What the data for this category looks like
Data sources include project settlement systems for the renovation and decoration industry, price monitoring APIs from building material circulation platforms, and decoration market filing data from local housing and urban-rural development departments. Full synchronization and calculation of the previous day’s data is completed every early morning, generating a daily profit margin snapshot. Each document corresponds to profit margin information for a single type of renovation scenario (such as residential full-house renovation, commercial office renovation) within a single region. The document structure includes fields such as region_code, scene_type, base_return_snapshot, material_cost_share, labor_cost_share, and update_time. base_return_snapshot is the measured value range of the day’s profit margin. material_cost_share and labor_cost_share are numerical ranges of the corresponding cost proportions, with units of proportional values.
What constraints these characteristics impose on the "database and operations" link
Multi-source data access verification constraints: Connect internal project settlement and external building material price API data sources. Configure cross-source data alignment verification rules to avoid deviations in profit margin data from different sources. Daily full-volume update write constraints: Batch write requests trigger intensively during the early morning period. Reserve sufficient database connection pool resources to avoid write blocking or request timeouts. Sub-scenario field constraints: Each document contains multiple proportional range fields. Configure format verification rules to filter abnormal data that is not a numerical range, ensuring consistency of data stored in the database. Daily archiving storage constraints: Partition and store historical data by the update_time field. This avoids excessive single-table data volume that would reduce subsequent query and report generation efficiency.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_POOL_MAX_CONN | 32–64 connections | Adapt to the peak batch write volume during early morning, avoid exhausting database connections |
SYNC_DATA_CHECK_INTERVAL | 5 minutes | Used for multi-source data alignment verification, matches the daily update schedule |
BATCH_INSERT_SIZE | 1000 records per batch | Balance single write efficiency and database load, adapts to the full update data volume |
FIELD_VALIDATION_ENABLED | Enabled | Perform format verification on proportional range fields, filter abnormal input |
STORAGE_PARTITION_POLICY | Partition by update_time day | Split historical data storage, reduce resource consumption for single-table queries |
DATA_BACKUP_CRON | 0 3 * | Execute data backup at 3 AM daily, avoid peak business write periods |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material types, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Database connection timeout, returning
ETIMEDOUTerror code. Cause: Docker internal network port mapping is not configured correctly, preventing the FastGPT container from accessing the local MongoDB instance. - Symptom: Database write queue is blocked during early morning batch writes, returning
503 Service Unavailablestatus code. Cause: The database connection pool size is not adjusted, and peak requests exceed the connection limit. - Symptom: The
base_return_snapshotfield of some documents is empty. Cause: Field required validation is not configured, and missing snapshot data returned by some data sources leads to write failure.
How to confirm the configuration is complete
- Run a manual data synchronization task, check the database write logs, confirm there are no entries that failed format validation.
- Check the creation status of database partition tables, confirm that corresponding partitions have been generated based on
update_time. - Simulate early morning batch write requests, monitor database connection count and write latency, confirm that connection pool resources are sufficient.
- Randomly select documents updated on the current day, verify that field formats meet preset proportional range requirements.
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.