Scalable Search Engine

Scalable Search Engine

Overview

A distributed search engine built with a multi-stage MapReduce framework, featuring tf-idf ranking algorithms and a fault-tolerant distributed backend capable of indexing large-scale datasets.

Architecture

MapReduce Pipeline

The search engine uses a custom MapReduce implementation for processing and indexing documents at scale:

• Stage 1: Document Processing - Tokenization, stemming, and stop-word removal
• Stage 2: Inverted Index Construction - Building term-to-document mappings
• Stage 3: TF-IDF Calculation - Computing relevance scores for ranking

Fault Tolerance

• Worker health monitoring with automatic task reassignment
• Checkpoint-based recovery for long-running jobs
• Distributed file system integration for data persistence

Features

• Distributed Indexing: Process millions of documents across multiple nodes
• TF-IDF Ranking: Relevance-based search results using term frequency-inverse document frequency
• Real-time Search: Sub-second query response times on indexed datasets
• REST API: Clean interface for search queries and index management
• React Frontend: Modern UI with instant search suggestions

Technical Stack

Component	Technology
Backend	Python, Flask
MapReduce	Custom Framework
Frontend	React, TypeScript
Search	TF-IDF, Inverted Index
Infrastructure	Distributed Workers

Results

• Indexed 100,000+ documents in distributed test environment
• Query latency under 200ms for complex searches
• Linear scaling with additional worker nodes