You need to enable JavaScript to run this app.
优惠活动
大模型
产品
解决方案
定价
更多

如何通过REST API批量关闭‘Planned Maintenance’邮件通知设置?

Hey there, let's tackle this bulk update for disabling "Planned Maintenance" email notifications via REST API. Here's a practical, scalable approach tailored for hundreds of user profiles:

1. Confirm API Endpoint & Permissions First

First, nail down the specifics of your platform's API:

  • Locate the endpoint that manages user notification preferences. This is typically something like PUT /api/users/{user_id}/notification-settings or PATCH /api/user-preferences/{user_id}.
  • Ensure your API authentication token has the right scopes—you'll need permissions to read user lists and modify notification settings (look for scopes like user_notifications_write or user_profile_edit).
  • Pro tip: Test the endpoint with a single user first to validate the payload works before scaling.
2. Fetch the Full List of Target User IDs

You'll need a complete list of user IDs to update:

  • Use your platform's bulk user API to pull the IDs. If you only need to update specific users (e.g., a department), add filter parameters like GET /api/users?department=operations.
  • Handle pagination! Most APIs limit results per page (e.g., 50 users per request), so loop through next links or increment page parameters to collect all IDs into an array.
3. Structure the Update Payload

Map out the exact payload to disable the Planned Maintenance emails. This varies by platform, but here's a common pattern:

  • For a PUT request (overwrites the full preference set):
{
  "notification_settings": {
    "alerts": {
      "planned_maintenance": {
        "email_enabled": false
      },
      // Keep other notification settings unchanged if using PUT
      "security_alerts": {"email_enabled": true}
    }
  }
}
  • For a PATCH request (only sends the changed field, more efficient):
{
  "notification_settings.alerts.planned_maintenance.email_enabled": false
}
4. Bulk Update with Rate Limiting & Error Handling

Don't flood the API—this will trigger rate limits and cause failures. Use controlled concurrency:

  • Here's a simplified Python example using requests and ThreadPoolExecutor to manage parallel requests safely:
import requests
from concurrent.futures import ThreadPoolExecutor

# Configure your API details
API_TOKEN = "your-authentication-token"
BASE_URL = "https://your-platform-api.com/api"
TARGET_USER_IDS = ["user_123", "user_456", ...] # From step 2

def update_user_notifications(user_id):
    headers = {
        "Authorization": f"Bearer {API_TOKEN}",
        "Content-Type": "application/json"
    }
    payload = {
        "notification_settings.alerts.planned_maintenance.email_enabled": false
    }
    
    try:
        response = requests.patch(
            f"{BASE_URL}/users/{user_id}/notification-settings",
            headers=headers,
            json=payload
        )
        response.raise_for_status()
        print(f"✅ Updated user {user_id}")
    except Exception as e:
        # Log failures for later retry
        print(f"❌ Failed to update {user_id}: {str(e)}")

# Limit concurrent requests to avoid rate limits (adjust based on your API's rate cap)
with ThreadPoolExecutor(max_workers=8) as executor:
    executor.map(update_user_notifications, TARGET_USER_IDS)
  • Always log failed updates so you can retry them later without reprocessing successful users.
5. Verify the Changes

After running the bulk update:

  • Spot-check 5-10 random users with a GET request to their notification settings endpoint to confirm the change stuck.
  • If your platform supports it, pull a bulk report of user notification preferences to validate all target users have the setting disabled.

内容的提问来源于stack exchange,提问作者Gary Sutherland

相关产品推荐
方舟 Agent Plan

超全模态模型 × Harness 升级,最新支持 Deepseek-V4.1-Flash、GLM-5.3 系列、Doubao-Seedream-5.0-pro、Kimi-K3 (部分), 限时 9.9 元起

最近更新时间:2026.05.26 11:12:56