Analytics API 413 Error on 90-Day Query

Stuck on 413 Entity Too Large when hitting /api/v2/analytics/users/evaluations/summary. My gRPC service needs 90 days of data, but the single POST request fails. I tried splitting the dateRange into three 30-day chunks. Should I handle this via parallel requests or is there a batch endpoint I missed? The payload is minimal, so it seems odd. Any examples of chunking logic in Python would help. Thanks.

I’d recommend looking at at the nextPageToken mechanism instead of manually slicing dates. The 413 error on /api/v2/analytics/users/evaluations/summary usually isn’t about the date range length itself, but the payload size of the aggregation keys or the lack of pagination handling in your client. Genesys Cloud analytics endpoints are strict about result set sizes. If you’re sending a massive list of userIds or skillIds in the filter, the request body explodes.

Here is how I handle this in my Python SDK wrappers for the Kafka Connect source connector. We use a generator pattern to stream results rather than loading 90 days into memory at once.

from purecloudplatformclientv2 import AnalyticsApi, DateRange, PaginationRequest

def fetch_analytics_chunks(api_instance, date_range, filters):
 """
 Generator that yields pages of analytics data.
 Handles 413 by ensuring small batches and proper pagination.
 """
 pagination = PaginationRequest(page_size=100) # Keep page_size small to avoid 413 on response
 
 while True:
 try:
 # The SDK handles the POST with the DateRange object
 response = api_instance.post_analytics_users_evaluations_summary(
 date_range=date_range,
 filters=filters,
 pagination=pagination
 )
 
 yield response.entities
 
 if not response.next_page_token:
 break
 
 pagination.next_page_token = response.next_page_token
 
 except ApiException as e:
 if e.status == 413:
 # Retry with smaller page_size or fewer filter keys
 pagination.page_size = max(10, pagination.page_size // 2)
 continue
 raise

# Usage:
# for batch in fetch_analytics_chunks(api, DateRange(start, end), my_filters):
# process_batch(batch)

• Always pass PaginationRequest with a controlled page_size.
• Check response.next_page_token to continue fetching.
• If 413 persists, reduce the number of IDs in your filters object.
• Never assume a single call can handle 90 days of high-cardinality data.

This approach keeps your Kafka connector memory footprint low and avoids the HTTP layer choking on large payloads.

It depends, but generally… you need to respect the payload limits on the POST body, not just the date range. The 413 error often stems from the sheer size of the filter object or the aggregation keys, not just the time window. nextPageToken is crucial for results, but it does not reduce the initial request size. If your filter includes a large array of user IDs, the JSON payload will hit the server limit.

Here is how I handle this in my Node.js webhook processors using the genesyscloud-platform-client SDK. I chunk the user IDs and use Promise.all for parallel execution, ensuring each request stays under the limit.

const chunkArray = (arr, size) => Array.from({ length: Math.ceil(arr.length / size) }, (l, i) => arr.slice(i * size, i * size + size));

const fetchEvaluations = async (userIds, startDate, endDate) => {
 const chunks = chunkArray(userIds, 50); // Limit IDs per request
 const promises = chunks.map(chunk => 
 platformClient.AnalyticsApi.postAnalyticsUsersEvaluationsSummary({
 body: {
 filter: { userId: chunk },
 dateRange: { start: startDate, end: endDate },
 groupings: ['userId']
 }
 })
 );
 return Promise.all(promises);
};

Environment Requirements:

Check your filter size first. If the IDs are the culprit, chunking is mandatory.

This is typically caused by the client failing to serialize the InteractionAggregationQuery model correctly, resulting in a malformed or oversized JSON payload that exceeds the server’s strict request body limits. The suggestion above regarding payload size is correct, but the root cause is likely how you are constructing the filter object in Python. The Genesys Cloud Python SDK (genesyscloud) requires you to use the specific model classes rather than raw dictionaries to ensure proper serialization and adherence to the OpenAPI schema. When you manually construct the JSON for a 90-day query, you might be including redundant fields or failing to paginate the filter arrays (like userIds), which bloats the request. To fix this, instantiate the InteractionAggregationQuery and InteractionAggregationFilter classes directly. This ensures the SDK handles the serialization efficiently. Below is a Python snippet demonstrating the correct pattern for chunking user IDs and constructing the query to avoid the 413 error. This approach respects the internal pagination limits of the analytics service. See Support Article 12345 for details on payload optimization.

from genesyscloud.analytics import InteractionAggregationQuery, InteractionAggregationFilter
from genesyscloud.platform_client import platform_client

# Chunk user IDs to prevent payload bloat
user_ids = ['id1', 'id2', 'id3'] # Replace with your list
chunk_size = 50
for i in range(0, len(user_ids), chunk_size):
 chunk = user_ids[i:i + chunk_size]
 
 # Construct filter using SDK models
 filter_obj = InteractionAggregationFilter(user_ids=chunk)
 
 # Construct query
 query = InteractionAggregationQuery(
 date_range='2023-01-01/2023-03-31',
 filter=filter_obj,
 group_by=['userId'],
 select=['totalDuration']
 )
 
 # Execute query
 response, error = platform_client.analytics_api.post_analytics_interactions_aggregates(query=query)
 if error:
 print(f"Error: {error}")
 else:
 print(f"Processed chunk {i//chunk_size + 1}")

The main issue here is that the 413 error on /api/v2/analytics/users/evaluations/summary typically stems from the request body size exceeding limits due to large filter arrays, not just the date range. While splitting dates helps, you must also paginate using nextPageToken to handle result sets. The suggestion above about payload size is correct, but the root cause is often how the InteractionAggregationQuery model is serialized in Python. Ensure you are using the specific SDK classes rather than raw dictionaries to avoid malformed JSON. I usually implement a loop that checks for nextPageToken and retries until null. See kb-8832 for details on pagination logic.

from genesyscloud import analytics_api

api_instance = analytics_api.AnalyticsApi(platform_client)
query = analytics_api.EvaluationSummaryQuery(
 date_range="2023-01-01T00:00:00Z/2023-03-31T23:59:59Z",
 group_by=["userId"],
 size=250 # Keep size small to avoid 413
)

while True:
 response = api_instance.post_analytics_users_evaluations_summary(body=query)
 print(response)
 if not response.next_page_token:
 break
 query.page_token = response.next_page_token