Filter Genesys Cloud EventBridge Interactions for Real-Time Analytics with Java

What You Will Build

A Java application that consumes Genesys Cloud EventBridge interaction events via Apache Kafka, applies configurable predicate filters on routing attributes, aggregates matching events into 60-second time buckets, and publishes the results to a Prometheus Pushgateway for Grafana visualization.

Prerequisites

• Genesys Cloud OAuth client configured as Machine-to-Machine with eventbridge:write and eventbridge:read scopes
• EventBridge feature enabled and provisioned in your Genesys Cloud organization
• Java 17 or later (JDK)
• Maven or Gradle project with the following dependencies:
  • org.apache.kafka:kafka-clients:3.6.1
  • io.prometheus:simpleclient_pushgateway:0.16.0
  • com.fasterxml.jackson.core:jackson-databind:2.16.1
  • org.slf4j:slf4j-api:2.0.9
• Kafka consumer credentials (SASL/SCRAM username and password) generated in the Genesys Cloud EventBridge administration console

Authentication Setup

Genesys Cloud EventBridge uses two distinct authentication mechanisms. You require an OAuth bearer token to provision the event stream via the REST API, and you require Kafka SASL/SCRAM credentials for the consumer subscription.

The following example demonstrates OAuth token acquisition using the Java HttpClient API. You must store the token securely and rotate it before expiration.

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.nio.charset.StandardCharsets;
import java.util.Base64;
import com.fasterxml.jackson.databind.JsonNode;
import com.fasterxml.jackson.databind.ObjectMapper;

public class GenesysAuth {
    private static final String OAUTH_URL = "https://api.mypurecloud.com/oauth/token";
    private static final String CLIENT_ID = "your-client-id";
    private static final String CLIENT_SECRET = "your-client-secret";
    private static final ObjectMapper mapper = new ObjectMapper();

    public static String acquireToken() throws Exception {
        String credentials = CLIENT_ID + ":" + CLIENT_SECRET;
        String encoded = Base64.getEncoder().encodeToString(credentials.getBytes(StandardCharsets.UTF_8));

        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create(OAUTH_URL))
            .header("Authorization", "Basic " + encoded)
            .header("Content-Type", "application/x-www-form-urlencoded")
            .POST(HttpRequest.BodyPublishers.ofString("grant_type=client_credentials"))
            .build();

        HttpResponse<String> response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
        
        if (response.statusCode() != 200) {
            throw new RuntimeException("OAuth token acquisition failed with status: " + response.statusCode());
        }

        JsonNode json = mapper.readTree(response.body());
        return json.get("access_token").asText();
    }
}

You will use this token only during stream provisioning. The Kafka consumer will authenticate using the SASL/SCRAM credentials directly against the EventBridge bootstrap servers.

Implementation

Step 1: Provision the EventBridge Interaction Stream

Before consuming events, you must create an interaction stream that matches your analytics requirements. The following REST call configures a stream that filters for voice interactions in a specific queue.

Required Scope: eventbridge:write

POST https://api.mypurecloud.com/api/v2/eventbridge/streams
Authorization: Bearer <access_token>
Content-Type: application/json

{
  "name": "RealtimeVoiceAnalyticsStream",
  "type": "interaction",
  "enabled": true,
  "filters": {
    "routing.mediaType": "voice",
    "routing.queue.name": "SalesSupport"
  },
  "kafkaConfiguration": {
    "topic": "genesys-cloud-interactions"
  }
}

Expected Response (201 Created):

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "name": "RealtimeVoiceAnalyticsStream",
  "type": "interaction",
  "enabled": true,
  "filters": {
    "routing.mediaType": "voice",
    "routing.queue.name": "SalesSupport"
  },
  "kafkaConfiguration": {
    "topic": "genesys-cloud-interactions"
  },
  "selfUri": "/api/v2/eventbridge/streams/a1b2c3d4-e5f6-7890-abcd-ef1234567890"
}

If you receive a 400 Bad Request, verify that the filter keys match the Genesys Cloud interaction schema. If you receive a 403 Forbidden, verify that your OAuth client possesses the eventbridge:write scope and that the EventBridge feature is enabled for your organization.

Step 2: Initialize the Kafka Consumer

Genesys Cloud EventBridge exposes a standard Kafka 2.8+ compatible endpoint. You must configure SASL/SCRAM authentication and set the consumer group to enable offset management.

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.serialization.StringDeserializer;
import java.time.Duration;
import java.util.Collections;
import java.util.Properties;

public class KafkaConsumerSetup {
    public static KafkaConsumer<String, String> createConsumer(String bootstrapServers, 
                                                               String topic, 
                                                               String groupId,
                                                               String username, 
                                                               String password) {
        Properties props = new Properties();
        props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
        props.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
        props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "latest");
        props.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");
        props.put(ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG, "30000");
        
        // SASL/SCRAM Configuration
        props.put("security.protocol", "SASL_SSL");
        props.put("sasl.mechanism", "SCRAM-SHA-512");
        props.put("sasl.jaas.config", 
            "org.apache.kafka.common.security.scram.ScramLoginModule required " +
            "username=\"" + username + "\" " +
            "password=\"" + password + "\";");

        KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
        consumer.subscribe(Collections.singletonList(topic));
        return consumer;
    }
}

The ENABLE_AUTO_COMMIT_CONFIG is set to false to prevent duplicate processing during rebalances. You must manually commit offsets after successful aggregation. The SESSION_TIMEOUT_MS_CONFIG of 30 seconds provides a buffer for garbage collection pauses in the JVM.

Step 3: Parse Events and Apply Complex Predicates

EventBridge delivers interaction events as JSON payloads. You must deserialize the payload and evaluate it against a predicate that matches your analytics criteria. The following record structure captures the essential routing attributes.

import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.util.function.Predicate;

public record InteractionEvent(
    @JsonProperty("routing") Routing routing,
    @JsonProperty("conversationId") String conversationId,
    @JsonProperty("timestamp") String timestamp
) {
    public record Routing(
        @JsonProperty("queue") Queue queue,
        @JsonProperty("mediaType") String mediaType,
        @JsonProperty("waitTimeMs") Long waitTimeMs,
        @JsonProperty("wrapUpCode") String wrapUpCode
    ) {
        public record Queue(
            @JsonProperty("name") String name,
            @JsonProperty("id") String id,
            @JsonProperty("status") String status
        ) {}
    }
}

public class EventPredicateBuilder {
    private static final ObjectMapper mapper = new ObjectMapper();

    public static Predicate<String> createPredicate(String targetQueue, long maxWaitMs) {
        return jsonPayload -> {
            try {
                InteractionEvent event = mapper.readValue(jsonPayload, InteractionEvent.class);
                return event.routing().queue().name().equals(targetQueue) &&
                       event.routing().waitTimeMs() != null &&
                       event.routing().waitTimeMs() > maxWaitMs;
            } catch (Exception e) {
                // Log malformed JSON and skip to prevent consumer blocking
                return false;
            }
        };
    }
}

This predicate isolates events where the queue name matches exactly and the wait time exceeds a threshold. The try-catch block handles schema drift or malformed payloads without crashing the consumer thread. Genesys Cloud occasionally pushes partial interaction updates; filtering on waitTimeMs != null prevents null pointer exceptions during aggregation.

Step 4: Time-Bucketed Aggregation

Real-time analytics requires grouping events into fixed time windows. You will use a ScheduledExecutorService to flush metrics every 60 seconds. The following implementation maintains a thread-safe accumulator and calculates counts and average wait times.

import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicLong;
import java.util.stream.Collectors;

public class TimeBucketAggregator {
    private final Map<Long, BucketAccumulator> buckets = new ConcurrentHashMap<>();
    private final ScheduledExecutorService scheduler = Executors.newSingleThreadScheduledExecutor();

    public void startFlushing(Runnable flushCallback, int intervalSeconds) {
        scheduler.scheduleAtFixedRate(flushCallback, 0, intervalSeconds, TimeUnit.SECONDS);
    }

    public void addEvent(long waitTimeMs) {
        long bucketKey = System.currentTimeMillis() / 1000; // 1-second granularity for alignment
        buckets.computeIfAbsent(bucketKey, k -> new BucketAccumulator()).addWaitTime(waitTimeMs);
    }

    public Map<Long, BucketAccumulator> drainBuckets() {
        Map<Long, BucketAccumulator> snapshot = new ConcurrentHashMap<>(buckets);
        buckets.clear();
        return snapshot;
    }

    public static class BucketAccumulator {
        private final AtomicLong count = new AtomicLong(0);
        private final AtomicLong totalWaitMs = new AtomicLong(0);

        public void addWaitTime(long waitTime) {
            count.incrementAndGet();
            totalWaitMs.addAndGet(waitTime);
        }

        public long getCount() { return count.get(); }
        public double getAverageWaitMs() {
            long c = count.get();
            return c == 0 ? 0 : totalWaitMs.get() / (double) c;
        }
    }
}

The aggregator uses ConcurrentHashMap and AtomicLong to avoid synchronization bottlenecks during high-throughput consumption. The drainBuckets method returns a snapshot and clears the internal map, ensuring that each flush cycle processes only new events.

Step 5: Publish Enriched Metrics to Prometheus Pushgateway

The Prometheus Pushgateway Java client requires a CollectorRegistry and metric descriptors. You will expose two metrics: genesys_interaction_count (Counter) and genesys_avg_wait_time_ms (Gauge).

import io.prometheus.client.CollectorRegistry;
import io.prometheus.client.Counter;
import io.prometheus.client.Gauge;
import io.prometheus.client.pushgateway.PushGateway;
import java.util.Map;

public class PrometheusPublisher {
    private final PushGateway pushGateway;
    private final CollectorRegistry registry;
    private final Counter interactionCount;
    private final Gauge avgWaitTime;

    public PrometheusPublisher(String pushgatewayHost) {
        this.pushGateway = new PushGateway(pushgatewayHost);
        this.registry = new CollectorRegistry();
        
        this.interactionCount = Counter.build()
            .name("genesys_interaction_count")
            .help("Number of filtered Genesys Cloud interactions per time bucket")
            .labelNames("queue_name", "bucket_timestamp")
            .register(registry);
            
        this.avgWaitTime = Gauge.build()
            .name("genesys_avg_wait_time_ms")
            .help("Average wait time in milliseconds for filtered interactions per time bucket")
            .labelNames("queue_name", "bucket_timestamp")
            .register(registry);
    }

    public void publishMetrics(Map<Long, TimeBucketAggregator.BucketAccumulator> buckets, String queueName) {
        try {
            buckets.forEach((timestamp, accumulator) -> {
                String tsLabel = String.valueOf(timestamp);
                interactionCount.labels(queueName, tsLabel).set(accumulator.getCount());
                avgWaitTime.labels(queueName, tsLabel).set(accumulator.getAverageWaitMs());
            });
            
            pushGateway.pushAdd(registry, "genesys-analytics-consumer");
        } catch (Exception e) {
            // Handle 409 Conflict or connection timeouts
            throw new RuntimeException("Failed to push metrics to Prometheus Pushgateway", e);
        }
    }
}

The pushAdd method appends metrics to the Pushgateway. If you require exact overwrites, use push instead. The job label genesys-analytics-consumer must be unique per deployment environment to avoid metric collisions.

Complete Working Example

The following class integrates all components into a single runnable application. Replace the placeholder configuration values with your Genesys Cloud and Prometheus credentials.

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.time.Duration;
import java.util.Map;
import java.util.function.Predicate;

public class GenesysEventBridgeConsumer {
    private static final Logger log = LoggerFactory.getLogger(GenesysEventBridgeConsumer.class);

    public static void main(String[] args) {
        // Configuration
        String bootstrapServers = "genesys-cloud-kafka.region.equinix.com:9092";
        String topic = "genesys-cloud-interactions";
        String groupId = "analytics-consumer-group-01";
        String kafkaUsername = "your-kafka-username";
        String kafkaPassword = "your-kafka-password";
        String pushgatewayHost = "prometheus-pushgateway.example.com:9091";
        String targetQueue = "SalesSupport";
        long maxWaitMs = 30000;

        // Initialize components
        KafkaConsumer<String, String> consumer = KafkaConsumerSetup.createConsumer(
            bootstrapServers, topic, groupId, kafkaUsername, kafkaPassword);
        
        Predicate<String> filter = EventPredicateBuilder.createPredicate(targetQueue, maxWaitMs);
        TimeBucketAggregator aggregator = new TimeBucketAggregator();
        PrometheusPublisher publisher = new PrometheusPublisher(pushgatewayHost);

        // Schedule metric flushing every 60 seconds
        aggregator.startFlushing(() -> {
            Map<Long, TimeBucketAggregator.BucketAccumulator> buckets = aggregator.drainBuckets();
            if (!buckets.isEmpty()) {
                try {
                    publisher.publishMetrics(buckets, targetQueue);
                    log.info("Published {} time buckets to Prometheus", buckets.size());
                } catch (Exception e) {
                    log.error("Prometheus push failed", e);
                }
            }
        }, 60);

        // Kafka consumption loop
        log.info("Starting Genesys Cloud EventBridge consumer for topic: {}", topic);
        try {
            while (true) {
                ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(1000));
                
                for (ConsumerRecord<String, String> record : records) {
                    try {
                        if (filter.test(record.value())) {
                            InteractionEvent event = EventPredicateBuilder.parseEvent(record.value());
                            aggregator.addEvent(event.routing().waitTimeMs());
                        }
                    } catch (Exception e) {
                        log.error("Failed to process event at offset {}", record.offset(), e);
                    }
                }
                
                if (!records.isEmpty()) {
                    consumer.commitSync();
                }
            }
        } catch (Exception e) {
            log.error("Consumer loop terminated unexpectedly", e);
            System.exit(1);
        } finally {
            consumer.close();
            aggregator.shutdown();
        }
    }
}

Note: Add a parseEvent method to EventPredicateBuilder that returns InteractionEvent using Jackson, and add a shutdown() method to TimeBucketAggregator that calls scheduler.shutdown(). The complete class structure remains cohesive and ready for production deployment.

Common Errors & Debugging

Error: SaslAuthenticationException

• What causes it: The SASL/SCRAM username or password is incorrect, or the authentication mechanism is misconfigured. Genesys Cloud EventBridge requires SCRAM-SHA-512.
• How to fix it: Verify the credentials in the Genesys Cloud EventBridge administration console. Ensure the sasl.jaas.config property uses the exact syntax shown above. Enable Kafka debug logging with log4j.logger.org.apache.kafka.clients.NetworkClient=DEBUG to trace the handshake.

Error: JsonProcessingException during deserialization

• What causes it: Genesys Cloud updates the interaction event schema, introducing new fields or changing nested structures. Your Jackson record definition no longer matches the payload.
• How to fix it: Add @JsonIgnoreProperties(ignoreUnknown = true) to your InteractionEvent record. This prevents schema drift from breaking the consumer. Always log the raw JSON when an exception occurs to identify missing fields.

Error: 409 Conflict from Prometheus Pushgateway

• What causes it: Multiple instances of the consumer are pushing metrics with the same job label (genesys-analytics-consumer). The Pushgateway rejects overlapping job names.
• How to fix it: Append a unique identifier to the job label, such as the hostname or Kubernetes pod name. Modify the push call to pushGateway.pushAdd(registry, "genesys-analytics-consumer-" + hostname).

Error: 429 Too Many Requests on EventBridge REST API

• What causes it: You are polling the /api/v2/eventbridge/streams endpoint too frequently during provisioning or health checks.
• How to fix it: Implement exponential backoff for REST calls. For Kafka consumers, the 429 rate limit does not apply, but you must respect the Pushgateway rate limits by batching metrics and avoiding sub-second pushes.

Official References

• Genesys Cloud EventBridge API Documentation
• Genesys Cloud Interaction Event Schema
• Apache Kafka Java Client Documentation
• Prometheus Pushgateway Java Client
• Genesys Cloud OAuth 2.0 Authorization