Read API Optimization with Elasticsearch

Overview

Solved critical performance bottlenecks in TC+ platform's read-heavy APIs by architecting an Elasticsearch-based solution. As MySQL grew and searches through names and listings became increasingly slow, I designed a hybrid architecture prioritizing availability over consistency for read operations.

The Performance Challenge

The TC+ platform faced escalating performance issues:

• Growing MySQL database making searches through names increasingly slow
• Multiple listing APIs with high latency affecting user experience
• Read-heavy operations bottlenecking the entire system
• Need for fast, scalable search capabilities across large datasets

My Role & Solution Architecture

• Analyzed system bottlenecks and identified read API optimization as critical priority
• Designed hybrid architecture with MySQL for transactional data and Elasticsearch for reads
• Created robust data pipeline ensuring MySQL-Elasticsearch synchronization
• Developed strategic indexing approach with per-client and common indexes
• Migrated all read-heavy APIs to Elasticsearch while maintaining data integrity

Technical Implementation

Hybrid Database Architecture

• MySQL: Remained as functional relational database for transactions
• Elasticsearch: Optimized for all read-heavy operations and searches
• Data Pipeline: Real-time synchronization between MySQL and Elasticsearch
• API Layer: Smart routing for read vs write operations

Indexing Strategy

• Per-Client Indexes: Dedicated indexes for each client's data isolation
• Common Indexes: Shared indexes for frequently accessed common data
• Optimized Mapping: Custom field mappings for search performance
• Index Lifecycle: Automated management and optimization policies

Infrastructure & Management

• Elastic Cloud: Managed Elasticsearch service reducing operational overhead
• Auto-scaling: Dynamic scaling based on query load
• Monitoring: Real-time performance metrics and alerting
• Backup & Recovery: Automated data protection and disaster recovery

Data Pipeline Design

• Real-time change data capture from MySQL
• Asynchronous processing to prevent blocking transactional operations
• Error handling and retry mechanisms for data consistency
• Bulk indexing optimization for large dataset updates
• Monitoring and alerting for pipeline health

Results & Impact

Key Achievements

• Significantly improved system performance by moving read operations to Elasticsearch
• Reduced search latency from seconds to sub-100ms response times
• Maintained data integrity through robust synchronization pipeline
• Eliminated operational overhead using Elastic Cloud managed services
• Enabled horizontal scaling for growing client data requirements
• Improved user experience across all listing and search functionalities

Design Principles

• Availability over Consistency: Optimized for read performance and system availability
• Data Separation: Clear boundaries between transactional and search data
• Scalability: Per-client indexing strategy supporting growth
• Operational Simplicity: Managed services reducing maintenance complexity

Tech Stack