What the Data for This Category Looks Like
Typical subjects include product designers, interaction designers, and front-end product teams. Daily documents cover interactive prototypes, page flowcharts, component specifications, and review records. Data sources are mostly Figma or Axure files, design systems, user feedback, and usability test records. Update frequency is weekly or per version release cycle. The data structure centers around page hierarchy, operation paths, component attributes, and review comments. It primarily consists of structured data linked via text and nodes, with no unified standardized numeric fields.
What Constraints Do These Characteristics Impose on Database and Operations Processes
The data characteristics of this category impose multiple constraints on database and operations processes. Unstructured design files and semi-structured review records require support for both document-oriented and relational storage capabilities. High-frequency updates on a weekly or per-version basis require database connection pools to adapt to frequent read/write operations and avoid connection exhaustion. Frequent adjustments to data structures alongside design iterations require database table structures to have flexible extensibility. Compatibility with export formats from Figma and Axure requires the operations link to configure corresponding parsing and adaptation rules. Associated queries for review records rely on stable index configurations to ensure response efficiency for design reviews.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://design-user:secure-pass@db-host:27017/design-db?authSource=admin | Stores unstructured Axure and Figma prototype files, supports flexible storage for multiple document types |
PG_URL | postgresql://review-user:secure-pass@pg-host:5432/design-review?sslmode=require | Stores structured review records, page flow data, and component attribute data, supports associated queries |
DB_MAX_LINK | 20–30 | Adapts to weekly high-frequency prototype file upload and review query operations, avoids database connection exhaustion |
REDIS_URL | redis://cache-user:secure-pass@redis-host:6379/0 | Caches frequently accessed component specifications and page path data, reduces read/write pressure on relational databases |
Three Common Misconfigurations
- Symptom: When passing database connection parameters via variables in a workflow, a parameter format error prompt is triggered, while manual execution of SQL statements shows no abnormalities. Cause: Special characters are not properly escaped, causing the connection string for
MONGODB_URIorPG_URLto be truncated, preventing a complete connection. - Symptom: In a FastGPT environment deployed via Docker, a connection timeout error is returned when the workflow calls database tools. Cause: The container network has not opened the ports corresponding to the database, or environment variables have not been correctly mapped to the database connection configuration inside the container.
- Symptom: After a successful MongoDB connection, component data from the design system cannot be read. Cause: The database user has not been granted read/write permissions for the corresponding collection, or the connection string did not specify the correct authentication database, leading to permission verification failure.
How to Verify Successful Configuration
- Run the database connectivity test script built into FastGPT v4.17.0 to verify whether the connection status of
MONGODB_URIandPG_URLis normal. - Upload a test Axure prototype file, confirm that parsed file data can be correctly written to the corresponding MongoDB collection.
- Access the component query interface of the design system, check whether the Redis cache can normally return frequently queried component specification data.
- Simulate submission of a design review record, confirm that structured data can be correctly inserted into the associated PostgreSQL table.
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 2026-10-19.