What the data for this category looks like
The data processed by this category comes from raw datasets, ETL operation logs, annotation platform output results, and sampling quality inspection results. The update frequency is daily or per batch task cycle. Daily associated documents include cleaning rule configuration files, quality inspection sampling statistics tables, abnormal data lists, and delivery reports. Fields include data ID, exception type, processing status, sampling coverage, etc. There are no unified mandatory units, and they adapt to the fields of the business dataset.
What constraints these characteristics impose on deployment and upgrade
Data sources are scattered and update rhythms are unstable. The deployment link must support multi-data source docking configuration. During upgrades, old version data source mapping rules must be compatible to avoid data pull failures. Daily documents are mostly structured configuration and list files. During deployment, document storage paths and permission rules must be preset to prevent loss of core files such as cleaning rules and quality inspection sampling tables. Single-task data volume fluctuates greatly. After upgrades, sufficient memory and computing resource buffers must be reserved to prevent overload in the abnormal value processing link. Network connectivity between the deployment environment, annotation platform, and ETL tools must also be ensured. Otherwise, data pull and processing will be interrupted.
How to set configurations
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 50–80 connections | Adapts to the concurrency requirements of daily or batch data processing, avoids connection pool exhaustion |
MONGODB_URI | mongodb://username:password@deployment_address:27017/clean_data | Stores structured documents such as abnormal data lists and quality inspection sampling tables |
REDIS_URL | redis://:custom_password@deployment_address:6379/0 | Caches cleaning rules and temporary sampling results to reduce repeated calculation overhead |
STORAGE_S3_ENDPOINT | https://s3-cn-beijing.aliyuncs.com | Stores raw dataset backups and delivery report files |
ROOT_KEY | Strong random string with length ≥32 characters | Ensures administrator permission security for the deployment environment, prevents unauthorized configuration modifications |
FE_DOMAIN | https://your-fastgpt-domain.com | Configures cross-domain access rules for the frontend page, ensures normal document preview functionality |
Three common mistakes
- Symptom: Cleaning rules cannot be loaded after deployment, and no data is returned from the associated raw dataset. Cause:
MONGODB_URIorREDIS_URLis not configured correctly, resulting in failure to read configuration files and temporary caches. - Symptom: After adding the data cleaning and quality inspection plugin to the workflow, no input or output parameters are displayed. Cause:
FE_DOMAINis not set correctly during deployment. The frontend cannot pull the plugin metadata interface, resulting in failed parameter rendering. - Symptom: Batch data cleaning tasks time out and fail, with logs showing connection pool exhaustion. Cause: The
DB_MAX_LINKconfiguration value is too low, cannot support concurrent database requests for batch tasks.
How to confirm the configuration is complete
- Log in to the deployment server, run the database connection test command, verify that the number of connections configured for
DB_MAX_LINKcan be established and reused normally. - Access the frontend domain name, open the cleaning rule management page, confirm that the configured cleaning rules and abnormal data lists can be loaded normally.
- Submit a batch of test data, run the cleaning task, check that there are no database connection or storage related errors in the task logs.
- Enter the workflow editor, add the data cleaning and quality inspection plugin, confirm that the input and output parameter list loads normally.
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.