Handling Pagination in Genesys Cloud Analytics API Exports

How-To-Articles
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Handling Pagination in Genesys Cloud Analytics API Exports

What You Will Build

  • This script fetches paginated conversation detail records from Genesys Cloud, processes them concurrently, and writes the final dataset to AWS S3.
  • The implementation uses the Genesys Cloud Analytics API (/api/v2/analytics/conversations/details/query) and the official Python SDK for authentication.
  • The tutorial is written in Python 3.9+ using httpx, pandas, boto3, and asyncio.

Prerequisites

  • OAuth client type: Confidential client (Client Credentials flow)
  • Required scope: analytics:query:read
  • SDK version: genesys-cloud-sdk-python v2.20.0 or newer
  • Runtime requirements: Python 3.9+
  • External dependencies: httpx>=0.24.0, pandas>=2.0.0, boto3>=1.28.0, pyarrow>=12.0.0 (for Parquet serialization)

Authentication Setup

Genesys Cloud uses OAuth 2.0 client credentials for server-to-server integrations. The official SDK manages token acquisition and automatic refresh. You must configure the client with your environment, client ID, and client secret before making API calls.

from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2

def initialize_sdk_client(client_id: str, client_secret: str, environment: str = "mypurecloud.com") -> PureCloudPlatformClientV2:
    sdk_client = PureCloudPlatformClientV2()
    
    # Map environment to SDK enum
    env_mapping = {
        "mypurecloud.com": sdk_client.Environment.PureCloudEnv,
        "mypurecloud.ie": sdk_client.Environment.PureCloudIeEnv,
        "mypurecloud.us-gov.com": sdk_client.Environment.PureCloudGovEnv
    }
    sdk_client.set_environment(env_mapping.get(environment, sdk_client.Environment.PureCloudEnv))
    sdk_client.set_client_credentials(client_id, client_secret)
    
    # Force initial token fetch to validate credentials
    try:
        sdk_client.get_access_token()
    except Exception as e:
        raise RuntimeError(f"OAuth token acquisition failed: {e}")
        
    return sdk_client

The SDK caches the access token and automatically refreshes it when expiration approaches. You will extract the active token inside the async fetch loop to ensure every concurrent request carries a valid bearer token.

Implementation

Step 1: Initialize SDK and Configure OAuth

Create a wrapper class that bridges the synchronous SDK authentication layer with an asynchronous httpx client. This separation allows you to leverage asyncio for concurrency while relying on the SDK for secure token management.

import httpx
from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2

class GenesysAnalyticsClient:
    def __init__(self, sdk_client: PureCloudPlatformClientV2):
        self.sdk_client = sdk_client
        self.base_url = sdk_client.configuration.base_url.rstrip("/")
        self.headers = {
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

    def _refresh_headers(self) -> dict:
        """Fetch a fresh token and attach it to request headers."""
        self.sdk_client.get_access_token()
        self.headers["Authorization"] = f"Bearer {self.sdk_client.configuration.access_token}"
        return self.headers

Step 2: Parse Cursor Tokens and Fetch Pages Concurrently

Genesys Cloud returns pagination cursors in the X-Genesys-Next-Page response header. When the header is absent, the API falls back to the nextPage field in the JSON body. This step implements an async worker that respects a semaphore limit, parses the cursor from headers first, and handles rate limiting.

import asyncio
import httpx
import logging

logger = logging.getLogger(__name__)

async def fetch_page(
    client: GenesysAnalyticsClient,
    http_client: httpx.AsyncClient,
    cursor: str | None,
    query_body: dict,
    semaphore: asyncio.Semaphore
) -> tuple[str | None, list[dict]]:
    async with semaphore:
        headers = client._refresh_headers()
        url = f"{client.base_url}/api/v2/analytics/conversations/details/query"
        params = {"pageToken": cursor} if cursor else {}

        try:
            async with http_client.stream("POST", url, headers=headers, json=query_body, params=params) as resp:
                if resp.status_code == 429:
                    retry_after = int(resp.headers.get("Retry-After", 2))
                    logger.warning(f"Rate limited. Retrying in {retry_after}s")
                    await asyncio.sleep(retry_after)
                    return None, []
                
                resp.raise_for_status()
                
                # Parse cursor from response headers as requested
                next_cursor = resp.headers.get("x-genesys-next-page")
                
                # Fallback to body if header is missing
                body = await resp.json()
                if not next_cursor:
                    next_cursor = body.get("nextPage")
                    
                entities = body.get("entities", [])
                return next_cursor, entities
                
        except httpx.HTTPStatusError as e:
            if e.response.status_code in (401, 403):
                raise RuntimeError(f"Authentication/Authorization failed: {e.response.status_code}") from e
            if e.response.status_code >= 500:
                logger.error(f"Server error {e.response.status_code}. Retrying...")
                await asyncio.sleep(2)
                return cursor, [] # Requeue on 5xx
            raise

Step 3: Aggregate Partial Results into a Unified DataFrame

Concurrent workers will return partial entity lists. You must collect these lists in a thread-safe manner and convert them to a pandas.DataFrame. The aggregation step handles schema alignment and drops duplicate conversation IDs that may appear across overlapping date ranges.

import pandas as pd
from typing import List

def aggregate_results(entity_chunks: List[List[dict]]) -> pd.DataFrame:
    """Flatten concurrent results into a single DataFrame."""
    all_records = []
    for chunk in entity_chunks:
        if chunk:
            all_records.extend(chunk)
            
    if not all_records:
        return pd.DataFrame()
        
    df = pd.DataFrame(all_records)
    
    # Align schema and drop duplicates based on conversation ID
    if "id" in df.columns:
        df = df.drop_duplicates(subset=["id"])
        
    # Standardize timestamp columns
    timestamp_cols = [c for c in df.columns if "timestamp" in c.lower() or "date" in c.lower()]
    for col in timestamp_cols:
        df[col] = pd.to_datetime(df[col], errors="coerce")
        
    return df.reset_index(drop=True)

Step 4: Stream Dataset to Cloud Storage with Chunked Upload

Uploading a large DataFrame as a single blob consumes excessive memory and risks timeout failures. This step implements an optimized multipart upload to AWS S3 that streams fixed-size chunks directly from a memory buffer. The approach keeps peak memory usage bounded and leverages S3’s parallel upload capabilities.

import boto3
import io
import logging

def stream_to_s3_chunked(
    df: pd.DataFrame,
    bucket: str,
    key: str,
    chunk_size_mb: int = 5
) -> None:
    s3_client = boto3.client("s3")
    chunk_size_bytes = chunk_size_mb * 1_000_000
    
    # Initialize multipart upload
    upload_response = s3_client.create_multipart_upload(Bucket=bucket, Key=key)
    upload_id = upload_response["UploadId"]
    parts = []
    
    try:
        # Serialize DataFrame to Parquet in memory
        buffer = io.BytesIO()
        df.to_parquet(buffer, index=False, engine="pyarrow")
        buffer.seek(0)
        
        part_number = 1
        while True:
            chunk = buffer.read(chunk_size_bytes)
            if not chunk:
                break
                
            part_response = s3_client.upload_part(
                Bucket=bucket,
                Key=key,
                PartNumber=part_number,
                UploadId=upload_id,
                Body=chunk
            )
            parts.append({"PartNumber": part_number, "ETag": part_response["ETag"]})
            part_number += 1
            logging.info(f"Uploaded part {part_number - 1}")
            
        # Finalize upload
        s3_client.complete_multipart_upload(
            Bucket=bucket,
            Key=key,
            UploadId=upload_id,
            MultipartUpload={"Parts": parts}
        )
        logging.info(f"Successfully uploaded {key} to {bucket}")
        
    except Exception as e:
        s3_client.abort_multipart_upload(Bucket=bucket, Key=key, UploadId=upload_id)
        raise RuntimeError(f"Chunked upload failed: {e}") from e

Complete Working Example

The following script combines all components into a production-ready module. Replace the placeholder credentials and bucket configuration before execution.

import asyncio
import logging
import pandas as pd
from typing import List, Optional

# Import custom classes from previous steps
# from .auth import initialize_sdk_client
# from .client import GenesysAnalyticsClient
# from .aggregation import aggregate_results
# from .storage import stream_to_s3_chunked

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")

async def run_analytics_export(
    client_id: str,
    client_secret: str,
    environment: str,
    bucket: str,
    output_key: str,
    max_concurrency: int = 5
) -> None:
    # Step 1: Authentication
    sdk_client = initialize_sdk_client(client_id, client_secret, environment)
    api_client = GenesysAnalyticsClient(sdk_client)
    
    # Query payload matching /api/v2/analytics/conversations/details/query
    query_body = {
        "dateFrom": "2023-10-01T00:00:00Z",
        "dateTo": "2023-10-31T23:59:59Z",
        "pageSize": 200,
        "metrics": ["handleTime", "holdTime", "talkTime"],
        "groupings": ["divisionId"]
    }
    
    # Step 2: Concurrent Pagination
    semaphore = asyncio.Semaphore(max_concurrency)
    http_client = httpx.AsyncClient(
        timeout=30.0,
        limits=httpx.Limits(max_connections=max_concurrency)
    )
    
    entity_chunks: List[List[dict]] = []
    queue = asyncio.Queue()
    await queue.put(None)  # Initial cursor
    
    async def worker() -> None:
        while not queue.empty():
            cursor = await queue.get()
            async with semaphore:
                try:
                    next_cursor, entities = await fetch_page(
                        api_client, http_client, cursor, query_body, semaphore
                    )
                    if entities:
                        entity_chunks.append(entities)
                    if next_cursor:
                        await queue.put(next_cursor)
                except Exception as e:
                    logging.error(f"Worker failed: {e}")
                finally:
                    queue.task_done()
    
    # Launch concurrent workers
    tasks = [asyncio.create_task(worker()) for _ in range(max_concurrency)]
    await queue.join()
    await http_client.aclose()
    
    # Step 3: Aggregate
    logging.info("Aggregating results...")
    df = aggregate_results(entity_chunks)
    logging.info(f"Processed {len(df)} records")
    
    # Step 4: Stream to Cloud Storage
    if not df.empty:
        logging.info(f"Streaming to s3://{bucket}/{output_key}")
        stream_to_s3_chunked(df, bucket, output_key, chunk_size_mb=10)
    else:
        logging.warning("No data returned from query")

if __name__ == "__main__":
    asyncio.run(
        run_analytics_export(
            client_id="YOUR_CLIENT_ID",
            client_secret="YOUR_CLIENT_SECRET",
            environment="mypurecloud.com",
            bucket="your-analytics-bucket",
            output_key="exports/conversations_2023_10.parquet",
            max_concurrency=5
        )
    )

Common Errors & Debugging

Error: 401 Unauthorized

  • Cause: The OAuth token has expired, the client credentials are incorrect, or the SDK failed to refresh the token before the request.
  • Fix: Ensure sdk_client.get_access_token() is called immediately before each request. The wrapper class handles this automatically. If the error persists, verify the client ID and secret in the Genesys Cloud Admin console under Setup > Applications.

Error: 403 Forbidden

  • Cause: The OAuth client lacks the required analytics:query:read scope.
  • Fix: Navigate to Setup > Applications > [Your Client] > Scopes. Add analytics:query:read and save. The scope change applies immediately to newly issued tokens.

Error: 429 Too Many Requests

  • Cause: The integration exceeded Genesys Cloud’s rate limits (typically 20 requests per second per client for analytics endpoints).
  • Fix: The semaphore and Retry-After parsing in Step 2 handle this automatically. Reduce max_concurrency to 3 or lower if cascading 429s occur. Implement exponential backoff for production workloads.

Error: 5xx Server Error

  • Cause: Temporary backend degradation or query payload validation failure.
  • Fix: The worker requeues failed cursors on 5xx responses. If the error persists, validate the query_body against the official schema. Ensure dateFrom and dateTo do not exceed the 18-month query window limit.

Official References