What the data for this category looks like
Data sources for this category include purchase orders, drawing specifications, supplier quality certificates, inspection equipment records, historical quality ledgers, and supplier portal information. Daily documents include inspection specifications, sampling plans, incoming material inspection reports, supplier audit reports, exception contact sheets, 8D correction reports, and concession acceptance approval forms. New data records are generated with each batch of incoming materials. Overall supplier quality evaluations are completed monthly or quarterly, and special audits are carried out irregularly. Data fields include batch number, inspection item, judgment result, supplier information, rectification status, and other items. There are no unified fixed units, and values vary based on the inspection item and document type.
What constraints do these data characteristics impose on deployment and upgrade
The data characteristics of this category impose multiple constraints on the deployment and upgrade process. Heterogeneous data from multiple sources requires adaptation to access logic for different formats. An extended module supporting multi-data source access must be configured during deployment. High-frequency batch-level updates require the database connection pool to match concurrent processing capabilities. Connection parameters must be set appropriately. Irregular special audits and quarterly evaluations create demand for batch data synchronization. The upgrade phase must be compatible with batch data import and processing workflows. Diverse document types and inconsistent fields require support for custom field mapping rules during deployment. This avoids increasing subsequent maintenance difficulty caused by hard-coded adaptations.
How to set configurations
The following configurations apply to FastGPT v4.17.0, optimized for the production ledger summary and report generation scenario for incoming material inspection and supplier quality.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 80–150 connections | This category generates new data with each batch of incoming materials, and has batch synchronization tasks monthly or quarterly. This setting balances connection resources and concurrent processing capabilities to meet high concurrent read and write demands |
MONGODB_URI | Configured as the dedicated cluster address for storing quality ledgers and historical inspection data | The data volume of this category grows with incoming material batches. A dedicated database supports long-term storage and high-frequency query demands |
REDIS_URL | Configured as a dedicated cache cluster address, set cache expiration time to 300–600 seconds | Batch data and supplier evaluation results of this category require frequent reading. Caching reduces database pressure, and the expiration duration matches the data update frequency |
STORAGE_S3_ENDPOINT | Configured as the object storage endpoint for storing inspection reports and supplier documents | Daily documents of this category are mostly in binary format. Dedicated object storage provides stable file access and scalability |
FILE_TOKEN_KEY | Use a randomly generated 32-bit key | Inspection reports and exception contact sheets for this category involve internal sensitive information. A dedicated key ensures the security of file access |
ROOT_KEY | Only configured during initial deployment, adjusted via background permission management afterward | System management permissions for this category must be strictly controlled to avoid unauthorized access to core configuration items |
Three common misconfigurations
- Symptom: After deploying via Docker, attempting to connect to a local database results in connection timeout or access denied errors. Cause: The database connection address inside the container was not pointed to the host intranet IP, or database port mapping was not enabled.
- Symptom: An incorrect storage endpoint is entered when configuring
STORAGE_S3_ENDPOINT, making uploaded supplier audit reports inaccessible. Cause: The object storage region and endpoint format were not verified, leading to token verification failure. - Symptom: Setting
DB_MAX_LINKto an excessively large value results in database connection exhaustion errors in the system. Cause: The concurrent read and write demands of this category were not matched, and too many idle connections occupied database resources.
How to confirm configurations are correctly set
- Perform a single batch of incoming material data import, and check system logs to confirm there are no database connection-related errors.
- Upload an inspection specification document, and verify that the document loads normally using the generated access link.
- View the system permission management interface, and confirm that
ROOT_KEYis only configured for designated administrator accounts. - Initiate a batch synchronization task for quarterly supplier quality evaluations, and confirm that the data synchronization process is not interrupted.
The values provided are common starting points and should be measured against the reader's own samples.