Deployment Pod Count
CheckDeployment Pod Count
Verifies Kubernetes Deployment pod countsCheckDeployment Pod Count
CheckDeployment Pod Count
Verifies Kubernetes Deployment pod countsCheck
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Kubernetes deployment survives Redis latency
Verify that your application handles an increased latency in a Redis cache properly, allowing for increased processing time while maintaining throughput.
Motivation
Latency issues in Redis can lead to degraded system performance, longer response times, and potentially lost or delayed data. By testing your system's resilience to Redis latency, you can ensure that it can handle increased processing time and maintain its throughput during increased latency. Additionally, you can identify any potential bottlenecks or inefficiencies in your system and take appropriate measures to optimize its performance and reliability.
Structure
We will verify that a load-balanced user-facing endpoint fully works while having all pods ready. As soon as we simulate Redis latency, we expect the system to maintain its throughput and indicate unavailability appropriately. We can introduce delays in Redis operations to simulate latency. The experiment aims to ensure that your system can handle increased processing time and maintain its throughput during increased latency. The performance should return to normal after the latency has ended.
Kubernetes deployment survives Redis downtime
Check that your application gracefully handles a Redis cache downtime and continues to deliver its intended functionality. The cache downtime may be caused by an unavailable Redis instance or a complete cluster.
Motivation
Redis downtime can lead to degraded system performance, lost data, and potentially long system recovery times. By testing your system's resilience to Redis downtime, you can ensure that it can handle the outage gracefully and continue to deliver its intended functionality. Additionally, you can identify any potential weaknesses in your system and take appropriate measures to improve its performance and resilience.
Structure
We will verify that a load-balanced user-facing endpoint fully works while having all pods ready. As soon as we simulate Redis downtime, we expect the system to indicate unavailability appropriately and maintain its throughput. We can block the traffic to the Redis instance to simulate downtime. The experiment aims to ensure that your system can gracefully handle the outage and continue delivering its intended functionality. The performance should return to normal after the Redis instance is available again.
Network outage for Kubernetes nodes in an availability zone
Achieve high availability of your Kubernetes cluster via redundancy across different Availability Zones. Check what happens to your Kubernetes cluster when one of the zones is down.
Motivation
Cloud providers host your deployments and services across multiple locations worldwide. From a reliability standpoint, regions and availability zones are most interesting. While the former refers to separate geographic areas spread worldwide, the latter refers to an isolated location within a region. For most use cases, applying deployments across availability zones is sufficient. Given that failures may happen at this level quite frequently, you should verify that your applications are still working in case of an outage.
Structure
We leverage the block traffic attack to simulate a full network loss in an availability zone. While the zone outage happens, we observe changes in the Kubernetes cluster with Steadybit's built-in visibility. Once the zone outage is over, we expect that all deployments will recover again within a specified time.
Solution Sketch
- AWS Regions and Zones
- Azure Regions and Zones
- GCP Regions and Zones
- Kubernetes liveness, readiness, and startup probes
Network loss for Kubernetes node's outgoing traffic in an availability zone
Achieve high availability of your Kubernetes cluster via redundancy across different Availability Zones. Check what happens to your Kubernetes cluster when one of the zones suffers from a network loss.
Motivation
Cloud provider host your deployments and services across multiple locations worldwide. From a reliability standpoint, regions and availability zones are most interesting. While the former refers to separate geographic areas spread worldwide, the latter refers to an isolated location within a region. For most use cases, applying deployments across availability zone is sufficient. Given that failures may happen at this level quite frequently, you should verify that your applications are still working in case of an outage.
Structure
We leverage the drop outgoing traffic to simulate network loss in an availability. If you want to test for a full outage of the zone, configure it to 100% loss. While the network loss happens, we observe changes of a Kubernetes cluster with Steadybit's built-in visibility. Once the network loss is over, we expect that all deployments will recover again within a specified time.
Solution Sketch
- AWS Regions and Zones
- Azure Regions and Zones
- GCP Regions and Zones
- Kubernetes liveness, readiness, and startup probes
