Building to Scale: How We're Preventing Server Crashes Before Our Game Launch

First, we'd like to apologize for missing last week's dev blog. We've been heads-down rethinking our infrastructure to ensure we're fully prepared for our upcoming launch. The good news is that this work has been incredibly productive! After extensive research, we've found a hosting service that's both cost-effective and powerful for our needs, as mentioned in our previous dev blog!
As we prepare for our upcoming launch, we're evaluating improvements to our existing Nakama backend infrastructure to ensure it can seamlessly scale to handle potential player spikes. While we already leverage Nakama as our game service platform, we're looking to optimize our deployment architecture for maximum scalability.
Evolving Our Current Nakama Implementation
We've been successfully using Nakama to power our game's backend services, benefiting from its robust feature set for authentication, matchmaking, and real-time communication. Now, we're focusing on enhancing our deployment architecture to handle launch day scenarios.
The Launch Day Challenge
Even with our established infrastructure, launch days present unique scaling challenges:
- Unpredictable player surges (potentially 10-100x normal traffic)
- Need for rapid scaling with zero player impact
- Maintaining consistent performance under variable load
- Ensuring geographic optimization for a global player base
Our current deployment has served us well during development and testing, but we need to ensure it's ready for the potentially massive influx of players at launch.
Optimizing Our Infrastructure for Scale
Here's how we're planning to enhance our existing Nakama deployment:
1. Moving from Static to Dynamic Scaling
Current Architecture: Right now, we're running a fixed number of servers behind a load balancer. This setup works well for our current player base, but it lacks the flexibility to rapidly adapt to changing demands. Our database is also configured as a single instance, which could become a bottleneck during high traffic.
Enhanced Architecture: We're transitioning to an auto-scaling architecture where our server pool can automatically expand and contract based on player demand. Instead of maintaining a fixed number of servers (currently 2), we'll configure our system to maintain those 2 servers as a minimum baseline but allow automatic expansion up to 50 servers if needed.
The load balancer will continuously monitor server metrics like CPU utilization, memory usage, and connection count. When these metrics exceed our defined thresholds (typically around 70% utilization), the system will automatically provision new servers. As demand decreases, unnecessary servers will be gracefully decommissioned.
We're also upgrading our database from a single instance to a distributed cluster spread across multiple availability zones. This will eliminate the database as a potential bottleneck and add redundancy to prevent data loss.
2. Optimizing Our Existing Match Handlers
Our current match handlers work well for our existing player numbers, but we're enhancing them to better support high-volume scenarios:
Performance Monitoring: We're adding timing metrics to our match processing functions to identify and address performance bottlenecks. The system will log warnings when match processing takes longer than expected (typically over 50ms), allowing us to quickly identify issues during high traffic.
Dynamic Update Frequency: We're implementing adaptive state synchronization based on server load and player count. When the system is under heavy load, non-critical updates may be sent less frequently, preserving bandwidth and processing power for essential game state updates. For example, cosmetic updates might be throttled while ensuring combat and scoring updates remain real-time.
Resource Allocation Optimization: We're enhancing how our match handlers allocate server resources, prioritizing active matches over idle ones and dynamically adjusting the computational resources dedicated to each match based on its complexity and player count.
3. Enhancing Our Existing Matchmaking System
Our matchmaking system works well under normal conditions, but we're optimizing it for player surges:
Load-Aware Matchmaking: We're adding server load awareness to our matchmaker. During periods of high demand, the system will slightly adjust matchmaking parameters to optimize for throughput while still maintaining match quality. For example, skill range tolerances might temporarily broaden slightly during peak loads.
Wait Time Estimation: We're implementing more accurate wait time estimation that factors in current server load, queue depth, and historical matching rates. This allows us to give players realistic expectations during high-traffic periods.
Priority Queuing: We're adding a system to ensure players who have been waiting longer receive priority, preventing indefinite waits during extremely high demand. The system will track wait times and dynamically adjust priority to ensure fair treatment.
Regional Load Balancing: For our global player base, we're enhancing region-based matchmaking to consider not just player location but also server capacity in each region. During regional peak times, matches might be allocated to less-loaded regions when doing so won't significantly impact latency.
Deployment Enhancements for Our Existing Service
We're implementing these key improvements to our current deployment:
1. Kubernetes-Based Auto-scaling
We're enhancing our existing Kubernetes deployment with improved auto-scaling capabilities. This includes creating a Horizontal Pod Autoscaler that monitors our servers and automatically adjusts the number of running instances based on demand.
The autoscaler will be configured with smart scaling behaviors that allow rapid scaling up when demand increases (responding within 60 seconds) but more conservative scaling down (waiting 5 minutes of lower demand before reducing capacity) to prevent oscillation and ensure smooth player experiences.
2. Database Scaling for Our Existing System
We're transitioning from our current database setup to a more scalable cluster with multiple nodes. This distributed database setup will allow us to:
- Handle much higher read/write volumes during player surges
- Provide redundancy in case of individual node failures
- Scale database capacity independently from the servers
- Maintain consistent performance even during peak load
The database will be configured as a StatefulSet in Kubernetes, allowing for orderly scaling and stable network identities for each database node.
3. Enhanced Monitoring for Our Services
We're significantly expanding our monitoring capabilities to provide better visibility into system performance:
Comprehensive Metrics Collection: Our enhanced monitoring will track detailed metrics like active matches, matchmaking queue depth, database query latency, and message processing times. This gives us a complete picture of system health.
Predictive Alerts: Rather than just alerting on failures, our new monitoring system will identify trends that might lead to issues and alert us before problems impact players. For example, it might notice gradually increasing matchmaking queue depths and alert us before wait times become problematic.
Player Experience Metrics: We're adding monitoring focused specifically on player experience, tracking metrics like matchmaking wait times, match join success rates, and connection stability. This helps us understand the actual player impact of any scaling issues.
Regional Performance Tracking: For our global player base, we're adding region-specific monitoring to identify and address performance issues that might only affect players in certain geographic areas.
Why We're Enhancing Our Existing Deployment
As current Nakama users, we're already familiar with its powerful capabilities, but we're making these enhancements to ensure our game can handle the unpredictable demands of launch day:
- Seamless Scaling: Our enhanced architecture allows our existing servers to dynamically scale without service disruption.
- Cost Optimization: By improving our auto-scaling configuration, we can maintain our current infrastructure costs during normal operation while having the capacity to scale when needed.
- Leveraging Existing Knowledge: Our team already knows Nakama well, so we're focusing on infrastructure improvements rather than learning a new system.
- Minimal Code Changes: These enhancements build upon our existing codebase with optimizations rather than requiring a complete rewrite.
- Improved Observability: Enhanced monitoring gives us better visibility into system performance during high-load periods.
By enhancing our existing infrastructure with these scaling optimizations, we're ensuring our game can handle the unpredictable demands of launch day while building on the foundation we've already established. Our approach focuses on making our current system more resilient and scalable rather than replacing what's already working well.
These improvements will give us the confidence that our game can provide a smooth experience for players regardless of how popular it becomes at launch, while allowing us to leverage our team's existing knowledge and our established Nakama codebase.
Stay Connected!
Thanks for following our development journey! We're getting closer to launch, and we can't wait to share our game with you all. To stay up-to-date with the latest news, behind-the-scenes content, and potential beta test opportunities:
🔹 Join our Discord: Be part of our growing community! Chat with developers, share feedback, and connect with other players. We're regularly sharing exclusive sneak peeks and hosting Q&A sessions with the team: Click to join our Discord!
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Your support means everything to us, and we can't wait to see you in-game soon! Until next week!
- The Dev Team 🎮