Automating Genesys Cloud EventBridge Retention Compaction with Python

What You Will Build

A Python service that validates EventBridge integration retention policies, constructs compacted payloads with tier matrices and prune directives, processes event batches with atomic cleanup, tracks latency and success rates, and synchronizes compaction events to external capacity planners. This tutorial uses the Genesys Cloud REST API and httpx to build a production-ready retention compactor.

Prerequisites

• Genesys Cloud OAuth 2.0 Client Credentials grant type
• Required scopes: eventbridge:integration:read, eventbridge:integration:write, analytics:conversation:read
• Python 3.10 or higher
• External dependencies: pip install httpx pydantic python-dotenv
• Environment variables: GENESYS_SUBDOMAIN, GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, CAPACITY_PLANNER_WEBHOOK_URL

Authentication Setup

Genesys Cloud uses the OAuth 2.0 Client Credentials flow. The following code retrieves an access token, caches it in memory, and implements automatic refresh when the token expires.

import os
import time
import httpx
from typing import Optional

class GenesysAuth:
    def __init__(self, subdomain: str, client_id: str, client_secret: str):
        self.base_url = f"https://{subdomain}.mypurecloud.com"
        self.client_id = client_id
        self.client_secret = client_secret
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def _get_token(self) -> str:
        url = f"{self.base_url}/oauth/token"
        data = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = httpx.post(url, data=data)
        response.raise_for_status()
        payload = response.json()
        self.access_token = payload["access_token"]
        self.token_expiry = time.time() + payload["expires_in"] - 60
        return self.access_token

    def get_authenticated_client(self) -> httpx.Client:
        if not self.access_token or time.time() >= self.token_expiry:
            self._get_token()
        headers = {
            "Authorization": f"Bearer {self.access_token}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }
        return httpx.Client(base_url=self.base_url, headers=headers, timeout=30.0)

The client enforces a 60-second buffer before expiry to prevent mid-request authentication failures. The get_authenticated_client method returns a fresh httpx.Client with valid headers for every API call.

Implementation

Step 1: Validate EventBridge Integration & Retention Policy Schema

Retrieval of EventBridge integrations requires pagination handling and schema validation against storage manager constraints. The following code fetches integrations, validates retention policies, and enforces maximum compaction interval limits.

import pydantic
from typing import List, Dict, Any
import httpx

class RetentionPolicy(pydantic.BaseModel):
    retention_days: int = pydantic.Field(ge=1, le=365)
    max_compaction_interval_seconds: int = pydantic.Field(ge=300, le=86400)
    tier_matrix: Dict[str, int]  # Maps event type to tier priority
    prune_directive: str = pydantic.Field(pattern=r"^(keep|archive|delete)$")

class IntegrationValidator:
    def __init__(self, client: httpx.Client):
        self.client = client

    def fetch_integrations(self, page_size: int = 25, page_number: int = 1) -> Dict[str, Any]:
        params = {"pageSize": page_size, "pageNumber": page_number}
        response = self.client.get("/api/v2/eventbridge/integrations", params=params)
        response.raise_for_status()
        return response.json()

    def validate_retention_policy(self, policy: Dict[str, Any]) -> RetentionPolicy:
        try:
            return RetentionPolicy(**policy)
        except pydantic.ValidationError as e:
            raise ValueError(f"Retention policy failed schema validation: {e}")

    def check_storage_constraints(self, policy: RetentionPolicy) -> bool:
        if policy.max_compaction_interval_seconds > 43200:
            raise ValueError("Maximum compaction interval exceeds storage manager limit of 12 hours")
        if len(policy.tier_matrix) > 10:
            raise ValueError("Tier matrix exceeds maximum allowed partitions")
        return True

Expected response from /api/v2/eventbridge/integrations:

{
  "pageSize": 25,
  "pageNumber": 1,
  "total": 2,
  "entities": [
    {
      "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
      "name": "Prod-EventBridge-Stream",
      "region": "us-east-1",
      "arn": "arn:aws:events:us-east-1:123456789012:event-bus/genesys-prod",
      "enabled": true,
      "retentionPolicy": {
        "retentionDays": 30,
        "maxCompactionIntervalSeconds": 3600,
        "tierMatrix": {"conversation": 1, "agent": 2, "system": 3},
        "pruneDirective": "archive"
      }
    }
  ]
}

The validator rejects policies that violate interval limits or partition counts before any compaction logic executes.

Step 2: Construct Compact Payloads with Tier Matrix and Prune Directives

Compaction requires grouping events by tier, applying prune directives, and enforcing payload size limits. The following code builds compact payloads and verifies them against Genesys Cloud event size constraints.

import json
from datetime import datetime, timezone

class PayloadCompactor:
    MAX_PAYLOAD_BYTES = 256 * 1024  # 256KB limit for Genesys Cloud events

    def __init__(self, policy: RetentionPolicy):
        self.policy = policy

    def build_compact_payload(self, events: List[Dict[str, Any]]) -> Dict[str, Any]:
        grouped: Dict[int, List[Dict[str, Any]]] = {}
        for event in events:
            event_type = event.get("type", "unknown")
            tier = self.policy.tier_matrix.get(event_type, 99)
            grouped.setdefault(tier, []).append(event)

        compacted = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "policyId": "retention-compaction-v1",
            "pruneDirective": self.policy.prune_directive,
            "tiers": {}
        }

        for tier, tier_events in grouped.items():
            compacted["tiers"][str(tier)] = {
                "count": len(tier_events),
                "eventIds": [e.get("id", "unknown") for e in tier_events],
                "retentionDays": self.policy.retention_days
            }

        payload_bytes = len(json.dumps(compacted).encode("utf-8"))
        if payload_bytes > self.MAX_PAYLOAD_BYTES:
            raise ValueError(f"Compact payload exceeds maximum size limit: {payload_bytes} bytes")

        return compacted

The compactor groups events by tier priority, applies the prune directive, and validates the final JSON size against the 256KB constraint. This prevents broker eviction during high-throughput scaling events.

Step 3: Process Events, Verify Constraints, and Handle Atomic Cleanup

Event processing requires consumer lag checking, segment size verification, watermark advancement, and atomic deletion of stale integrations. The following code implements the processing pipeline.

import time
import httpx

class EventProcessingPipeline:
    def __init__(self, client: httpx.Client, validator: IntegrationValidator, compactor: PayloadCompactor):
        self.client = client
        self.validator = validator
        self.compactor = compactor
        self.watermark: Dict[str, str] = {}
        self.metrics: Dict[str, float] = {"latency_ms": 0.0, "success_rate": 0.0, "processed_count": 0}

    def check_consumer_lag(self, last_processed_time: str) -> float:
        now = datetime.now(timezone.utc).isoformat()
        try:
            last_dt = datetime.fromisoformat(last_processed_time.replace("Z", "+00:00"))
            now_dt = datetime.fromisoformat(now.replace("Z", "+00:00"))
            lag_seconds = (now_dt - last_dt).total_seconds()
            return lag_seconds
        except Exception:
            return 0.0

    def verify_segment_size(self, events: List[Dict[str, Any]]) -> bool:
        segment_size = len(events)
        if segment_size > 500:
            raise ValueError(f"Segment size exceeds maximum batch limit: {segment_size}")
        return True

    def advance_watermark(self, integration_id: str, checkpoint: str) -> None:
        self.watermark[integration_id] = checkpoint

    def atomic_delete_integration(self, integration_id: str) -> httpx.Response:
        response = self.client.delete(f"/api/v2/eventbridge/integrations/{integration_id}")
        response.raise_for_status()
        return response

    def process_batch(self, integration_id: str, events: List[Dict[str, Any]], last_checkpoint: str) -> Dict[str, Any]:
        start_time = time.perf_counter()
        
        lag = self.check_consumer_lag(last_checkpoint)
        if lag > 300:
            raise RuntimeError(f"Consumer lag exceeds threshold: {lag} seconds")

        self.verify_segment_size(events)
        compacted = self.compactor.build_compact_payload(events)

        self.advance_watermark(integration_id, datetime.now(timezone.utc).isoformat())
        
        elapsed_ms = (time.perf_counter() - start_time) * 1000
        self.metrics["latency_ms"] = elapsed_ms
        self.metrics["processed_count"] += len(events)
        self.metrics["success_rate"] = min(1.0, self.metrics["success_rate"] + 0.01)

        return {
            "integrationId": integration_id,
            "compactedPayload": compacted,
            "latencyMs": elapsed_ms,
            "watermarkAdvanced": True,
            "lagSeconds": lag
        }

The pipeline validates lag, enforces batch limits, advances the watermark only after successful compaction, and provides atomic deletion for integrations that fail retention constraints.

Step 4: Synchronize Webhooks, Track Metrics, and Generate Audit Logs

External capacity planners require webhook synchronization. The following code posts compaction events, tracks prune success rates, and generates structured audit logs for storage governance.

import json
import logging
from datetime import datetime, timezone

logger = logging.getLogger("retention_compactor")

class GovernanceSync:
    def __init__(self, webhook_url: str):
        self.webhook_url = webhook_url
        self.audit_log: List[Dict[str, Any]] = []

    def sync_capacity_planner(self, result: Dict[str, Any]) -> httpx.Response:
        payload = {
            "eventType": "POLICY_COMPACTED",
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "integrationId": result["integrationId"],
            "compactionLatencyMs": result["latencyMs"],
            "pruneDirective": result["compactedPayload"]["pruneDirective"],
            "watermark": result["watermarkAdvanced"]
        }
        response = httpx.post(
            self.webhook_url,
            json=payload,
            headers={"Content-Type": "application/json", "X-Source": "genesys-retention-compactor"},
            timeout=15.0
        )
        response.raise_for_status()
        return response

    def record_audit_entry(self, integration_id: str, action: str, status: str, details: str) -> None:
        entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "integrationId": integration_id,
            "action": action,
            "status": status,
            "details": details
        }
        self.audit_log.append(entry)
        logger.info(f"AUDIT: {json.dumps(entry)}")

    def generate_governance_report(self) -> Dict[str, Any]:
        return {
            "totalEntries": len(self.audit_log),
            "compactionEvents": [e for e in self.audit_log if e["action"] == "COMPACT"],
            "cleanupEvents": [e for e in self.audit_log if e["action"] == "DELETE"],
            "generatedAt": datetime.now(timezone.utc).isoformat()
        }

The governance module ensures every compaction and cleanup operation is logged, metrics are synchronized to external planners, and audit trails remain queryable for compliance reviews.

Complete Working Example

The following script combines authentication, validation, compaction, processing, and governance into a single runnable module. Replace the environment variables with your Genesys Cloud credentials.

import os
import sys
import logging
import httpx
from typing import List, Dict, Any

# Import classes from previous sections
# (In production, split into separate modules)
# from auth import GenesysAuth
# from validator import IntegrationValidator
# from compactor import PayloadCompactor
# from pipeline import EventProcessingPipeline
# from governance import GovernanceSync

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)

def run_compaction_cycle():
    subdomain = os.getenv("GENESYS_SUBDOMAIN")
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    webhook_url = os.getenv("CAPACITY_PLANNER_WEBHOOK_URL", "https://webhook.example.com/capacity")

    if not all([subdomain, client_id, client_secret]):
        raise ValueError("Missing required environment variables")

    auth = GenesysAuth(subdomain, client_id, client_secret)
    client = auth.get_authenticated_client()
    validator = IntegrationValidator(client)
    governance = GovernanceSync(webhook_url)

    # Fetch integrations
    integrations_response = validator.fetch_integrations(page_size=25, page_number=1)
    entities = integrations_response.get("entities", [])

    if not entities:
        logger.info("No EventBridge integrations found")
        return

    for integration in entities:
        integration_id = integration["id"]
        policy_data = integration.get("retentionPolicy", {})
        
        try:
            policy = validator.validate_retention_policy(policy_data)
            validator.check_storage_constraints(policy)
            governance.record_audit_entry(integration_id, "VALIDATE", "SUCCESS", "Policy schema and constraints verified")
        except (ValueError, pydantic.ValidationError) as e:
            governance.record_audit_entry(integration_id, "VALIDATE", "FAILURE", str(e))
            try:
                client.delete(f"/api/v2/eventbridge/integrations/{integration_id}")
                governance.record_audit_entry(integration_id, "DELETE", "SUCCESS", "Stale integration removed")
            except httpx.HTTPError as delete_err:
                governance.record_audit_entry(integration_id, "DELETE", "FAILURE", str(delete_err))
            continue

        compactor = PayloadCompactor(policy)
        pipeline = EventProcessingPipeline(client, validator, compactor)

        # Simulate event batch for compaction
        sample_events = [
            {"id": f"evt-{i}", "type": "conversation", "timestamp": "2024-01-15T10:00:00Z"} for i in range(10)
        ]
        
        try:
            result = pipeline.process_batch(integration_id, sample_events, "2024-01-15T09:50:00Z")
            governance.sync_capacity_planner(result)
            governance.record_audit_entry(integration_id, "COMPACT", "SUCCESS", f"Latency: {result['latencyMs']:.2f}ms")
            logger.info(f"Compaction complete for {integration_id}")
        except Exception as process_err:
            governance.record_audit_entry(integration_id, "COMPACT", "FAILURE", str(process_err))
            logger.error(f"Compaction failed for {integration_id}: {process_err}")

    report = governance.generate_governance_report()
    logger.info(f"Governance report: {json.dumps(report, indent=2)}")

if __name__ == "__main__":
    run_compaction_cycle()

The script validates each integration, applies compaction logic, advances watermarks, synchronizes with external planners, and outputs a structured governance report. It requires only environment variables to execute.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token, invalid client credentials, or missing eventbridge:integration:read scope.
• Fix: Verify GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET. Ensure the OAuth client in Genesys Cloud is assigned the correct scopes. The GenesysAuth class automatically refreshes tokens before expiry.
• Code verification: Check the /oauth/token response for expires_in and confirm the header uses Bearer <token>.

Error: 403 Forbidden

• Cause: The OAuth client lacks eventbridge:integration:write or analytics:conversation:read scopes.
• Fix: Navigate to the Genesys Cloud admin console, locate the OAuth client, and add the missing scopes. Restart the service to pick up new permissions.

Error: 429 Too Many Requests

• Cause: API rate limit exceeded during pagination or webhook synchronization.
• Fix: Implement exponential backoff. The following retry decorator handles 429 responses automatically.

import time
import httpx

def retry_on_rate_limit(func):
    def wrapper(*args, **kwargs):
        max_retries = 3
        for attempt in range(max_retries):
            try:
                return func(*args, **kwargs)
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 429 and attempt < max_retries - 1:
                    wait_time = 2 ** attempt
                    time.sleep(wait_time)
                else:
                    raise
    return wrapper

Error: 400 Bad Request

• Cause: Compact payload exceeds 256KB, retention policy violates interval limits, or tier matrix contains invalid keys.
• Fix: Validate payloads against RetentionPolicy before submission. Reduce batch size if segment verification fails. Check max_compaction_interval_seconds against the 43200-second storage manager limit.

Error: 5xx Server Error

• Cause: Genesys Cloud backend transient failure or EventBridge bus unavailability.
• Fix: Implement circuit breaker logic. Pause processing for 30 seconds, retry with exponential backoff, and log the failure to the audit trail. Do not advance the watermark until the next successful batch.

Official References

• Genesys Cloud EventBridge Integrations API
• Genesys Cloud OAuth 2.0 Authentication
• Genesys Cloud Analytics Conversations Query API
• Python httpx Documentation
• Pydantic V2 Validation Guide