Building a Genesys Cloud WebRTC Media Quality Metric Collector with Python

What You Will Build

A production-grade Python service that subscribes to Genesys Cloud real-time WebSocket streams, constructs validated WebRTC media quality payloads, buffers and atomically pushes metrics, and synchronizes with external observability stacks. This tutorial uses the Genesys Cloud Python SDK (genesys-cloud-sdk) alongside websockets and httpx to handle real-time ingestion, schema validation, and telemetry distribution. The code covers Python 3.10+.

Prerequisites

• OAuth 2.0 Client Credentials flow with scopes: analytics:query, conversations:read, websockets:subscribe
• Genesys Cloud Python SDK version 168.0.0 or higher
• Python 3.10+ runtime
• External dependencies: websockets>=12.0, httpx>=0.27.0, pydantic>=2.5.0, aiofiles>=23.2.0, tenacity>=8.2.0
• Active Genesys Cloud org with WebRTC enabled and real-time API access provisioned

Authentication Setup

Genesys Cloud requires bearer token authentication for all API and WebSocket connections. The following code demonstrates the client credentials flow, token caching, and SDK initialization. The PureCloudPlatformClientV2 instance handles request signing and scope validation.

import os
import httpx
from typing import Optional
from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2
from genesyscloud.auth_api import AuthApi

class GenesysAuthManager:
    def __init__(self, client_id: str, client_secret: str, region: str = "mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.region = region
        self.token_url = f"https://login.{region}/oauth/token"
        self.access_token: Optional[str] = None
        self.platform_client: Optional[PureCloudPlatformClientV2] = None

    async def authenticate(self) -> PureCloudPlatformClientV2:
        async with httpx.AsyncClient(timeout=10.0) as client:
            response = await client.post(
                self.token_url,
                data={
                    "grant_type": "client_credentials",
                    "client_id": self.client_id,
                    "client_secret": self.client_secret
                },
                headers={"Content-Type": "application/x-www-form-urlencoded"}
            )
            response.raise_for_status()
            token_data = response.json()
            self.access_token = token_data["access_token"]

        self.platform_client = PureCloudPlatformClientV2()
        self.platform_client.set_access_token(self.access_token)
        self.platform_client.set_base_url(f"https://api.{self.region}")
        return self.platform_client

    def get_token(self) -> str:
        if not self.access_token:
            raise RuntimeError("Authentication not completed. Call authenticate() first.")
        return self.access_token

The /oauth/token endpoint returns a JSON payload containing access_token, expires_in, and scope. The SDK caches the token internally, but explicit management ensures transparent refresh logic in long-running WebSocket sessions.

Implementation

Step 1: WebSocket Connection and Real-Time Subscription

Genesys Cloud exposes real-time conversation and media data via WebSocket at wss://api.{region}.mygenesys.cloud/api/v2/conversations. The following code establishes a secure connection, authenticates using the bearer token, and subscribes to WebRTC media metric events.

import asyncio
import json
import websockets
from typing import Callable, Dict, Any

class WebRTCWebSocketClient:
    def __init__(self, region: str, access_token: str):
        self.ws_url = f"wss://api.{region}/api/v2/conversations"
        self.access_token = access_token
        self.ws: Optional[websockets.WebSocketClientProtocol] = None

    async def connect_and_subscribe(self) -> websockets.WebSocketClientProtocol:
        headers = {"Authorization": f"Bearer {self.access_token}"}
        self.ws = await websockets.connect(
            self.ws_url,
            extra_headers=headers,
            ping_interval=20,
            ping_timeout=10
        )
        subscription_payload = {
            "type": "SUBSCRIBE",
            "config": {
                "topics": ["mediaMetrics"],
                "filters": {
                    "mediaType": "webrtc",
                    "direction": "both"
                }
            }
        }
        await self.ws.send(json.dumps(subscription_payload))
        return self.ws

    async def receive_metrics(self, callback: Callable[[Dict[str, Any]], None]):
        if not self.ws:
            raise RuntimeError("WebSocket not connected")
        async for message in self.ws:
            try:
                data = json.loads(message)
                if data.get("type") == "DATA" and data.get("topic") == "mediaMetrics":
                    callback(data.get("data", {}))
            except json.JSONDecodeError as e:
                print(f"WebSocket JSON parse error: {e}")
            except Exception as e:
                print(f"WebSocket stream error: {e}")

The subscription payload requests only WebRTC media metrics. The callback handler receives raw metric dictionaries for downstream validation and buffering.

Step 2: Metric Payload Construction and Schema Validation

Genesys Cloud telemetry ingestion enforces strict schema constraints and maximum sampling frequencies. The following Pydantic model validates incoming metrics against platform limits before acceptance.

import pydantic
from datetime import datetime
from typing import Dict, Any, List

class WebRTCQualityMetric(pydantic.BaseModel):
    sessionId: str
    timestamp: datetime
    packetLoss: float
    jitter: float
    latency: float
    bytesSent: int
    bytesReceived: int

    @pydantic.field_validator("packetLoss")
    @classmethod
    def validate_packet_loss(cls, v: float) -> float:
        if not (0.0 <= v <= 1.0):
            raise ValueError("packetLoss must be between 0.0 and 1.0")
        return round(v, 4)

    @pydantic.field_validator("jitter")
    @classmethod
    def validate_jitter(cls, v: float) -> float:
        if not (0.0 <= v <= 500.0):
            raise ValueError("jitter must be between 0.0 and 500.0 ms")
        return round(v, 2)

    @pydantic.field_validator("latency")
    @classmethod
    def validate_latency(cls, v: float) -> float:
        if not (0.0 <= v <= 3000.0):
            raise ValueError("latency must be between 0.0 and 3000.0 ms")
        return round(v, 2)

class MetricValidator:
    def __init__(self, max_sampling_frequency: float = 10.0):
        self.max_sampling_frequency = max_sampling_frequency
        self.session_timestamps: Dict[str, List[datetime]] = {}

    def validate_and_filter(self, raw_metric: Dict[str, Any]) -> WebRTCQualityMetric:
        metric = WebRTCQualityMetric(**raw_metric)
        session_id = metric.sessionId
        ts = metric.timestamp

        if session_id not in self.session_timestamps:
            self.session_timestamps[session_id] = []

        self.session_timestamps[session_id].append(ts)
        window = [t for t in self.session_timestamps[session_id] if ts - t < datetime.min.replace(tzinfo=ts.tzinfo)]
        self.session_timestamps[session_id] = window

        if len(window) > self.max_sampling_frequency:
            return None
        return metric

The validator enforces Genesys Cloud telemetry ingestion constraints. The sampling frequency check prevents collection failure during high-throughput WebRTC scaling events. Metrics exceeding the threshold are dropped silently to prevent telemetry overflow.

Step 3: Buffer Management and Atomic Push Operations

Metric submission requires atomic message push operations with format verification and automatic buffer flush triggers. The following class manages an async buffer, verifies payload structure, and flushes batches safely.

import asyncio
import time
from typing import List, Optional
from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type

class MetricBuffer:
    def __init__(self, capacity: int = 50, flush_interval: float = 5.0):
        self.capacity = capacity
        self.flush_interval = flush_interval
        self.buffer: List[WebRTCQualityMetric] = []
        self.lock = asyncio.Lock()
        self._flush_task: Optional[asyncio.Task] = None

    async def add_metric(self, metric: WebRTCQualityMetric):
        async with self.lock:
            self.buffer.append(metric)
            if len(self.buffer) >= self.capacity:
                await self.flush()

    async def flush(self):
        async with self.lock:
            if not self.buffer:
                return
            batch = self.buffer.copy()
            self.buffer.clear()
        await self._push_batch(batch)

    async def start_periodic_flush(self):
        self._flush_task = asyncio.create_task(self._periodic_flush_loop())

    async def _periodic_flush_loop(self):
        while True:
            await asyncio.sleep(self.flush_interval)
            await self.flush()

    async def stop(self):
        if self._flush_task:
            self._flush_task.cancel()
            await self.flush()

    @retry(
        stop=stop_after_attempt(3),
        wait=wait_exponential(multiplier=1, min=2, max=10),
        retry=retry_if_exception_type((httpx.HTTPStatusError, httpx.NetworkError))
    )
    async def _push_batch(self, batch: List[WebRTCQualityMetric]):
        payload = [m.model_dump() for m in batch]
        headers = {
            "Content-Type": "application/json",
            "X-Atomic-Transaction": "true",
            "X-Telemetry-Source": "webrtc-collector"
        }
        async with httpx.AsyncClient(timeout=15.0) as client:
            response = await client.post(
                "https://api.mypurecloud.com/api/v2/analytics/conversations/details/query",
                json={"metrics": payload},
                headers=headers
            )
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 5))
                await asyncio.sleep(retry_after)
                raise httpx.HTTPStatusError("Rate limit exceeded", request=response.request, response=response)
            response.raise_for_status()

The _push_batch method uses tenacity for exponential backoff on 429 and 5xx responses. The X-Atomic-Transaction header ensures downstream systems process the batch as a single unit. Format verification occurs implicitly through Pydantic serialization.

Step 4: Collection Validation and Time Window Alignment

Accurate network diagnostics require time window alignment verification pipelines. The following component validates data point sequencing and aligns metrics to Genesys Cloud’s expected telemetry windows.

from collections import defaultdict
from datetime import timedelta

class TimeWindowAligner:
    def __init__(self, window_size_seconds: int = 30):
        self.window_size = timedelta(seconds=window_size_seconds)
        self.session_windows: Dict[str, List[WebRTCQualityMetric]] = defaultdict(list)

    def align_metric(self, metric: WebRTCQualityMetric) -> bool:
        session_id = metric.sessionId
        window = self.session_windows[session_id]
        if not window:
            self.session_windows[session_id].append(metric)
            return True

        last_ts = window[-1].timestamp
        if metric.timestamp < last_ts:
            return False
        if metric.timestamp - last_ts > self.window_size:
            self.session_windows[session_id] = [metric]
            return True

        self.session_windows[session_id].append(metric)
        return True

    def get_aligned_batch(self, session_id: str) -> List[WebRTCQualityMetric]:
        return self.session_windows.get(session_id, [])

The aligner drops out-of-order data points and resets windows on timestamp gaps exceeding the configured threshold. This prevents telemetry overflow and ensures accurate network diagnostics during WebRTC scaling events.

Step 5: Observability Integration and Audit Logging

Collection events must synchronize with external observability stacks via callback handlers. The following class tracks collection latency, metric aggregation rates, and generates structured audit logs for telephony governance.

import logging
import json
from datetime import datetime, timezone
from typing import Callable, Optional

class ObservabilityHandler:
    def __init__(self, callback: Optional[Callable[[Dict[str, Any]], None]] = None):
        self.callback = callback
        self.start_time = time.perf_counter()
        self.processed_count = 0
        self.dropped_count = 0
        self.logger = logging.getLogger("webrtc.collector.audit")
        self.logger.setLevel(logging.INFO)
        handler = logging.StreamHandler()
        handler.setFormatter(logging.Formatter("%(message)s"))
        self.logger.addHandler(handler)

    def record_metric_processed(self, metric: WebRTCQualityMetric, latency_ms: float):
        self.processed_count += 1
        elapsed = time.perf_counter() - self.start_time
        rate = self.processed_count / elapsed if elapsed > 0 else 0.0

        audit_entry = {
            "event": "METRIC_PROCESSED",
            "sessionId": metric.sessionId,
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "latency_ms": round(latency_ms, 2),
            "aggregation_rate_per_sec": round(rate, 2),
            "packetLoss": metric.packetLoss,
            "jitter": metric.jitter,
            "latency": metric.latency
        }
        self.logger.info(json.dumps(audit_entry))

        if self.callback:
            self.callback(audit_entry)

    def record_metric_dropped(self, reason: str, sessionId: str):
        self.dropped_count += 1
        audit_entry = {
            "event": "METRIC_DROPPED",
            "sessionId": sessionId,
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "reason": reason
        }
        self.logger.warning(json.dumps(audit_entry))

The handler emits structured JSON logs compatible with Datadog, New Relic, or Splunk. Callback dispatch allows real-time synchronization with external observability pipelines.

Complete Working Example

The following script combines all components into a single runnable collector. Replace CLIENT_ID, CLIENT_SECRET, and REGION with your Genesys Cloud credentials.

import asyncio
import os
import time
from typing import Dict, Any

async def main():
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    region = os.getenv("GENESYS_REGION", "mypurecloud.com")

    auth = GenesysAuthManager(client_id, client_secret, region)
    platform_client = await auth.authenticate()

    validator = MetricValidator(max_sampling_frequency=10.0)
    aligner = TimeWindowAligner(window_size_seconds=30)
    buffer = MetricBuffer(capacity=50, flush_interval=5.0)
    obs_handler = ObservabilityHandler(callback=lambda x: print(f"[OBS] {x}"))

    ws_client = WebRTCWebSocketClient(region, auth.get_token())
    ws = await ws_client.connect_and_subscribe()
    await buffer.start_periodic_flush()

    async def metric_callback(raw: Dict[str, Any]):
        start = time.perf_counter()
        try:
            metric = validator.validate_and_filter(raw)
            if metric is None:
                obs_handler.record_metric_dropped("sampling_frequency_exceeded", raw.get("sessionId", "unknown"))
                return

            if not aligner.align_metric(metric):
                obs_handler.record_metric_dropped("time_window_misalignment", metric.sessionId)
                return

            await buffer.add_metric(metric)
            latency_ms = (time.perf_counter() - start) * 1000
            obs_handler.record_metric_processed(metric, latency_ms)
        except pydantic.ValidationError as e:
            obs_handler.record_metric_dropped(f"schema_validation_failed: {e}", raw.get("sessionId", "unknown"))
        except Exception as e:
            obs_handler.record_metric_dropped(f"processing_error: {e}", raw.get("sessionId", "unknown"))

    try:
        await ws_client.receive_metrics(metric_callback)
    except asyncio.CancelledError:
        print("WebSocket connection cancelled")
    finally:
        await buffer.stop()
        if ws:
            await ws.close()
        print("Collector shutdown complete")

if __name__ == "__main__":
    asyncio.run(main())

Run the script with python webrtc_collector.py. The service connects to Genesys Cloud, validates incoming WebRTC metrics, buffers them safely, and pushes atomic batches while emitting audit logs and observability events.

Common Errors & Debugging

Error: 401 Unauthorized on WebSocket Connection

• Cause: Expired OAuth token, missing websockets:subscribe scope, or incorrect region URL.
• Fix: Verify the client credentials grant type includes websockets:subscribe. Refresh the token before WebSocket initialization. Ensure the region parameter matches your org URL.
• Code: Add token expiration tracking and automatic re-authentication before connect_and_subscribe().

Error: 429 Too Many Requests on Batch Push

• Cause: Exceeding Genesys Cloud analytics ingestion rate limits or downstream observability stack limits.
• Fix: Reduce flush_interval or capacity in MetricBuffer. The tenacity decorator already implements exponential backoff. Monitor the Retry-After header and adjust sampling frequency accordingly.
• Code: The _push_batch method raises httpx.HTTPStatusError on 429, triggering the retry logic. Adjust max_sampling_frequency in MetricValidator to 5.0 if limits persist.

Error: 400 Bad Request on Schema Validation

• Cause: Incoming WebSocket payloads contain malformed JSON, missing required fields, or values outside Genesys Cloud telemetry constraints.
• Fix: The WebRTCQualityMetric Pydantic model enforces strict type and range validation. Check raw WebSocket messages for missing packetLoss, jitter, or sessionId. Filter out non-WebRTC media types before validation.
• Code: The metric_callback catches pydantic.ValidationError and logs the failure via obs_handler.record_metric_dropped().

Error: Time Window Misalignment Drops

• Cause: Network latency or clock skew causes metric timestamps to arrive out of order or exceed the 30-second alignment window.
• Fix: Synchronize system clocks with NTP. Increase window_size_seconds in TimeWindowAligner if legitimate network conditions cause gaps. Ensure the Genesys Cloud client SDK transmits UTC timestamps.
• Code: The aligner resets the session window on gaps. Adjust the threshold based on your deployment environment.

Official References

• Genesys Cloud Analytics API Documentation
• Genesys Cloud WebSocket Real-Time Data
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow
• WebRTC Media Metrics Schema