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Encountering 502 errors in production environments can be both frustrating and disruptive, especially when they occur at scale.
In this blog, I’ll walk you through the systematic approach I used to diagnose, troubleshoot, and ultimately reduce these errors to just a handful per day. This real-world case study sheds light on common misconfigurations, overlooked bottlenecks, and key best practices that can make a significant difference in stabilizing containerized applications running on AWS Fargate.
What Went Wrong?
I worked on a challenging case where an application running on Amazon ECS with the AWS Fargate launch type was experiencing a high volume of 502 Bad Gateway errors behind an Application Load Balancer (ALB). The service was intermittently failing, generating 30 to 50 such errors per day, severely impacting reliability and user experience.
Error Metrics Observed
1. Since ALB access logs were enabled, we observed that elb_status_code was 502 and target_status_code was “-”, indicating the request wasn’t reaching the target.
2. The request_processing_time showed values for some requests, while response_processing_time was -1, meaning the target was closing the connection before a response was sent.
Digging Into the Root Cause
Upon deeper investigation, we identified several contributing factors:
1. Task CPU Configuration: Each task was allocated only 0.256 vCPU, and CPU utilization consistently spiked above 90%, triggering frequent scaling events.
2. Target Tracking Policy: Scaling was configured based on both CPU utilization at 60% and memory utilization at 60%.
3. No Slow Start Duration: There was no slow start configuration on the ALB target group, which meant new targets started receiving traffic immediately upon registration, even before being fully ready.
4. High Request Load: The service began throwing 502 errors when it received around 800 requests per second, despite normal traffic being only 100–120 requests per second.
How We Resolved It — and What We Learnt
● Resource Optimization
After analyzing CPU usage patterns, I recommended increasing the task size to at least 2 vCPUs, as the workload was evidently CPU-intensive. This adjustment significantly stabilized resource utilization and reduced the frequency of scaling events.
● Scaling Policy Conflict Resolution
We discovered a conflict between the CPU and memory-based target tracking policies:
1. CPU utilization consistently exceeded 80%, triggering scale-out.
2. Memory utilization, on the other hand, remained below 35%, often triggering scale-in.
This imbalance led to premature scale-in events, causing task deregistration and resulting in 502 errors when requests were routed to targets during the deregistration process. To resolve this, we removed the memory-based policy and retained CPU utilization at a 60% threshold for more accurate scaling behavior.
● Introducing Slow Start for Stability
The absence of a slow start duration in the target group configuration allowed the load balancer to immediately route traffic to newly launched tasks, even before they had completed initialization. This contributed to the 502 errors observed during scaling events. To mitigate this, we configured a 60-second slow start period, aligning with the typical time required for tasks to become healthy and ready to serve traffic.
● Traffic-Based Scaling Enhancement
To further fine-tune auto-scaling and better respond to fluctuating traffic loads, we implemented a scaling policy based on ALBRequestCountPerTarget. This approach aligned more closely with the application’s actual request patterns and ensured a more responsive and resilient scaling mechanism.
Wrapping Up
To address issues like this effectively:
● Observe application startup and shutdown behavior to avoid premature traffic routing.
● Be cautious when combining multiple scaling policies, especially if your application is primarily sensitive to a single metric (CPU vs memory).
● Always consider configuring slow start durations for ALB target groups to allow your application time to warm up before handling production traffic.
● Match your scaling strategies with actual usage patterns—for example, by using ALB request count for scaling when appropriate.
While ALB is a powerful and reliable service, troubleshooting issues behind the scenes requires a clear understanding of application behavior, resource metrics, and AWS service configurations. When those elements are aligned, stability and performance naturally follow.
Explore our other related resources on AWS Fargate, you might find them useful.
Monitoring Long-Running Fargate Tasks
Mastering AWS Fargate Cost Optimization: Tips for Right-Sizing and Task Scheduling with ECS Fargate
