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Tips for troubleshooting the Adaptive Decision Manager service

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The incorrect or incomplete configuration of the Adaptive Decision Manager (ADM) service causes many of the problems and technical issues related to adaptive models. Use the following general guidelines to investigate and solve the most common problems.

  1. In the header of Dev Studio, click Configure Decisioning Infrastructure Services Adaptive Decision Manager .

    1. Ensure that there is an ADM node with a status of NORMAL. If there is no node, add one.

    2. Click the ADM node to view additional metrics.

      Clicking on the node will show additional metrics around total number of model updates, number of models currently updating and models waiting update and some more metrics around the update times.
    3. Click Edit settings, and then ensure that the ADM settings are correct.

      The ADM node contains settings related to monitoring. These settings control the data that is written to the ADM data mart and the schedule for ADM data monitoring.
      ADM settings
      ADM settings include node type, model and predictor
                                                data snapshot, snapshot schedule, thread count, and
                                                more.
      For more information, see Configuring the Adaptive Decision Manager service.
  2. Test run the strategy that references your adaptive model to verify that the model identifier values are as expected.

    1. In the navigation pane of Dev Studio, click Records.

    2. Expand the Decision category, and then click Strategy.

    3. Click the strategy that references your adaptive model.

    4. Click Actions Run .

    5. In the Run dialog box, click Run.

    6. View the results for the model identifiers.

    7. If you encounter any issues with the strategy, ask a strategy designer for help.

  3. Enable extra logging for ADM to get detailed logs when you start and stop the ADM node.

    1. In the header of Dev Studio, click Configure System Operations Logs .

    2. Click Logging Level Settings .

    3. In the Logger name field, enter com.pega.decision.adm.

    4. Set the Current level to All, and then close the dialog box.

    5. Click Log Files to view or download the current log files.

    Enabling logging at the whole ADM package level provides the most comprehensive information to help you trace the root cause of the issue. If the log files are too extensive, you can configure a reduced logging scope.
    Scenario Packages
    Monitoring, snapshot, and data mart
    • com.pega.decision.adm.client
    • com.pega.decision.adm.datamart
    Models not being created
    • com.pega.decision.adm.persistence.prpc.ADMDataSetSaveOperation
    • com.pega.decision.adm.client.impl.cache.ModelCache
    • com.pega.decision.adm.service.ADMService
    • com.pega.decision.adm.server
    • com.pega.decision.adm.server.arbiter
    • com.pega.decision.adm.server.consumer.adm
    Commit log
    • com.pega.decision.adm.client
    • com.pega.decision.adm.commitlog
    • com.pega.decision.adm.persistence
  4. Ensure that the pxHandleResponses real-time data flow is running.

    This data flow is not part of the core ADM, but if this data flow is not running, then predictions that use a response timeout will not emit a negative response (NoResponse) when the specified waiting time expires.

    In one-to-one customer engagement use cases, where Pega Customer Decision Hub is used, the data flow is started by the PegaMKT-Engine agent schedule. The pxHandleResponses data flow is a managed run that should always be running.

  5. Open the adaptive model rule, check the settings, possible outcomes, and context identifiers.

  6. Open the pxDecisionResults data set.

    The common predictor values (ADM inputs) and model executions are stored in the pxDecisionResults data set. You can browse or retrieve the results by interaction ID and subject ID. Each decision result may reference (pzModelExecutionReferences) one or more model executions. An incoming response to a decision result updates associated model executions if the pyOutcome field in the response matches one of the allowed outcomes as defined in the adaptive model rule.
  7. In the header of Dev Studio, click Configure Decisioning Model Management .

    1. In the Latest responses section, check whether any responses have been sent to a model.

      The Model Management landing page contains information around the models created by ADM, positive and negative responses sent to an ADM model, and the processed and recorded responses. For more information, see Adaptive model details.
    2. Expand the row for a response to view the details.

      There is a separate page for each model execution that the decision result is referencing. This page contains all the information contained in the model execution, for example, parameter values and context key values, and indicates whether the model execution is d, Ignored, or Monitored. An adaptive model execution can be Ignored if the outcome does not match any of the possible outcomes defined in the adaptive model rule.

      Model executions are also used for predictive models and predictions, which use them for monitoring.

  8. Check which models are executed in the decision strategy. Use the strategy test panel and the Executed models under internal fields to track which model executions are propagated.

    The Group By and Data Join components both have options that control which model executions are propagated.
  9. Capture response flow and set response strategy.

    Responses can be captured by interaction ID (and subject ID) or by past period (only subject ID). The strategy requires an external input shape.
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