Linear Search

algorithms

Linear Search

Linear search is one of the simplest search algorithms. It checks each item in a list one by one until it finds the target value or reaches the end of the list.

How it works

  1. Start at the beginning of the array.
  2. Compare the current item with the target.
  3. If they match, return the index.
  4. If no match is found, return -1.

Example

function linearSearch(arr, target) {
  for (let i = 0; i < arr.length; i++) {
    if (arr[i] === target) {
      return i;
    }
  }
  return -1;
}

console.log(linearSearch([10, 20, 30, 40], 30)); // 2

Time complexity

  • Best case: O(1)
  • Average case: O(n)
  • Worst case: O(n)

When to use it

Linear search is useful for small arrays or unsorted data where simplicity matters more than speed.

Case Study

In Progress

Bible Verse — Case Study

Production SaaS Platform · Full-Stack · Founder & Sole Engineer

A domain-driven SaaS platform with five independently scalable system boundaries: scripture content delivery, RAG-backed AI study, real-time community interaction, async media processing, and infrastructure services — built and operated end-to-end.

Our Results

37K+
Verses Indexed
5
AI Models
5
Bounded Domains
3
Job Queues

How We Built It

  • RAG pipeline grounding AI responses in actual scripture rather than model memory
  • Hybrid Llama / OpenAI routing — local inference for cost, API fallback for quality at the edge
  • Non-blocking media processing — FFmpeg jobs enqueued via BullMQ, API never waits on transcoding
  • Cross-instance real-time consistency via Redis pub/sub behind WebSocket and WebRTC layers

Lessons Learned

  • Domain boundaries enforced at the service layer prevent coupling long before scale demands microservices.
  • RAG retrieval quality matters more than model size — better embeddings outperform a larger model on poor context.
  • Async queue design should be first-class, not bolted on; BullMQ worker isolation saved the request path repeatedly.

Stack

Nuxt 3TypeScriptNitroPostgreSQLPrismaRedisBullMQWeaviateMinIOFFmpegWebRTCWebSocketsLlama 3.2OpenAI APIKubernetes
View Full Case Study

Written by

Full-Stack Engineer & Systems Architect

5+ years building production systems · AI, Backend & Infrastructure · Founder of Bible Logic

Full-stack engineer with 5+ years of hands-on experience designing and shipping production systems — from Nuxt 3 frontends and Nitro APIs to self-hosted Kubernetes clusters, RAG pipelines, and real-time AI applications. Everything I write comes from systems I've designed, deployed, and operated in production.

5+ Years Experience AI Systems Specialist Kubernetes & Infrastructure
Nuxt 3TypeScriptPostgreSQLKubernetesRAG / LLMWebRTCAWS IVSRedis