Process batch job

Large-scale simulations are enabled by performing strategy execution in batch across system nodes. The assignment, queuing, and management of large-scale simulations are governed by the ProcessBatchJob agent configuration. The agent is scheduled to run at a specified frequency (in seconds) to trigger the checking of assignments in the [email protected] work queue.

If there are assignments, they will be queued to create threads based on the thread configuration for each node. The status of the work item is updated as it progresses in this process and you can monitor the assignment by viewing the instances in the work queue. How many threads can be run in a specific node is something that you define on the Topology landing page. You need to have the ProcessBatchJob agent configured in your ruleset to use this functionality.

Proposition cache synchronization

The proposition cache works on a single Pega Platform node. When Pega Platform runs on multiple system nodes that are connected to the same database, Decision Management uses the system pulse to ensure the consistency of propositions across all nodes. The proposition cache is invalidated when a proposition is saved (by adding or changing a proposition) or deleted.

Adding records that result in the proposition cache becoming invalid is done through two declare trigger rules that run the pyRefreshPropositions activity (pyPropositionSaved and pyPropositionRemoved in Data-pxStrategyResult).

If your installation consists of different Pega Platform nodes that connect to the same database, enable proposition cache synchronization by adding the PRPC:Administrators access group to the Pega-RULES: Core Engine Processing Agent data instance for every active node.

ADM data mart agent

Adaptive Decision Manager can capture historical data for reporting purposes. The ADM Data Mart is implemented by periodically starting the ADMSnapshot agent ( Pega-DecisionEngine ruleset, PRPC:Administrators access group).

The agent runs the pzGetAllModelDetails activity. This activity captures the state of models, predictors, and predictor binning in the ADM system at a particular point in time and writes that information to a table using the Data-Decision-ADM-ModelSnapshot and Data-Decision-ADM-PredictiveBinningSnapshot classes. The Data-Decision-ADM-ModelSnapshot class is mapped to the PR_DATA_DM_ADMMART_MDL_FACT table and the Data-Decision-ADM-PredictorBinningSnapshot class is mapped to the PR_DATA_DM_ADMMART_PRED table in PegaDATA database.

By default, the ADMSnapshot agent is scheduled to run once per day. The Data Mart settings in the Adaptive Decision Manager section of the Services landing page allow you to define how often the activity runs to capture the state of models and predictor binning.

Initialize DSM feature toggles

A feature toggle enables or disables particular decisioning functionality through a corresponding instance of the dynamic system settings rule. A dynamic system setting corresponding to a particular feature toggle has the following format: features/<featurename> and can be set to true or false.

The InitializeDSMFeatureToggles agent ( Pega-DecisionEngine ruleset, PRPC:Administrators access group) runs at the Pega Platform startup and caches feature toggles in an object called FeatureManager. The feature toggles with their dynamic system setting set to true are initialized at the Pega Platform startup and enable particular functionalities of the platform.

Start DSM services

The StartDSMServices agent (Pega-DecisionEngine ruleset, PRPC:Administrators access group) runs at the Pega Platform startup and registers the natural language processing (NLP) pulse extension with the engine. NLP uses the core engine's pulse extension mechanism to notify all nodes about the updates so that those nodes can clear the cached model and reload from the database.

Start Simulation Run