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Dynatrace

important

When creating a Dynatrace query:

  • Mark the service as a key request.
  • Ensure that the entity selector for metrics is a service or service method.

Before you begin​

This page assumes you have followed the rest of the steps to set up CV. To learn more, go to Configure CV

Add Dynatrace as a health source​

This option is available only if you have configured the service and environment as fixed values.

A Health Source is basically a mapping of a Harness Monitored Service to the Service in a deployment environment monitored by an APM or logging tool.

In Health Sources, click Add. The Add New Health Source settings appear.

  1. In Select health source type, select Dynatrace.

  2. In Health Source Name, enter a name for the Health Source. For example Quickstart.

  3. Under Connect Health Source, click Select Connector.

  4. In Connector settings, you can either choose an existing connector or click New Connector to create a new Connector.

  5. After selecting the connector, click Apply Selected. The Connector is added to the Health Source.

  6. In Select Feature, a Dynatrace feature is selected by default.

  7. Click Next. The Customize Health Source settings appear.

    The subsequent steps in Customize Health Source depend on the Health Source type you selected.

  8. In Find a Dynatrace service, enter the name of the desired Dynatrace service.

  9. In Select Metric Packs to be monitored, you can select Infrastructure or Performance or both.

  10. Click Add Metric if you want to add any specific metric to be monitored (optional) or simply click Submit.

  11. If you click Add Metric, click Map Metric(s) to Harness Services.

  12. In Metric Name, enter the name of the metric.

  13. In Group Name, enter the group name of the metric.

  14. Click Query Specifications and mapping.

  15. In Metric, choose the desired metric from the list.

  16. Click Fetch Records to retrieve data for the provided query.

  17. In Assign, choose the services for which you want to apply the metric. Available options are:

    • Continuous Verification
    • Health Score
    • SLI

  18. In Risk Category, select a risk type.

  19. In Deviation Compared to Baseline, select one of the options based on the selected risk type.

  20. Click Submit. The Health Source is displayed in the Verify step.

You can add one or more Health Sources for each APM or logging provider.

Sample Dynatrace queries​

Latency​

  • Latency trend over time: timeseries(avg(response.time))
  • Latency distribution: histogram(response.time)
  • Latency by application version: avg(response.time) by application.version
  • Latency by geographical region: avg(response.time) by geoip.country_name
  • Latency spike detection: spike(response.time)
  • Latency comparison between environments: avg(response.time) by environment
  • Latency by HTTP method: avg(response.time) by http.method
  • Latency by service: avg(response.time) by service.name
  • Latency anomaly detection: anomaly(response.time)
  • Latency percentiles: percentile(response.time, 50), percentile(response.time, 90), percentile(response.time, 99)

Traffic​

  • Requests per minute trend: timeseries(count(request) / 60)
  • Requests by HTTP status code: count(request) by http.status_code
  • Requests by user agent: count(request) by useragent.name
  • Requests by endpoint and HTTP method: count(request) by endpoint, http.method
  • Requests by response time range: histogram(response.time)
  • Requests by geo-location: count(request) by geoip.country_name
  • Slow endpoint detection: top(avg(response.time), 10, endpoint)
  • Requests by hostname: count(request) by hostname
  • Requests by service: count(request) by service.name
  • Requests by HTTP version: count(request) by http.version

Errors​

  • Error rate trend over time: timeseries(count(error) / count(request) * 100)
  • Top error types: count(error) by errorType
  • Error rate by geographical region: count(error) by geoip.country_name
  • Error rate by application version: count(error) by application.version
  • Error rate by HTTP status code: count(error) by http.status_code
  • Error rate by service: count(error) by service.name
  • Error rate by user agent: count(error) by useragent.name
  • Error spike detection: spike(count(error))
  • Error anomaly detection: anomaly(count(error))

Saturation​

  • CPU utilization across hosts: avg(cpu.usage) by host
  • Memory utilization across hosts: avg(memory.usage) by host
  • Disk utilization across hosts: avg(disk.usage) by host
  • Network utilization across hosts: avg(network.usage) by host
  • CPU utilization by geographical region: avg(cpu.usage) by geoip.country_name
  • Memory utilization by geographical region: avg(memory.usage) by geoip.country_name
  • Disk utilization by geographical region: avg(disk.usage) by geoip.country_name
  • Network utilization by geographical region: avg(network.usage) by geoip.country_name