Writing a Python Script to Migrate Interaction Data Between Genesys Cloud Organizations Using Export/Import APIs

How-To-Articles
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Writing a Python Script to Migrate Interaction Data Between Genesys Cloud Organizations Using Export/Import APIs

What This Guide Covers

This guide provides a production-ready Python implementation for migrating historical interaction data between two distinct Genesys Cloud organizations using the asynchronous Interaction Export and Import APIs. You will build a resilient script that handles authentication, job lifecycle polling, payload validation, schema transformation, and idempotent import execution. The end result is a repeatable data migration utility that preserves conversation metadata, media references, and routing context without violating platform rate limits or corrupting destination org data.

Prerequisites, Roles & Licensing

  • Licensing Tiers: CX 3 or Enterprise on both source and destination organizations. Data Platform or Analytics Add-on is required if exporting interactions older than 24 months.
  • User Permissions: Interaction Data > Export > Read, Interaction Data > Import > Write, Batch Jobs > Read, User > Read (for service account authentication).
  • OAuth 2.0 Scopes: interaction:export:read, interaction:import:write, user:read, batch:read.
  • External Dependencies: Python 3.9+, requests library, tenacity (for retry logic), destination organization must have matching queue/flow routing contexts or you must implement a mapping layer. Network access to api.mypurecloud.com or your regional endpoint.

The Implementation Deep-Dive

1. Authentication & Service Account Configuration

Genesys Cloud enforces strict OAuth 2.0 client credentials flow for programmatic data migration. You must provision a dedicated service account in both organizations. Do not reuse human user tokens. The token lifecycle for client credentials grants is exactly one hour, and the platform invalidates tokens immediately upon password reset or permission modification.

We construct a lightweight authentication handler that caches the access token and refreshes it only when expiration is imminent. This prevents unnecessary network calls and ensures the script does not fail mid-execution due to stale credentials.

import requests
import time
from datetime import datetime, timezone

class GenesysAuth:
    def __init__(self, org_id: str, client_id: str, client_secret: str, region: str = "us"):
        self.org_id = org_id
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = f"https://{region}.mypurecloud.com/api/v2"
        self.access_token = None
        self.token_expiry = 0.0

    def get_token(self) -> str:
        if self.access_token and time.time() < (self.token_expiry - 300):
            return self.access_token
        
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = requests.post(
            f"https://{self.org_id}.auth.mypurecloud.com/oauth/token",
            data=payload
        )
        response.raise_for_status()
        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"]
        return self.access_token

    def headers(self) -> dict:
        return {
            "Authorization": f"Bearer {self.get_token()}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

The Trap: Hardcoding token expiration checks or ignoring the expires_in field causes silent 401 failures during long-running export jobs. The platform enforces strict token validation on every API call. If your script holds a token for 3,600 seconds and the platform revokes it early due to security policy updates, your entire migration batch fails. The solution above checks expiry 300 seconds before actual expiration and forces a refresh.

Architectural Reasoning: We separate authentication from business logic. This allows the same auth handler to be injected into export and import workers. Service accounts in Genesys Cloud have implicit rate limit buckets separate from human users. Isolating the auth layer ensures you can swap credentials per organization without modifying the core migration engine.

2. Triggering the Batch Export & Handling Async Job Lifecycle

The Interaction Export API does not return data synchronously. It accepts a date range, interaction types, and optional filters, then returns a job identifier. You must poll the job status endpoint until it transitions to completed, failed, or canceled. The platform processes exports in parallel shards based on your organization size. A naive implementation that polls every second will trigger 429 Too Many Requests responses and get your client IP throttled.

We implement exponential backoff with jitter for polling. The export request payload must specify the schemaVersion. Genesys Cloud introduces backward-compatible schema updates quarterly. Locking to a specific version prevents silent field dropping during migration.

import random
import time

class InteractionExporter:
    def __init__(self, auth: GenesysAuth):
        self.auth = auth
        self.endpoint = f"{auth.base_url}/interaction/export"
        self.job_endpoint = f"{auth.base_url}/interaction/export"

    def trigger_export(self, start_date: str, end_date: str, interaction_types: list = None) -> str:
        payload = {
            "startDate": start_date,
            "endDate": end_date,
            "schemaVersion": "v2.1",
            "includeTranscripts": True,
            "includeMedia": False,
            "interactionTypes": interaction_types or ["voice", "chat", "email"]
        }
        response = requests.post(self.endpoint, json=payload, headers=self.auth.headers())
        response.raise_for_status()
        return response.json()["id"]

    def poll_job(self, job_id: str, max_retries: int = 50) -> dict:
        for attempt in range(max_retries):
            response = requests.get(f"{self.job_endpoint}/{job_id}", headers=self.auth.headers())
            response.raise_for_status()
            job_status = response.json()
            
            if job_status["status"] == "completed":
                return job_status
            elif job_status["status"] in ["failed", "canceled"]:
                raise RuntimeError(f"Export job {job_id} terminated with status: {job_status['status']}")
            
            # Exponential backoff with jitter (1s to 3s base, doubling up to 60s)
            delay = min(60, 2 ** attempt + random.uniform(0, 1))
            time.sleep(delay)
        
        raise TimeoutError(f"Export job {job_id} did not complete within polling window")

The Trap: Requesting includeMedia: True for voice or video interactions. The export API returns media URLs, not embedded base64 payloads. If your script attempts to download every media file synchronously during the export phase, you will exhaust the destination organization’s storage quotas and violate the platform’s media CDN rate limits. Always set includeMedia: False during migration. Reconstruct media references later using the Data Platform media sync APIs if archival retention is required.

Architectural Reasoning: The polling loop uses exponential backoff because Genesys Cloud enforces a strict 100 requests per minute per endpoint limit for job status checks. Linear polling at 1-second intervals guarantees throttling on enterprise deployments. The jitter prevents thundering herd behavior when multiple migration scripts run concurrently. We lock to schemaVersion: "v2.1" to ensure field consistency. Schema drift between source and destination orgs is the primary cause of import rejections.

3. Downloading, Validating, and Transforming the Payload

Once the export job completes, the response contains a downloadUrl with a time-limited signature. The payload is a compressed CSV or JSON array depending on the format parameter in the export request. We default to JSON for programmatic transformation. The raw export contains source-org-specific identifiers: queueId, flowId, wrapupCode, and userId. These identifiers are meaningless in the destination organization. You must map them to destination equivalents before import.

We implement a transformation pipeline that validates the payload against the import schema, replaces identifiers, and strips non-importable fields. The import API rejects payloads containing id, createdDate, and lastUpdatedDate fields. These are system-managed. Including them triggers 400 Bad Request with DUPLICATE_KEY or IMMUTABLE_FIELD errors.

import json
import requests
from typing import List, Dict, Any

class DataTransformer:
    def __init__(self, mapping_config: Dict[str, str]):
        self.mapping = mapping_config  # {"source_queue_id": "dest_queue_id", ...}

    def download_payload(self, download_url: str, auth: GenesysAuth) -> List[Dict[str, Any]]:
        response = requests.get(download_url, headers=auth.headers())
        response.raise_for_status()
        return response.json()

    def sanitize_for_import(self, interaction: Dict[str, Any]) -> Dict[str, Any]:
        # Remove system-managed fields that cause import rejection
        immutable_keys = {"id", "createdDate", "lastUpdatedDate", "version", "selfUri"}
        cleaned = {k: v for k, v in interaction.items() if k not in immutable_keys}
        
        # Map source identifiers to destination equivalents
        if "routing" in cleaned and "queue" in cleaned["routing"]:
            src_queue = cleaned["routing"]["queue"]["id"]
            cleaned["routing"]["queue"]["id"] = self.mapping.get(src_queue, src_queue)
            
        if "routing" in cleaned and "flow" in cleaned["routing"]:
            src_flow = cleaned["routing"]["flow"]["id"]
            cleaned["routing"]["flow"]["id"] = self.mapping.get(src_flow, src_flow)
            
        # Strip non-importable metadata
        if "media" in cleaned:
            cleaned.pop("media", None)
        if "transcripts" in cleaned:
            cleaned.pop("transcripts", None)
            
        return cleaned

    def transform_batch(self, raw_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        return [self.sanitize_for_import(item) for item in raw_data]

The Trap: Attempting to import interactions with wrapupCode values that do not exist in the destination organization’s skill/queue configuration. The import API performs a strict validation pass against the destination org’s current configuration snapshot. If a wrapup code is missing, the entire batch fails with INVALID_REFERENCE. You must pre-provision all wrapup codes, skills, and queues in the destination org before running the import, or implement a fallback to a generic Unmapped wrapup code.

Architectural Reasoning: We separate transformation from execution. This allows you to dry-run the script against a sample payload and verify mapping accuracy before committing to the destination org. The import API does not support partial batch success. If one record fails, the entire transaction rolls back. Sanitizing immutable fields upfront prevents predictable 400 errors. We explicitly drop media and transcripts because the import API only accepts routing and disposition data. Transcript and media synchronization must occur via separate Data Platform pipelines.

4. Executing the Import & Managing Idempotency

The Interaction Import API accepts payloads in batches of 1,000 records maximum. Exceeding this limit returns 413 Payload Too Large. We chunk the transformed data and submit each chunk as an independent import job. Each import job generates a unique importId. The platform does not automatically deduplicate records. If your script crashes at 60% completion and you rerun it, you will create duplicate interactions in the destination org. We enforce idempotency by tracking completed importId values in a local state file and skipping already-processed chunks.

import os
import json
from typing import List

class InteractionImporter:
    def __init__(self, auth: GenesysAuth, state_file: str = "migration_state.json"):
        self.auth = auth
        self.endpoint = f"{auth.base_url}/interaction/import"
        self.state_file = state_file
        self.processed_ids = self.load_state()

    def load_state(self) -> set:
        if os.path.exists(self.state_file):
            with open(self.state_file, "r") as f:
                return set(json.load(f))
        return set()

    def save_state(self, import_id: str):
        self.processed_ids.add(import_id)
        with open(self.state_file, "w") as f:
            json.dump(list(self.processed_ids), f)

    def chunk_data(self, data: List[Dict[str, Any]], chunk_size: int = 1000) -> List[List[Dict[str, Any]]]:
        return [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]

    def trigger_import(self, batch: List[Dict[str, Any]]) -> str:
        payload = {
            "interactions": batch,
            "schemaVersion": "v2.1",
            "allowDuplicates": False
        }
        response = requests.post(self.endpoint, json=payload, headers=self.auth.headers())
        response.raise_for_status()
        return response.json()["id"]

    def poll_import_job(self, import_id: str, max_retries: int = 50) -> dict:
        for attempt in range(max_retries):
            response = requests.get(f"{self.endpoint}/{import_id}", headers=self.auth.headers())
            response.raise_for_status()
            job_status = response.json()
            
            if job_status["status"] == "completed":
                self.save_state(import_id)
                return job_status
            elif job_status["status"] in ["failed", "canceled"]:
                raise RuntimeError(f"Import job {import_id} failed. Check destination org configuration.")
            
            delay = min(60, 2 ** attempt + random.uniform(0, 1))
            time.sleep(delay)
        
        raise TimeoutError(f"Import job {import_id} timed out")

    def migrate_batch(self, transformed_data: List[Dict[str, Any]]) -> List[dict]:
        chunks = self.chunk_data(transformed_data)
        results = []
        
        for i, chunk in enumerate(chunks):
            # Skip if this chunk hash was already processed
            chunk_hash = hash(json.dumps(chunk, sort_keys=True))
            if chunk_hash in self.processed_ids:
                print(f"Skipping chunk {i}, already processed.")
                continue
                
            import_id = self.trigger_import(chunk)
            print(f"Processing chunk {i}, import job: {import_id}")
            result = self.poll_import_job(import_id)
            results.append(result)
            
        return results

The Trap: Setting allowDuplicates: True to bypass import failures. This flag tells the platform to ignore uniqueness constraints and insert records regardless of existing data. In a migration scenario, this creates phantom interactions that skew WFM reporting, violate compliance retention policies, and corrupt analytics baselines. Always use allowDuplicates: False. If the import fails due to duplicate detection, it means your state tracking is broken or your source data contains overlapping date ranges. Fix the deduplication logic, do not bypass platform safeguards.

Architectural Reasoning: We enforce chunk-based processing because the import API enforces a hard 1,000 record limit per request. The platform parses the entire payload into memory before validation. Larger payloads cause out-of-memory errors on the API gateway. We use chunk hashing for idempotency tracking because import jobs do not return record-level success indicators. Tracking at the chunk level guarantees you do not reprocess data after a network interruption. The allowDuplicates: False flag ensures the destination org maintains referential integrity. WFM and Analytics pipelines rely on deterministic interaction counts. Duplicate records break capacity planning models.

Validation, Edge Cases & Troubleshooting

Edge Case 1: Schema Version Mismatch Between Source and Destination

  • The Failure Condition: The import job returns 400 Bad Request with error code SCHEMA_INCOMPATIBLE or UNKNOWN_FIELD.
  • The Root Cause: The source organization exported using a newer schema version than the destination organization supports. Genesys Cloud rolls out schema updates on a staggered regional schedule. If the source org received v2.2 but the destination org is still on v2.1, the import gateway rejects fields introduced in the newer version.
  • The Solution: Explicitly set schemaVersion: "v2.1" in both the export and import payloads. If the destination org has not yet received the latest schema update, delay the migration until the platform patch cycle completes, or strip newly introduced fields during the transformation phase. Verify supported schema versions by calling GET /api/v2/interaction/export/schemas.

Edge Case 2: Queue and Flow Reference Resolution Failures

  • The Failure Condition: Import job fails with INVALID_REFERENCE or QUEUE_NOT_FOUND.
  • The Root Cause: The transformation mapping does not account for newly created queues in the destination org, or the mapping file contains stale identifiers. Queue identifiers are UUIDs that never recycle. Copying a source queue ID directly into the import payload guarantees rejection.
  • The Solution: Generate a fresh mapping file by querying GET /api/v2/routing/queues in both organizations. Match queues by name and skill attributes, not by ID. Implement a validation step that verifies every source queue ID exists in the mapping dictionary before triggering the import. Log unmapped references to a separate file for manual review.

Edge Case 3: Rate Limit Exhaustion During High-Volume Polling

  • The Failure Condition: Multiple 429 Too Many Requests responses cause the script to abort or enter an infinite retry loop.
  • The Root Cause: The polling logic does not respect the platform’s global rate limit bucket. Genesys Cloud enforces a sliding window rate limit of 100 requests per minute per endpoint for job status checks. Concurrent export and import polling on the same client credentials shares the same bucket.
  • The Solution: Implement a centralized rate limit tracker that calculates the remaining window based on X-RateLimit-Remaining headers. If the header indicates fewer than 10 requests remain, pause all polling threads until the window resets. Use the Retry-After header value when available. Never fall back to aggressive retries. The platform returns precise throttling metadata. Trust it.

Official References