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Delegate YAML,
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Get back to coding
Flightcrew is a config copilot that helps engineers bulletproof and scale their infrastructure.
Flightcrew is a config copilot that helps engineers bulletproof and scale their infrastructure.
We’ve found 2 Availability risks for the Payments deployment
Here are a few things you can try:
Update HPA - Scaling Behavior
Update resource and requests HPA scaling behavior to prevent OOMs.
Update HPA - Custom Scaler
Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.
Check Upstream Services
Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request
Okay confirmed. What can I do?
Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable
Timeouts have increased 20% w/w
408 timeouts have increased during peak weekday traffic
Pods are within 10% of Memory limit
You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week
We’ve found 2 Availability risks for the Payments deployment
Here are a few things you can try:
Update HPA - Scaling Behavior
Update resource and requests HPA scaling behavior to prevent OOMs.
Update HPA - Custom Scaler
Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.
Check Upstream Services
Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request
Okay confirmed. What can I do?
Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable
Timeouts have increased 20% w/w
408 timeouts have increased during peak weekday traffic
Pods are within 10% of Memory limit
You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week
We’ve found 2 Availability risks for the Payments deployment
Here are a few things you can try:
Update HPA - Scaling Behavior
Update resource and requests HPA scaling behavior to prevent OOMs.
Update HPA - Custom Scaler
Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.
Check Upstream Services
Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request
Okay confirmed. What can I do?
Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable
Timeouts have increased 20% w/w
408 timeouts have increased during peak weekday traffic
Pods are within 10% of Memory limit
You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week
We’ve found 2 Availability Risks for the Payments deployment
Here are a few things you can try:
Update HPA - Scaling Behavior
Update resource and requests HPA scaling behavior to prevent OOMs.
Update HPA - Custom Scaler
Scale on QueueLength to allow Payments pods to preemptively scale up when traffic increases.
Check Upstream Services
Timeouts may be related to the upstream Catalog deployment, which recently began using twice as many DB connections per request
Okay confirmed. What can I do?
Can you generate a PR to resize the HPA? I’ll take a look at the others once we’re stable
Timeouts have increased 20% w/w
408 timeouts have increased during peak weekday traffic
Pods are within 10% of Memory limit
You’ve defined a utilization SLO of <90% for Payments. Utilization crossed this threshold three times last week
Fix issues before they become incidents
Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.
Fix issues before they become incidents
Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.
Fix issues before they become incidents
Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.
Smart scaling on Kubernetes
Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, node lifecycles and cold-start optimization.
Smart scaling on Kubernetes
Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, node lifecycles and cold-start optimization.
Smarter platform workflows
Personalize abstractions and simplify maintenance with a living database of safe & optimized configuration.
Smarter platform workflows
Personalize abstractions and simplify maintenance with a living database of safe & optimized configuration.
Fix issues before they become incidents
Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.
Fix issues before they become incidents
Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.
Smart scaling on Kubernetes
Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, node lifecycles and cold-start optimization.
Smarter platform workflows
Personalize abstractions and simplify maintenance with a living database of safe & optimized configuration.
Fix issues before they become incidents
Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.
Fix issues before they become incidents
Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.
Smart scaling on Kubernetes
Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, node lifecycles and cold-start optimization.
Smarter platform workflows
Personalize abstractions and simplify maintenance with a living database of safe & optimized configuration.