Case Study: Building a High-Performance Document Search Engine Using Elasticsearch

Industry: Research & Document Management

Challenge: A business was struggling with its document management system that stored over 1 billion research documents. Their previous setup using a traditional SQL database couldn’t handle the complex full-text search queries and large dataset efficiently, causing the server to freeze or crash frequently during heavy search operations. Users had to wait for minutes, sometimes hours, to retrieve relevant documents, severely impacting productivity. Manual restarts were often needed, adding further delays.

The client needed a faster, scalable solution that could handle complex full-text search queries in seconds, improve uptime, and ensure that users could quickly access the most relevant documents without server interruptions.

Solution: Our team implemented a robust document search engine using Elasticsearch, designed to manage massive datasets and handle complex queries efficiently. Here’s how we approached the project:

1. Custom Indexing with Data Cleanup:
   • We built a Python script to process and clean the raw data, preparing it for ingestion into Elasticsearch. This included removing duplicate entries, normalizing text, and ensuring consistent metadata fields.
   • Custom indexing was applied, allowing for advanced search capabilities, including phrase matching, fuzzy searches, and complex filtering based on document metadata (e.g., author, date, type).
2. Improved Search Methods:
   • Implemented a variety of search techniques, including full-text search, proximity search, wildcard search, and Boolean queries.
   • Highlighting feature: Important terms in search results were highlighted to give users a clear view of why a document was relevant to their query.
3. Elasticsearch Setup on Premises:
   • We configured Elasticsearch on a dedicated on-premise server, optimizing it for handling large datasets and ensuring that it could scale to accommodate future data growth.
   • Set up Node.js for the backend to manage search queries, with an Angular 15-based UI and React Native for mobile, allowing users to search and access documents seamlessly across platforms.
4. Server Optimization & Monitoring:
   • Previously, the SQL-based server would often crash during heavy search operations. With Elasticsearch’s distributed nature, we optimized the system for parallel processing of queries and set up real-time monitoring to prevent server overload.
   • Implemented auto-restart mechanisms and error handling to ensure high availability and minimal downtime.
5. Fast Query Response Time:
   • Leveraging Elasticsearch’s distributed architecture, complex queries that previously took minutes now executed in under 2 seconds, even when handling full-text search across billions of documents.

Results:

• Instant Search Results: Search results were now available in seconds, with friction-free retrieval of relevant documents, even from a dataset exceeding 1 billion records.
• Reduced Downtime: Server crashes and manual restarts were eliminated, thanks to Elasticsearch’s resilience and error handling mechanisms.
• Enhanced User Experience: With advanced search techniques and real-time highlighting, users could quickly find what they were looking for without digging through irrelevant results.
• Scalable Solution: The solution was designed to grow with the client’s data needs, allowing for future expansion without sacrificing performance.

Technologies Used:

• Backend: Node.js, Express, Elasticsearch
• Frontend: Angular 15, React Native (Mobile)
• Data Processing: Python for data cleanup and custom indexing
• Server Setup: On-premise deployment of Elasticsearch, managed with real-time monitoring and auto-restart mechanisms