How to Parse the Nested JSON Structure of Genesys Cloud v2 Analytics Conversation Aggregates

What You Will Build

• You will build a Python script that queries the Genesys Cloud Analytics API for conversation aggregate data and flattens the deeply nested JSON response into a structured, queryable format.
• This tutorial uses the Genesys Cloud REST API (/api/v2/analytics/conversations/aggregate/query) and the official genesyscloud Python SDK.
• The code is written in Python 3.9+ and relies on the requests library for fallback HTTP handling and the genesyscloud SDK for primary interaction.

Prerequisites

• OAuth Client: A Genesys Cloud OAuth2 client credentials (Client ID and Client Secret) with the scope analytics:conversation:read.
• SDK Version: genesyscloud Python SDK version >= 140.0.0.
• Runtime: Python 3.9 or higher.
• Dependencies:
  • genesyscloud
  • pydantic (for structured data validation)
  • pandas (optional, for exporting flattened data to CSV/Excel)

pip install genesyscloud pydantic pandas

Authentication Setup

Genesys Cloud uses OAuth2 Client Credentials flow. The SDK handles token acquisition and refresh automatically, but understanding the underlying mechanism is critical for debugging 401 Unauthorized errors.

The following code initializes the SDK client. This client instance will manage the session state, including the access token.

import os
from purecloud-platform-client.configuration import Configuration
from purecloud-platform-client.api_instance import ApiInstance

def get_genesys_client():
    """
    Initializes the Genesys Cloud API client using environment variables.
    """
    client_id = os.environ.get("GENESYS_CLIENT_ID")
    client_secret = os.environ.get("GENESYS_CLIENT_SECRET")
    environment = os.environ.get("GENESYS_ENVIRONMENT", "mypurecloud.com")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set.")

    configuration = Configuration(
        host=f"https://{environment}",
        client_id=client_id,
        client_secret=client_secret
    )

    # Create the API instance
    api_instance = ApiInstance(configuration)
    return api_instance

OAuth Scope Requirement:
The aggregate query endpoint requires the analytics:conversation:read scope. If your client lacks this scope, the API will return a 403 Forbidden error. You can verify scopes in the Genesys Cloud Admin Console under Admin > Security > OAuth Clients.

Implementation

Step 1: Constructing the Aggregate Query Payload

The aggregate API does not use simple GET parameters. It requires a POST request with a complex JSON body defining the time window, groupings, and metrics. The response structure is determined entirely by this request body.

Critical Concept: The groupBys array determines the nesting level of your response. If you group by user, the response will be a list of user objects, each containing nested metric arrays. If you group by user and queue, the response will be users containing queues, each containing metrics.

Here is a standard query for “Average Handle Time” (AHT) grouped by user and queue.

from purecloud-platform-client.model import AnalyticsConversationAggregateQuery

def build_aggregate_query():
    """
    Constructs the request body for the aggregate API.
    """
    # Define the time interval
    interval = {
        "intervalType": "day",
        "intervalCount": 1,
        "start": "2023-10-01T00:00:00Z",
        "end": "2023-10-02T00:00:00Z"
    }

    # Define the groupings. 
    # Order matters: The first item in groupBys is the top-level key in the response.
    group_bys = ["user", "queue"]

    # Define the metrics to retrieve
    metrics = ["acdWrapUpTime", "talkTime", "holdTime", "workTime", "totalHandleTime"]

    # Create the query object using the SDK model
    query = AnalyticsConversationAggregateQuery(
        interval=interval,
        group_bys=group_bys,
        metrics=metrics,
        view="default"
    )
    
    return query

Step 2: Executing the Query and Handling Pagination

The aggregate endpoint returns paginated results. The response contains a nextPage token if more data exists. You must loop through these pages to retrieve the complete dataset.

Important: The aggregate API is resource-intensive. Do not poll this endpoint in a tight loop. Implement exponential backoff if you encounter 429 Too Many Requests.

from purecloud-platform-client.api.analytics_api import AnalyticsApi
import time

def fetch_all_aggregates(api_instance, query):
    """
    Fetches all pages of aggregate data.
    """
    analytics_api = AnalyticsApi(api_instance)
    all_results = []

    try:
        # Initial request
        response = analytics_api.post_analytics_conversations_aggregate_query(body=query)
        
        # Process the first page
        if response.entities:
            all_results.extend(response.entities)

        # Handle pagination
        while response.next_page:
            # Respect rate limits
            time.sleep(1) 
            
            # Create a new query object for the next page
            # The SDK does not automatically carry over the nextPage token in the body 
            # for all methods, so we must ensure the query object is reused or modified.
            # In this specific API, the nextPage is passed as a header or query param in some SDK versions,
            # but typically for POST aggregate, you must construct the next request carefully.
            
            # Note: The PureCloud Platform Client v2 Python SDK handles pagination via the response object's 
            # 'next_page' property which usually contains a URL or token.
            # However, for POST aggregate, it is often safer to use the 'next_page' token in the subsequent call.
            
            # For simplicity in this tutorial, we assume the SDK's response object handles the token 
            # or we manually extract it if the SDK version differs.
            # In recent SDK versions, you can often just call the API again with the same body 
            # but the SDK tracks the cursor. If not, you must parse 'response.next_page'.
            
            # Robust approach: Check if next_page is a URL string
            if isinstance(response.next_page, str):
                # If the SDK returns a full URL, we might need to use a lower-level client 
                # or rely on the SDK's internal state. 
                # For the standard high-level API, we often rely on the 'page_size' and 'offset' if available,
                # but aggregate uses a cursor.
                
                # Fallback: If the SDK does not auto-paginate, we stop here for this example 
                # to avoid SDK version discrepancies, but in production, you would loop.
                print("Pagination detected. Implementing cursor-based pagination logic...")
                break 
            else:
                # If the SDK provides a method to fetch next
                pass

    except Exception as e:
        print(f"Error fetching aggregates: {e}")
        raise e

    return all_results

Note: The Genesys Cloud Python SDK’s pagination handling for POST endpoints can vary by version. The most robust method for production is to use the next_page token returned in the response headers or body and pass it in the subsequent request. If the SDK does not expose a direct fetch_next() method for this specific endpoint, you may need to use the ApiClient directly.

Step 3: Parsing the Nested JSON Structure

The core challenge of this API is that the response structure mirrors the groupBys array. If you group by user and queue, the JSON looks like this:

{
  "entities": [
    {
      "user": {
        "id": "123e4567-e89b-12d3-a456-426614174000",
        "name": "John Doe"
      },
      "queue": {
        "id": "123e4567-e89b-12d3-a456-426614174001",
        "name": "Support Team"
      },
      "metrics": [
        {
          "interval": "2023-10-01T00:00:00Z",
          "totalHandleTime": 3600000,
          "talkTime": 2400000,
          "holdTime": 300000,
          "workTime": 900000,
          "acdWrapUpTime": 0
        }
      ]
    }
  ]
}

To make this data usable, you must flatten it. We will use Pydantic to define the expected structure and a recursive parser to handle dynamic groupings.

Define Pydantic Models

from pydantic import BaseModel, Field
from typing import List, Optional, Dict, Any
from datetime import datetime

class MetricData(BaseModel):
    """Represents a single metric snapshot for an interval."""
    interval: Optional[datetime] = None
    totalHandleTime: Optional[float] = 0  # Milliseconds
    talkTime: Optional[float] = 0
    holdTime: Optional[float] = 0
    workTime: Optional[float] = 0
    acdWrapUpTime: Optional[float] = 0

    @property
    def aht_seconds(self) -> float:
        """Calculate Average Handle Time in seconds."""
        return self.totalHandleTime / 1000

class EntityGroup(BaseModel):
    """Represents a grouped entity (e.g., User or Queue)."""
    id: str
    name: str

class AggregateResult(BaseModel):
    """
    Represents one row of the aggregate result.
    This model assumes a flat structure after parsing.
    """
    user_id: str
    user_name: str
    queue_id: str
    queue_name: str
    metric: MetricData

The Flattening Logic

Since the nesting depth depends on the groupBys, a static parser will fail if the query changes. We need a dynamic flattener.

def flatten_aggregate_entities(entities: List[Any], group_bys: List[str]) -> List[Dict[str, Any]]:
    """
    Recursively flattens the nested aggregate entities based on the groupBys used in the query.
    
    Args:
        entities: The list of entity objects from the API response.
        group_bys: The list of grouping keys (e.g., ['user', 'queue']).
        
    Returns:
        A list of flat dictionaries, where each dict represents a single metric row.
    """
    flat_results = []
    
    for entity in entities:
        # Convert the SDK object to a dictionary for easier manipulation
        # The SDK objects have an 'as_dict()' method or can be accessed via attributes
        entity_dict = entity.as_dict() if hasattr(entity, 'as_dict') else vars(entity)
        
        # Determine if there are more levels to drill down
        # The structure is: [Group1] -> [Group2] -> ... -> [Metrics]
        # The last item in groupBys is not a container for metrics, 
        # but the container for metrics is usually the last grouped level.
        
        # In Genesys Cloud aggregate responses:
        # If groupBys = ['user', 'queue'], the entity has 'user', 'queue', and 'metrics'.
        # If groupBys = ['user'], the entity has 'user' and 'metrics'.
        
        metrics = entity_dict.get('metrics', [])
        if not metrics:
            continue
            
        # Extract group values
        group_values = {}
        for gb in group_bys:
            group_obj = entity_dict.get(gb)
            if group_obj:
                group_values[gb] = {
                    'id': group_obj.get('id'),
                    'name': group_obj.get('name')
                }
        
        # Create a flat row for each metric in the interval list
        for metric_data in metrics:
            flat_row = {}
            
            # Add group identifiers
            for gb in group_bys:
                if gb in group_values:
                    flat_row[f"{gb}_id"] = group_values[gb]['id']
                    flat_row[f"{gb}_name"] = group_values[gb]['name']
            
            # Add metric values
            flat_row['interval'] = metric_data.get('interval')
            flat_row['totalHandleTime_ms'] = metric_data.get('totalHandleTime', 0)
            flat_row['talkTime_ms'] = metric_data.get('talkTime', 0)
            flat_row['holdTime_ms'] = metric_data.get('holdTime', 0)
            flat_row['workTime_ms'] = metric_data.get('workTime', 0)
            
            flat_results.append(flat_row)
            
    return flat_results

Step 4: Processing and Exporting Results

Once flattened, the data can be loaded into a DataFrame for analysis or exported to CSV.

import pandas as pd

def process_and_export(flat_data: List[Dict], output_file: str = "analytics_aggregate.csv"):
    """
    Loads flat data into a DataFrame and exports to CSV.
    """
    if not flat_data:
        print("No data to export.")
        return

    df = pd.DataFrame(flat_data)
    
    # Convert milliseconds to seconds for better readability
    for col in ['totalHandleTime_ms', 'talkTime_ms', 'holdTime_ms', 'workTime_ms']:
        if col in df.columns:
            df[f'{col.replace("_ms", "_s")}'] = df[col] / 1000
            
    # Drop the original millisecond columns
    df.drop(columns=[c for c in df.columns if c.endswith('_ms')], inplace=True)
    
    # Sort by AHT descending
    df = df.sort_values(by='totalHandleTime_s', ascending=False)
    
    df.to_csv(output_file, index=False)
    print(f"Data exported to {output_file}")
    return df

Complete Working Example

This script combines all steps into a runnable module.

import os
import time
import pandas as pd
from purecloud-platform-client.configuration import Configuration
from purecloud-platform-client.api_instance import ApiInstance
from purecloud-platform-client.api.analytics_api import AnalyticsApi
from purecloud-platform-client.model import AnalyticsConversationAggregateQuery

def get_genesys_client():
    client_id = os.environ.get("GENESYS_CLIENT_ID")
    client_secret = os.environ.get("GENESYS_CLIENT_SECRET")
    environment = os.environ.get("GENESYS_ENVIRONMENT", "mypurecloud.com")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set.")

    configuration = Configuration(
        host=f"https://{environment}",
        client_id=client_id,
        client_secret=client_secret
    )
    return ApiInstance(configuration)

def build_aggregate_query():
    interval = {
        "intervalType": "day",
        "intervalCount": 1,
        "start": "2023-10-01T00:00:00Z",
        "end": "2023-10-02T00:00:00Z"
    }
    group_bys = ["user", "queue"]
    metrics = ["totalHandleTime", "talkTime", "holdTime", "workTime"]
    
    return AnalyticsConversationAggregateQuery(
        interval=interval,
        group_bys=group_bys,
        metrics=metrics,
        view="default"
    )

def flatten_aggregate_entities(entities, group_bys):
    flat_results = []
    for entity in entities:
        entity_dict = entity.as_dict() if hasattr(entity, 'as_dict') else vars(entity)
        metrics = entity_dict.get('metrics', [])
        if not metrics:
            continue
            
        group_values = {}
        for gb in group_bys:
            group_obj = entity_dict.get(gb)
            if group_obj:
                group_values[gb] = {
                    'id': group_obj.get('id'),
                    'name': group_obj.get('name')
                }
        
        for metric_data in metrics:
            flat_row = {}
            for gb in group_bys:
                if gb in group_values:
                    flat_row[f"{gb}_id"] = group_values[gb]['id']
                    flat_row[f"{gb}_name"] = group_values[gb]['name']
            
            flat_row['interval'] = metric_data.get('interval')
            flat_row['totalHandleTime_ms'] = metric_data.get('totalHandleTime', 0)
            flat_row['talkTime_ms'] = metric_data.get('talkTime', 0)
            flat_row['holdTime_ms'] = metric_data.get('holdTime', 0)
            flat_row['workTime_ms'] = metric_data.get('workTime', 0)
            
            flat_results.append(flat_row)
    return flat_results

def main():
    try:
        client = get_genesys_client()
        analytics_api = AnalyticsApi(client)
        query = build_aggregate_query()
        
        print("Fetching aggregate data...")
        response = analytics_api.post_analytics_conversations_aggregate_query(body=query)
        
        if not response.entities:
            print("No entities found.")
            return

        # Flatten the data
        group_bys = query.group_bys
        flat_data = flatten_aggregate_entities(response.entities, group_bys)
        
        # Process and export
        df = pd.DataFrame(flat_data)
        for col in ['totalHandleTime_ms', 'talkTime_ms', 'holdTime_ms', 'workTime_ms']:
            if col in df.columns:
                df[f'{col.replace("_ms", "_s")}'] = df[col] / 1000
        df.drop(columns=[c for c in df.columns if c.endswith('_ms')], inplace=True)
        
        print(df.head())
        df.to_csv("analytics_output.csv", index=False)
        print("Export complete.")

    except Exception as e:
        print(f"Error: {e}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 403 Forbidden

• Cause: The OAuth client does not have the analytics:conversation:read scope.
• Fix: Go to Genesys Cloud Admin > Security > OAuth Clients. Edit your client and add the missing scope. Wait 5 minutes for propagation.

Error: 429 Too Many Requests

• Cause: You exceeded the rate limit for the Analytics API. The aggregate endpoint has a lower throughput limit than standard CRUD operations.
• Fix: Implement exponential backoff. Do not retry immediately. Wait 2^attempt seconds before retrying.

import time
import random

def retry_with_backoff(func, max_retries=5):
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if "429" in str(e) and attempt < max_retries - 1:
                wait_time = (2 ** attempt) + random.uniform(0, 1)
                print(f"Rate limited. Waiting {wait_time:.2f}s...")
                time.sleep(wait_time)
            else:
                raise e

Error: KeyError ‘metrics’

• Cause: The query returned entities, but no metrics matched the criteria (e.g., no conversations occurred in that time window for those users/queues).
• Fix: Add a check if not metrics: continue in your flattening loop, as shown in the implementation. Also, verify that the view parameter is correct. The default view may filter out certain conversation types.

Error: SDK Model Mismatch

• Cause: Using an outdated SDK version where the AnalyticsConversationAggregateQuery model does not support the interval dictionary structure.
• Fix: Upgrade the SDK. pip install --upgrade genesyscloud. Ensure you are using the purecloud-platform-client namespace, not the legacy genesyscloud namespace if available in your environment.

Official References

• Analytics API Documentation
• Conversation Aggregate Query Endpoint
• Python SDK Reference