What this category’s data looks like
Data related to infrastructure construction project yield rates comes from project measurement and payment ledgers, progress cost documents issued by cost consulting institutions, current building material market price databases, and project financing interest records. The update rhythm is daily: current period yield calculation data corresponding to the day’s completed construction volume is finalized each day. Full-cycle cumulative yield calculation results are synchronized weekly. Each data document includes: unique project identifier, calculation date, current period completed construction volume, unit cost reference value, current period yield coefficient, cumulative yield coefficient, financing cost parameter, and risk adjustment parameter. For field units: construction volume uses cubic meters or square meters, unit cost reference value uses yuan, and yield coefficients are recorded as unitless numerical values.
What constraints do these characteristics impose on the "database and operations" link
Multi-source heterogeneous data sources require the database to support cross-data-source connections. Connection pools must be properly configured to handle concurrent requests. Frequent daily updates and weekly batch synchronization require incremental update and scheduled archiving mechanisms. This avoids excessive storage and computing resource usage from full synchronization. Fields contain multiple associated parameters. Multi-dimensional indexes must be established to optimize associated query efficiency and ensure real-time performance of daily yield reports. Long-term project data retention requires a reasonable archiving strategy to reduce online storage pressure. Additionally, the diversity of data fields and units requires strict format verification rules to prevent format errors in imported data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb.maxPoolSize | 15–20 | Handle multi-data-source concurrent requests, match daily high-frequency daily report data pulling and update operations |
database.syncInterval | 300 seconds | Match the daily incremental update rhythm, avoid resource occupation from frequent synchronization |
database.readPreference | secondaryPreferred | Prioritize reading market data from replica sets to reduce pressure on the primary node |
database.archiveRetentionDays | 730 days | Match the long-term data retention requirements of infrastructure projects, retain two years of historical data |
database.connectionTimeoutMS | 10000 milliseconds | Address network latency for cross-data-source connections, avoid timeout errors |
database.validateSchema | Enable | Verify units and formats of fields such as construction volume and cost, prevent exceptions during data import |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material types, data volume and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: The database connection plugin in the workflow returns a "connect ETIMEDOUT" error, and the database can be accessed normally locally. Cause: The database access whitelist is not configured, or the outbound ports of the FastGPT deployment environment are not open, resulting in cross-environment connection failure.
- Phenomenon: Requests for infrastructure construction project data cannot be distinguished from other business data in MongoDB logs. Cause: A dedicated database namespace is not specified in the connection string, or an independent access user is not created for the infrastructure construction project dataset, resulting in inability to filter logs by source.
- Phenomenon: Query execution time increases significantly when using the new version of the FastGPT database connection plugin, while the old version maintains a stable execution time of approximately 0.2 seconds. Cause: The connection pool size is not adjusted to a value suitable for the scenario, or unnecessary full Schema verification is enabled, resulting in additional format verification steps being executed for each request.
How to confirm the configuration is correct
- Run a daily report data pulling workflow once, check the field integrity of the returned results, and confirm that all configured fields are correctly returned.
- View the FastGPT database connection logs, confirm that the request source has been correctly marked, and logs can be filtered by project ID or database namespace.
- Simulate multiple concurrent requests, check the database connection pool usage status, and confirm that no abnormal state of connection exhaustion occurs.
- Check the execution logs of the data archiving task, confirm that historical data is automatically archived according to the configured cycle.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.