Aggregating Custom Genesys Cloud Analytics Metrics with Python, InfluxDB, and FastAPI

What You Will Build

This tutorial delivers a Python service that extracts raw conversation event data from Genesys Cloud, calculates average handle time per skill group, writes downsampled metrics to a time-series database, and serves filtered results through a FastAPI REST endpoint. The implementation uses the Genesys Cloud Analytics API with httpx for explicit HTTP lifecycle control, pandas for aggregation, the InfluxDB v2 Python client for storage, and FastAPI for dashboard consumption. The language is Python 3.10+.

Prerequisites

• OAuth client type: Machine-to-Machine (Client Credentials)
• Required scopes: analytics:conversation:read, conversation:detail:read
• SDK/API version: Genesys Cloud API v2, Python httpx 0.27+, influxdb-client 1.38+, fastapi 0.109+, pandas 2.1+
• Runtime: Python 3.10 or higher
• External dependencies: pip install httpx influxdb-client fastapi uvicorn pandas pydantic python-dotenv
• Active Genesys Cloud organization with at least one skill group assigned to conversations

Authentication Setup

Genesys Cloud uses OAuth 2.0 Client Credentials flow. The following code implements token acquisition, caching, and automatic refresh before API calls.

import os
import time
import httpx
from typing import Optional

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, base_url: str = "https://api.mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = base_url
        self.token: Optional[str] = None
        self.token_expiry: float = 0
        self.token_url = f"{base_url}/oauth/token"

    def _is_token_valid(self) -> bool:
        return self.token is not None and time.time() < self.token_expiry - 60

    def get_token(self) -> str:
        if self._is_token_valid():
            return self.token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = httpx.post(self.token_url, data=payload, timeout=15.0)
        response.raise_for_status()
        token_data = response.json()

        self.token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"]
        return self.token

The client stores the bearer token and refreshes it when the remaining lifetime drops below 60 seconds. Every subsequent API call must attach Authorization: Bearer <token> to the request headers.

Implementation

Step 1: Initialize Client and Query Raw Conversation Data

The Analytics Conversations Details Query endpoint returns granular conversation records. You must specify date boundaries, metrics, and groupings. The API enforces pagination via the pageSize and nextPageToken parameters.

Required scope: analytics:conversation:read

import httpx
import pandas as pd
from typing import List, Dict, Any

class GenesysAnalyticsClient:
    def __init__(self, auth: GenesysAuth):
        self.auth = auth
        self.base_url = auth.base_url
        self.endpoint = "/api/v2/analytics/conversations/details/query"

    def query_conversations(self, start_date: str, end_date: str, page_size: int = 1000) -> pd.DataFrame:
        headers = {
            "Authorization": f"Bearer {self.auth.get_token()}",
            "Content-Type": "application/json"
        }
        
        payload = {
            "dateFrom": start_date,
            "dateTo": end_date,
            "metrics": ["handleTime", "talkTime", "waitTime", "wrapTime"],
            "dimension": "conversationId",
            "groupings": ["skill"],
            "pageSize": page_size,
            "includeMetricsWithNoData": False
        }

        all_rows: List[Dict[str, Any]] = []
        next_page_token: Optional[str] = None

        while True:
            request_payload = payload.copy()
            if next_page_token:
                request_payload["nextPageToken"] = next_page_token

            response = httpx.post(
                f"{self.base_url}{self.endpoint}",
                headers=headers,
                json=request_payload,
                timeout=30.0
            )
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 5))
                time.sleep(retry_after)
                continue
                
            response.raise_for_status()
            data = response.json()

            if data.get("data"):
                all_rows.extend(data["data"])

            next_page_token = data.get("nextPageToken")
            if not next_page_token:
                break

        return pd.DataFrame(all_rows)

Expected response structure contains a data array where each object includes conversationId, skill array, and metric objects with value fields. The loop consumes nextPageToken until it is null. The 429 handler respects the Retry-After header to prevent rate-limit cascade failures.

Step 2: Compute Derived KPIs with Aggregation

Raw conversation records require transformation before storage. The goal is average handle time per skill group. The pipeline flattens the nested skill array, filters out null metrics, and computes the mean.

def compute_aht_per_skill(df: pd.DataFrame) -> pd.DataFrame:
    if df.empty:
        return pd.DataFrame(columns=["skill", "avg_handle_time", "conversation_count"])

    # Flatten the skill array into individual rows
    df_exploded = df.explode("skill")
    
    # Filter rows with valid handleTime and non-null skill
    df_valid = df_exploded[
        (df_exploded["skill"].notna()) & 
        (df_exploded["skill"] != "") &
        (df_exploded["metrics"]["handleTime"]["value"].notna())
    ].copy()

    # Extract numeric handle time in seconds
    df_valid["handle_time_sec"] = df_valid["metrics"]["handleTime"]["value"]

    # Aggregate by skill
    agg_result = df_valid.groupby("skill").agg(
        avg_handle_time=("handle_time_sec", "mean"),
        conversation_count=("handle_time_sec", "count")
    ).reset_index()

    # Round to two decimal places for storage efficiency
    agg_result["avg_handle_time"] = agg_result["avg_handle_time"].round(2)
    return agg_result

Edge cases include conversations without assigned skills, zero-duration records, and metric objects missing the value key. The filter chain removes invalid entries before aggregation. The output DataFrame contains one row per skill with the computed average.

Step 3: Persist to Time-Series Database with Downsampling

InfluxDB v2 requires a bucket, organization, and token. The following function writes aggregated points with a timestamp bucket aligned to hourly intervals. Downsampling is handled server-side via a Flux task, but the write client demonstrates tag indexing and retention alignment.

Required scope: None (database operation)

from influxdb_client import InfluxDBClient, Point, WritePrecision
from influxdb_client.client.write_api import SYNCHRONOUS
import pandas as pd

class InfluxMetricsStore:
    def __init__(self, url: str, token: str, org: str, bucket: str):
        self.client = InfluxDBClient(url=url, token=token, org=org)
        self.bucket = bucket
        self.org = org
        self.write_api = self.client.write_api(write_options=SYNCHRONOUS)

    def write_aht_metrics(self, df: pd.DataFrame, timestamp: pd.Timestamp) -> None:
        if df.empty:
            return

        points = []
        for _, row in df.iterrows():
            point = (
                Point("analytics_metrics")
                .tag("skill", str(row["skill"]))
                .field("avg_handle_time", float(row["avg_handle_time"]))
                .field("conversation_count", int(row["conversation_count"]))
                .time(timestamp, WritePrecision.S)
            )
            points.append(point)

        self.write_api.write(bucket=self.bucket, org=self.org, record=points)
        self.client.close()

    def create_downsample_task(self, task_name: str, downsample_interval: str = "1h") -> None:
        flux_script = f"""
import "math"

option task = {{name: "{task_name}", every: {downsample_interval}}}

from(bucket: "{self.bucket}")
  |> range(start: -{downsample_interval})
  |> filter(fn: (r) => r._measurement == "analytics_metrics")
  |> aggregateWindow(every: {downsample_interval}, fn: mean, createEmpty: false)
  |> yield(name: "downsampled")
"""
        # Task creation requires the HTTP API or influx CLI. 
        # This placeholder shows the exact Flux payload required.
        # In production, execute via: influx task create --name <name> --org <org> --flux <script>
        print(f"Task Flux Script Generated: {flux_script}")

The write_aht_metrics method converts each DataFrame row into an InfluxDB Point. Tags enable fast filtering by skill. The create_downsample_task method outputs the exact Flux script required to configure a continuous aggregation task. InfluxDB executes the task on a schedule and writes results to a separate bucket, reducing query latency for dashboards.

Step 4: Expose REST Endpoint with Query Filtering

FastAPI provides automatic OpenAPI documentation and type validation. The endpoint accepts date ranges, skill filters, and interval parameters. It queries InfluxDB using the InfluxDB Python client’s query API and returns JSON.

from fastapi import FastAPI, Query
from pydantic import BaseModel
from typing import List, Optional
from influxdb_client import InfluxDBClient

app = FastAPI(title="Genesys Analytics Aggregator")

class MetricResponse(BaseModel):
    skill: str
    avg_handle_time: float
    conversation_count: int
    timestamp: str

@app.get("/metrics/aht", response_model=List[MetricResponse])
def get_aht_metrics(
    start_date: str = Query(..., description="ISO 8601 start date"),
    end_date: str = Query(..., description="ISO 8601 end date"),
    skill: Optional[str] = Query(None, description="Filter by skill group"),
    interval: str = Query("1h", description="Aggregation interval")
):
    client = InfluxDBClient(
        url=os.getenv("INFLUX_URL"),
        token=os.getenv("INFLUX_TOKEN"),
        org=os.getenv("INFLUX_ORG")
    )
    query_api = client.query_api()
    
    filter_clause = ""
    if skill:
        filter_clause = f' and r.skill == "{skill}"'

    flux_query = f"""
from(bucket: "{os.getenv('INFLUX_BUCKET')}")
  |> range(start: "{start_date}", stop: "{end_date}")
  |> filter(fn: (r) => r._measurement == "analytics_metrics"{filter_clause})
  |> filter(fn: (r) => r._field == "avg_handle_time")
  |> yield(name: "mean")
"""
    
    result = query_api.query(org=os.getenv("INFLUX_ORG"), query=flux_query)
    metrics = []
    for table in result:
        for record in table.records:
            metrics.append(MetricResponse(
                skill=record.values.get("skill", "unknown"),
                avg_handle_time=record.get_value(),
                conversation_count=0,
                timestamp=str(record.get_time())
            ))
            
    client.close()
    return metrics

The endpoint constructs a dynamic Flux query based on query parameters. It filters by measurement, applies optional skill constraints, and extracts the _value field. The response model enforces type safety before JSON serialization. Dashboard consumers can request specific time windows and skill groups without full table scans.

Complete Working Example

The following script combines authentication, data extraction, aggregation, storage, and API exposure into a single executable module. Replace environment variables with valid credentials before execution.

import os
import time
import httpx
import pandas as pd
from typing import Optional
from influxdb_client import InfluxDBClient, Point, WritePrecision
from influxdb_client.client.write_api import SYNCHRONOUS
from fastapi import FastAPI, Query
from pydantic import BaseModel
from typing import List
import uvicorn

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, base_url: str = "https://api.mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = base_url
        self.token: Optional[str] = None
        self.token_expiry: float = 0
        self.token_url = f"{base_url}/oauth/token"

    def _is_token_valid(self) -> bool:
        return self.token is not None and time.time() < self.token_expiry - 60

    def get_token(self) -> str:
        if self._is_token_valid():
            return self.token
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = httpx.post(self.token_url, data=payload, timeout=15.0)
        response.raise_for_status()
        token_data = response.json()
        self.token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"]
        return self.token

def fetch_and_aggregate(start_date: str, end_date: str) -> pd.DataFrame:
    auth = GenesysAuth(
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET")
    )
    headers = {
        "Authorization": f"Bearer {auth.get_token()}",
        "Content-Type": "application/json"
    }
    payload = {
        "dateFrom": start_date,
        "dateTo": end_date,
        "metrics": ["handleTime"],
        "dimension": "conversationId",
        "groupings": ["skill"],
        "pageSize": 1000,
        "includeMetricsWithNoData": False
    }
    
    all_rows = []
    next_page_token = None
    while True:
        req_payload = payload.copy()
        if next_page_token:
            req_payload["nextPageToken"] = next_page_token
        response = httpx.post(
            f"{auth.base_url}/api/v2/analytics/conversations/details/query",
            headers=headers,
            json=req_payload,
            timeout=30.0
        )
        if response.status_code == 429:
            time.sleep(int(response.headers.get("Retry-After", 5)))
            continue
        response.raise_for_status()
        data = response.json()
        if data.get("data"):
            all_rows.extend(data["data"])
        next_page_token = data.get("nextPageToken")
        if not next_page_token:
            break
            
    df = pd.DataFrame(all_rows)
    if df.empty:
        return pd.DataFrame(columns=["skill", "avg_handle_time", "conversation_count"])
        
    df_exploded = df.explode("skill")
    df_valid = df_exploded[
        (df_exploded["skill"].notna()) & 
        (df_exploded["skill"] != "") &
        (df_exploded["metrics"]["handleTime"]["value"].notna())
    ].copy()
    df_valid["handle_time_sec"] = df_valid["metrics"]["handleTime"]["value"]
    agg_result = df_valid.groupby("skill").agg(
        avg_handle_time=("handle_time_sec", "mean"),
        conversation_count=("handle_time_sec", "count")
    ).reset_index()
    agg_result["avg_handle_time"] = agg_result["avg_handle_time"].round(2)
    return agg_result

def store_metrics(df: pd.DataFrame, timestamp: pd.Timestamp) -> None:
    client = InfluxDBClient(
        url=os.getenv("INFLUX_URL"),
        token=os.getenv("INFLUX_TOKEN"),
        org=os.getenv("INFLUX_ORG")
    )
    write_api = client.write_api(write_options=SYNCHRONOUS)
    points = []
    for _, row in df.iterrows():
        point = (
            Point("analytics_metrics")
            .tag("skill", str(row["skill"]))
            .field("avg_handle_time", float(row["avg_handle_time"]))
            .field("conversation_count", int(row["conversation_count"]))
            .time(timestamp, WritePrecision.S)
        )
        points.append(point)
    write_api.write(bucket=os.getenv("INFLUX_BUCKET"), org=os.getenv("INFLUX_ORG"), record=points)
    client.close()

app = FastAPI(title="Genesys Analytics Aggregator")

class MetricResponse(BaseModel):
    skill: str
    avg_handle_time: float
    conversation_count: int
    timestamp: str

@app.get("/metrics/aht", response_model=List[MetricResponse])
def get_aht_metrics(
    start_date: str = Query(...),
    end_date: str = Query(...),
    skill: Optional[str] = Query(None),
    interval: str = Query("1h")
):
    client = InfluxDBClient(
        url=os.getenv("INFLUX_URL"),
        token=os.getenv("INFLUX_TOKEN"),
        org=os.getenv("INFLUX_ORG")
    )
    query_api = client.query_api()
    filter_clause = f' and r.skill == "{skill}"' if skill else ""
    flux_query = f"""
from(bucket: "{os.getenv('INFLUX_BUCKET')}")
  |> range(start: "{start_date}", stop: "{end_date}")
  |> filter(fn: (r) => r._measurement == "analytics_metrics"{filter_clause})
  |> filter(fn: (r) => r._field == "avg_handle_time")
  |> yield(name: "mean")
"""
    result = query_api.query(org=os.getenv("INFLUX_ORG"), query=flux_query)
    metrics = []
    for table in result:
        for record in table.records:
            metrics.append(MetricResponse(
                skill=record.values.get("skill", "unknown"),
                avg_handle_time=record.get_value(),
                conversation_count=0,
                timestamp=str(record.get_time())
            ))
    client.close()
    return metrics

if __name__ == "__main__":
    # Example aggregation run
    df = fetch_and_aggregate("2024-01-01T00:00:00Z", "2024-01-01T23:59:59Z")
    if not df.empty:
        store_metrics(df, pd.Timestamp.now())
    uvicorn.run(app, host="0.0.0.0", port=8000)

The script executes a single aggregation cycle on startup, writes results to InfluxDB, and launches the FastAPI server on port 8000. Dashboard tools can query /metrics/aht?start_date=...&end_date=...&skill=Support.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token, incorrect client ID/secret, or missing Authorization header.
• Fix: Verify credentials in GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET. Ensure the GenesysAuth class refreshes the token before each request. Check that the client application is enabled in the Genesys Cloud admin console.
• Code fix: The _is_token_valid method subtracts 60 seconds from expiry to prevent edge-case expiration during request transmission.

Error: 403 Forbidden

• Cause: Missing OAuth scope. The Analytics API requires analytics:conversation:read.
• Fix: Navigate to the Genesys Cloud OAuth client configuration and add analytics:conversation:read and conversation:detail:read. Restart the application to force token reissuance with updated scopes.
• Code fix: Validate scope presence in the token payload if custom middleware is required. The SDK does not expose scope claims directly, so rely on admin console configuration.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud rate limits (typically 100 requests per second per client for Analytics endpoints).
• Fix: Implement exponential backoff. The provided code reads the Retry-After header and sleeps accordingly. For high-volume pipelines, introduce a request queue with token bucket rate limiting.
• Code fix: The while True pagination loop includes a 429 handler that pauses execution. Add jitter to retry delays in production to prevent thundering herd scenarios.

Error: 5xx Internal Server Error

• Cause: Genesys Cloud backend outage, malformed date format, or invalid metric names.
• Fix: Validate ISO 8601 date strings. Ensure metric names match the API contract (handleTime, talkTime, waitTime). Implement circuit breaker logic for consecutive 5xx responses.
• Code fix: Wrap httpx.post in a retry decorator with max attempts set to 3. Log the full request body and response headers for incident analysis.

Error: InfluxDB Write Timeout

• Cause: Network latency, bucket retention policy violation, or missing organization ID.
• Fix: Verify INFLUX_ORG and INFLUX_BUCKET values. Check InfluxDB logs for write failures. Ensure the client closes connections after batch writes to prevent file descriptor exhaustion.
• Code fix: The store_metrics function calls client.close() explicitly. Use connection pooling via InfluxDBClient configuration for sustained workloads.

Official References

• Genesys Cloud Analytics Conversations Details Query API
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• InfluxDB v2 Python Client Documentation
• FastAPI Query Parameters and Validation
• Pandas GroupBy Aggregation Reference