Project Name
Cut API Latency by 60% for an E-Commerce Platform at Scale
Our client is a mid-market e-commerce platform serving 50,000 daily active users across global markets, operating on AWS infrastructure with a Spring Boot microservices back-end. The business runs a high read-to-write ratio workload; product browsing and user profile retrieval vastly outnumber write operations, making database read performance a direct lever on revenue.
The platform had scaled rapidly through 2024, and by mid-2025 its original single-database architecture was no longer adequate for the traffic profile. The engagement was initiated to modernise the data layer before the next major commercial event cycle, with zero downtime as a hard operational constraint from the business.
The platform faced critical performance, scalability, and database architecture challenges that directly impacted user experience, operational efficiency, and infrastructure costs.
- Excessive Read Latency on Product Catalog: Complex 12-table JOINs across categories, variants, and pricing pushed p95 API latency to 480ms, well above the 200ms abandonment threshold.
- Unscalable User Profile Queries: User profiles stored in unpredictable JSON columns required unpacking and eight secondary queries per request, pushing p95 latency to 340ms.
- Full-Table Scans on Search and Filter: Ad-hoc WHERE clauses without effective index coverage triggered full-table scans, driving p95 latency to 620ms and limiting real-time filtering.
- Concentrated Database Load on a Single RDS Instance: Three services generated 73% of database load on one Amazon RDS PostgreSQL instance, creating contention and driving monthly RDS costs to $4,200.
- No Caching Layer for Read-Heavy Endpoints: Product catalog queries hit the database on every request despite a 25-to-1 read-to-write ratio, creating unnecessary database load at scale.
- Zero-Downtime Migration Constraint: The platform had to remain fully operational throughout migration, requiring incremental deployment, percentage-based traffic splitting, and immediate rollback at every phase.
Ksolves, an AI-first Big Data consulting company, followed a data-first approach: profile before migrating, stage every cutover, and preserve ACID guarantees for transactional data. The solution used polyglot persistence, assigning each data domain to the store best suited to its access pattern. A phased 14-week dual-write strategy enabled incremental migration from PostgreSQL to MongoDB and Redis with rollback capability throughout.
- MongoDB Atlas Migration, Eliminating JOIN Complexity: Product catalog and user profile data were restructured into native MongoDB Atlas document schemas, eliminating complex JOINs and enabling sub-100ms reads on the two highest-traffic endpoints.
- Redis Cache-Aside Layer - 78% Cache Hit Rate: Redis ElastiCache was implemented with domain-specific TTLs: 15 minutes for products, 5 minutes for profiles, and 60 minutes for categories. The 78% cache hit rate significantly reduced database round-trips and served high-volume reads from memory.
- Dual-Write Bridge Service: A Spring Boot bridge wrote mutations to both PostgreSQL and MongoDB, while feature flags gradually shifted reads from 0% to 100% MongoDB across six phases. PostgreSQL remained the write source of truth throughout.
- MongoDB Atlas Search, Eliminating Full-Table Scans: Search and filtering were migrated to MongoDB Atlas Search, replacing full-table scans with native full-text and faceted indexes and removing the 620ms search latency bottleneck.
- Parallelized Spring Batch Backfill, 52-Hour Migration Window: Historical data backfill used eight parallel Spring Batch steps, reducing the estimated 18-day migration window to 52 hours and enabling the Phase 3 cutover on schedule.
Technology Stack
| Category | Technology |
|---|---|
| Database | MongoDB Atlas |
| Database | PostgreSQL (RDS) |
| Processing | Redis (ElastiCache) |
| Platform | Spring Boot (Java) |
| Infrastructure | AWS CloudWatch / Datadog APM |
| Integration | Spring Batch |
The platform moved from 73% concentrated RDS load, five endpoints above the 200ms UX threshold, and a 0.8% error rate to a polyglot architecture delivering 4× read throughput at 41% lower monthly infrastructure cost.
- Product API Latency Reduced by 60%: p95 product catalog latency dropped from 480ms to 190ms, bringing the highest-traffic endpoint below the 200ms UX threshold.
- User Profile Latency Reduced by 72%: MongoDB Atlas eliminated JSON unpacking and eight secondary queries, reducing p95 profile latency from 340ms to 95ms.
- Read Throughput Increased 4× Under Equivalent Load: The architecture sustained 4,800 RPS at the same 50,000 DAU load that previously capped at 1,200 RPS, while RDS CPU utilization fell from 82% to 34%.
- Monthly Infrastructure Cost Reduced by 41%: Lower RDS load enabled instance downsizing, reducing monthly AWS RDS spend from $4,200 to $2,480 and delivering significant savings within three months.
- Error Rate Reduced by 85%: Platform 5xx errors fell from 0.8% to 0.12%, while the platform handled a 3× flash-sale traffic spike with zero database incidents.
- Zero Downtime Achieved Across the Full Migration: Dual-write and feature-flag traffic splitting migrated read traffic across six phases without maintenance downtime or disruption to live order processing, while preserving rollback capability.
The platform had outgrown a single PostgreSQL architecture, with rising latency, concentrated database load, and increasing infrastructure costs limiting scalability. Ksolves addressed these challenges at the data layer by combining MongoDB Atlas, Redis, Atlas Search, and a zero-downtime dual-write migration strategy. The result was a more scalable and resilient architecture that delivered 4× higher read throughput, 60% lower product API latency, 85% fewer errors, and 41% lower RDS costs. Most importantly, the migration was completed without disrupting live order processing, giving the platform the foundation to scale confidently with future demand.
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