thundering Herd, Cache Penetration, Cache Breakdown, and Cache Crash can significantly degrade system reliability if not handled properly.

In this blog, i jot down notes on what is smoke test, how it got its name, and how to approach the same in k6.

The term smoke testing originates from hardware testing, where engineers would power on a circuit or device and check if smoke appeared.

If smoke was detected, it indicated a fundamental issue, and further testing was halted. This concept was later adapted to software engineering.

What is Smoke Testing?

Smoke testing is a subset of test cases executed to verify that the major functionalities of an application work as expected. If a smoke test fails, the build is rejected, preventing further testing of a potentially unstable application. This test helps catch major defects early, saving time and effort.

Key Characteristics

• Ensures that the application is not broken in major areas.
• Runs quickly and is not exhaustive.
• Usually automated as part of a CI/CD pipeline.

Writing a Basic Smoke Test with K6

A basic smoke test using K6 typically checks API endpoints for HTTP 200 responses and acceptable response times.

import http from 'k6/http';
import { check } from 'k6';

export let options = {
    vus: 1, // 1 virtual user
    iterations: 5, // Runs the test 5 times
};

export default function () {
    let res = http.get('https://example.com/api/health');
    check(res, {
        'is status 200': (r) => r.status === 200,
        'response time < 500ms': (r) => r.timings.duration < 500,
    });
}

Advanced Smoke Test Example

import http from 'k6/http';
import { check, sleep } from 'k6';

export let options = {
    vus: 2, // 2 virtual users
    iterations: 10, // Runs the test 10 times
};

export default function () {
    let res = http.get('https://example.com/api/login');
    check(res, {
        'status is 200': (r) => r.status === 200,
        'response time < 400ms': (r) => r.timings.duration < 400,
    });
    sleep(1);
}

Running and Analyzing Results

Execute the test using

k6 run smoke-test.js

Sample Output

checks...
 is status 200
 response time < 500ms

If any of the checks fail, K6 will report an error, signaling an issue in the application.

Smoke testing with K6 is an effective way to ensure that key functionalities in your application work as expected. By integrating it into your CI/CD pipeline, you can catch major defects early, improve application stability, and streamline your development workflow.

When running load tests with K6, two fundamental aspects that shape test execution are the number of Virtual Users (VUs) and the test duration. These parameters help simulate realistic user behavior and measure system performance under different load conditions.

In this blog, i jot down notes on virtual users and test duration in options. Using this we can ramp up users.

1. Defining VUs and Duration in K6
2. Basic VU and Duration Configuration
3. Specifying VUs and Duration from the Command Line
4. Ramp Up and Ramp Down with Stages
5. Custom Execution Scenarios
   1. Syntax of Custom Execution Scenarios
   2. Different Executors in K6
   3. Example: Ramping VUs Scenario
   4. Example: Constant Arrival Rate Scenario
   5. Example: Per VU Iteration Scenario
6. Choosing the Right Configuration
7. References

Defining VUs and Duration in K6

K6 offers multiple ways to define VUs and test duration, primarily through options in the test script or the command line.

Basic VU and Duration Configuration

The simplest way to specify VUs and test duration is by setting them in the options object of your test script.

import http from 'k6/http';
import { sleep } from 'k6';

export const options = {
  vus: 10, // Number of virtual users
  duration: '30s', // Duration of the test
};

export default function () {
  http.get('https://test.k6.io/');
  sleep(1);
}

This script runs a load test with 10 virtual users for 30 seconds, making requests to the specified URL.

Specifying VUs and Duration from the Command Line

You can also set the VUs and duration dynamically using command-line arguments without modifying the script.

k6 run --vus 20 --duration 1m script.js

This command runs the test with 20 virtual users for 1 minute.

Ramp Up and Ramp Down with Stages

Instead of a fixed number of VUs, you can simulate user load variations over time using stages. This helps to gradually increase or decrease the load on the system.

export const options = {
  stages: [
    { duration: '30s', target: 10 }, // Ramp up to 10 VUs
    { duration: '1m', target: 50 },  // Ramp up to 50 VUs
    { duration: '30s', target: 10 }, // Ramp down to 10 VUs
    { duration: '20s', target: 0 },  // Ramp down to 0 VUs
  ],
};

This test gradually increases the load, sustains it, and then reduces it, simulating real-world traffic patterns.

Custom Execution Scenarios

For more advanced load testing strategies, K6 supports scenarios, allowing fine-grained control over execution behavior.

Syntax of Custom Execution Scenarios

A scenarios object defines different execution strategies. Each scenario consists of

• executor: Defines how the test runs (e.g., ramping-vus, constant-arrival-rate, etc.).
• vus: Number of virtual users (for certain executors).
• duration: How long the scenario runs.
• iterations: Total number of iterations per VU (for certain executors).
• stages: Used in ramping-vus to define load variations over time.
• rate: Defines the number of iterations per time unit in constant-arrival-rate.
• preAllocatedVUs: Number of VUs reserved for the test.

Different Executors in K6

K6 provides several executors that define how virtual users (VUs) generate load

1. shared-iterations – Distributes a fixed number of iterations across multiple VUs.
2. per-vu-iterations – Each VU runs a specific number of iterations independently.
3. constant-vus – Maintains a fixed number of VUs for a set duration.
4. ramping-vus – Increases or decreases the number of VUs over time.
5. constant-arrival-rate – Ensures a constant number of requests per time unit, independent of VUs.
6. ramping-arrival-rate – Gradually increases or decreases the request rate over time.
7. externally-controlled – Allows dynamic control of VUs via an external API.

Example: Ramping VUs Scenario

export const options = {
  scenarios: {
    ramping_users: {
      executor: 'ramping-vus',
      startVUs: 0,
      stages: [
        { duration: '30s', target: 20 },
        { duration: '1m', target: 100 },
        { duration: '30s', target: 0 },
      ],
    },
  },
};

export default function () {
  http.get('https://test-api.example.com');
  sleep(1);
}

Example: Constant Arrival Rate Scenario

export const options = {
  scenarios: {
    constant_request_rate: {
      executor: 'constant-arrival-rate',
      rate: 50, // 50 iterations per second
      timeUnit: '1s', // Per second
      duration: '1m', // Test duration
      preAllocatedVUs: 20, // Number of VUs to allocate
    },
  },
};

export default function () {
  http.get('https://test-api.example.com');
  sleep(1);
}

Example: Per VU Iteration Scenario

export const options = {
  scenarios: {
    per_vu_iterations: {
      executor: 'per-vu-iterations',
      vus: 10,
      iterations: 50, // Each VU executes 50 iterations
      maxDuration: '1m',
    },
  },
};

export default function () {
  http.get('https://test-api.example.com');
  sleep(1);
}

Choosing the Right Configuration

• Use fixed VUs and duration for simple, constant load testing.
• Use stages for ramping up and down load gradually.
• Use scenarios for more complex and controlled testing setups.

References

1. Scenarios & Executors – https://grafana.com/docs/k6/latest/using-k6/scenarios/

Many Thanks to Shrini for documenting it last year. This serves as a good reference to improve my skills. Hope it will help many.

What Participants wanted to improve

Go a bit slower so that everyone can understand clearly without feeling rushed.

Provide more basics and examples to make learning easier for beginners.

Spend the first week explaining programming basics so that newcomers don’t feel lost.

Teach flowcharting methods to help participants understand the logic behind coding.

Try teaching Scratch as an interactive way to introduce programming concepts.

Offer weekend batches for those who prefer learning on weekends.

Encourage more conversations so that participants can actively engage in discussions.

Create sub-groups to allow participants to collaborate and support each other.

Get “cheerleaders” within the team to make the classes more fun and interactive.

Increase promotion efforts to reach a wider audience and get more participants.

Provide better examples to make concepts easier to grasp.

Conduct more Q&A sessions so participants can ask and clarify their doubts.

Ensure that each participant gets a chance to speak and express their thoughts.

Showing your face in videos can help in building a more personal connection with the learners.

Organize mini-hackathons to provide hands-on experience and encourage practical learning.

Foster more interactions and connections between participants to build a strong learning community.

Encourage participants to write blogs daily to document their learning and share insights.

Motivate participants to give talks in class and other communities to build confidence.

Other Learnings & Suggestions

Avoid creating WhatsApp groups for communication, as the 1024 member limit makes it difficult to manage multiple groups.

Telegram works fine for now, but explore using mailing lists as an alternative for structured discussions.

Mute groups when necessary to prevent unnecessary messages like “Hi, Hello, Good Morning.”

Teach participants how to join mailing lists like ChennaiPy and KanchiLUG and guide them on asking questions in forums like Tamil Linux Community.

Show participants how to create a free blog on platforms like dev.to or WordPress to share their learning journey.

Avoid spending too much time explaining everything in-depth, as participants should start coding a small project by the 5th or 6th class.

Present topics as solutions to project ideas or real-world problem statements instead of just theory.

Encourage using names when addressing people, rather than calling them “Sir” or “Madam,” to maintain an equal and friendly learning environment.

Zoom is costly, and since only around 50 people complete the training, consider alternatives like Jitsi or Google Meet for better cost-effectiveness.

Will try to incorporate these learnings in our upcoming sessions.

Let’s make this learning experience engaging, interactive, and impactful!

In our previous blog on K6, we ran a script.js to test an api. As an output we received some metrics in the cli.

In this blog we are going to delve deep in to understanding metrics in K6.

1. HTTP Request Metrics

http_reqs

• Description: Total number of HTTP requests initiated during the test.
• Usage: Indicates the volume of traffic generated. A high number of requests can simulate real-world usage patterns.

http_req_duration

• Description: Time taken for a request to receive a response (in milliseconds).
• Components:
  • http_req_connecting: Time spent establishing a TCP connection.
  • http_req_tls_handshaking: Time for completing the TLS handshake.
  • http_req_waiting (TTFB): Time spent waiting for the first byte from the server.
  • http_req_sending: Time taken to send the HTTP request.
  • http_req_receiving: Time spent receiving the response data.
• Usage: Identifies performance bottlenecks like slow server responses or network latency.

http_req_failed

• Description: Proportion of failed HTTP requests (ratio between 0 and 1).
• Usage: Highlights reliability issues. A high failure rate indicates problems with server stability or network errors.

2. VU (Virtual User) Metrics

vus

• Description: Number of active Virtual Users at any given time.
• Usage: Reflects concurrency level. Helps analyze how the system performs under varying loads.

vus_max

• Description: Maximum number of Virtual Users during the test.
• Usage: Defines the peak load. Useful for stress testing and capacity planning.

3. Iteration Metrics

iterations

• Description: Total number of script iterations executed.
• Usage: Measures the test’s progress and workload. Useful in endurance (soak) testing to observe long-term stability.

iteration_duration

• Description: Time taken to complete one iteration of the script.
• Usage: Helps identify performance degradation over time, especially under sustained load.

4. Data Transfer Metrics

data_sent

• Description: Total amount of data sent over the network (in bytes).
• Usage: Monitors network usage. High data volumes might indicate inefficient request payloads.

data_received

• Description: Total data received from the server (in bytes).
• Usage: Detects bandwidth usage and helps identify heavy response payloads.

5. Custom Metrics (Optional)

While K6 provides default metrics, you can define custom metrics like Counters, Gauges, Rates, and Trends for specific business logic or technical KPIs.

Example

import { Counter } from 'k6/metrics';

let myCounter = new Counter('my_custom_metric');

export default function () {
  myCounter.add(1); // Increment the custom metric
}

Interpreting Metrics for Performance Optimization

• Low http_req_duration + High http_reqs = Good scalability.
• High http_req_failed = Investigate server errors or timeouts.
• High data_sent / data_received = Optimize payloads.
• Increasing iteration_duration over time = Possible memory leaks or resource exhaustion.