How We Rewrote Streaming Architecture For 15x Efficiency

In highly dynamic systems where users rely on real-time data and interactions, architectural inefficiencies can escalate into crippling bottlenecks. This was the case with one of our client’s real-time streaming infrastructure, which originally handled tens of thousands of concurrent users through independent Python processes. These legacy connections, while functional, were draining system resources and introducing latency at precisely the moments we could least afford it, during live events with heavy spikes in activity.

The tipping point came when the system failed to scale smoothly during peak demand, exposing fundamental flaws in our architectural approach. Rather than incrementally patching issues, we committed to a ground-up rewrite, one that would future-proof the platform while drastically reducing resource usage, and latency.

What followed was a shift from Python to Golang, from multi-process chaos to a clean, consolidated goroutines-based model, and from high operational overhead to measurable efficiency. This is the story of how the Axelerant team re-architected a critical streaming service to deliver 15x performance gains, while ensuring reliability, scalability, and observability across the board.

Scaling Issues In The Previous Architecture

Originally, the system managed streaming through multiple separate Python processes. Each process maintained an independent connection to the streaming API. While functional in low-load scenarios, this architecture proved inefficient at scale:

• High CPU Utilization: Each streaming process independently consumed between 1.5 to 2.5 cores, depending on the event volume and number of active users. When traffic spiked during high-profile events, the cumulative load overwhelmed the available vCPU resources, leading to throttling and dropped packets.
• Excessive Memory Footprint: The memory overhead was particularly unsustainable, with peaks as high as 6 GB per pod due to duplicated data handling, caching, and redundant data serialization.
• Latency Instability: Multiple processes meant data synchronization delays and inconsistent message propagation across user sessions. This caused visible latency spikes that degraded the real-time experience.
• Operational Overhead: Managing lifecycle events (start/stop, recovery, restarts) for each process introduced deployment inconsistencies and made troubleshooting and scaling harder.

This fragmented, heavyweight model required a complete rethinking to support sustained user growth.

The Rewrite From Python To Go

The architecture overhaul was driven by the need to streamline connections, minimize compute waste, and enhance the platform's ability to handle sudden traffic bursts.

• Choice Of Language: Go was chosen for its low-latency concurrency model, lightweight memory footprint, and robust standard library. Unlike Python, Go supports native threading via goroutines without the Global Interpreter Lock (GIL), making it ideal for real-time data streaming workloads.
• Single Connection Model: Instead of spinning multiple processes per connection, the new architecture routes all streaming through a single connection, shared across goroutines. This eliminated duplicate processes, simplified state management, and drastically reduced resource usage.
• Modular Refactor: The transition was staged to avoid regressions. We built connection handling, buffer management, message parsing, and error recovery as unit-testable modules from the ground up. Performance benchmarks guided every phase before production rollout.

Engineering The New Architecture

Our new architecture rests on a foundation optimized for real-time, high-concurrency workloads:

• Rewrote streaming logic in Go for efficiency, using channels and goroutines for asynchronous message handling.
• Retained Python for admin and support services that didn’t require high-throughput performance.

The Numbers Don’t Lie

The rewrite resulted in significant improvements across key performance metrics:

• CPU Usage: Our streaming workload now runs on 0.1 core, down from a fluctuating range of 1.5–2.5 cores. This allowed us to consolidate services and freed up compute for other mission-critical operations.
• Memory Usage: A single Go process now peaks at just 150 MB, a 40x improvement that stabilizes our memory allocation and eliminates OOM kill risks.
• Latency Spikes: Average message latency fell below 200ms, and jitter (latency variance) reduced by over 90%, resulting in consistently responsive user interactions.
• Operational Cost: Monthly AWS costs dropped significantly as we transitioned to more efficient pod and node utilization models.

Visual Proof Of Performance:

The system passed its ultimate test during a high-traffic event with concurrent spikes exceeding 20K markets, without a single service degradation alert.

Built For Resilience

To ensure that our gains wouldn’t compromise reliability, we embedded resilience features at every layer:

• Auto-Scaling: Kubernetes HPA scales services based on custom metrics including event rate and memory load, not just CPU.
• Failover Strategy: In the event of outages (which occur quarterly), fallback modules route traffic to Casino/Fancy API streams to ensure continuous user engagement.
• Backup & Disaster Recovery: Snapshots of stateful services are scheduled via AWS Backup. Multi-region replication protects against zonal outages.

Lessons Learned

1. Choose Languages By Use Case: Go is purpose-built for real-time, high-throughput systems. Python remains strong for scripting and orchestration.
2. Invest In Observability Early: Comprehensive monitoring systems reduced debugging cycles and enabled proactive scaling.
3. Build In Iterations, Validate Often: Staging environments, load test scripts, and feature flags ensured smooth rollout without disruption.

Looking Ahead

With the foundational rewrite complete, we’re now focused on preparing the platform to accommodate 100K+ concurrent users and integrate multi-tenant support across white-label clients.

This rewrite wasn’t just about optimization, it was about architectural evolution. By rethinking how we manage streaming workloads, we’ve created a blueprint for future scalability, cost efficiency, and performance excellence.

If your team is experiencing scale-related bottlenecks in real-time systems, don’t just scale up. Architect smarter. The returns can be exponential.