What the data for this category looks like
Civil aviation airport internal office and employee service data primarily originates from flight dispatch systems, employee attendance management systems, logistics maintenance work order systems, and post-training document libraries, among others. Data is updated at real-time, daily, and weekly intervals: flight dynamics are synchronized in real time, attendance reports are generated daily, and maintenance inspection records are archived weekly. Documents are split into structured and unstructured categories. Structured fields include flight number, employee ID, job type, and device ID. Flight numbers use alphanumeric combinations, and attendance duration is measured in hours. Unstructured data includes work order text, training PDFs, and similar materials.
What constraints these characteristics impose during deployment and upgrade
Real-time flight dynamic data requires the database connection pool during deployment to support high-frequency read and write operations, to prevent service interruptions caused by connection exhaustion. Multi-source heterogeneous data (structured attendance data, unstructured work orders) requires storage configurations compatible with both relational and unstructured storage. Relevant object storage parameters such as STORAGE_S3_ENDPOINT must be planned in advance. Data with different update frequencies requires corresponding indexing strategies: real-time data needs high-frequency index update configuration, while historical archived data can use batch indexing. Airport internal office data involves layered job permissions. Relevant permission verification logic must be configured in advance during deployment to prevent cross-job data leaks.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 100–150 | Matches the high-frequency read/write data characteristics of airport multi-system scenarios, prevents connection pool exhaustion |
MONGODB_URI | Exclusive connection address pointing to the airport's internal structured data cluster | Isolates office data from other business data to ensure access permission control |
STORAGE_S3_ENDPOINT | Access address of the airport's internal object storage service | Stores unstructured data such as maintenance work orders and training documents, adapts to multi-source data storage requirements |
ROOT_KEY | Exclusive key generated according to the airport's internal permission management rules | Controls the platform's highest permissions, prevents unauthorized access to sensitive office data |
REDIS_URL | Configured as the airport's internal cache cluster address, with a timeout set to 300 seconds | Caches frequently accessed flight dynamics and employee job information to reduce database read/write pressure |
Three common mistakes
- Phenomenon: When starting a container via Docker Desktop, the loading spinner continues to display, deployment initialization cannot be completed, and container logs prompt database connection failure. Cause:
MONGODB_URIis not correctly configured to point to an available database cluster, so the container cannot establish a database connection after startup, blocking the startup process. - Phenomenon: After deploying with docker-compose, structured data indexes cannot be added in the platform, there is no corresponding entry in the configuration interface, and clicking index management has no response. Cause: The multi-data source index adaptation logic is not enabled in the deployment configuration, or the data directory mapping is not completed in the storage associated with
STORAGE_S3_ENDPOINTin advance, causing the index generation process to not trigger. - Phenomenon: After entering the FastGPT container, executing
npm listcannot retrieve the mongoose version, and its presence can be confirmed by checking package.json in the project root directory. Cause: The global query tool for npm dependencies is not installed globally in the container, or the dependency query command is not executed in the project root directory, resulting in failure to traverse the embedded mongoose dependency.
How to confirm configurations are correct
- Log in to the FastGPT admin backend, check the database connection status panel to confirm that the database connection is normal.
- Upload a sample airport maintenance work order file to verify whether the unstructured data storage and index generation process triggers normally.
- Create a test account to verify whether the permission verification logic complies with the airport's internal job permission rules.
- Simulate a real-time flight data write request to confirm that no abnormal errors occur in the database connection pool.
The values provided 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 2026-09-28.