What the data for this category looks like
This category of data primarily comes from customers' historical business systems, standard CSV or Excel templates, database export files, and FastGPT product import logs. Daily supporting documents include data import templates, migration execution plans, field mapping comparison tables, and migration verification reports. The data is updated per order or irregularly, with no fixed cycle. Data fields include business entity fields, import status identifiers, error codes and prompt information, and units cover data volume, file storage capacity, and similar metrics.
Constraints imposed by these characteristics on the scheduled tasks and automation link
The diversity of data sources requires scheduled tasks to support multi-type data source access configuration, adapting to different import paths such as local files and database exports. Unlike basic configurations for customer onboarding and tenant initialization, this category processes existing business data, so tasks must support batch import and verification of historical data. The irregular update rhythm requires task triggering methods to support both scheduled scheduling and manual triggering, adapting to temporary migration needs. The high time consumption of field mapping and dirty data cleaning requires scheduled tasks to reuse pre-configured mapping rules and cleaning strategies, reducing repetitive operations. The serious consequences of data errors require tasks to include built-in retry mechanisms, breakpoint resume capabilities, and complete log retention to facilitate problem tracing and repair.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Task triggering method | Scheduled scheduling + manual triggering | Adapt to irregular migration needs, balance automatic execution and temporary adjustments. This configuration is natively supported in FastGPT v4.17.0. |
| Data source access type | Local file / database export / API pull | Cover common data source channels for this category, meet import scenarios of different customers |
| Field mapping rule reuse | Save preset mapping tables | Reduce repeated configuration, match the high-time-consuming field mapping link, improve migration efficiency |
| Task retry times | 3 times (60 second interval) | Respond to temporary network fluctuations or small-probability import errors, avoid task termination after a single failure |
| Log retention duration | 30 days | Meet the needs of problem tracing and migration verification report generation |
| Single import data limit | 100,000 entries | Balance import efficiency and system load, calibrated based on actual testing |
Three common mistakes
- Phenomenon: API request failed: 404 - Resource not found. Reason: The data source connection address configured in the scheduled task does not correctly match the FastGPT import interface path, or the target database table has been deleted.
- Phenomenon: Global variable values are not automatically updated. Reason: The variable synchronization switch is not enabled in the scheduled task configuration, or the corresponding global variable field is not bound in the mapping rule.
- Phenomenon: The category call count statistics field is empty after import. Reason: The call count field was not correctly associated with the target business field during field mapping, or the dirty data cleaning rule filtered this field.
How to confirm configuration is complete
- Navigate to the FastGPT scheduled task management page, view the created customer data import and migration tasks, and confirm that the triggering method, data source type, and preset configuration are consistent.
- Upload a test CSV template, run a manual trigger task, and check whether the import log is generated and contains no errors.
- Modify the preset field mapping rules, save the changes, and review the task configuration again to confirm that the mapping rules have been synchronized and updated.
- Check the task log retention duration setting, and confirm that it matches the preset 30-day retention cycle.
The values listed above 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 2026-10-19.