linkedin api organizationalEntityShareStatistics returns same data regardless of timeRange parameters

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Summary

The LinkedIn API endpoint for organizationalEntityShareStatistics has been returning inconsistent data since January 31, 2026. Despite changing the timeRange parameters, the response remains the same, suggesting cached or incorrect data. The issue is characterized by:

  • No effect from changing timeIntervals.timeRange.start
  • No effect from changing timeIntervals.timeRange.end
  • No effect from changing timeIntervals.timeGranularityType
  • Data always representing the last 7 days
  • Same number of objects returned
  • Missing timeRange per object in the response

Root Cause

The root cause of this issue is unclear, but it may be related to a recent undocumented change or a bug in the LinkedIn API. Possible causes include:

  • Cache invalidation issues
  • Incorrect data processing
  • Changes to the API endpoint

Why This Happens in Real Systems

This issue can occur in real systems due to:

  • Lack of testing for edge cases
  • Insufficient documentation of API changes
  • Inadequate caching mechanisms
  • Complexity of the API endpoint

Real-World Impact

The impact of this issue includes:

  • Inaccurate analytics and insights
  • Incorrect decision-making based on faulty data
  • Wasted resources on debugging and troubleshooting
  • Loss of trust in the LinkedIn API

Example or Code (if necessary and relevant)

import requests

url = "https://api.linkedin.com/rest/organizationalEntityShareStatistics"
params = {
    "timeIntervals.timeRange.start": "2026-01-01",
    "timeIntervals.timeRange.end": "2026-01-31",
    "timeIntervals.timeGranularityType": "DAY"
}

response = requests.get(url, params=params)
print(response.json())

How Senior Engineers Fix It

Senior engineers can fix this issue by:

  • Verifying API documentation for any changes
  • Testing the API endpoint with different parameters
  • Checking for cache invalidation issues
  • Contacting LinkedIn API support for assistance
  • Implementing workarounds or fallbacks for incorrect data

Why Juniors Miss It

Junior engineers may miss this issue due to:

  • Lack of experience with the LinkedIn API
  • Insufficient knowledge of caching mechanisms
  • Inadequate testing of edge cases
  • Overreliance on documentation without verifying the API behavior

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