Building a Google Style Search Autocomplete System Using Spring Boot, React, Elasticsearch, Redis, Kafka and AWS

A complete step-by-step guide to building a scalable Google-style search autocomplete system using Spring Boot, React, Elasticsearch, Redis, Kafka, PostgreSQL, Docker and AWS deployment architecture.

Building a Google Style Search Autocomplete System

Search autocomplete is one of the most common features in modern applications. Whenever a user types a few characters into Google, Amazon, YouTube, or any large platform, the system predicts the most relevant search terms instantly. In this project, we will build a production-style autocomplete system using React, Spring Boot, Elasticsearch, Redis, Kafka and AWS.

What We Will Build

  • Real-time search suggestions while typing
  • Google-style autocomplete dropdown
  • Fast prefix based search
  • Search ranking based on popularity
  • Redis caching for high performance
  • Kafka based search analytics pipeline
  • Trending search calculation
  • Fuzzy search for spelling mistakes
  • AWS based frontend deployment

High Level System Architecture

The system follows a scalable microservice architecture where frontend, search processing, analytics, caching and storage layers are separated.

Users

   |
   |

React Frontend
(AWS S3 + CloudFront)

   |
   |

Spring Boot Search Service

   |
   |--------------------------
   |                          |
Redis Cache             Elasticsearch

   |

Kafka Event Stream

   |

Analytics Service

   |

PostgreSQL

Technology Stack

Why Elasticsearch Instead of Database Search?

A simple SQL query like LIKE '%spring%' works for small applications, but large search systems require specialized search engines. Elasticsearch provides indexing, prefix matching, fuzzy search, ranking and very fast response times.

Why Redis Cache?

Autocomplete receives repeated requests. Thousands of users may search the same words such as 'spring boot' or 'java tutorial'. Redis stores frequently requested suggestions in memory and avoids unnecessary Elasticsearch calls.

Why Kafka?

Search requests generate valuable business data. Kafka allows us to capture search events asynchronously without slowing down the user search experience.

Final Project Structure

search-autocomplete-platform

|
|-- frontend
|   |-- React Application
|
|-- services
|   |
|   |-- search-service
|   |   |-- Spring Boot API
|   |
|   |-- analytics-service
|       |-- Kafka Consumer
|
|-- infrastructure
|   |
|   |-- docker-compose.yml
|   |-- elasticsearch
|   |-- redis
|   |-- kafka
|
|-- README.md

Development Environment Requirements

  • Java 21
  • Node.js 20+
  • Docker Desktop
  • Maven
  • Git
  • IDE such as IntelliJ IDEA or VS Code

Create Project Workspace

mkdir search-autocomplete-platform

cd search-autocomplete-platform

mkdir frontend
mkdir services
mkdir infrastructure

Setup Docker Infrastructure

Before writing application code, we will start PostgreSQL, Redis, Elasticsearch and Kafka using Docker Compose.

version: '3.8'

services:

 postgres:
  image: postgres:16
  environment:
   POSTGRES_DB: searchdb
   POSTGRES_USER: admin
   POSTGRES_PASSWORD: admin123
  ports:
   - '5432:5432'

 redis:
  image: redis:7
  ports:
   - '6379:6379'

 elasticsearch:
  image: elasticsearch:8.13.0
  environment:
   discovery.type: single-node
   xpack.security.enabled: false
  ports:
   - '9200:9200'

 kafka:
  image: bitnami/kafka
  ports:
   - '9092:9092'

Start Infrastructure

cd infrastructure

docker compose up

Verify Services

  • PostgreSQL running on port 5432
  • Redis running on port 6379
  • Elasticsearch running on port 9200
  • Kafka running on port 9092

Development Flow

  • Step 1: Start infrastructure using Docker
  • Step 2: Create Spring Boot search service
  • Step 3: Add PostgreSQL keyword storage
  • Step 4: Add Elasticsearch autocomplete index
  • Step 5: Add Redis caching
  • Step 6: Add Kafka analytics pipeline
  • Step 7: Create React search interface
  • Step 8: Deploy frontend to AWS

What We Build in Next Part

In the next part, we will create the complete Spring Boot backend including entity design, Elasticsearch indexing, Redis caching, REST APIs, and search ranking logic.