Building a Daily Genesys Cloud Analytics Export Job to S3 with Python

What You Will Build

• A Python script that queries the Genesys Cloud Analytics API for conversation details and exports the resulting JSON data to an Amazon S3 bucket.
• This solution uses the Genesys Cloud REST API v2 (/api/v2/analytics/conversations/details/query) and the boto3 library for S3 interactions.
• The implementation covers Python 3.9+ with requests for API communication and boto3 for cloud storage.

Prerequisites

• OAuth Client Type: Confidential Client (Client Credentials Grant).
• Required Scopes: analytics:query:read is mandatory for accessing analytics data.
• SDK/API Version: Genesys Cloud API v2.
• Language/Runtime: Python 3.9 or higher.
• External Dependencies:
  • requests: For handling HTTP requests to Genesys Cloud.
  • boto3: The AWS SDK for Python.
  • python-dotenv: For managing environment variables securely.

Install the dependencies via pip:

pip install requests boto3 python-dotenv

Authentication Setup

Genesys Cloud uses OAuth 2.0 for authentication. For a server-to-server integration like this export job, the Client Credentials Grant is the standard flow. This flow requires your client ID and client secret, which you obtain from the Genesys Cloud Admin Console under Administration > Security > OAuth Clients.

The authentication endpoint is https://api.mypurecloud.com/oauth/token. You must pass your credentials in the body with the grant type set to client_credentials.

Token Management Code

In a production environment, you should cache the access token and refresh it only when it expires (typically every hour). The following function handles the initial token retrieval and basic error handling for authentication failures.

import os
import requests
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

GENESYS_CLIENT_ID = os.getenv("GENESYS_CLIENT_ID")
GENESYS_CLIENT_SECRET = os.getenv("GENESYS_CLIENT_SECRET")
GENESYS_BASE_URL = "https://api.mypurecloud.com"

def get_access_token() -> str:
    """
    Retrieves an OAuth access token from Genesys Cloud.
    
    Returns:
        str: The JWT access token.
    
    Raises:
        requests.exceptions.HTTPError: If authentication fails.
    """
    auth_url = f"{GENESYS_BASE_URL}/oauth/token"
    
    payload = {
        "grant_type": "client_credentials",
        "client_id": GENESYS_CLIENT_ID,
        "client_secret": GENESYS_CLIENT_SECRET
    }
    
    headers = {
        "Content-Type": "application/x-www-form-urlencoded"
    }
    
    try:
        response = requests.post(auth_url, data=payload, headers=headers)
        response.raise_for_status()
        
        token_data = response.json()
        return token_data["access_token"]
        
    except requests.exceptions.HTTPError as http_err:
        if response.status_code == 401:
            raise RuntimeError("Authentication failed: Invalid Client ID or Secret.") from http_err
        elif response.status_code == 403:
            raise RuntimeError("Authentication failed: Client does not have required scopes.") from http_err
        else:
            raise RuntimeError(f"Authentication failed with status {response.status_code}: {http_err}") from http_err
    except requests.exceptions.RequestException as req_err:
        raise RuntimeError(f"Network error during authentication: {req_err}") from req_err

Implementation

Step 1: Constructing the Analytics Query

The core of this export is the query sent to the Genesys Cloud Analytics API. You do not pull raw call logs; instead, you define a time window and specific metrics. For a daily export, you will typically query the previous day’s data to ensure completeness.

The endpoint is POST /api/v2/analytics/conversations/details/query. The request body requires a query object containing dateFrom, dateTo, and view.

Required Scope: analytics:query:read

The Query Payload

You must specify a view. For conversation details, the standard view is "conversationDetail". You can also filter by specific entities (like a queue or user) if needed, but for a full daily export, you often leave the entity filters empty.

import json
from datetime import datetime, timedelta

def build_daily_query(date: datetime) -> dict:
    """
    Constructs the analytics query payload for a specific date.
    
    Args:
        date (datetime): The date for which to retrieve analytics.
        
    Returns:
        dict: The JSON-serializable query payload.
    """
    # Define the time window: Start of the day to end of the day
    start_of_day = date.replace(hour=0, minute=0, second=0, microsecond=0)
    end_of_day = date.replace(hour=23, minute=59, second=59, microsecond=0)
    
    query_payload = {
        "dateFrom": start_of_day.isoformat() + "Z",
        "dateTo": end_of_day.isoformat() + "Z",
        "view": "conversationDetail",
        "groupBy": [],
        "filter": {
            "type": "and",
            "clauses": []
        },
        "select": [
            "conversationId",
            "channel",
            "mediaType",
            "startTime",
            "endTime",
            "duration",
            "wrapUpCode",
            "queueId",
            "userId"
        ]
    }
    
    return query_payload

Step 2: Executing the Query and Handling Pagination

Genesys Cloud Analytics queries can return large datasets. The API returns a maximum of 10,000 records per request. If your daily volume exceeds this, you must implement pagination using the nextPageUri provided in the response.

The following function executes the query and iterates through all pages until all data is retrieved. It also implements exponential backoff for 429 Too Many Requests errors, which are common in high-throughput analytics jobs.

import time
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def fetch_analytics_data(access_token: str, query_payload: dict) -> list:
    """
    Fetches all analytics data based on the query payload, handling pagination.
    
    Args:
        access_token (str): The OAuth access token.
        query_payload (dict): The analytics query definition.
        
    Returns:
        list: A list of all conversation detail records.
    """
    api_url = f"{GENESYS_BASE_URL}/api/v2/analytics/conversations/details/query"
    headers = {
        "Authorization": f"Bearer {access_token}",
        "Content-Type": "application/json"
    }
    
    all_records = []
    next_page_uri = None
    max_retries = 3
    
    while True:
        url = next_page_uri if next_page_uri else api_url
        method = "GET" if next_page_uri else "POST"
        
        for attempt in range(max_retries):
            try:
                if method == "POST":
                    response = requests.post(url, json=query_payload, headers=headers)
                else:
                    # For pagination, we use GET with the nextPageUri
                    # The nextPageUri already contains the query parameters
                    response = requests.get(url, headers=headers)
                
                # Handle Rate Limiting (429)
                if response.status_code == 429:
                    retry_after = int(response.headers.get("Retry-After", 5))
                    logger.warning(f"Rate limited. Waiting {retry_after} seconds before retry.")
                    time.sleep(retry_after)
                    continue
                
                response.raise_for_status()
                data = response.json()
                break # Success, exit retry loop
                
            except requests.exceptions.HTTPError as e:
                if attempt == max_retries - 1:
                    logger.error(f"Failed to fetch data after {max_retries} attempts: {e}")
                    raise
                time.sleep(2 ** attempt) # Exponential backoff
            except requests.exceptions.RequestException as e:
                logger.error(f"Network error: {e}")
                raise

        # Accumulate data
        if "data" in data and data["data"]:
            all_records.extend(data["data"])
            logger.info(f"Fetched {len(data['data'])} records. Total so far: {len(all_records)}")
        
        # Check for pagination
        next_page_uri = data.get("nextPageUri")
        if not next_page_uri:
            logger.info("No more pages to fetch.")
            break
            
        logger.info(f"Fetching next page...")
        # Small delay to be respectful to the API
        time.sleep(0.5)

    return all_records

Step 3: Writing Data to Amazon S3

Once the data is retrieved, it must be serialized and uploaded to S3. We will use boto3 to create a client and upload the JSON data. It is best practice to structure the S3 key with the date to allow for easy retrieval and lifecycle management.

Required AWS Permissions: The IAM user or role running this script must have s3:PutObject permission on the target bucket.

import boto3
from botocore.exceptions import ClientError, NoCredentialsError
import io

def upload_to_s3(bucket_name: str, key_name: str, data: list) -> bool:
    """
    Uploads a list of records as a JSON file to an S3 bucket.
    
    Args:
        bucket_name (str): The name of the S3 bucket.
        key_name (str): The S3 object key (path/filename).
        data (list): The list of records to upload.
        
    Returns:
        bool: True if successful, False otherwise.
    """
    try:
        # Serialize data to JSON string
        json_data = json.dumps(data, indent=2, default=str)
        
        # Convert string to bytes for S3 upload
        file_obj = io.BytesIO(json_data.encode('utf-8'))
        
        # Initialize S3 client
        s3_client = boto3.client('s3')
        
        # Upload the object
        s3_client.put_object(
            Bucket=bucket_name,
            Key=key_name,
            Body=file_obj,
            ContentType='application/json'
        )
        
        logger.info(f"Successfully uploaded {len(data)} records to s3://{bucket_name}/{key_name}")
        return True
        
    except NoCredentialsError:
        logger.error("AWS credentials not found. Ensure AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are set.")
        return False
    except ClientError as e:
        logger.error(f"AWS Client Error: {e}")
        return False
    except Exception as e:
        logger.error(f"Unexpected error during S3 upload: {e}")
        return False

Complete Working Example

The following script combines all the components into a single executable file. It calculates the previous day’s date, fetches the analytics data, and uploads it to S3.

import os
import sys
import logging
import requests
import boto3
import io
import json
from datetime import datetime, timedelta
from dotenv import load_dotenv

# --- Configuration ---
load_dotenv()

GENESYS_CLIENT_ID = os.getenv("GENESYS_CLIENT_ID")
GENESYS_CLIENT_SECRET = os.getenv("GENESYS_CLIENT_SECRET")
GENESYS_BASE_URL = "https://api.mypurecloud.com"
S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME", "genesys-analytics-exports")
AWS_REGION = os.getenv("AWS_REGION", "us-east-1")

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

# --- Authentication ---

def get_access_token() -> str:
    auth_url = f"{GENESYS_BASE_URL}/oauth/token"
    payload = {
        "grant_type": "client_credentials",
        "client_id": GENESYS_CLIENT_ID,
        "client_secret": GENESYS_CLIENT_SECRET
    }
    headers = {"Content-Type": "application/x-www-form-urlencoded"}
    
    try:
        response = requests.post(auth_url, data=payload, headers=headers)
        response.raise_for_status()
        return response.json()["access_token"]
    except requests.exceptions.HTTPError as e:
        logger.error(f"Authentication failed: {e}")
        raise
    except Exception as e:
        logger.error(f"Unexpected auth error: {e}")
        raise

# --- Analytics Query ---

def build_daily_query(date: datetime) -> dict:
    start_of_day = date.replace(hour=0, minute=0, second=0, microsecond=0)
    end_of_day = date.replace(hour=23, minute=59, second=59, microsecond=0)
    
    return {
        "dateFrom": start_of_day.isoformat() + "Z",
        "dateTo": end_of_day.isoformat() + "Z",
        "view": "conversationDetail",
        "groupBy": [],
        "filter": {"type": "and", "clauses": []},
        "select": ["conversationId", "channel", "startTime", "endTime", "duration"]
    }

def fetch_analytics_data(access_token: str, query_payload: dict) -> list:
    api_url = f"{GENESYS_BASE_URL}/api/v2/analytics/conversations/details/query"
    headers = {
        "Authorization": f"Bearer {access_token}",
        "Content-Type": "application/json"
    }
    
    all_records = []
    next_page_uri = None
    max_retries = 3
    
    while True:
        url = next_page_uri if next_page_uri else api_url
        method = "GET" if next_page_uri else "POST"
        
        for attempt in range(max_retries):
            try:
                if method == "POST":
                    response = requests.post(url, json=query_payload, headers=headers)
                else:
                    response = requests.get(url, headers=headers)
                
                if response.status_code == 429:
                    retry_after = int(response.headers.get("Retry-After", 5))
                    logger.warning(f"Rate limited. Waiting {retry_after}s.")
                    import time
                    time.sleep(retry_after)
                    continue
                
                response.raise_for_status()
                data = response.json()
                break
                
            except requests.exceptions.HTTPError as e:
                if attempt == max_retries - 1:
                    raise
                import time
                time.sleep(2 ** attempt)
            except Exception as e:
                raise

        if "data" in data and data["data"]:
            all_records.extend(data["data"])
            logger.info(f"Fetched {len(data['data'])} records.")
        
        next_page_uri = data.get("nextPageUri")
        if not next_page_uri:
            break
            
        import time
        time.sleep(0.5)

    return all_records

# --- S3 Export ---

def upload_to_s3(bucket_name: str, key_name: str, data: list) -> bool:
    try:
        json_data = json.dumps(data, indent=2, default=str)
        file_obj = io.BytesIO(json_data.encode('utf-8'))
        
        s3_client = boto3.client('s3', region_name=AWS_REGION)
        s3_client.put_object(
            Bucket=bucket_name,
            Key=key_name,
            Body=file_obj,
            ContentType='application/json'
        )
        logger.info(f"Uploaded to s3://{bucket_name}/{key_name}")
        return True
    except Exception as e:
        logger.error(f"S3 upload failed: {e}")
        return False

# --- Main Execution ---

def main():
    try:
        # 1. Determine the date (default to yesterday)
        target_date = datetime.now() - timedelta(days=1)
        date_str = target_date.strftime("%Y-%m-%d")
        logger.info(f"Starting export for date: {date_str}")
        
        # 2. Authenticate
        logger.info("Authenticating with Genesys Cloud...")
        token = get_access_token()
        
        # 3. Build Query
        query = build_daily_query(target_date)
        
        # 4. Fetch Data
        logger.info("Fetching analytics data...")
        records = fetch_analytics_data(token, query)
        logger.info(f"Total records fetched: {len(records)}")
        
        if not records:
            logger.warning("No records found for this date. Skipping upload.")
            return

        # 5. Upload to S3
        s3_key = f"conversations/{date_str}/details.json"
        logger.info(f"Uploading to S3: {s3_key}")
        success = upload_to_s3(S3_BUCKET_NAME, s3_key, records)
        
        if success:
            logger.info("Export job completed successfully.")
        else:
            logger.error("Export job failed during S3 upload.")
            sys.exit(1)
            
    except Exception as e:
        logger.error(f"Job failed with critical error: {e}")
        sys.exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The Client ID or Client Secret is incorrect, or the OAuth client is disabled.
• Fix: Verify the credentials in your .env file against the Genesys Cloud Admin Console. Ensure the client is enabled.

Error: 403 Forbidden

• Cause: The OAuth client lacks the analytics:query:read scope.
• Fix: In the Genesys Cloud Admin Console, edit the OAuth client and add the analytics:query:read scope to the list of allowed scopes.

Error: 429 Too Many Requests

• Cause: You have exceeded the Genesys Cloud API rate limits. Analytics queries are heavy on the server.
• Fix: The code above implements exponential backoff. If you consistently hit this limit, reduce the frequency of your queries or break the daily export into smaller hourly chunks.

Error: AWS Credentials Not Found

• Cause: The script cannot find AWS credentials.
• Fix: Ensure AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are set in your environment, or configure the AWS CLI (aws configure) on the machine running the script. Alternatively, run the script on an EC2 instance with an IAM role attached.

Official References

• Genesys Cloud Analytics Conversations Details Query API
• Genesys Cloud OAuth 2.0 Documentation
• Boto3 S3 Documentation