Client

A fast-growing platform with increasing API traffic across mobile and web clients. Their existing API struggled with:

• Slow response times

• Heavy database load

• Inefficient query patterns

• Unpredictable performance during peak traffic

They needed a high-performance REST/GraphQL API that could scale while staying fast.

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Project Overview

We redesigned and optimized the client's API layer by:

• Improving REST and GraphQL resolver performance

• Optimizing heavy, inefficient database queries

• Adding Redis for caching hot data

• Leveraging CDN caching for static/edge responses

• Adding performance monitoring and metrics

The result:
Massively faster response times and a lighter database workload.

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Key Challenges

1. Slow Endpoints

Certain API routes took 800ms to 2s, especially ones with:

• Joins across multiple tables

• Non-indexed fields

• N+1 queries in GraphQL resolvers

2. Heavy Load on Database

The DB was overwhelmed due to repetitive queries from multiple services.

3. No Caching Strategy

Every request hit the database — even static data like categories or public content.

4. High Latency for Global Users

Users far from the server experienced 1–2s added latency.

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Our Solution

1. REST & GraphQL API Refactoring

We improved core API architecture:

REST Improvements

• Batch fetching

• Reduced payload sizes

• Query parameter validation

• Pagination & filtering

• Async processing for heavy tasks

GraphQL Improvements

• Avoided N+1 query issues using DataLoader

• Optimized resolvers for minimal DB calls

• Field-level caching for expensive fields

• Preloading associations

This drastically reduced backend computation.

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2. Deep Database Query Optimization

We audited DB performance using tools like:

• pg_stat_statements (Postgres)

• EXPLAIN ANALYZE

• Slow query logs

Optimizations included:

• Creating missing indexes

• Rewriting slow queries

• Using materialized views

• Partitioning large tables

• Normalizing heavy data structures

Result:
Queries that took 1.2 seconds → now execute in 40–80ms.

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3. Redis Caching Layer

We introduced Redis for:

✔ Hot Data Caching

• User profiles

• Product lists

• Category trees

• Frequently accessed dashboards

✔ API Response Caching

Storing complete REST/GraphQL responses.

✔ Expiry Policies

• TTL for live data

• Invalidation triggers for updates

✔ Session Caching (Optional)

Result:
Database load reduced 35–60% during peak time.

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4. CDN Caching for Global Performance

We added CDN caching (Cloudflare/AWS CloudFront):

• Static JSON

• Public GET endpoints

• Common GraphQL responses for non-authenticated users

• Edge caching based on URL & query params

Global users now get responses in 50–150ms.

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5. Background Jobs for Heavy Workflows

Tasks like:

• Analytics updates

• Report generation

• Notification sending

were moved to:

• Worker queues

• Background jobs

• Asynchronous processing

Reducing pressure on the main API.

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6. Performance Monitoring & Metrics

We implemented:

• Prometheus + Grafana dashboards

• Query-level tracing

• API latency monitoring

• Redis hit/miss ratio tracking

Provided full visibility into performance over time.

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Architecture Diagram (Text Version)

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Results & Impact

⚡ Response Time Reduced by 70–90%

Heavy endpoints dropped from 1–2 seconds → 100–200ms.

💾 Database Load Reduced by 40–60%

Redis + query optimization dramatically reduced DB calls.

🌍 Global Users Experienced Faster APIs

CDN-level caching improved latency across continents.

🔒 Stable API Under High Traffic

System now stable during:

• Sales events

• Feature launches

• Traffic spikes

🚀 Better Developer Experience

Clean resolvers, optimized queries, and caching reduced debugging overhead.

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Conclusion

By optimizing database queries, improving REST/GraphQL design, and adding Redis + CDN caching, we built a high-performance API capable of handling heavy traffic with minimal latency and reduced infrastructure load.

The platform is now:

• Faster

• More scalable

• More efficient

• Better suited for global growth