Intelligent Product Discovery with Apache Solr
Transforming online shopping with lightning-fast search, smart faceting, and personalized recommendations
Business Context
A leading global fashion marketplace needed to transform their product discovery experience to compete with industry giants
Client Overview
Our client operates one of the fastest-growing online fashion marketplaces, connecting millions of shoppers with hundreds of thousands of independent sellers worldwide. With a catalog of over 10 million products spanning apparel, accessories, beauty, and lifestyle categories, providing a seamless product discovery experience was critical to their success.
Their existing search infrastructure, built on a legacy SQL-based system, struggled to handle the scale and complexity of modern e-commerce requirements. Search queries took 3-5 seconds to return results, faceted navigation was limited, and personalization was virtually non-existent. With cart abandonment rates exceeding 75% and customer complaints about search relevance increasing, they needed a transformative solution.
The business imperative was clear: build an intelligent, scalable product discovery platform that could deliver Amazon-level search experiences while maintaining the unique character and diversity that made their marketplace special.
Key Challenges
Eight critical pain points blocking growth and degrading customer experience
Slow Search Performance
SQL-based search taking 3-5 seconds for complex queries, causing user frustration and high bounce rates.
Poor Search Relevance
Users frequently reported irrelevant results. No semantic understanding, typo tolerance, or synonym handling.
Limited Faceted Navigation
Only basic category and price filters available. No dynamic facets, multi-select, or attribute-based filtering.
No Autocomplete/Suggestions
Users had to type complete queries. No typeahead suggestions, trending searches, or query assistance.
Zero Personalization
Same search results for all users regardless of browsing history, preferences, or purchase behavior.
Scalability Constraints
Database couldn't handle peak traffic loads. Frequent timeouts during sales events and promotions.
No Business Rules Engine
Unable to boost promoted products, manage merchandising rules, or implement strategic product placement.
Poor Mobile Experience
Mobile search even slower than desktop. No mobile-optimized facets or touch-friendly filtering interface.
Business Impact
These challenges resulted in a 75% cart abandonment rate, 40% search exit rate, and estimated $15M annual revenue loss. Customer satisfaction scores for search and discovery were at an all-time low of 2.3/5, threatening the platform's competitive position in a crowded marketplace.
Solution Architecture
A comprehensive product discovery platform built on Apache Solr with intelligent query processing and real-time data synchronization
Apache Solr Search Engine
Full-text search with edismax query parser, multi-field querying across title, brand, description, and attributes. Sub-100ms response times at scale.
Dynamic Faceted Navigation
Real-time facet generation based on search results. Multi-select filters, range facets for price/rating, hierarchical category navigation.
Intelligent Query Processing
Synonym expansion, spell checking, fuzzy matching, phrase boosting. Handles typos and natural language queries seamlessly.
Business Boosting Engine
Function queries for relevance tuning. Boost in-stock items, high-rated products, promotional items, and preferred brands dynamically.
Spring Boot API Layer
RESTful microservices for search, suggestions, and faceting. Handles authentication, caching, and orchestration between Solr and MySQL.
MySQL Source of Truth
Normalized database schema for products, variants, attributes, and inventory. Real-time sync to Solr via Change Data Capture.
Core Features Delivered
Search Capabilities
- • Full-text search across all product fields
- • Typeahead autocomplete with trending queries
- • Spell correction and fuzzy matching
- • Synonym expansion (tv → television)
- • Multi-language support
Business Intelligence
- • Dynamic result boosting by business rules
- • Promotional product placement
- • Inventory-aware ranking
- • Personalization hooks for ML models
- • A/B testing framework for relevance
Measurable Results
Transformative impact on business metrics and customer satisfaction within 6 months of launch
Technical Performance
- 10M+ products indexed and searchable
- 50K queries/sec handled at peak traffic
- 99.95% uptime maintained consistently
- <100ms average search response time
Business Impact
- 3.2x ROI achieved in first year
- 85% reduction in search-related support tickets
- 40% increase in average order value
- Top 3 search experience rating in industry benchmarks
Technical Architecture
Multi-layer architecture with real-time synchronization and high availability
Frontend Layer
React 18 + Next.js 14
API Layer
Spring Boot 3
Search Layer
Apache Solr 9.x
Data Layer
MySQL 8 + Kafka
Data Synchronization Flow
MySQL Update: Product data modified in source database (inventory, price, attributes)
CDC Capture: Debezium captures change event and publishes to Kafka topic
Stream Processing: Kafka Streams transforms and enriches data for Solr format
Solr Update: Document updated in Solr index via atomic update API
Real-time Search: Updated product immediately available in search results (<2s latency)
Infrastructure & Deployment
Cloud Infrastructure
- • AWS EKS for container orchestration
- • 6-node SolrCloud cluster across 3 AZs
- • Auto-scaling based on query load
- • AWS RDS Multi-AZ for MySQL
- • ElastiCache Redis for caching
DevOps & Monitoring
- • GitLab CI/CD with blue-green deployment
- • Prometheus + Grafana monitoring
- • ELK stack for log aggregation
- • Automated backup & disaster recovery
- • Performance testing with JMeter
Business Impact & Conclusion
Transforming product discovery into a competitive advantage
Revenue Growth
The new product discovery platform drove $22M in additional annual revenue through improved conversion rates, higher average order values, and reduced cart abandonment. Search-driven transactions now account for 68% of total revenue, up from 42% pre-implementation.
Customer Experience
Customer satisfaction scores for search and discovery improved from 2.3/5 to 4.4/5. Users now spend 35% more time on the platform, view 50% more products per session, and report significantly higher confidence in finding what they're looking for.
Operational Excellence
The platform handles peak loads 10x higher than the legacy system while maintaining sub-100ms response times. Operational costs reduced by 40% through efficient infrastructure utilization and automated scaling. The merchandising team can now deploy new boosting rules and promotional campaigns in minutes instead of days.
Competitive Advantage
Industry benchmarks now rank the client's search experience in the top 3 for fashion e-commerce. The platform has become a key differentiator in customer acquisition, with 42% of new users citing "easy product discovery" as their primary reason for choosing the marketplace over competitors.
Conclusion
This project demonstrates how strategic investment in search infrastructure can transform e-commerce performance. By leveraging Apache Solr's powerful capabilities—full-text search, faceted navigation, intelligent query processing, and real-time indexing—we delivered a product discovery experience that rivals industry giants while maintaining the marketplace's unique character. The platform continues to evolve with ML-powered personalization and advanced recommendation features currently in development.
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