Constructing an Analytics API aggregation query that groups by queue and media type

What You Will Build

• The code submits a POST request to the Genesys Cloud Analytics API to retrieve aggregated queue performance metrics grouped by queue identifier and media type.
• This implementation uses the Genesys Cloud v2 REST API endpoint /api/v2/analytics/queues/details/query.
• The tutorial covers Python 3.9+ using the httpx library with production-grade error handling, pagination, and retry logic.

Prerequisites

• OAuth client type: Confidential Client (Client Credentials Grant)
• Required OAuth scope: analytics:queue:view
• API version: Genesys Cloud v2 REST API
• Language/runtime: Python 3.9 or higher
• External dependencies: httpx>=0.25.0, typing, time, json

Authentication Setup

Genesys Cloud uses OAuth 2.0 for all API authentication. Server-to-server integrations require the Client Credentials flow. You must store the access token securely and handle expiration, as tokens are valid for approximately one hour.

The following code demonstrates a minimal token fetcher with basic caching. In production, you should implement a thread-safe cache or use a dedicated token manager.

import httpx
import time
from typing import Optional

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mygen"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://login.{environment}.com/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_access_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry:
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "scope": "analytics:queue:view"
        }
        
        with httpx.Client() as client:
            response = client.post(
                self.token_url,
                auth=(self.client_id, self.client_secret),
                data=payload
            )
            response.raise_for_status()
            
            data = response.json()
            self.access_token = data["access_token"]
            self.token_expiry = time.time() + data["expires_in"] - 300
            
        return self.access_token

The scope parameter explicitly requests analytics:queue:view. Genesys Cloud enforces scope validation at the API gateway. If you request additional scopes like analytics:conversation:view without administrative approval, the token endpoint returns a 401 Unauthorized response.

Implementation

Step 1: Initialize HTTP client and build the aggregation query payload

Analytics queries use POST instead of GET because the request body contains complex JSON structures. The groupBys array determines how the analytics engine partitions the data. When you pass ["queue", "mediaType"], the engine returns one row per unique combination of queue and media type.

import httpx
from typing import Dict, Any, List

def build_aggregation_query(
    date_from: str,
    date_to: str,
    metrics: List[str],
    size: int = 50
) -> Dict[str, Any]:
    """
    Constructs the JSON payload for the queue analytics aggregation query.
    """
    return {
        "dateFrom": date_from,
        "dateTo": date_to,
        "groupBys": ["queue", "mediaType"],
        "metrics": metrics,
        "size": size
    }

def execute_query_step(
    base_url: str,
    token: str,
    query_payload: Dict[str, Any]
) -> httpx.Response:
    """
    Sends the aggregation query to the Genesys Cloud API.
    """
    url = f"{base_url}/api/v2/analytics/queues/details/query"
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }
    
    with httpx.Client(timeout=30.0) as client:
        response = client.post(url, json=query_payload, headers=headers)
        return response

The dateFrom and dateTo fields must use ISO 8601 format with UTC timezone. The size parameter controls pagination batch size. Genesys Cloud caps this value at 1000. Setting it too high increases memory consumption and triggers serialization timeouts on the backend.

Step 2: Execute the query with pagination and retry logic

The Analytics API returns paginated results. The response object contains a nextPageUri field when additional data exists. You must follow this URI for subsequent requests. The nextPageUri is a fully qualified URL that already contains the query parameters, so you send it as a POST request with an empty body or the original body depending on the endpoint. For queue analytics, you POST the original payload to the nextPageUri or use the URI directly with a POST request.

Additionally, the Analytics backend enforces strict rate limits. A 429 Too Many Requests response includes a Retry-After header. You must implement exponential backoff to avoid cascading failures.

import time
import logging
from typing import Generator, Dict, Any, List

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

def fetch_paginated_results(
    base_url: str,
    token: str,
    query_payload: Dict[str, Any],
    max_retries: int = 3,
    backoff_base: float = 2.0
) -> Generator[Dict[str, Any], None, None]:
    """
    Iterates through paginated analytics results with 429 retry logic.
    Yields individual response bodies.
    """
    url = f"{base_url}/api/v2/analytics/queues/details/query"
    current_payload = query_payload
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }

    with httpx.Client(timeout=45.0) as client:
        while url:
            attempt = 0
            while attempt < max_retries:
                try:
                    response = client.post(url, json=current_payload, headers=headers)
                    
                    if response.status_code == 429:
                        retry_after = float(response.headers.get("Retry-After", backoff_base ** attempt))
                        logger.warning(f"Rate limited. Retrying in {retry_after} seconds.")
                        time.sleep(retry_after)
                        attempt += 1
                        continue
                    
                    response.raise_for_status()
                    yield response.json()
                    
                    # Pagination logic
                    next_page = response.json().get("nextPageUri")
                    if next_page:
                        url = next_page
                        current_payload = {}  # Subsequent pages use empty body or original payload per spec
                        # Genesys Analytics pagination requires POST to nextPageUri with empty body
                    else:
                        url = None
                    break
                    
                except httpx.HTTPStatusError as exc:
                    if exc.response.status_code in (401, 403):
                        logger.error(f"Authentication/Authorization failed: {exc.response.status_code}")
                        raise
                    if exc.response.status_code >= 500 and attempt < max_retries - 1:
                        time.sleep(backoff_base ** attempt)
                        attempt += 1
                        continue
                    raise
                except httpx.RequestError as exc:
                    logger.error(f"Network error: {exc}")
                    raise

The nextPageUri handling is critical. Genesys Cloud Analytics pagination does not use query parameters like ?page=2. Instead, it returns a stateful URI that encodes the cursor position. You must POST to that exact URI. The payload for subsequent requests is typically empty or a minimal continuation object. The code above resets current_payload to an empty dictionary for continuation, which aligns with the v2 Analytics specification.

Step 3: Process and transform the response entities

The response contains an entities array. Each entity represents one unique queue and mediaType combination. The metrics appear as top-level fields in the entity object. You must extract the relevant fields and structure them for downstream consumption.

def process_analytics_entities(
    generator: Generator[Dict[str, Any], None, None]
) -> List[Dict[str, Any]]:
    """
    Flattens paginated responses into a unified list of queue/mediaType metrics.
    """
    aggregated_data: List[Dict[str, Any]] = []
    
    for batch in generator:
        entities = batch.get("entities", [])
        for entity in entities:
            record = {
                "queue_id": entity.get("queueId"),
                "queue_name": entity.get("queueName"),
                "media_type": entity.get("mediaType"),
                "offered": entity.get("offered", 0),
                "answered": entity.get("answered", 0),
                "abandoned": entity.get("abandoned", 0),
                "service_level_percent": entity.get("serviceLevelPercent", 0.0),
                "avg_handle_time": entity.get("avgHandleTime", 0.0)
            }
            aggregated_data.append(record)
            
    return aggregated_data

The groupBys parameter dictates the entity structure. When grouping by queue and mediaType, the engine returns rows like {"queueId": "123", "queueName": "Support US", "mediaType": "voice", "offered": 150, ...}. If you omit mediaType from groupBys, the engine aggregates across all media types, and the mediaType field becomes null or "all". Always verify the groupBys array matches your downstream data model.

Complete Working Example

The following script combines authentication, query construction, pagination, retry logic, and result processing into a single executable module. Replace the placeholder credentials and environment variables before execution.

import httpx
import time
import logging
from typing import Dict, Any, List, Generator, Optional

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

class GenesysAnalyticsClient:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mygen"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.environment = environment
        self.base_url = f"https://{environment}.mygen.com"
        self.token_url = f"https://login.{environment}.com/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_access_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry:
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "scope": "analytics:queue:view"
        }
        
        with httpx.Client() as client:
            response = client.post(
                self.token_url,
                auth=(self.client_id, self.client_secret),
                data=payload
            )
            response.raise_for_status()
            
            data = response.json()
            self.access_token = data["access_token"]
            self.token_expiry = time.time() + data["expires_in"] - 300
            
        return self.access_token

    def fetch_queue_analytics(
        self,
        date_from: str,
        date_to: str,
        metrics: List[str],
        page_size: int = 100
    ) -> List[Dict[str, Any]]:
        token = self.get_access_token()
        
        query_payload = {
            "dateFrom": date_from,
            "dateTo": date_to,
            "groupBys": ["queue", "mediaType"],
            "metrics": metrics,
            "size": page_size
        }

        def pagination_generator() -> Generator[Dict[str, Any], None, None]:
            url = f"{self.base_url}/api/v2/analytics/queues/details/query"
            current_payload = query_payload
            headers = {
                "Authorization": f"Bearer {token}",
                "Content-Type": "application/json",
                "Accept": "application/json"
            }
            max_retries = 3
            backoff_base = 2.0

            with httpx.Client(timeout=45.0) as client:
                while url:
                    attempt = 0
                    while attempt < max_retries:
                        try:
                            response = client.post(url, json=current_payload, headers=headers)
                            
                            if response.status_code == 429:
                                retry_after = float(response.headers.get("Retry-After", backoff_base ** attempt))
                                logger.warning(f"Rate limited. Retrying in {retry_after} seconds.")
                                time.sleep(retry_after)
                                attempt += 1
                                continue
                            
                            response.raise_for_status()
                            yield response.json()
                            
                            next_page = response.json().get("nextPageUri")
                            if next_page:
                                url = next_page
                                current_payload = {}
                            else:
                                url = None
                            break
                            
                        except httpx.HTTPStatusError as exc:
                            if exc.response.status_code in (401, 403):
                                logger.error(f"Auth failed: {exc.response.status_code}")
                                raise
                            if exc.response.status_code >= 500 and attempt < max_retries - 1:
                                time.sleep(backoff_base ** attempt)
                                attempt += 1
                                continue
                            raise
                        except httpx.RequestError as exc:
                            logger.error(f"Network error: {exc}")
                            raise

        aggregated_data: List[Dict[str, Any]] = []
        for batch in pagination_generator():
            entities = batch.get("entities", [])
            for entity in entities:
                record = {
                    "queue_id": entity.get("queueId"),
                    "queue_name": entity.get("queueName"),
                    "media_type": entity.get("mediaType"),
                    "offered": entity.get("offered", 0),
                    "answered": entity.get("answered", 0),
                    "abandoned": entity.get("abandoned", 0),
                    "service_level_percent": entity.get("serviceLevelPercent", 0.0),
                    "avg_handle_time": entity.get("avgHandleTime", 0.0)
                }
                aggregated_data.append(record)
                
        return aggregated_data

if __name__ == "__main__":
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    ENVIRONMENT = "mygen"
    
    client = GenesysAnalyticsClient(CLIENT_ID, CLIENT_SECRET, ENVIRONMENT)
    
    results = client.fetch_queue_analytics(
        date_from="2023-10-01T00:00:00.000Z",
        date_to="2023-10-31T23:59:59.999Z",
        metrics=["offered", "answered", "abandoned", "serviceLevelPercent", "avgHandleTime"],
        page_size=100
    )
    
    for row in results:
        print(row)

This script handles token refresh, constructs the aggregation payload, iterates through paginated results, implements exponential backoff for rate limits, and transforms the raw JSON into a flat list of dictionaries. You can pipe the output to CSV, load it into a data warehouse, or feed it into a reporting engine.

Common Errors & Debugging

Error: 400 Bad Request

• What causes it: Invalid date range, unsupported metric names, or malformed groupBys array. The Analytics engine validates metric compatibility with the selected entity type. Queue endpoints reject conversation-level metrics.
• How to fix it: Verify that all strings in the metrics array exist in the Queue Analytics metric catalog. Ensure dateFrom precedes dateTo. Check that groupBys contains only valid keys like queue, mediaType, skill, or wrapupCode.
• Code showing the fix: Replace unsupported metrics with valid queue metrics. Use ["offered", "answered", "abandoned", "serviceLevelPercent", "avgHandleTime"] for queue endpoints.

Error: 401 Unauthorized or 403 Forbidden

• What causes it: Expired access token, missing analytics:queue:view scope, or the OAuth client lacks permission to view queue analytics.
• How to fix it: Regenerate the token using the get_access_token method. Verify the client credentials scope in the Genesys Cloud admin console. Grant the analytics:queue:view scope to the OAuth client.
• Code showing the fix: The authentication setup section already implements token expiration checking. If the error persists, inspect the token payload via a JWT decoder to confirm the scope claim contains analytics:queue:view.

Error: 429 Too Many Requests

• What causes it: Exceeding the Analytics API rate limit. The queue aggregation endpoint typically allows 10 requests per second per organization. Large date ranges or frequent polling trigger rate limiting.
• How to fix it: Implement exponential backoff. Read the Retry-After header from the response. The complete working example includes a retry loop that sleeps for the specified duration before resuming.
• Code showing the fix: The pagination_generator method checks response.status_code == 429 and executes time.sleep(retry_after). Adjust max_retries and backoff_base if your workload requires longer recovery windows.

Error: 502 Bad Gateway or 504 Gateway Timeout

• What causes it: The Analytics backend requires time to aggregate historical data. Queries spanning more than 30 days or requesting high-cardinality groupings may exceed the proxy timeout.
• How to fix it: Reduce the date range to weekly chunks. Lower the size parameter. Remove high-cardinality groupings like agent or skill if they are not required. Implement client-side timeout handling and retry with a narrower window.
• Code showing the fix: Wrap the httpx.Client call with a timeout parameter. Catch httpx.TimeoutException and split the date range into smaller intervals before requeuing the query.

Official References

• Genesys Cloud Analytics Queues API Documentation
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• Genesys Cloud Python SDK Reference
• Analytics Query Best Practices and Rate Limits