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AppDynamics

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AppDynamics is a performance management program for applications. It helps users to gain a comprehensive understanding of the impact of technical difficulties on business goals, allowing IT teams to prioritize their efforts in a way that improves ROI.

AppDynamics parameters and supported features in Nobl9
General support:
Release channel: Stable, Beta
Connection method: Agent, Direct
Replay and SLI Analyzer: Historical data limit 30 days
Event logs: Supported
Query checker: Not supported
Query parameters retrieval: Supported
Timestamp cache persistence: Supported
Query parameters:
Query interval: 1 min
Query delay: 1 min
Jitter: 15 sec
Timeout: 30 sec
Agent details and minimum required versions for supported features:
Plugin name: n9appd
Query delay environment variable: APPD_QUERY_DELAY
Replay and SLI Analyzer: 0.68.0
Query parameters retrieval: 0.73.2
Timestamp cache persistence: 0.65.0
Additional notes:
No support for metric paths with Wild Cards
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Creating SLOs with AppDynamics​

Nobl9 Web​

Follow the instructions below to create your SLOs with AppDynamics in the UI:

  1. Navigate to Service Level Objectives.

  2. Click .
    The SLO wizard opens.

  3. In step 1, select the Service the SLO will be associated with.

  4. In step 2, select AppDynamics as the data source for your SLO.

    1. Enter the Application Name.

      • The Application Name refers to the name of the logical business application defined in AppDynamics.
      • To ingest Database Metrics, you must enter Database Monitoring in the Application Name field.

    2. Then specify the Metric. You can choose either:

      • A Threshold Metric where a single time series is evaluated against a threshold
      • A Ratio Metric that allows you to enter two-time series to compare. You can choose one of the following metric types:
        • Good Counter, meaning a ratio of good requests and total requests
        • Bad Counter, meaning a ratio of bad requests and total requests
      1. Choose the Data Count Method for your ratio metric:
      • Non-incremental: counts incoming metric values one-by-one. So the resulting SLO graph is pike-shaped.
      • Incremental: counts the incoming metric values incrementally, adding every next value to previous values. It results in a constantly increasing SLO graph.

  5. Configure the metrics for your SLO. The following are query examples:

    1. Threshold metric for AppDynamics:
      Metric Path: End User Experience|App|End User Response Time 95th percentile (ms)

    2. Ratio metric for AppDynamics:
      Metric Path for Good Counter (numerator): End User Experience|App|Very Slow Requests
      Metric Path for Bad Counter (numerator): End User Experience|App|Slow Requests
      Metric Path for Total Counter (denominator): End User Experience|App|Normal Requests

    SLI values for good and total
    When choosing the query for the ratio SLI (countMetrics), keep in mind that the values ​​resulting from that query for both good and total:
    • Must be positive.
    • While we recommend using integers, fractions are also acceptable.
      • If using fractions, we recommend them to be larger than 1e-4 = 0.0001.
    • Shouldn't be larger than 1e+20.

  6. In step 3, define a Time Window for the SLO.

  • Rolling time windows are better for tracking the recent user experience of a service.

  • Calendar-aligned windows are best suited for SLOs that are intended to map to business metrics measured on a calendar-aligned basis, such as every calendar month or every quarter.

  1. In step 4, specify the Error Budget Calculation Method and the expected Objective(s).

  2. In step 5, set the SLO Name and the Alert Policy for your SLO. Optionally, you can mark your SLO with Labels.

  3. When you’re done, click CREATE SLO.

sloctl​

Sample Appdynamics threshold SLO
apiVersion: n9/v1alpha
kind: SLO
metadata:
  name: api-server-slo
  displayName: API Server SLO
  project: default
  labels:
    area:
      - latency
      - slow-check
    env:
      - prod
      - dev
    region:
      - us
      - eu
    team:
      - green
      - sales
  annotations:
    area: latency
    env: prod
    region: us
    team: sales
spec:
  description: Example AppDynamics SLO
  indicator:
    metricSource:
      name: app-dynamics
      project: default
      kind: Agent
  budgetingMethod: Occurrences
  objectives:
    - displayName: Good response (200)
      value: 200
      name: ok
      target: 0.95
      rawMetric:
        query:
          appDynamics:
            applicationName: api-server
            metricPath: Overall Application Performance|Average Response Time (ms)
      op: lte
      primary: true
  service: api-server
  timeWindows:
    - unit: Month
      count: 1
      isRolling: false
      calendar:
        startTime: 2022-12-01T00:00:00.000Z
        timeZone: UTC
  alertPolicies:
    - fast-burn-5x-for-last-10m
  attachments:
    - url: https://docs.nobl9.com
      displayName: Nobl9 Documentation
  anomalyConfig:
    noData:
      alertMethods:
        - name: slack-notification
          project: default

Important notes:

Metric specification from AppDynamics has two mandatory fields:

  • applicationName – mandatory, string, Application name, meaning the name of the logical business application defined in AppDynamics.

    • To ingest Database Metrics, you must enter Database Monitoring in the Application Name field.

    For more details, refer to Configuring Agents in Kubernetes | AppDynamics documentation.

    On top of the same validation rules for appDynamics sections as for the rawMetric (threshold metric), there is an additional requirement that all applicationName fields must have the same value within a single SLO.

  • metricPath – mandatory, string, full metric path that can be obtained from AppDynamics Controller UI (dashboard).

    metricPath must refer to a single time series.

    Example: "End User Experience|App|End User Response Time 95th percentile (ms)"

    The list of metrics Full Path and their Application Names can be copied directly from the AppDynamics dashboard.

Querying the AppDynamics server​

The Nobl9 agent leverages the Time Range API parameters. It pulls data at a per-minute interval from the AppDynamics server.

For a more in-depth look, consult additional resources:
WildcardsAppDynamics docsConfiguring Agents in KubernetesAppDynamics docsConfigure Resource URL SegmentsAppDynamics docsAppDynamics ConceptsAppDynamics docsMetric and Snapshot APIAppDynamics docsAgent metricsAgentCreating SLOs via TerraformTerraformCreating agents via TerraformTerraform
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