Scheduling Genesys Cloud Purge API Interaction Cleanup via Python
What You Will Build
- You will build a Python scheduler that constructs, validates, and posts Genesys Cloud data purge schedules for interaction cleanup, tracks execution metrics, and emits compliance webhooks.
- You will use the Genesys Cloud CX Data Purge API v2, Conversations Search API, and Platform Webhook API.
- You will implement the solution in Python 3.9+ using
httpx,pydantic,croniter, and the officialgenesys-cloud-sdk.
Prerequisites
- OAuth client type and required scopes: Machine-to-machine OAuth client with
purge:manage,data:purge:read,conversation:read, andwebhook:managescopes. - SDK version or API version:
genesys-cloud-sdk>=130.0.0, Genesys Cloud API v2. - Language/runtime requirements: Python 3.9+,
asyncio, standard librarylogging,datetime,time. - External dependencies:
pip install genesys-cloud-sdk httpx pydantic croniter
Authentication Setup
Genesys Cloud uses OAuth 2.0 client credentials flow for server-to-server integrations. The official SDK handles token acquisition and refresh automatically when configured, but you must explicitly set the credentials before issuing API calls. The token expires after thirty minutes, and the SDK caches and rotates it transparently.
import os
from genesyscloud import PlatformClient
def initialize_platform_client() -> PlatformClient:
platform_client = PlatformClient()
login_url = os.getenv("GENESYS_LOGIN_URL", "https://login.us.genesyscloud.com")
client_id = os.getenv("GENESYS_CLIENT_ID")
client_secret = os.getenv("GENESYS_CLIENT_SECRET")
if not client_id or not client_secret:
raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET environment variables are required.")
platform_client.set_oauth_client_credentials(client_id, client_secret, login_url)
return platform_client
Implementation
Step 1: Validate Cron Directives and Purge Constraints
Before scheduling, you must validate the cron expression against Genesys Cloud constraints. The platform enforces a maximum_schedule_interval (typically seven days) and rejects overlapping schedules for the same entity. You will use croniter to verify syntax and calculate the next execution window. You will also enforce a purge_constraints dictionary that defines retention limits and batch boundaries.
import logging
from datetime import datetime, timedelta, timezone
from typing import Dict, Any
from croniter import croniter, CroniterBadCronError, CroniterNotAlphaError
from pydantic import BaseModel, validator
logger = logging.getLogger(__name__)
class PurgeConstraints(BaseModel):
maximum_schedule_interval_days: int = 7
minimum_retention_days: int = 30
maximum_batch_size: int = 10000
allowed_entities: list = ["conversation", "interaction", "voice", "chat"]
class ScheduleValidator:
def __init__(self, constraints: PurgeConstraints):
self.constraints = constraints
def validate_cron_and_constraints(self, cron_expression: str, retention_days: int, batch_size: int) -> Dict[str, Any]:
try:
cron = croniter(cron_expression)
next_run = cron.get_next(datetime)
except (CroniterBadCronError, CroniterNotAlphaError) as e:
raise ValueError(f"Cron directive validation failed: {e}")
# Calculate interval between runs to enforce maximum_schedule_interval
previous_run = cron.get_prev(datetime)
interval = (next_run - previous_run).days
if interval > self.constraints.maximum_schedule_interval_days:
raise ValueError(
f"Cron interval ({interval} days) exceeds maximum_schedule_interval_limit ({self.constraints.maximum_schedule_interval_days} days)."
)
if retention_days < self.constraints.minimum_retention_days:
raise ValueError(
f"Retention period ({retention_days} days) violates retention_policy_calculation minimum ({self.constraints.minimum_retention_days} days)."
)
if batch_size > self.constraints.maximum_batch_size:
raise ValueError(
f"Batch size ({batch_size}) exceeds batch_size_optimization maximum ({self.constraints.maximum_batch_size})."
)
return {
"cron_valid": True,
"next_execution_utc": next_run.isoformat(),
"interval_days": interval,
"retention_compliant": True,
"batch_optimized": True
}
Step 2: Overlapping Job Checking and Active Interaction Verification
Genesys Cloud rejects schedules that overlap with existing purge jobs for the same data entity. You must query existing schedules, parse their cron expressions, and detect conflicts. You must also verify that no active interactions match the purge filter before enabling the schedule. This prevents premature deletion during scaling events or ongoing routing.
import httpx
from genesyscloud.data_purge.api import DataPurgeApi
from genesyscloud.conversations.api import ConversationsApi
async def check_overlapping_schedules(platform_client: PlatformClient, target_cron: str, entity: str) -> bool:
api: DataPurgeApi = platform_client.data_purge
existing_schedules = []
cursor = None
# Pagination handling for /api/v2/data/purge/schedules
while True:
try:
resp = api.get_data_purge_schedules(expands=["purgeConfig"], cursor=cursor)
existing_schedules.extend(resp.entities)
cursor = resp.next_page_cursor
if not cursor:
break
except Exception as e:
logger.error(f"Failed to fetch existing schedules: {e}")
raise
target_cron_obj = croniter(target_cron)
for schedule in existing_schedules:
if not schedule.enabled:
continue
if schedule.purge_config and schedule.purge_config.entity == entity:
try:
existing_cron_obj = croniter(schedule.cron_expression)
# Check for exact match or dangerous overlap within a 1-hour window
target_next = target_cron_obj.get_next(datetime)
existing_next = existing_cron_obj.get_next(datetime)
if abs((target_next - existing_next).total_seconds()) < 3600:
logger.warning(f"Overlapping cron detected with schedule ID: {schedule.id}")
return True
except Exception:
continue
return False
async def verify_no_active_interactions(platform_client: PlatformClient, filter_type: str) -> bool:
api: ConversationsApi = platform_client.conversations
# Query active interactions matching the purge matrix filter
search_body = {
"query": f"status:active AND type:{filter_type}",
"pageSize": 100
}
try:
resp = api.post_conversations_search(body=search_body)
if resp.total_count and resp.total_count > 0:
logger.warning(f"Active interactions detected ({resp.total_count}). Purge schedule must remain disabled until clearance.")
return False
return True
except Exception as e:
logger.error(f"Active interaction verification failed: {e}")
raise
Step 3: Atomic HTTP POST, Retention Calculation, and Webhook Alignment
You will construct the schedule payload using the validated parameters, map internal references to the official API schema, and issue an atomic HTTP POST. You will implement retry logic for 429 rate limits, track scheduling latency, and register a webhook for external compliance auditors. The webhook ensures alignment with governance pipelines.
import time
import json
from typing import Optional
class PurgeScheduler:
def __init__(self, platform_client: PlatformClient):
self.platform_client = platform_client
self.metrics = {"total_attempts": 0, "success_count": 0, "failure_count": 0, "avg_latency_ms": 0.0}
self.audit_log = []
async def create_purge_schedule(
self,
purge_ref: str,
cron_expression: str,
entity: str,
retention_days: int,
batch_size: int,
webhook_url: str
) -> Dict[str, Any]:
self.metrics["total_attempts"] += 1
start_time = time.perf_counter()
# Map internal purge-matrix to official API structure
payload = {
"name": f"Interaction Cleanup - {purge_ref}",
"description": f"Scheduled purge for {entity} with {retention_days} day retention.",
"enabled": False, # Enabled after verification
"cronExpression": cron_expression,
"purgeConfig": {
"entity": entity,
"filter": {
"type": entity,
"status": "closed"
},
"retentionDays": retention_days,
"batchSize": batch_size
}
}
# Atomic HTTP POST with 429 retry logic
async with httpx.AsyncClient(timeout=30.0) as client:
headers = {
"Authorization": f"Bearer {self.platform_client.auth_token.get_access_token()}",
"Content-Type": "application/json"
}
url = f"https://{self.platform_client.default_domain}/api/v2/data/purge/schedules"
max_retries = 3
for attempt in range(max_retries):
response = await client.post(url, headers=headers, json=payload)
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
logger.info(f"Rate limited (429). Retrying in {retry_after}s (attempt {attempt + 1}).")
await asyncio.sleep(retry_after)
continue
response.raise_for_status()
break
else:
raise Exception("Max retries exceeded for purge schedule creation.")
schedule_data = response.json()
latency_ms = (time.perf_counter() - start_time) * 1000
self._update_metrics(latency_ms, True)
self._log_audit("SCHEDULE_CREATED", purge_ref, latency_ms)
# Register compliance webhook
await self._register_compliance_webhook(schedule_data["id"], webhook_url)
return {
"schedule_id": schedule_data["id"],
"purge_ref": purge_ref,
"cron": cron_expression,
"latency_ms": latency_ms,
"status": "created"
}
async def _register_compliance_webhook(self, schedule_id: str, target_url: str):
webhook_payload = {
"name": f"Compliance Auditor - Purge {schedule_id}",
"targets": [{"type": "web", "address": target_url}],
"events": ["purge.schedule.triggered", "purge.schedule.completed", "purge.schedule.failed"],
"enabled": True
}
async with httpx.AsyncClient(timeout=20.0) as client:
headers = {
"Authorization": f"Bearer {self.platform_client.auth_token.get_access_token()}",
"Content-Type": "application/json"
}
url = f"https://{self.platform_client.default_domain}/api/v2/platform/webhooks/v2"
resp = await client.post(url, headers=headers, json=webhook_payload)
resp.raise_for_status()
logger.info(f"Compliance webhook registered for schedule {schedule_id}")
def _update_metrics(self, latency_ms: float, success: bool):
if success:
self.metrics["success_count"] += 1
else:
self.metrics["failure_count"] += 1
total = self.metrics["total_attempts"]
prev_avg = self.metrics["avg_latency_ms"]
self.metrics["avg_latency_ms"] = ((prev_avg * (total - 1)) + latency_ms) / total
def _log_audit(self, event: str, ref: str, latency: float):
entry = {
"timestamp": datetime.now(timezone.utc).isoformat(),
"event": event,
"purge_ref": ref,
"latency_ms": latency,
"success_rate": self.metrics["success_count"] / max(1, self.metrics["total_attempts"])
}
self.audit_log.append(entry)
logger.info(f"AUDIT: {json.dumps(entry)}")
Complete Working Example
The following script combines authentication, validation, verification, scheduling, and audit tracking into a single executable module. Replace the environment variables with your credentials before running.
import asyncio
import os
import logging
from genesyscloud import PlatformClient
from pydantic import ValidationError
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")
async def main():
try:
platform_client = PlatformClient()
login_url = os.getenv("GENESYS_LOGIN_URL", "https://login.us.genesyscloud.com")
client_id = os.getenv("GENESYS_CLIENT_ID")
client_secret = os.getenv("GENESYS_CLIENT_SECRET")
platform_client.set_oauth_client_credentials(client_id, client_secret, login_url)
# 1. Validate cron and constraints
validator = ScheduleValidator(
constraints=PurgeConstraints(maximum_schedule_interval_days=7, minimum_retention_days=30, maximum_batch_size=5000)
)
validation_result = validator.validate_cron_and_constraints(
cron_expression="0 2 * * 0", # Runs every Sunday at 02:00 UTC
retention_days=45,
batch_size=3000
)
print("Validation passed:", validation_result)
# 2. Check overlapping jobs
has_overlap = await check_overlapping_schedules(platform_client, "0 2 * * 0", "conversation")
if has_overlap:
raise RuntimeError("Scheduling aborted due to overlapping cron directive.")
# 3. Verify active interactions
is_clear = await verify_no_active_interactions(platform_client, "conversation")
if not is_clear:
raise RuntimeError("Scheduling aborted due to active interactions matching purge matrix.")
# 4. Create schedule and register compliance webhook
scheduler = PurgeScheduler(platform_client)
result = await scheduler.create_purge_schedule(
purge_ref="PROD-INT-CLEANUP-001",
cron_expression="0 2 * * 0",
entity="conversation",
retention_days=45,
batch_size=3000,
webhook_url="https://compliance.example.com/webhooks/genesys-purge"
)
print("Schedule created:", result)
print("Audit log:", scheduler.audit_log)
print("Metrics:", scheduler.metrics)
except ValidationError as ve:
logging.error(f"Schema validation failed: {ve}")
except Exception as e:
logging.error(f"Execution failed: {e}")
if __name__ == "__main__":
asyncio.run(main())
Common Errors & Debugging
Error: 400 Bad Request (Invalid Cron or Constraint Violation)
- What causes it: The cron expression contains unsupported syntax (e.g.,
LorWmodifiers not supported by Genesys Cloud cron parser), or theretentionDaysfalls below the organization’s policy minimum. - How to fix it: Use standard UNIX cron syntax. Verify retention days against your tenant’s
purge_constraints. TheScheduleValidatorclass will catch these before the HTTP POST. - Code showing the fix: The
validate_cron_and_constraintsmethod raises aValueErrorwith the exact constraint violation. Catch it and adjust the parameters before retrying.
Error: 403 Forbidden (Missing OAuth Scopes)
- What causes it: The OAuth client lacks
purge:manageorwebhook:managescopes. Genesys Cloud returns 403 when the token does not cover the requested resource path. - How to fix it: Navigate to the Genesys Cloud admin console, edit the OAuth client, and append the missing scopes. Regenerate the token.
- Code showing the fix: The SDK will raise
genesyscloud.platform_client.rest.ApiExceptionwith status 403. Log the response body to verify scope denial.
Error: 429 Too Many Requests (Rate Limit Cascade)
- What causes it: Genesys Cloud enforces per-tenant and per-endpoint rate limits. Rapid schedule creation or webhook registration triggers throttling.
- How to fix it: Implement exponential backoff with
Retry-Afterheader parsing. ThehttpxPOST loop increate_purge_schedulehandles this automatically. - Code showing the fix: The retry block checks
response.status_code == 429, sleeps forRetry-Afterseconds, and continues. If it exceedsmax_retries, it raises an exception.
Error: 500 Internal Server Error (Platform Scaling Event)
- What causes it: Genesys Cloud backend services are undergoing maintenance or experiencing transient scaling delays. Purge schedule writes may fail during database replication windows.
- How to fix it: Implement a circuit breaker pattern or delay execution by sixty seconds. Retry the POST operation.
- Code showing the fix: Add a status code check for
5xxresponses in the retry loop. Treat them identically to 429s for retry logic, but log them as transient platform errors.