What the Data for This Category Looks Like
The data for this category mainly comes from animal quarantine information, supplier batch records, production traceability systems, temperature collection devices, metal detection devices, and laboratory test results. Daily generated documents include quarantine certificates, slaughter and cutting records, ingredient marinating records, thermal processing records, cold chain temperature records, and microbial test reports. Data updates proceed according to production batches and each shift’s work cycle. Cold chain temperature data is collected and updated continuously. Data fields include batch number, quarantine number, processing link parameters, temperature values, test results, and timestamps. Units include ℃, batch numbers, standard time formats, and others.
What Constraints These Characteristics Impose on Deployment and Upgrade
This category’s data sources are scattered, and it includes real-time cold chain temperature data collected continuously. During deployment, configure stable database connections and sufficient storage capacity to support high-frequency concurrent read and write operations. The data includes highly compliant quarantine and test documents. During deployment, define unified field mapping rules in advance to ensure structured and unstructured data from different sources can be correctly identified and associated. During the upgrade process, avoid interrupting continuously collected cold chain temperature data. Configure a task mechanism that supports breakpoint resume. Ensure the association between original batches and quarantine information is not damaged after the upgrade. In addition, store records of raw and cooked processing zones in separate categories. Plan a data partitioning strategy in advance during deployment to avoid confusion of records related to data cross-contamination.
Configuration Settings
This configuration guide is written for FastGPT v4.17.0. The following configuration items are adapted for the production ledger scenario of this category:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 200–300 connections | Supports high-frequency concurrent read and write operations for multiple data sources, avoids connection pool exhaustion, and adapts to the bulk data import requirements of this category |
PG_URL | Points to a dedicated PostgreSQL instance, set connection timeout to 60 seconds | Stores structured data such as batch associations and quarantine numbers; the timeout configuration adapts to long connection requirements during bulk imports |
REDIS_URL | Configured as an independent Redis instance, set memory threshold to 8 GB | Caches continuously collected cold chain temperature data and temporary task statuses, reduces real-time read and write pressure on the database |
STORAGE_S3_ENDPOINT | Adapted to compliant cloud storage or local object storage endpoints | Stores document-type data such as quarantine certificates and test reports, meets the compliance storage requirements of this category |
ROOT_KEY | Generate a strong random string based on the deployment environment | Controls platform backend permissions, prevents unauthorized access to configurations and core production data |
Three Common Misconfigurations
- Issue: Database connection errors appear every few hours after local Docker deployment. Recovery requires deleting the PostgreSQL container and redeploying. Cause: No reasonable threshold configured for
DB_MAX_LINK, high-frequency data read and write operations cause connection pool exhaustion, and the automatic connection recycling mechanism is not enabled. - Issue: Container startup fails when deploying on a Huawei Euler ARM64 architecture system. Cause: FastGPT images adapted for ARM64 architecture are not used, or an ARM64-compatible database instance corresponding to
PG_URLis not configured. - Issue: Summary logic for some historical ledgers malfunctions after attempting a direct upgrade to the latest version. Cause: Step-by-step upgrades were not performed according to version iteration instructions, and database migration scripts for intermediate versions were skipped, leading to incompatible association fields for batch data.
How to Verify Proper Configuration
- Run a bulk import simulation of production batch data, check that there are no connection exhaustion errors in the database connection logs, and confirm that the
DB_MAX_LINKconfiguration is effective. - Upload a quarantine certificate document, check that the storage path matches the storage endpoint configured in
STORAGE_S3_ENDPOINT, and confirm that the document can be read and associated normally. - Start a continuous temperature data collection simulation task, check that temporary task statuses in the Redis cache are updated normally, and confirm that the
REDIS_URLconfiguration is effective. - Attempt to access the platform configuration page using an unauthorized key, confirm that access is denied, and verify that the
ROOT_KEYconfiguration is effective.
The values provided are common starting points and should be evaluated against the reader’s own samples.