What the data for this category looks like
This category includes four core document types: ad review forms, qualification attachments, delivery records, and rectification notices. Data sources include ad platform backends, customer qualification upload portals, contract systems, and public regulatory rule databases. Updates trigger per single ad campaign or monthly cycle. Each record contains fields such as subject information, review node, delivery period, and compliance status. There is no unified fixed unit; match corresponding metadata fields based on document type. Archiving and retrieval tasks account for most daily operational time.
Constraints on database and operations workflows
Multi-source data access requires cross-system permission isolation and incremental sync configuration to avoid pulling redundant duplicate data. Non-fixed update cycles need on-demand triggered sync tasks to support temporary data writes for single ad campaigns. Differentiated fields across multiple document types require databases with flexible schema extensions, without hard-coded field structures. High compliance risks demand databases with multi-replica backups and real-time audit logging to prevent data tampering. High time costs in archiving and retrieval require optimizing index structures to reduce query response times.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://compliance_user:secure_pass@db-host:27017/compliance_db?replicaSet=rs0 | Stores unstructured qualification attachments and delivery records; configure replica set to enhance high availability |
PG_URL | postgresql://meta_user:secure_pass@pg-host:5432/compliance_meta | Stores structured ad review forms and compliance rule metadata; supports multi-field associated queries |
DB_MAX_LINK | 15–25 connections | Balances concurrent load for batch archiving tasks and real-time queries; prevents database connection pool exhaustion |
REDIS_URL | redis://:redis_pass@cache-host:6379/1 | Caches frequently accessed regulatory rules and qualification verification results; reduces repeated database requests |
| Database Index Strategy | Create joint indexes based on delivery period and compliance status | Optimizes batch query efficiency for monthly archiving and regulatory audits |
| Backup Strategy | Daily full backup + hourly incremental backup | Meets traceability requirements for regulatory audits; prevents data loss |
Three Common Mistakes
- Replacing the MongoDB image version during deployment causes startup failure with an incompatible version prompt. Cause: Failed to confirm the compatible MongoDB image version per the FastGPT v4.17.0 official documentation, and used an unspecified non-compatible image.
- Sustained increase in database load when repeating the same compliance query. Cause: Did not configure
REDIS_URLto enable caching; every query directly requests the database without reusing historical query results. - Unable to locate the storage location of specific delivery records. Cause: Did not partition database tables and collections per business logic, or did not establish clear metadata indexes, leading to ambiguous query scope.
How to Confirm Configurations Are Properly Set
- Run the database connectivity test script provided by FastGPT official to confirm normal connections for
MONGODB_URI,PG_URL, andREDIS_URL. - Manually trigger an archiving task for temporary delivery data, check if corresponding records are generated in the database, and verify that the
DB_MAX_LINKconfiguration's concurrent load does not trigger connection exhaustion errors. - Submit two identical compliance qualification queries, confirm that the response time of the second query is shorter than the first, and verify that the cache configuration is active.
- View the database audit log to confirm that all data write operations are fully recorded, meeting compliance audit requirements.
The values provided here are common starting points and should be evaluated 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.