What Data for This Category Looks Like
Data for this category comes from raw material qualification certificates, ERP/MES systems, weighing equipment, ovens or fryer lines, laboratories, and food traceability ledgers. Daily documents include formulas and feeding lists, production records, baking or steaming curves, metal detection records, cleaning and disinfection records, and factory inspection reports. Data is updated per production batch, shift, or daily. Fields cover material codes, feeding quantities, baking temperatures and durations, test results, shelf lives, and other relevant metrics. Units include grams, degrees Celsius, seconds, batch numbers, and similar identifiers.
What Constraints These Characteristics Impose on Database and Operations Workflows
This category has numerous formulas, frequent line changes, and products intended for direct consumer consumption. This requires databases to support high-frequency, small-batch writes and fast batch-by-batch queries. Multi-source heterogeneous data originates from equipment, systems, laboratories, and other channels. It must be stored in a unified format and adapted to data synchronization with varying update frequencies. Frequent allergen and cleaning switching requires data fields to accurately associate materials with production batches, to avoid mixing cross-contamination data. Bulk data generated daily or per shift must support efficient bulk import. It must also support fast extraction of full-link data for a single batch during food recalls. Operations must balance high-concurrency queries and stable storage capabilities.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fastgpt?authSource=admin | Supports unstructured storage for multi-source batch data, and fast retrieval of full-link data by batch |
PG_URL | postgresql://user:pass@host:5432/fastgpt?sslmode=require | Stores structured data such as formulas and test reports that require transaction guarantees |
DB_MAX_LINK | 20–50 connections | Matches the connection requirements for high-frequency batch writes and multi-terminal queries, prevents connection exhaustion from impacting business |
REDIS_URL | redis://:pass@host:6379/0 | Caches frequently queried batch information and allergen association data, reduces load on primary databases |
Three Common Configuration Mistakes
- Symptom: After starting FastGPT v4.17.0, the container status shows normal, but accessing port 3000 prompts a database connection failure. Manual verification of the MongoDB account password results in a login failure. Cause: The authentication parameter in the
MONGODB_URIconfiguration is missing, or the instance port does not match the configuration, preventing a database connection from being established. - Symptom: The client closes the SSE connection after calling the conversation API, and the production record for the corresponding batch is not written to the database. Cause: The request timeout retry mechanism is not configured, and local caching for temporarily storing incomplete write requests is not enabled, resulting in data loss when the connection is interrupted.
- Symptom: A timeout error occurs when bulk importing multiple production records, and the number of returned query results is lower than expected. Cause: The
DB_MAX_LINKconfiguration value is too low, and concurrent write requests exceed the connection pool limit, causing some requests to be rejected.
How to Confirm the Configuration Is Correct
- Run the connection command for the corresponding database, verify that the instances configured with
MONGODB_URIandPG_URLcan read and write data normally. - View the running logs after starting the FastGPT v4.17.0 service, confirm that there are no error messages about database connection failures or connection pool exhaustion.
- Import a simulated production batch data, confirm that the data has been correctly written to the corresponding database table through the system query function.
- Initiate multiple concurrent query or write requests, confirm that the service does not show abnormal errors related to connections.
The values provided are common starting points and should be measured against the reader's own samples.