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This week's problem took me 24 hours to solve the first time. Let's see how you do.

Today we will cover:

• Rotate Array

• Time-Series Database

Read time: under 4 minutes

CODING CHALLENGE

Rotate Array

Given an integer array nums, rotate the array to the right by k steps, where k is non-negative.

Example 1:

Input: nums = [1,2,3,4,5,6,7], k = 3
Output: [5,6,7,1,2,3,4]
Explanation:
rotate 1 steps to the right: [7,1,2,3,4,5,6]
rotate 2 steps to the right: [6,7,1,2,3,4,5]
rotate 3 steps to the right: [5,6,7,1,2,3,4]

Example 2:

Input: nums = [-1,-100,3,99], k = 2
Output: [3,99,-1,-100]
Explanation: 
rotate 1 steps to the right: [99,-1,-100,3]
rotate 2 steps to the right: [3,99,-1,-100]

Solve the problem here before reading the solution.

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SOLUTION

To solve this problem efficiently, we'll implement an in-place rotation algorithm that minimizes extra space usage and handles cases where k might be larger than the array length.

The algorithm works as follows:

1. First, we use k %= len(nums) to handle cases where k is larger than the array length.

2. We define a reverse helper function that reverses elements in-place.

3. Reverse the entire array.

4. Reverse the first k elements.

5. Reverse the remaining elements.

Time complexity is O(n), where n is the length of the array. We perform three reverse operations, each taking O(n/2) time. Space complexity is O(1) as we modify the array in-place.

SYSTEM DESIGN

Time-Series Database

Regular databases struggle when dealing with time-based data like stock prices, sensor readings, or server metrics. When you're collecting millions of data points every second, you need a specialized solution. This is where Time-Series Databases (TSDBs) come in.

A TSDB is designed specifically to handle data that changes over time. Instead of updating existing values like traditional databases, it stores each new measurement as a separate data point with its own timestamp. This approach makes it much more efficient at handling continuous streams of data.

Let's look at how TSDBs are different from regular databases:

1. Data Structure: TSDBs organize data by time intervals rather than by rows and columns. This makes it faster to query data for specific time ranges.

2. Compression: Since time-series data often follows patterns, TSDBs use special compression techniques. For example, if a temperature sensor reports 20°C for several hours, the database can store this efficiently instead of saving thousands of identical readings.

3. Data Retention: TSDBs often include built-in features to automatically delete or compress old data. You might want to keep detailed data for the last week but only hourly averages for older data.

4. Time-Based Queries: TSDBs are optimized for common time-series operations like finding rates of change, averaging over time periods, or detecting trends.

Popular time-series databases include InfluxDB, Prometheus, and TimescaleDB. Each has its strengths:

• InfluxDB is purpose-built for time-series data and offers excellent write performance

• Prometheus is designed for monitoring and includes built-in alerting

• TimescaleDB extends PostgreSQL, making it familiar for teams already using PostgreSQL

Here's how they compare:

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NEWS

This Week in the Tech World

Apple Unveils M5 MacBook Pro: Apple launched the 14-inch MacBook Pro with M5 chip, delivering 3.5x faster AI performance, 1.6x faster graphics, and up to 24 hours of battery life. Starts at $1,599, available October 22.

Python Dominates TIOBE Index: Python maintained its lead with 24.45% in October's TIOBE Index, holding a historic 15% advantage over second-place C. This marks the largest language lead in TIOBE history. SQL reentered the top 10.

OpenAI Partners With Broadcom: OpenAI and Broadcom announced a deal to co-develop 10 gigawatts of custom AI chips, reducing OpenAI's reliance on Nvidia. Deployment starts late 2026. Broadcom stock jumped 10% on the announcement.

GitHub Copilot Chat Flaw: Security researchers discovered a critical vulnerability in GitHub Copilot Chat that leaked AWS keys and private repository data through hidden markdown prompts. GitHub patched the flaw in August.

Google Launches Gemini Enterprise: Google introduced Gemini Enterprise at $30 per user monthly, a comprehensive AI platform for building custom workplace agents. Early customers include Figma, Klarna, and Virgin Voyages with 50+ agents deployed.

Microsoft Releases Agent Framework: Microsoft launched a preview of its open-source Agent Framework for .NET and Python, enabling developers to easily create AI agents and multi-agent workflows for enterprise applications.

Meta Hires AI Talent for $1.5B: Meta acquired Thinking Machines Lab co-founder Andrew Tulloch with a compensation package reportedly reaching $1.5 billion over six years, underscoring the escalating AI talent arms race.

Microsoft Patches 172 Flaws: Microsoft's October Patch Tuesday fixed 172 vulnerabilities including six zero-days, two actively exploited in the wild. The company removed the legacy Agere Modem driver entirely rather than patching it.

BONUS

Just for laughs 😏

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