Real-Time Data Streaming with Spring WebFlux and SSE

Guneet Nagia — Sat, 22 Feb 2025 12:50:56 +0000

I recently built a real-time data streaming solution using Spring WebFlux and Server-Sent Events (SSE) to push short bursts of data to customers via an API. Here’s why it worked and how it stacks up.

I needed a lightweight, secure way to deliver short-duration updates. SSE won over alternatives:

Vs. Kafka/Pulsar: Too heavy for my needs—SSE is simpler, no broker required.
Vs. Long Polling: Less efficient—SSE uses one connection, not constant requests.
Vs. WebSockets: Overkill for one-way data—SSE is simpler and secure by design.

SSE shines because it’s:

Lightweight: Runs on HTTP, low overhead.
Great for Short Bursts: Perfect for event-driven updates.
Simple: Easy to set up and use.
Secure: One-way flow plus custom logic (e.g., auth, filtering).
Scalable: Handles concurrency well.

Spring WebFlux Boost

Spring WebFlux made it real-time and scalable:

Reactive: Non-blocking, ideal for streaming.
Scalable: Manages tons of connections effortlessly.
Flexible: Easy to add auth and filtering.

The Result

Customers hit an API, get authenticated, and receive filtered, real-time data over SSE. It’s fast, secure, and scales with WebFlux’s reactive power.

When to Use It

SSE is best for one-way, short-burst updates. Need two-way? Try WebSockets. Big data pipelines? Kafka or Pulsar. For my case, SSE nailed it.

Takeaway

Spring WebFlux + SSE = a clean, efficient streaming solution. Try it if you need simple, real-time data delivery.

Thoughts? Share below!

Seamless Database Migration in a Microservices Architecture: Cloud-Native, Event-Driven Strategies

Guneet Nagia — Sat, 22 Feb 2025 12:23:36 +0000

Downtime during a database migration can tank a production app—trust me, I’ve been there. As a solution design, I tackled this head-on, designing a cloud-native, event-driven solution using Kafka, AWS, and microservices. Stick around to see how I replaced DynamoDB with MongoDB without breaking a sweat.

Here’s how I tackled this at scale: a phased, zero-downtime migration using AWS, Kafka, and event-driven microservices. Check out this diagram for the flow:

The approach introduces an abstraction layer between services and databases, leveraging Kafka for event-driven reliability. Here’s how it works:

Phase 1: Current State

Data Flow:

The Writer Service writes data to Amazon DynamoDB.
Reader Services fetch data from DynamoDB.
Archival Service moves older data to an Amazon S3 Bucket for storage.

Retention Policy: Data in DynamoDB is retained for 15 days before archival.

Phase 2: Transition (Dual Write & Validation)

Data Replication:

Introduce a Kafka Sink Connector to sync data from Writer Service to MongoDB.
Continue writing to DynamoDB while also storing data in MongoDB.

New Archival Process:

A new S3 bucket is created to store data from MongoDB.

New Service Introduction:

A New Service API is introduced to provide the same responses as the existing database.
A toggle flag is implemented in the API to switch between DynamoDB and MongoDB.

Validation Steps:

Validate responses from New Service against existing Reader Services.
Ensure consistency in both S3 archival processes.

Phase 3: Full Migration & Decommissioning

Switching Data Source:

Change the toggle flag to make Reader Services fetch data from MongoDB.

Enhancements & Optimization:

Fully transition reading services to the New Service.

Decommissioning Old Infrastructure:

Decommission DynamoDB and remove dependency on the old archival process.

Final Validation & Completion:

Validate that all data is successfully migrated and all services function as expected.

DEV Community: Guneet Nagia