Data profile for this use case
Data sources for urban commercial bank investment research include internal credit ledgers, regional economic statistical reports, regulatory submission documents, local industry research reports, and central bank monetary policy tool data. Update frequencies vary widely:
- Internal credit data updates on a T+1 basis
- Regional economic data updates monthly or quarterly
- Regulatory files update per their submission cycles
- Industry research reports are released in real time
Document structures fall into three categories:
- Structured credit ledgers, which include fields such as customer ID, credit limit, non-performing rate, and more
- Semi-structured research reports, which include publishing institution, release time, and core viewpoints
- Unstructured regulatory documents
Field units include ten thousand yuan, %, person-times, and others. Some fields must adapt to regional exclusive statistical standards.
Constraints for database and operations workflows
Multiple source data types require the platform to support access via multiple database protocols. It must be compatible with MongoDB, MSSQL, Oracle, and other storage formats.
Uneven scheduling pressure arises from data sources with different update frequencies. Deploy layered synchronization policies to avoid resource preemption.
Regional exclusive statistical fields require the database to support custom field mapping. This prevents data format incompatibility issues.
Financial data compliance requirements mandate full auditability of all database operations. Local data must not be transferred across regions. The platform must support local deployment.
Configuration guidelines
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
FILE_PARSE_MAX_SIZE | 1000 MB | Urban commercial bank investment research data includes large regulatory files and industry research reports. This setting accommodates maximum single file size requirements |
DB_CONNECTION_TIMEOUT | 30 seconds | Matches typical connection latency for urban commercial bank local networks. Prevents unnecessary timeout interruptions |
MAX_CONNECTION_POOL_SIZE | 20-50 | Aligns with concurrent query scale for urban commercial bank investment research scenarios. Prevents resource exhaustion |
DATA_SYNC_SCHEDULE | Configured per data source type | Use 0 0 1 * * ? for internal credit data. Use 0 0 0 1 * * ? for regional economic data. This adapts to different update frequencies |
ORACLE_CONNECT_ENABLE | Enabled | Urban commercial banks commonly use Oracle to store core credit and regulatory data. This setting loads the corresponding driver |
AUDIT_LOG_RETENTION_DAYS | 90 days | Meets retention requirements for financial data compliance audits |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Symptom: MongoDB connection status query fails. Logs return a
Connection refusederror. Cause: The IP of the platform deployment node was not added to the access whitelist of the urban commercial bank's internal MongoDB instance. External access is blocked. - Symptom: Connecting to the MSSQL database in an orchestration flow returns the error
Failed to connect to jyfkk:1433 - 38BBDDC3AF7F0000. Local MongoDB visualization tools can connect normally. Cause: The database connection string does not correctly specify the MSSQL instance name, or the platform has not configured MSSQL port forwarding rules. - Symptom: The database connection configuration interface only displays three database type options. Oracle cannot be selected. Cause: The
ORACLE_CONNECT_ENABLEconfiguration item is not enabled. The platform has not loaded the corresponding database driver component.
How to confirm successful configuration
- Run a database connection test operation. Adjust connection parameters based on data source type until the test passes.
- Trigger a full data synchronization task. Verify that the number of synced documents matches the expected data volume from the source.
- View the audit log module. Confirm that all database operations are properly recorded and that retention duration meets compliance requirements.
- Call the investment research data query interface. Verify that returned fields match the preset investment research data field structure.
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.