How to Parse the Nested JSON Structure of a Genesys Cloud v2.analytics.conversation.aggregate Event

What You Will Build

• This tutorial demonstrates how to query, retrieve, and flatten the deeply nested JSON structure returned by the Genesys Cloud Analytics API.
• It uses the GET /api/v2/analytics/conversations/aggregate endpoint to retrieve aggregated conversation metrics.
• The implementation covers Python (using requests and pandas) and JavaScript (using axios and native JSON processing).

Prerequisites

• OAuth Client Type: Machine-to-Machine (M2M) or Confidential Client.
• Required Scopes: analytics:conversation:read is mandatory for accessing conversation data. If you need to filter by specific user attributes, user:read may also be beneficial for resolving IDs to names in a secondary step.
• SDK/API Version: Genesys Cloud API v2.
• Language/Runtime Requirements:
  • Python 3.8+
  • Node.js 16+
• External Dependencies:
  • Python: requests, pandas (for flattening), purecloudplatformclientv2 (optional, but this tutorial uses raw requests for transparency on JSON structure).
  • JavaScript: axios.

Authentication Setup

Genesys Cloud uses OAuth 2.0. For backend integrations and data extraction scripts, the Client Credentials Grant flow is the standard approach. You must store your Client ID and Client Secret as environment variables.

Python Authentication Helper

import os
import requests
from typing import Optional

def get_access_token(client_id: str, client_secret: str, env_name: str = "us") -> str:
    """
    Retrieves an OAuth 2.0 access token from Genesys Cloud.
    
    Args:
        client_id: Your OAuth Client ID.
        client_secret: Your OAuth Client Secret.
        env_name: The environment prefix (e.g., 'us', 'eu', 'au').
    
    Returns:
        Access token string.
    """
    if env_name == "us":
        token_url = "https://api.mypurecloud.com/oauth/token"
    elif env_name == "eu":
        token_url = "https://api.eu.mypurecloud.com/oauth/token"
    else:
        raise ValueError("Unsupported environment. Use 'us' or 'eu'.")

    headers = {
        "Content-Type": "application/x-www-form-urlencoded"
    }
    payload = {
        "grant_type": "client_credentials",
        "client_id": client_id,
        "client_secret": client_secret
    }

    try:
        response = requests.post(token_url, headers=headers, data=payload)
        response.raise_for_status()
        token_data = response.json()
        return token_data["access_token"]
    except requests.exceptions.HTTPError as e:
        print(f"Authentication failed: {e.response.text}")
        raise
    except requests.exceptions.RequestException as e:
        print(f"Network error during authentication: {e}")
        raise

# Usage
# TOKEN = get_access_token(os.getenv("GENESYS_CLIENT_ID"), os.getenv("GENESYS_CLIENT_SECRET"))

JavaScript Authentication Helper

const axios = require('axios');

async function getAccessToken(clientId, clientSecret, env = 'us') {
    const baseUrl = env === 'eu' ? 'https://api.eu.mypurecloud.com' : 'https://api.mypurecloud.com';
    const tokenUrl = `${baseUrl}/oauth/token`;

    const payload = new URLSearchParams();
    payload.append('grant_type', 'client_credentials');
    payload.append('client_id', clientId);
    payload.append('client_secret', clientSecret);

    try {
        const response = await axios.post(tokenUrl, payload, {
            headers: {
                'Content-Type': 'application/x-www-form-urlencoded'
            }
        });
        return response.data.access_token;
    } catch (error) {
        if (error.response) {
            console.error("Authentication failed:", error.response.data);
        } else {
            console.error("Network error:", error.message);
        }
        throw error;
    }
}

// Usage
// const TOKEN = await getAccessToken(process.env.GENESYS_CLIENT_ID, process.env.GENESYS_CLIENT_SECRET);

Implementation

The GET /api/v2/analytics/conversations/aggregate endpoint returns a complex JSON object. The primary challenge is not the HTTP call, but navigating the entities array, which contains nested objects for metrics, groups, and time series data.

The response structure generally looks like this:

1. entities: An array of result sets.
2. entities[i].groups: An array of group definitions (e.g., by Queue, by User).
3. entities[i].metrics: An array of metric objects (e.g., handle-time, wait-time).
4. entities[i].timeSeries: An array of time buckets containing the actual metric values.

Step 1: Constructing the Query Payload

You must POST a query body to the endpoint. The body defines what you want to aggregate.

import json
from datetime import datetime, timedelta

def build_query_payload(start_date: str, end_date: str, queue_id: str) -> dict:
    """
    Builds the JSON payload for the analytics aggregate query.
    
    Args:
        start_date: ISO 8601 start date (inclusive).
        end_date: ISO 8601 end date (exclusive).
        queue_id: The ID of the queue to filter by.
    
    Returns:
        Dictionary representing the query body.
    """
    query = {
        "dateFrom": start_date,
        "dateTo": end_date,
        "groupBy": ["queue"],
        "metrics": ["handle-time", "wait-time", "conversations"],
        "filter": {
            "type": "queue",
            "id": queue_id
        }
    }
    return query

Step 2: Executing the API Call

The endpoint is POST /api/v2/anversations/aggregate. Note that this is a POST request, not a GET, because the filter/query complexity requires a body.

Python Implementation

def fetch_aggregate_data(token: str, query: dict, env: str = "us") -> dict:
    """
    Sends the query to Genesys Cloud and returns the raw JSON response.
    
    Scope Required: analytics:conversation:read
    """
    base_url = "https://api.eu.mypurecloud.com" if env == "eu" else "https://api.mypurecloud.com"
    url = f"{base_url}/api/v2/analytics/conversations/aggregate"

    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }

    try:
        response = requests.post(url, headers=headers, json=query)
        
        # Handle Rate Limiting (429)
        if response.status_code == 429:
            retry_after = int(response.headers.get('Retry-After', 5))
            print(f"Rate limited. Retrying in {retry_after} seconds...")
            import time
            time.sleep(retry_after)
            response = requests.post(url, headers=headers, json=query)
        
        response.raise_for_status()
        return response.json()
    
    except requests.exceptions.HTTPError as e:
        if e.response.status_code == 401:
            raise Exception("Invalid or expired token.")
        elif e.response.status_code == 403:
            raise Exception("Insufficient scopes. Ensure 'analytics:conversation:read' is granted.")
        else:
            raise Exception(f"HTTP Error: {e.response.status_code} - {e.response.text}")

JavaScript Implementation

async function fetchAggregateData(token, query, env = 'us') {
    const baseUrl = env === 'eu' ? 'https://api.eu.mypurecloud.com' : 'https://api.mypurecloud.com';
    const url = `${baseUrl}/api/v2/analytics/conversations/aggregate`;

    const headers = {
        'Authorization': `Bearer ${token}`,
        'Content-Type': 'application/json',
        'Accept': 'application/json'
    };

    try {
        const response = await axios.post(url, query, { headers });
        return response.data;
    } catch (error) {
        if (error.response) {
            if (error.response.status === 429) {
                console.warn("Rate limited. Implement exponential backoff in production.");
            } else if (error.response.status === 401) {
                throw new Error("Invalid or expired token.");
            } else if (error.response.status === 403) {
                throw new Error("Insufficient scopes. Check 'analytics:conversation:read'.");
            }
        }
        throw error;
    }
}

Step 3: Parsing the Nested JSON Structure

The raw response contains an entities array. Each entity corresponds to a group defined in your groupBy clause. If you grouped by queue, you will get one entity per queue. Inside each entity, the timeSeries array contains the data points.

Python: Flattening with Pandas

Pandas is ideal here because it can handle multi-level indexing and JSON normalization.

import pandas as pd

def parse_analytics_response(raw_json: dict) -> pd.DataFrame:
    """
    Parses the nested Genesys Cloud analytics response into a flat DataFrame.
    
    The structure is:
    entities -> [
        {
            groups: [{id: 'queue-id', name: 'Sales'}],
            metrics: [{name: 'handle-time', type: 'duration'}],
            timeSeries: [
                {
                    timestamp: '2023-10-01T00:00:00.000Z',
                    metrics: [
                        {name: 'handle-time', value: 120.5, count: 50},
                        {name: 'conversations', value: 50, count: 50}
                    ]
                }
            ]
        }
    ]
    """
    if not raw_json.get('entities'):
        return pd.DataFrame()

    records = []
    
    # Iterate through each entity (e.g., each Queue)
    for entity in raw_json['entities']:
        # Extract Group Information (e.g., Queue Name/ID)
        group_info = {}
        if entity.get('groups'):
            for group in entity['groups']:
                # Assuming we grouped by queue
                if group.get('type') == 'queue':
                    group_info['queue_id'] = group.get('id')
                    group_info['queue_name'] = group.get('name')
        
        # Extract Metric Definitions (optional, but good for validation)
        metric_names = [m['name'] for m in entity.get('metrics', [])]
        
        # Iterate through Time Series
        for ts in entity.get('timeSeries', []):
            timestamp = ts.get('timestamp')
            
            # Create a row for this time bucket
            row = {
                'timestamp': timestamp,
                **group_info # Unpack queue info
            }
            
            # Map metric values
            for metric_val in ts.get('metrics', []):
                metric_name = metric_val.get('name')
                if metric_name:
                    row[f'{metric_name}_value'] = metric_val.get('value')
                    row[f'{metric_name}_count'] = metric_val.get('count')
            
            records.append(row)

    df = pd.DataFrame(records)
    
    # Convert timestamp to datetime object
    if not df.empty and 'timestamp' in df.columns:
        df['timestamp'] = pd.to_datetime(df['timestamp'])
    
    return df

# Example Usage
# data_frame = parse_analytics_response(api_response)
# print(data_frame.head())

JavaScript: Flattening with Map/Reduce

In JavaScript, we will transform the nested structure into an array of flat objects suitable for insertion into a database or CSV export.

function parseAnalyticsResponse(rawJson) {
    if (!rawJson.entities || rawJson.entities.length === 0) {
        return [];
    }

    const flatData = [];

    rawJson.entities.forEach(entity => {
        // 1. Extract Group Metadata (e.g., Queue details)
        let groupMetadata = {};
        if (entity.groups) {
            entity.groups.forEach(group => {
                if (group.type === 'queue') {
                    groupMetadata = {
                        queueId: group.id,
                        queueName: group.name
                    };
                }
            });
        }

        // 2. Process Time Series
        if (entity.timeSeries) {
            entity.timeSeries.forEach(ts => {
                const timestamp = ts.timestamp;
                
                // Initialize row with metadata and timestamp
                const row = { ...groupMetadata, timestamp };

                // 3. Flatten Metrics
                if (ts.metrics) {
                    ts.metrics.forEach(metric => {
                        // Create keys like 'handle-time_value', 'handle-time_count'
                        const valueKey = `${metric.name}_value`;
                        const countKey = `${metric.name}_count`;
                        
                        row[valueKey] = metric.value;
                        row[countKey] = metric.count;
                    });
                }

                flatData.push(row);
            });
        }
    });

    return flatData;
}

// Example Usage
// const flatRows = parseAnalyticsResponse(apiResponse);
// console.log(flatRows[0]);

Complete Working Example

Below is a complete, runnable Python script that authenticates, queries, parses, and outputs the data to CSV.

import os
import requests
import pandas as pd
from datetime import datetime, timedelta
import csv

def get_access_token(client_id: str, client_secret: str) -> str:
    token_url = "https://api.mypurecloud.com/oauth/token"
    headers = {"Content-Type": "application/x-www-form-urlencoded"}
    payload = {
        "grant_type": "client_credentials",
        "client_id": client_id,
        "client_secret": client_secret
    }
    response = requests.post(token_url, headers=headers, data=payload)
    response.raise_for_status()
    return response.json()["access_token"]

def fetch_and_parse_analytics(token: str, queue_id: str, days_back: int = 7) -> pd.DataFrame:
    # Calculate date range
    end_date = datetime.utcnow()
    start_date = end_date - timedelta(days=days_back)
    
    query = {
        "dateFrom": start_date.isoformat() + "Z",
        "dateTo": end_date.isoformat() + "Z",
        "groupBy": ["queue"],
        "metrics": ["handle-time", "wait-time", "conversations"],
        "filter": {
            "type": "queue",
            "id": queue_id
        }
    }

    url = "https://api.mypurecloud.com/api/v2/analytics/conversations/aggregate"
    headers = {
        "Authorization": f"Bearer {token}",
        "Content-Type": "application/json",
        "Accept": "application/json"
    }

    response = requests.post(url, headers=headers, json=query)
    response.raise_for_status()
    raw_data = response.json()

    # Flatten the JSON
    records = []
    if raw_data.get('entities'):
        for entity in raw_data['entities']:
            group_info = {}
            if entity.get('groups'):
                for g in entity['groups']:
                    if g.get('type') == 'queue':
                        group_info = {'queue_id': g.get('id'), 'queue_name': g.get('name')}
            
            for ts in entity.get('timeSeries', []):
                row = {'timestamp': ts.get('timestamp'), **group_info}
                for m in ts.get('metrics', []):
                    row[f"{m['name']}_value"] = m.get('value')
                    row[f"{m['name']}_count"] = m.get('count')
                records.append(row)

    df = pd.DataFrame(records)
    if not df.empty:
        df['timestamp'] = pd.to_datetime(df['timestamp'])
    return df

def main():
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    target_queue_id = os.getenv("TARGET_QUEUE_ID") # e.g., "a1b2c3d4-e5f6-..."

    if not all([client_id, client_secret, target_queue_id]):
        raise ValueError("Missing environment variables: GENESYS_CLIENT_ID, GENESYS_CLIENT_SECRET, TARGET_QUEUE_ID")

    print("Authenticating...")
    token = get_access_token(client_id, client_secret)
    
    print("Fetching analytics data...")
    df = fetch_and_parse_analytics(token, target_queue_id, days_back=7)

    if df.empty:
        print("No data returned. Check date range and queue ID.")
        return

    print("Data parsed successfully.")
    print(df.head())

    # Export to CSV
    output_file = "analytics_export.csv"
    df.to_csv(output_file, index=False)
    print(f"Data exported to {output_file}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 403 Forbidden (Insufficient Scope)

Cause: The OAuth token does not include the analytics:conversation:read scope. This is the most common error when first attempting to access analytics endpoints.

Fix:

1. Go to the Genesys Cloud Admin Console.
2. Navigate to Platform > Integrations > OAuth 2.0 Clients.
3. Select your client.
4. Ensure Analytics > Conversation > Read is checked in the Scopes section.
5. Save and re-generate the token.

Error: 422 Unprocessable Entity (Invalid Query)

Cause: The query body sent to the aggregate endpoint is malformed. Common issues include:

• dateFrom is after dateTo.
• Invalid metric names (e.g., using handleTime instead of handle-time).
• Grouping by a field not supported for the selected metrics.

Fix:

• Validate metric names against the Analytics API documentation.
• Ensure ISO 8601 format for dates: 2023-10-01T00:00:00.000Z.

Error: Empty entities Array

Cause: The query executed successfully, but no data matched the filter.

Fix:

1. Verify the queue_id is correct and active.
2. Check the date range. If you query for a future date, you will get empty results.
3. Ensure there was actual conversation activity in the selected queue during the period.

Error: KeyError in Python Parsing

Cause: The JSON structure varies slightly based on the groupBy configuration. If you group by user instead of queue, the groups array will contain type: "user" instead of type: "queue".

Fix:

• Always check group.get('type') before assuming the structure.
• Use .get() method on dictionaries to avoid crashes on missing keys.

Official References

• Genesys Cloud Analytics API Documentation
• OAuth 2.0 Client Credentials Grant
• Python Requests Library
• Pandas Documentation