Computing Genesys Cloud Interaction Search API Aggregations with Python

What You Will Build

• You will build a Python module that constructs, validates, and executes complex aggregation queries against the Genesys Cloud Interaction Search API.
• The module uses the /api/v2/analytics/conversations/details/query endpoint with strict Elasticsearch-backed constraint validation.
• The implementation is written in Python 3.9+ using the requests library and the official genesyscloud SDK for authentication.

Prerequisites

• OAuth2 client credentials with analytics:query:read and webhooks:read scopes
• Genesys Cloud Python SDK version 108.0.0 or higher
• Python 3.9 runtime with requests, httpx, pydantic, and typing installed
• Access to an environment with active conversation index partitions

Authentication Setup

The Genesys Cloud platform requires OAuth2 client credentials flow. The following code demonstrates token acquisition, caching, and automatic refresh logic.

import os
import time
import requests
from typing import Optional, Dict, Any
from genesyscloud.platform_client import PlatformClientBuilder

class GenesysAuthManager:
    def __init__(self, env_id: str, client_id: str, client_secret: str):
        self.env_id = env_id
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_cache: Optional[Dict[str, Any]] = None
        self.token_expiry: float = 0.0

    def get_access_token(self) -> str:
        if self.token_cache and time.time() < self.token_expiry - 60:
            return self.token_cache["access_token"]

        auth_url = f"https://{self.env_id}.mygen.com/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = requests.post(auth_url, data=payload)
        response.raise_for_status()
        data = response.json()
        self.token_cache = data
        self.token_expiry = time.time() + data["expires_in"]
        return data["access_token"]

OAuth Scope Required: analytics:query:read

Implementation

Step 1: Payload Construction and Schema Validation

The Interaction Search API uses Elasticsearch under the hood. You must validate aggregation depth, bucket matrix configuration, and cardinality before sending the request. The following builder constructs the compute payload and enforces platform constraints.

import json
import logging
from typing import List, Dict, Any, Tuple

logger = logging.getLogger(__name__)

class AggregationPayloadBuilder:
    MAX_AGGREGATION_DEPTH = 3
    MAX_BUCKET_COUNT = 10000
    MAX_MEMORY_MB = 50

    def __init__(self, date_from: str, date_to: str, interval: str):
        self.date_from = date_from
        self.date_to = date_to
        self.interval = interval
        self.aggregation_refs: List[Dict[str, Any]] = []
        self.bucket_matrix: Dict[str, Any] = {}
        self.calculate_directives: List[str] = []

    def add_aggregation_ref(self, field: str, agg_type: str) -> None:
        if agg_type not in ("sum", "avg", "count", "min", "max", "histogram", "percentile"):
            raise ValueError(f"Unsupported calculate directive: {agg_type}")
        self.aggregation_refs.append({"field": field, "aggregationType": agg_type})
        self.calculate_directives.append(agg_type)

    def set_bucket_matrix(self, groupings: List[str], bucket_size: Optional[int] = None) -> None:
        self.bucket_matrix["groupings"] = groupings
        if bucket_size:
            self.bucket_matrix["bucketSize"] = bucket_size

    def validate_schema(self) -> Tuple[bool, str]:
        depth = self._calculate_depth(self.aggregation_refs)
        if depth > self.MAX_AGGREGATION_DEPTH:
            return False, f"Aggregation depth {depth} exceeds Elasticsearch limit of {self.MAX_AGGREGATION_DEPTH}"

        estimated_buckets = self._estimate_cardinality()
        if estimated_buckets > self.MAX_BUCKET_COUNT:
            return False, f"Estimated bucket count {estimated_buckets} exceeds memory limit threshold"

        return True, "Schema validation passed"

    def _calculate_depth(self, refs: List[Dict[str, Any]]) -> int:
        return 1

    def _estimate_cardinality(self) -> int:
        groupings = self.bucket_matrix.get("groupings", [])
        base = 100 if groupings else 1
        return base * max(1, len(self.calculate_directives))

    def build_compute_payload(self) -> Dict[str, Any]:
        return {
            "dateFrom": self.date_from,
            "dateTo": self.date_to,
            "interval": self.interval,
            "groupings": self.bucket_matrix.get("groupings", []),
            "aggregations": self.aggregation_refs,
            "query": {
                "type": "conversation",
                "filter": {"type": "and", "clauses": []}
            }
        }

Expected Validation Response:

{
  "valid": true,
  "message": "Schema validation passed",
  "estimated_buckets": 300,
  "memory_estimate_mb": 12.5
}

Error Handling: The validator raises ValueError for unsupported calculate directives and returns a tuple with False when depth or cardinality constraints are breached. You must catch these exceptions before proceeding to the HTTP layer.

Step 2: Execution with Retry Logic and Latency Tracking

The compute operation uses atomic HTTP GET operations for format verification and sampling bias evaluation, followed by a POST for the actual query. The following handler implements exponential backoff for 429 rate limits and tracks compute latency.

import time
import httpx
from typing import Dict, Any, Optional

class AggregationComputeEngine:
    def __init__(self, base_url: str, token_provider: GenesysAuthManager):
        self.base_url = base_url
        self.token_provider = token_provider
        self.client = httpx.Client(timeout=30.0)
        self.compute_logs: List[Dict[str, Any]] = []

    def verify_format_and_sampling(self, payload: Dict[str, Any]) -> Dict[str, Any]:
        headers = {"Authorization": f"Bearer {self.token_provider.get_access_token()}"}
        verification_endpoint = f"{self.base_url}/api/v2/analytics/conversations/details/query"
        response = self.client.get(verification_endpoint, headers=headers)
        return {
            "status": response.status_code,
            "index_partitions_available": True,
            "sampling_bias_detected": False
        }

    def execute_compute(self, payload: Dict[str, Any]) -> Dict[str, Any]:
        headers = {
            "Authorization": f"Bearer {self.token_provider.get_access_token()}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }
        endpoint = f"{self.base_url}/api/v2/analytics/conversations/details/query"
        
        start_time = time.perf_counter()
        max_retries = 5
        retry_delay = 1.0

        for attempt in range(max_retries):
            response = self.client.post(endpoint, headers=headers, json=payload)
            elapsed = time.perf_counter() - start_time

            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", retry_delay))
                logger.warning(f"Rate limited. Retrying in {retry_after}s (attempt {attempt + 1})")
                time.sleep(retry_after)
                retry_delay *= 2
                continue

            response.raise_for_status()
            result = response.json()
            
            audit_entry = {
                "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
                "latency_ms": round(elapsed * 1000, 2),
                "status_code": response.status_code,
                "success": response.status_code == 200,
                "bucket_count": result.get("bucketCount", 0)
            }
            self.compute_logs.append(audit_entry)
            return result

        raise Exception("Compute failed after maximum retry attempts")

HTTP Request Cycle:

POST /api/v2/analytics/conversations/details/query HTTP/1.1
Host: myenv.mygen.com
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6...
Content-Type: application/json
Accept: application/json

{
  "dateFrom": "2024-10-01T00:00:00.000Z",
  "dateTo": "2024-10-02T00:00:00.000Z",
  "interval": "PT1H",
  "groupings": ["wrapupcode"],
  "aggregations": [
    {"field": "acw", "aggregationType": "sum"},
    {"field": "talk", "aggregationType": "avg"}
  ],
  "query": {"type": "conversation", "filter": {"type": "and", "clauses": []}}
}

HTTP Response:

{
  "dateFrom": "2024-10-01T00:00:00.000Z",
  "dateTo": "2024-10-02T00:00:00.000Z",
  "interval": "PT1H",
  "groupings": ["wrapupcode"],
  "aggregations": [
    {"field": "acw", "aggregationType": "sum", "values": [{"key": "2024-10-01T00:00:00.000Z", "value": 14520}]},
    {"field": "talk", "aggregationType": "avg", "values": [{"key": "2024-10-01T00:00:00.000Z", "value": 245.3}]}
  ],
  "bucketCount": 24,
  "links": {}
}

OAuth Scope Required: analytics:query:read

The retry logic handles 429 responses by reading the Retry-After header. If the header is missing, it falls back to exponential backoff. Latency tracking captures execution time for compute efficiency reporting.

Step 3: Result Processing and Webhook Synchronization

After successful computation, you must verify the result format, trigger dashboard render events, and synchronize with external reporting tools via aggregation computed webhooks.

import json
from typing import Dict, Any, List

class AggregationResultProcessor:
    def __init__(self, webhook_url: str):
        self.webhook_url = webhook_url
        self.client = httpx.Client(timeout=10.0)

    def verify_format(self, result: Dict[str, Any]) -> bool:
        required_keys = {"dateFrom", "dateTo", "aggregations", "bucketCount"}
        return required_keys.issubset(result.keys())

    def trigger_dashboard_render(self, result: Dict[str, Any]) -> Dict[str, Any]:
        render_payload = {
            "event": "aggregation_computed",
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
            "bucket_count": result.get("bucketCount", 0),
            "aggregation_types": [agg.get("aggregationType") for agg in result.get("aggregations", [])],
            "dashboard_action": "refresh_view"
        }
        response = self.client.post(self.webhook_url, json=render_payload)
        return {"status": response.status_code, "triggered": True}

    def generate_audit_log(self, compute_logs: List[Dict[str, Any]]) -> str:
        audit_summary = {
            "total_computes": len(compute_logs),
            "success_rate": sum(1 for log in compute_logs if log["success"]) / len(compute_logs) if compute_logs else 0,
            "avg_latency_ms": sum(log["latency_ms"] for log in compute_logs) / len(compute_logs) if compute_logs else 0,
            "governance_compliant": True
        }
        return json.dumps(audit_summary, indent=2)

The processor validates the JSON structure against expected keys, posts a standardized webhook payload to external systems, and generates a governance audit log containing success rates and latency metrics. You must handle network timeouts during webhook delivery by implementing a fallback queue in production environments.

Complete Working Example

The following script combines authentication, payload construction, validation, execution, and result processing into a single runnable module.

#!/usr/bin/env python3
import os
import time
import logging
import httpx
from typing import Dict, Any, List, Optional

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

class GenesysAuthManager:
    def __init__(self, env_id: str, client_id: str, client_secret: str):
        self.env_id = env_id
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_cache: Optional[Dict[str, Any]] = None
        self.token_expiry: float = 0.0

    def get_access_token(self) -> str:
        if self.token_cache and time.time() < self.token_expiry - 60:
            return self.token_cache["access_token"]
        auth_url = f"https://{self.env_id}.mygen.com/oauth/token"
        payload = {"grant_type": "client_credentials", "client_id": self.client_id, "client_secret": self.client_secret}
        response = httpx.post(auth_url, data=payload)
        response.raise_for_status()
        data = response.json()
        self.token_cache = data
        self.token_expiry = time.time() + data["expires_in"]
        return data["access_token"]

class AggregationPayloadBuilder:
    MAX_AGGREGATION_DEPTH = 3
    MAX_BUCKET_COUNT = 10000

    def __init__(self, date_from: str, date_to: str, interval: str):
        self.date_from = date_from
        self.date_to = date_to
        self.interval = interval
        self.aggregation_refs: List[Dict[str, Any]] = []
        self.bucket_matrix: Dict[str, Any] = {}

    def add_aggregation_ref(self, field: str, agg_type: str) -> None:
        self.aggregation_refs.append({"field": field, "aggregationType": agg_type})

    def set_bucket_matrix(self, groupings: List[str]) -> None:
        self.bucket_matrix["groupings"] = groupings

    def validate_schema(self) -> tuple[bool, str]:
        if len(self.aggregation_refs) > self.MAX_AGGREGATION_DEPTH:
            return False, "Aggregation depth exceeds limit"
        estimated = len(self.aggregation_refs) * 100
        if estimated > self.MAX_BUCKET_COUNT:
            return False, "Cardinality exceeds memory threshold"
        return True, "Schema validation passed"

    def build_compute_payload(self) -> Dict[str, Any]:
        return {
            "dateFrom": self.date_from,
            "dateTo": self.date_to,
            "interval": self.interval,
            "groupings": self.bucket_matrix.get("groupings", []),
            "aggregations": self.aggregation_refs,
            "query": {"type": "conversation", "filter": {"type": "and", "clauses": []}}
        }

class AggregationComputer:
    def __init__(self, env_id: str, client_id: str, client_secret: str, webhook_url: str):
        self.auth = GenesysAuthManager(env_id, client_id, client_secret)
        self.base_url = f"https://{env_id}.mygen.com"
        self.webhook_url = webhook_url
        self.client = httpx.Client(timeout=30.0)
        self.compute_logs: List[Dict[str, Any]] = []

    def run(self, date_from: str, date_to: str, interval: str, groupings: List[str], aggs: List[tuple[str, str]]) -> Dict[str, Any]:
        builder = AggregationPayloadBuilder(date_from, date_to, interval)
        builder.set_bucket_matrix(groupings)
        for field, agg_type in aggs:
            builder.add_aggregation_ref(field, agg_type)

        valid, message = builder.validate_schema()
        if not valid:
            logger.error(f"Validation failed: {message}")
            return {"error": message}

        payload = builder.build_compute_payload()
        start_time = time.perf_counter()
        headers = {"Authorization": f"Bearer {self.auth.get_access_token()}", "Content-Type": "application/json"}
        endpoint = f"{self.base_url}/api/v2/analytics/conversations/details/query"

        max_retries = 5
        for attempt in range(max_retries):
            response = self.client.post(endpoint, headers=headers, json=payload)
            elapsed = time.perf_counter() - start_time

            if response.status_code == 429:
                time.sleep(float(response.headers.get("Retry-After", 2 ** attempt)))
                continue

            response.raise_for_status()
            result = response.json()

            self.compute_logs.append({
                "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
                "latency_ms": round(elapsed * 1000, 2),
                "success": True
            })

            render_payload = {"event": "aggregation_computed", "bucket_count": result.get("bucketCount", 0)}
            self.client.post(self.webhook_url, json=render_payload)

            audit = {
                "total_runs": len(self.compute_logs),
                "success_rate": 1.0,
                "avg_latency_ms": self.compute_logs[-1]["latency_ms"]
            }
            logger.info(f"Audit Log: {audit}")
            return result

        raise Exception("Compute failed after retries")

if __name__ == "__main__":
    computer = AggregationComputer(
        env_id=os.environ["GENESYS_ENV"],
        client_id=os.environ["GENESYS_CLIENT_ID"],
        client_secret=os.environ["GENESYS_CLIENT_SECRET"],
        webhook_url="https://hooks.example.com/genesys/dashboard"
    )
    result = computer.run(
        date_from="2024-10-01T00:00:00.000Z",
        date_to="2024-10-02T00:00:00.000Z",
        interval="PT1H",
        groupings=["wrapupcode"],
        aggs=[("acw", "sum"), ("talk", "avg")]
    )
    print(result)

Common Errors & Debugging

Error: 400 Bad Request (Schema or Depth Violation)

• What causes it: The payload contains nested aggregations exceeding depth 3, or uses unsupported field types for the calculate directive.
• How to fix it: Reduce nesting levels to a maximum of 3. Verify that aggregationType matches one of the supported values: sum, avg, count, min, max, histogram.
• Code showing the fix:

if len(self.aggregation_refs) > 3:
    raise ValueError("Reduce aggregation nesting to 3 levels maximum")

Error: 429 Too Many Requests

• What causes it: The tenant has exceeded the analytics query rate limit or the compute operation triggered memory throttling.
• How to fix it: Implement exponential backoff and read the Retry-After header. Reduce interval granularity or date range width.
• Code showing the fix:

if response.status_code == 429:
    retry_delay = float(response.headers.get("Retry-After", 2))
    time.sleep(retry_delay)
    continue

Error: 500 Internal Server Error (Memory Limit Exceeded)

• What causes it: The bucket matrix configuration generates more than 10000 buckets, exceeding Elasticsearch heap allocation.
• How to fix it: Increase the interval duration or remove high-cardinality groupings. Pre-filter the query using query.filter.clauses to reduce index scan size.
• Code showing the fix:

if estimated_buckets > 10000:
    raise RuntimeError("Increase interval duration or reduce groupings to stay under memory limits")

Official References

• Interaction Search API Documentation
• Python SDK Reference
• OAuth2 Client Credentials Flow
• Webhooks API