Client

A fast-growing digital product company with over 1 million active users, generating analytics events and interactions at extremely high volume thousands of requests per second.

They needed a scalable analytics backend capable of:

• Real-time event ingestion

• Large-volume processing

• Fast querying

• Low infrastructure cost

• Always-on uptime

Their old architecture was unable to handle the load.

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Project Overview

The goal was to build a fault-tolerant, highly scalable real-time analytics system capable of:

• Tracking user behavior

• Storing metrics

• Generating dashboards

• Powering recommendations

• Real-time activity streams

We built a high-performance analytics pipeline using:

Spring Boot 3.4.5 • JDK 17 • Kafka • Zookeeper • PostgreSQL • Redis Cache • Nginx • Tomcat • Cloudflare CDN • AWS EC2 autoscaling

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Key Challenges

1. Extremely High Traffic

• 1M+ active users

• Thousands of requests per second

• Spikes during promotions or peak usage

2. Real-Time Requirements

Analytics must be processed:

• Within milliseconds

• With no message loss

• Distributed horizontally

3. Database Overload

Postgres alone couldn’t handle raw event ingestion.

4. Message Ordering & Consistency

Analytics events needed guaranteed delivery and ordering.

5. Global User Base

Low latency required worldwide.

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Our Solution

1. High-Performance Event Ingestion API (Spring Boot 3.4.5)

We created a non-blocking reactive API using:

• Spring Boot 3.4.5

• JDK 17

• Spring WebFlux

• Tomcat (as embedded server, tuned for high concurrency)

Optimizations included:

• Connection pooling

• Zero-copy request handling

• Async processing

• Automatic backpressure handling

The API could easily handle 10k+ requests/sec.

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2. Kafka as the Real-Time Event Pipeline

We deployed a Kafka cluster backed by Zookeeper for:

• High-throughput message ingestion

• Partitioning for horizontal scalability

• Guaranteed ordering per user/session

• Replication & fault tolerance

Why Kafka?
Because it can handle millions of messages per second with low latency.

We used:

• Topic sharding (user_id hashing)

• Acks=all for durability

• Retention policies for raw data

• Stream filtering and enrichment

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3. Real-Time Consumers & Processing Layer

We built analytics consumers using Spring Boot microservices:

• Event parser

• User activity aggregator

• Real-time metrics generator

• Storage writer services

Consumers were horizontally scalable:

• Auto-scaling groups on EC2

• Manual + metric-based scaling

• Dedicated partitions

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4. PostgreSQL for Processed Analytics Storage

Postgres used for:

• Aggregated metrics

• User insights

• Reporting dashboards

Optimizations:

• Partitioned tables

• Read replicas

• Index tuning

• Connection pooling (HikariCP)

We also used Redis for:

• Hot data

• Caching analytics

• Reducing Postgres load

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5. Nginx + Cloudflare for Global Load Handling

To manage millions of global requests:

Cloudflare

• Edge-level caching

• WAF security

• Bot filtering

• Rate limiting

• Low-latency routing

Nginx

• Reverse proxy

• Load balancing

• SSL termination

• Static asset caching

This reduced backend load by 30–50%.

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6. AWS EC2 Auto Scaling

We deployed:

• EC2 for microservices

• Auto-scaling on CPU & Kafka queue depth

• Dedicated Kafka nodes

• Dedicated PostgreSQL instance

• S3 for cold backups

• CloudWatch for real-time metrics

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7. Backup & Reliability Strategy

Kafka

• Multi-broker replication

• Automatic failover

Postgres

• Daily snapshots

• PITR (point-in-time recovery)

• Multi-AZ replication

Application Services

• Blue/Green deployments

• Canary releases

• Zero downtime upgrades

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Architecture Diagram (Text Version)

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Results / Impact

⚡ Real-Time Throughput: 10,000+ Requests/Second

System remained stable during heavy traffic spikes.

🧠 Accurate real-time analytics

Event processing delay: < 200 ms

🗃 Postgres load reduced by 60%

Thanks to:

• Kafka buffering

• Redis caching

• Optimized write batching

🌍 Global low latency

Cloudflare delivered <80ms latency worldwide.

💰 Cost Efficient

Optimized resources and event processing reduced cloud bills.

🔒 Highly Reliable

Zero data loss with Kafka replication and Postgres backups.

🚀 Scalable Architecture

Easily supports growth from 1M → 5M active users.

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Conclusion

By combining Spring Boot 3.4.5, Kafka, PostgreSQL, Nginx, Cloudflare, and AWS EC2, we built a high-performance, real-time user analytics platform capable of sustaining millions of users and thousands of requests per second all with stability, speed, and efficiency.

This architecture is modern, scalable, and future-proof for any fast-growing product.