What the data for this category looks like
Data for this category mainly comes from advertising platform APIs, CRM systems, front-end attribution SDKs, and landing page form systems. Update frequency is daily or weekly batch updates. Daily associated documents include attribution rule configuration tables, lead distribution tables, channel effect reports, and abnormal lead checklists. Fields are mostly identification and time-related content such as channel identifiers, lead source parameters, assigned sales employee IDs, and conversion timestamps. No complex measurement units are used.
What constraints these characteristics impose on deployment and upgrades
Multi-data source docking requirements demand configuration of connection parameters for multiple external systems during deployment. Batch update rhythms require ensuring batch task scheduling does not occupy core business resources during upgrades. Daily document rule and report content requires configuring stable storage and import/export capabilities during upgrades to avoid data loss. Time-consuming steps such as channel parameter verification and duplicate lead deduplication require reserving sufficient database connection and cache resources during deployment. This prevents blocking caused by concurrent tasks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 80–120 connections | This category handles batch leads from multiple data sources daily or weekly. Sufficient connection count prevents concurrent query blocking |
MONGODB_URI | Cluster address with read-write separation tags | Attribution data storage volume is large. Cluster deployment improves read-write performance and availability |
REDIS_URL | 600-second expiration cache configuration | Caches channel parameter verification results to avoid timeouts from repeated calls to external advertising platform APIs |
STORAGE_S3_ENDPOINT | Storage node matched to business region | Storage of attribution reports and abnormal checklists requires low-latency access |
ROOT_KEY | 32-character random string | Ensures access security for internal configuration interfaces. Prevents unauthorized modification of core rules |
Three common errors
- Symptom: Lead distribution table upload results in empty fields. Cause:
FE_DOMAINis not configured correctly, leading to failed front-end uploaded parameter verification. - Symptom: Database calls from tools return no output. Cause: Access permission configuration for
PG_URLis not filled in correctly, resulting in rejected database connections. - Symptom: Batch attribution task execution times out. Cause:
DB_MAX_LINKis set too low. Concurrent queries exhaust the connection pool, leading to task blocking.
How to confirm proper configuration
- In the system settings of FastGPT v4.17.0, view the
DB_MAX_LINKparameter. Run the connection pool test script, and verify that the concurrent connection count reaches the configured threshold. - Upload a test lead distribution table. Verify that all fields are correctly synchronized to the corresponding storage without missing or incorrect content.
- Initiate a small-batch attribution test task. Check that task logs have no timeout or connection error messages.
- Use the
ROOT_KEYto call the internal configuration interface. Verify that attribution rule configurations can be read and modified normally.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.