Honor Retry-After and RateLimit Headers in SharePoint Online Throttling
Throttling in SharePoint Online is a mechanism implemented to prevent overuse of resources and maintain optimal performance and reliability. When throttling occurs, SharePoint Online returns HTTP status codes, such as 429 (“Too many requests”) or 503 (“Server Too Busy”). This article focuses on the best practices for handling throttling by honoring the Retry-After and RateLimit headers provided in the response.
Understanding Throttling in SharePoint Online
Throttling in SharePoint Online can occur at both the user and application levels. User throttling restricts the number of calls and operations made by applications on behalf of a user, while application throttling imposes limits on applications within a tenant based on the number of licenses purchased per organization.
Handling 429 and 503 Errors with Retry
To handle throttling effectively, it is crucial to honor the Retry-After and RateLimit headers provided in the response. The following Python code snippet demonstrates how to handle 429 and 503 errors with a maximum retry of three times:
Sample Python Code
import requests
import time
class GraphAPIHandler:
def __init__(self, tenant_id, client_id, client_secret, user_agent):
self.tenant_id = tenant_id
self.client_id = client_id
self.client_secret = client_secret
self.user_agent = user_agent
self.access_token = None
def get_access_token(self):
token_url = f'https://login.microsoftonline.com/{self.tenant_id}/oauth2/v2.0/token'
payload = {
'client_id': self.client_id,
'client_secret': self.client_secret,
'scope': 'https://graph.microsoft.com/.default',
'grant_type': 'client_credentials'
}
headers = {
'User-Agent': self.user_agent
}
response = requests.post(token_url, data=payload, headers=headers)
response_data = response.json()
if 'access_token' in response_data:
self.access_token = response_data['access_token']
else:
raise Exception('Failed to obtain access token')
def make_graph_api_request(self, url):
headers = {
'Authorization': f'Bearer {self.access_token}',
'User-Agent': self.user_agent
}
max_retries = 3
retry_wait_time = 1 # Wait time in seconds between retries
for retry in range(max_retries):
response = requests.get(url, headers=headers)
if response.status_code == 200:
return response.json()
elif response.status_code == 429 or response.status_code == 503:
if 'Retry-After' in response.headers:
# Retry-After header provides the recommended wait time
wait_time = int(response.headers.get('Retry-After', 0))
if wait_time > 0:
print(f'Received {response.status_code} status code. Waiting for {wait_time} seconds before retrying...')
time.sleep(wait_time)
else:
print(f'Received {response.status_code} status code, but Retry-After header was not provided. Waiting for {retry_wait_time} seconds before retrying...')
time.sleep(retry_wait_time)
else:
# If Retry-After header is not available, use a default wait time
print(f'Received {response.status_code} status code, but Retry-After header was not provided. Waiting for {retry_wait_time} seconds before retrying...')
time.sleep(retry_wait_time)
else:
# Other error occurred, handle it accordingly
raise Exception(f"Request failed with status code: {response.status_code}")
# Reached maximum retries without a successful response
raise Exception("Maximum retry attempts reached. Unable to complete the request.")
# Usage example
tenant_id = '<your_tenant_id>'
client_id = '<your_client_id>'
client_secret = '<your_client_secret>'
user_agent = 'NONISV|CompanyName|AppName/Versio' # If ISV Application type, use ISV|CompanyName|AppName/Version
graph_handler = GraphAPIHandler(tenant_id, client_id, client_secret, user_agent)
try:
graph_handler.get_access_token()
graph_api_url = 'https://graph.microsoft.com/v1.0/me'
user_data = graph_handler.make_graph_api_request(graph_api_url)
print('User data:', user_data)
except Exception as e:
print('An error occurred:', str(e))
By incorporating the above code into your SharePoint Online application, you can handle 429 and 503 errors effectively. The code retries the request with appropriate wait times based on the Retry-After header provided in the response. It allows a maximum of three retries before raising an exception if a successful response is not received. Adjust the retry_wait_time and max_retries variables as per your application’s requirements.
Conclusion
By following the best practices mentioned in this article and honoring the Retry-After and RateLimit headers in SharePoint Online, you can handle throttling more effectively. Remember to reduce concurrent requests, avoid request spikes, utilize Microsoft Graph APIs, and implement proper retry mechanisms for 429 and 503 errors. With these practices in place, you can optimize your application’s performance and ensure a reliable and responsive experience for SharePoint Online users.
Although the best practice for avoiding SharePoint Online throttling is to optimize your code to minimize the impact of service-related limits, SharePoint Online provides a number of tools to manage and troubleshoot throttling in your tenant. For example, you can use the SharePoint Online Component-based Scalable Logical Architecture(CSLA) tool, monitor the health of your environment, and identify potential throttling issues. The tool, accessed through an elevated command prompt, enables you to configure throttling policies, restrict access to specific endpoints and resources, and monitor the health of your SharePoint Online deployments. Additionally, the SharePoint Online Knowledge Base contains information regarding the most common throttling scenarios and possible solutions.
Even with all the available tools, it can be difficult to detect and address throttling issues in your SharePoint Online environment. To manage and troubleshoot potential throttling issues, perform continuous monitoring of your environment. Monitor the health of your SharePoint Online deployments and identify potential misuse. Also, keep in mind that throttling can occur for any number of reasons—from network bottlenecks to limited server resources and performance degradation.
Published on:
Learn moreRelated posts
What’s new and coming next to Microsoft 365 Copilot and Teams
Hat on, roadmap rolling. ✨ I follow the Microsoft Admin Center (MAC) Message Center all the time — or to be precise, my AI teammate follows it...
How SharePoint Integration Improves Dynamics 365 Document Management
Managing business documents inside Dynamics 365 can become increasingly difficult as CRM data grows. Sales teams upload proposals, support tea...
Microsoft Purview | Data Lifecycle Management – Archive OneDrive and SharePoint files under retention
Microsoft Purview Data Lifecycle Management will enable archiving OneDrive and SharePoint files under retention policies to reduce storage cos...
Microsoft 365 Copilot app: Simplified, chat-centered experience
Microsoft 365 Copilot app updates introduce a streamlined, chat-centered experience with simplified navigation, a new Tasks tab, and consolida...
Microsoft 365 Copilot: Support for real-time screen sharing in Copilot voice sessions
Microsoft 365 Copilot will support real-time screen and camera sharing in voice sessions, enabling Copilot to analyze visual content and provi...
Microsoft Teams: Guest invitation emails will be sent from the inviter’s email address
Microsoft Teams guest invitation emails will soon be sent from the inviter’s email address instead of a no-reply address, improving clarity an...
Microsoft Cans Power BI App for Microsoft 365 Usage
Microsoft has announced that the Microsoft 365 Usage Analytics Power BI app will retire on August 1, 2026. The alternative is the usage report...
D365 CE 2026 Release Wave 1: For Sales & Service Teams
Microsoft continues to evolve Dynamics 365 Customer Engagement with updates that affect how organizations manage sales pipelines, customer ser...
SharePoint Framework (SPFx) roadmap update – May 2026
SPFx is powering the future of Microsoft 365 with AI driven portals and deep integrations across SharePoint and Microsoft 365. The May 2026 up...
File-level backup and restore in Microsoft 365
Microsoft 365 Copilot become more and more crucial component. AFI.AI offers the backup of this technology as a first vendor on the market. The...