You can use the decision engine to implement high available event-driven decisioning on Red Hat OpenShift Container Platform.

An event models a fact that happened in a specific point in time. The decision engine offers a rich set of temporal operators to compare, correlate, and accumulate events. In event-driven decisioning, the decision engine processes complex series of decisions based on events. Every event can alter the state of the engine, influencing decisions for subsequent events.

You cannot use a standard deployment of Red Hat Process Automation Manager on Red Hat OpenShift Container Platform, as described in Deploying a Red Hat Process Automation Manager environment on Red Hat OpenShift Container Platform using Operators, to run high available event-driven decisioning. The deployment includes Process Server (KIE Server) pods, which remain independent of each other when scaled. The states of the pods are not synchronized. Therefore, only stateless calls can be processed reliably.

The Complex Event Processing (CEP) API is useful for event-driven decisioning with the decision engine. The decision engine uses CEP to detect and process multiple events within a collection of events, to uncover relationships that exist between events, and to infer new data from the events and their relationships. For more information about CEP in the decision engine, see Decision engine in Red Hat Process Automation Manager.

You can implement high available event-driven decisioning on Red Hat OpenShift Container Platform based on the reference implementation provided with Red Hat Process Automation Manager. This implementation provides an environment with safe failover.

In this reference implementation, you can scale the pod with the processing code. The replicas of the pod are not independent. One of the replicas is automatically designated leader. If the leader ceases to function, another replica is automatically made leader, and the processing continues without interruption or data loss.

The election of the leader is implemented with Kubernetes ConfigMaps. Coordination of the leader with other replicas is performed with exchanged messages through Kafka. The leader is always the first to process an event. When processing is complete, the leader notifies other replicas. A replica that is not the leader starts executing an event only after it has been completely processed on the leader.

When a new replica joins the cluster, this replica requests a snapshot of the current Drools session from the leader. The leader can use a recent existing snapshot if one is available in a Kafka topic. If a recent snapshot is not available, the leader produces a new snapshot on demand. After receiving the snapshot, the new replica deserializes it and eventually executes the last events not included in the snapshot before starting to process new events in coordination with the leader.

The high-availability (HA) CEP server runs on the Red Hat OpenShift Container Platform environment. It includes all necessary Drools rules and other code required to process events.

You must prepare the source, build it, and then deploy it on Red Hat OpenShift Container Platform.

You must adapt your CEP client code to communicate with the HA CEP server image. You can use the sample project included in the reference implementation for your client code. You can run your client code inside or outside the OpenShift environment.

When developing client and server code for high-availability CEP, you must follow certain additional requirements.

kie-remote API

The client code must use the kie-remote API and not the kie API. The kie-remote API is specified in the org.kie:kie-remote Maven artifact. You can find the source code in the kie-remote Maven module.

Explicit timestamps

The decision engine needs to determine the sequence in which events happen. For this reason, every event must have an associated timestamp assigned to it. In a high-availability environment, make this timestamp a property of the JavaBean that models the event. You must then annotate the event class with the @Timestamp annotation, where the name of the timestamp attribute itself is the parameter, as in the following example:

@Role(Role.Type.EVENT)
@Timestamp("myTime")
public class StockTickEvent implements Serializable {

    private String company;
    private double price;
    private long myTime;
}

If you do not provide a timestamp attribute, Drools assigns a timestamp to every event based on the time when the event is inserted by the client into a remote session. However, this mechanism depends on the clocks on the client machines. If clocks between different clients diverge, inconsistencies can occur between events inserted by these hosts.

Lambda expressions for non-memory actions

Working memory actions (actions to insert, modify, or delete information in the working memory of the decision engine) must be processed on every node of the cluster. Actions that are not memory actions must be executed only on the leader.

For example, the code might include the following rule:

rule FindAdult when
  $p : Person(age >= 18)
then
  modify($p) { setAdult(true) }; // working memory action
  sendEmailTo($p); // side effect
end

When this rule is triggered, the person must be marked as an adult on every node. However, only the leader must send the email, so that only one copy of the email is sent.

Therefore, in your code, you must wrap the email action (called a side effect) in a lambda expression, as shown in the following example:

rule FindAdult when
  $p : Person(age >= 18)
then
  modify($p) { setAdult(true) };
  DroolsExecutor.getInstance().execute( () -> sendEmailTo($p) );
end