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Datadog APM Probe Templates

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Pre-built Datadog APM Probe templates for validating service health using Datadog metrics during chaos experiments. These templates query Datadog container metrics or APM metrics and compare the result against a threshold you configure at runtime.

Here are Datadog probe templates that you can use in your chaos experiments.

[object Object]

Datadog CPU Check

Validates the CPU utilisation of a service using Datadog container metrics. The template queries container.cpu.usage filtered by Kubernetes deployment and namespace.

Required probe variables:

  • SERVICE_NAME: Kubernetes deployment name of the target service
  • NAMESPACE: Kubernetes namespace of the target service

Required inputs:

  • CONNECTOR_ID: Datadog connector identifier
Use cases
  • Verify CPU stays within limits during pod CPU hog faults
  • Monitor container CPU after resource stress experiments
  • Validate autoscaling behavior under load chaos
  • Detect CPU saturation before it affects latency
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[object Object]

Datadog Memory Check

Validates the memory utilisation of a service using Datadog container metrics. The template queries container.memory.usage filtered by Kubernetes deployment and namespace.

Required probe variables:

  • SERVICE_NAME: Kubernetes deployment name of the target service
  • NAMESPACE: Kubernetes namespace of the target service

Required inputs:

  • CONNECTOR_ID: Datadog connector identifier
Use cases
  • Verify memory stays within limits during memory hog faults
  • Monitor container memory after OOM-related chaos
  • Validate memory limits during stress experiments
  • Detect memory leaks under sustained load
View details
[object Object]

Datadog P95 Latency Check

Validates the 95th percentile latency of a service using Datadog APM metrics (latency_p95).

Required probe variables:

  • SERVICE_NAME: Datadog APM service name
  • ENV: Datadog APM environment tag (optional)

Required inputs:

  • CONNECTOR_ID: Datadog connector identifier
Use cases
  • Verify tail latency stays within SLO during network chaos
  • Monitor p95 latency after dependency failures
  • Validate latency recovery after fault injection
  • Detect latency regressions during rollout chaos
View details
[object Object]

Datadog P99 Latency Check

Validates the 99th percentile latency of a service using Datadog APM metrics (latency_p99).

Required probe variables:

  • SERVICE_NAME: Datadog APM service name
  • ENV: Datadog APM environment tag (optional)

Required inputs:

  • CONNECTOR_ID: Datadog connector identifier
Use cases
  • Verify worst-case latency during high-percentile SLO checks
  • Monitor p99 latency after infrastructure faults
  • Validate tail latency recovery after chaos injection
  • Detect outlier latency spikes during experiments
View details
[object Object]

Datadog Avg Latency Check

Validates the average latency of a service using Datadog APM metrics (latency_avg).

Required probe variables:

  • SERVICE_NAME: Datadog APM service name
  • ENV: Datadog APM environment tag (optional)

Required inputs:

  • CONNECTOR_ID: Datadog connector identifier
Use cases
  • Verify mean latency stays within budget during chaos
  • Monitor average response time after fault injection
  • Validate latency stability across experiment phases
  • Compare baseline and post-chaos average latency
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[object Object]

Datadog Error Rate Check

Validates the error rate of a service using Datadog APM metrics (error_rate).

Required probe variables:

  • SERVICE_NAME: Datadog APM service name
  • ENV: Datadog APM environment tag (optional)

Required inputs:

  • CONNECTOR_ID: Datadog connector identifier
Use cases
  • Verify error rate stays below threshold during chaos
  • Monitor error budget consumption during fault injection
  • Validate service recovery after dependency failures
  • Detect error spikes during network or pod chaos
View details

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