What this category of data looks like
Data sources for this category include the Food Safety Sampling and Monitoring System, the State Administration for Market Regulation Sampling Results Query System, the laboratory LIMS system, and food producer and operator accounts. Daily documents include sampling plans, sampling slips, inspection reports, unqualified food verification and disposal reports, and risk control notices. The data update frequency is monthly. Core fields cover sampling number, inspection items, unqualified judgment items, disposal measures, and more. Units include batches, copies, concentration units, and others.
What constraints these characteristics impose on the database and operations layer
The monthly batch update feature requires the database to support efficient batch writing and import, to avoid performance loss from single-item writing. Data from multiple sources has large format differences, so the system must support access to structured and semi-structured data, reducing pre-work for format conversion. The accuracy of core fields directly affects regulatory disposal effects, so mandatory validation rules must be configured at the database layer to prevent missing or incorrect fields from entering the system. For scenarios with collaborative use by multiple subjects, the database must support fine-grained permission division, ensuring different roles can only access data within their authorized scope. As monthly sampling batches increase, storage expansion space must be reserved to avoid data overload affecting query efficiency.
How to set configurations
| Config Item | Suggested Value | Basis for This Setting |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/food_safety_inspect?authSource=admin | Adapt to multi-source semi-structured data storage, compatible with non-standard documents such as sampling slips and inspection reports |
PG_URL | postgresql://user:pass@host:5432/food_safety?sslmode=require | Store standardized sampling plans, disposal accounts and other structured data, ensuring associated query efficiency |
DB_MAX_LINK | 20–30 | Match the concurrency requirements of monthly batch data import, avoid database overload caused by too high connection count |
REDIS_URL | redis://:pass@host:6379/0 | Cache frequently queried sampling result statistics, reduce direct database query pressure |
| Workflow concurrency threshold | 1 time per 1–2 seconds | Match abnormal issues of high-concurrency calls, avoid situations where MCP calls return empty results |
Three common mistakes
- Phenomenon: When the workflow calls MCP, empty results are returned after 2–3 calls per second. Cause: Concurrency call frequency is not limited, exceeding the carrying capacity of the database or external services.
- Phenomenon: Results queried from database nodes cannot be directly used for report generation, requiring additional code parsing. Cause: Unified return mapping rules are not configured for the structured format of sampling data, resulting in result formats that do not meet preset template requirements.
- Phenomenon: Database connection count is too high, triggering an error prompt of connection pool exhaustion. Cause: A reasonable
DB_MAX_LINKparameter is not set, too many connections are occupied during batch data import and not released in time.
How to confirm the configuration is correct
- Use the database node of FastGPT v4.17.0 to perform a monthly batch data import test, verify whether data can be completely written to the corresponding database table or collection, and check the import success rate judgment standard.
- Initiate a high-frequency call test, simulate workflow calls of 1 time per 1–2 seconds, check whether MCP return results meet expectations, and adjust concurrency limit parameters.
- View the database connection pool monitoring panel, confirm that the current connection count does not exceed the set
DB_MAX_LINKvalue, and there is no connection overflow. - Randomly select sampling data samples, verify the field integrity and format consistency of database query results, and match the preset account summary requirements.
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 2026-09-28.