LinkedIn API Batch Request Optimization
As developers scale applications that integrate with the LinkedIn API, optimizing API calls becomes crucial. High-frequency or bulk data requests can quickly hit rate limits, increase latency, and slow down workflows. This is where batch requests come in.
Batch requests allow multiple API calls to be combined into a single HTTP request, significantly reducing overhead and improving performance. In this guide, we’ll explore how LinkedIn API batch requests work, provide step-by-step instructions to implement them, and share best practices for optimizing your API performance.
What Are Batch Requests in LinkedIn API?
Batch requests enable developers to bundle multiple API calls into a single HTTP request. Instead of making separate calls for each endpoint, a single batch request can include multiple operations, reducing network latency and the total number of requests sent to the LinkedIn API.
Structure of a Batch Request
Each batch request consists of:
-
- Request Body: A JSON array containing individual API calls with their endpoints, HTTP methods, and parameters.
-
- Headers: Standard LinkedIn API authentication and content type headers.
Common Use Cases for Batch Requests
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- Profile Data Retrieval: Fetching profile data for multiple users simultaneously.
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- Content Posting: Posting updates or articles to several LinkedIn connections in one request.
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- Message Sending: Sending personalized messages to multiple recipients.
Batch requests are particularly useful for automation tools, CRMs, and analytics platforms where efficiency is a priority.
How to Implement Batch Requests
-
- Prepare the Batch Request Body
Each operation in the batch must include:
- Prepare the Batch Request Body
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- Method: HTTP method (GET, POST, etc.).
-
- Relative URL: API endpoint relative to the LinkedIn base URL.
-
- Headers (Optional): Headers specific to the operation.
-
- Body (Optional): Data payload for POST/PUT requests.
Example JSON Structure for a Batch Request:
{
"batch": [
{
"method": "GET",
"url": "/v2/me"
},
{
"method": "GET",
"url": "/v2/connections?q=viewer&start=0&count=10"
},
{
"method": "POST",
"url": "/v2/ugcPosts",
"body": {
"author": "urn:li:person:123456",
"lifecycleState": "PUBLISHED",
"specificContent": {
"com.linkedin.ugc.ShareContent": {
"shareCommentary": { "text": "Hello, LinkedIn!" },
"shareMediaCategory": "NONE"
}
},
"visibility": { "com.linkedin.ugc.MemberNetworkVisibility": "PUBLIC" }
}
}
]
}-
- Send the Batch Request
Send the batch request to the /v2/batch endpoint with proper authentication.
- Send the Batch Request
Python Example:
import requests
BASE_URL = "https://api.linkedin.com/v2/batch"
ACCESS_TOKEN = "YOUR_ACCESS_TOKEN"
batch_payload = {
"batch": [
{"method": "GET", "url": "/v2/me"},
{"method": "GET", "url": "/v2/connections?q=viewer&start=0&count=10"},
{
"method": "POST",
"url": "/v2/ugcPosts",
"body": {
"author": "urn:li:person:123456",
"lifecycleState": "PUBLISHED",
"specificContent": {
"com.linkedin.ugc.ShareContent": {
"shareCommentary": {"text": "Hello, LinkedIn!"},
"shareMediaCategory": "NONE"
}
},
"visibility": {"com.linkedin.ugc.MemberNetworkVisibility": "PUBLIC"}
}
}
]
}
headers = {
"Authorization": f"Bearer {ACCESS_TOKEN}",
"Content-Type": "application/json"
}
response = requests.post(BASE_URL, json=batch_payload, headers=headers)
print(response.json())-
- Test Your Batch Request
Use tools like Postman to validate your request structure and troubleshoot issues before integrating into production.
- Test Your Batch Request
Section 3: Managing Batch Request Responses
Parsing Batch Responses
The LinkedIn API returns a JSON object containing the responses for each batched operation. Each response includes:
-
- Status Code: HTTP status for the operation.
-
- Headers: Any response-specific headers.
-
- Body: Data returned from the operation.
Example Batch Response:
{
"results": [
{
"status": 200,
"body": { "id": "123", "firstName": "John", "lastName": "Doe" }
},
{
"status": 429,
"body": { "message": "Too Many Requests" }
},
{
"status": 201,
"body": { "postId": "789" }
}
]
}Handling Partial Successes and Failures
-
- Log and retry failed requests individually (e.g., for 429 or 500 errors).
-
- Use response codes to identify and handle errors programmatically.
Example Python Code for Parsing Responses:
for result in response.json().get("results", []):
if result["status"] == 200:
print("Success:", result["body"])
else:
print("Error:", result["body"])Logging and Debugging
-
- Log all responses to track successful and failed operations.
-
- Use unique identifiers in batch requests to correlate responses with requests.
Best Practices for Batch Request Optimization
1. Limit the Number of Requests in a Batch
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- LinkedIn recommends limiting batch requests to 20 operations per request to reduce the risk of failures.
2. Prioritize Critical Requests
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- Include high-priority operations at the beginning of the batch to ensure they’re processed even if the batch is interrupted.
3. Monitor API Rate Limits
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- Use the X-RateLimit-Limit and X-RateLimit-Remaining headers to ensure batch requests do not exceed your allocated quota.
4. Cache Responses
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- Store frequently used data to reduce the need for repetitive requests.
5. Handle Errors Gracefully
-
- Implement retry mechanisms for failed operations, such as exponential backoff for rate limit errors.
Trade-offs of Batch Requests vs. Individual Requests
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- Advantages: Reduces network overhead, improves latency, and optimizes API quotas.
-
- Disadvantages: Harder to debug and handle failures for individual requests within a batch.
Conclusion
Batch requests are a powerful feature of the LinkedIn API, enabling developers to optimize performance, reduce overhead, and stay within rate limits. By following the steps and best practices outlined in this guide, you can effectively use batch requests to enhance your application’s efficiency and scalability.
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