Catch and kill infrastructure errors

Catch and kill infrastructure errors

Catch and kill infrastructure errors

Catch and kill infrastructure errors

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

Flightcrew helps engineers catch configuration issues before they’re incidents. No more blindspots, no more tuning. Actually magic numbers.

The upstream Orders service is sending more requests, many of them are retries due to ineffective connection management. Lower connection_pool_size and raise idle_timeout on that service to avoid these retries.

Why?

A new version of the upstream Orders service has increased traffic on Payments by 30%

Why?

The Payments service is At Risk of failing

The Payments service is At Risk of failing

The Payments service is At Risk of failing

Why?

A new version of the upstream Orders service has increased traffic on Payments by 30%

Whats a quick fix?

The upstream Orders service is sending more requests, many of them are retries due to ineffective connection management. Lower connection_pool_size and raise idle_timeout on that service to avoid these retries.

And what should I do long-term?

Update Order's Autoscaling Strategy

Change to Keda and scale based on order queue length to reduce cold-start times and high-traffic. Here’s a PR


Consider increasing the number of nodes in the cluster to handle these extra pods. 

How can I make this more resilient?

Improve Resiliency with 4 changes 


Allow for more graceful shutdowns while scaling down


A stale default setting allows for 0% availability during rollouts


Your Karpenter version will be out of sync with Kubernetes in 30 days 


Settings allow running as root user

The upstream Orders service is sending more requests, many of them are retries due to ineffective connection management. Lower connection_pool_size and raise idle_timeout on that service to avoid these retries.

Why?

A new version of the upstream Orders service has increased traffic on Payments by 30%

Why?

The Payments service is At Risk of failing

The Payments service is At Risk of failing

The Payments service is At Risk of failing

Why?

A new version of the upstream Orders service has increased traffic on Payments by 30%

Whats a quick fix?

The upstream Orders service is sending more requests, many of them are retries due to ineffective connection management. Lower connection_pool_size and raise idle_timeout on that service to avoid these retries.

And what should I do long-term?

Update Order's Autoscaling Strategy

Change to Keda and scale based on order queue length to reduce cold-start times and high-traffic. Here’s a PR


Consider increasing the number of nodes in the cluster to handle these extra pods. 

How can I make this more resilient?

Improve Resiliency with 4 changes 


Allow for more graceful shutdowns while scaling down


A stale default setting allows for 0% availability during rollouts


Your Karpenter version will be out of sync with Kubernetes in 30 days 


Settings allow running as root user

The upstream Orders service is sending more requests, many of them are retries due to ineffective connection management. Lower connection_pool_size and raise idle_timeout on that service to avoid these retries.

Why?

A new version of the upstream Orders service has increased traffic on Payments by 30%

Why?

The Payments service is At Risk of failing

The Payments service is At Risk of failing

The Payments service is At Risk of failing

Why?

A new version of the upstream Orders service has increased traffic on Payments by 30%

Whats a quick fix?

The upstream Orders service is sending more requests, many of them are retries due to ineffective connection management. Lower connection_pool_size and raise idle_timeout on that service to avoid these retries.

And what should I do long-term?

Update Order's Autoscaling Strategy

Change to Keda and scale based on order queue length to reduce cold-start times and high-traffic. Here’s a PR


Consider increasing the number of nodes in the cluster to handle these extra pods. 

How can I make this more resilient?

Improve Resiliency with 4 changes 


Allow for more graceful shutdowns while scaling down


A stale default setting allows for 0% availability during rollouts


Your Karpenter version will be out of sync with Kubernetes in 30 days 


Settings allow running as root user

Fix issues before they become incidents

Fix issues before they become incidents

Fix issues before they become incidents

Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.

Bulletproof your infrastructure with proactive fixes for misconfigurations, bottlenecks, stale values, and dependencies.

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

Smart scaling on Kubernetes

Smart scaling on Kubernetes

Smart scaling on Kubernetes

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter versioning and cold-start optimization.

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter versioning and cold-start optimization.

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter versioning and cold-start optimization.

Autoscaling becomes easy when you let Flightcrew worry about things like KEDA logic, Karpenter versioning and cold-start optimization.

Smarter platform workflows

Smarter platform workflows

Smarter platform workflows

Smarter platform workflows

Personalize abstractions and simplify maintenance with a living database of configuration intelligence.

Personalize abstractions and simplify maintenance with a living database of configuration intelligence.

Personalize abstractions and simplify maintenance with a living database of configuration intelligence.

Personalize abstractions and simplify maintenance with a living database of configuration intelligence.

Engineering teams use Flightcrew to make
every pull request more reliable

Engineering teams use Flightcrew to make
every pull request more reliable

Engineering teams use Flightcrew to make every pull request more reliable

Engineering teams use Flightcrew to make
every pull request more reliable