What data for this category looks like
Data sources include compliant public financial market data APIs and institutional custody report APIs. Update schedule: Full daily report updates are completed T+1 after each trading day close. Some intraday high-frequency market data sub-items are pushed as real-time snapshots during trading hours. Document structure uses structured CSV or relational database tables layered by statistical cycle and product category. Fields include: product unique identifier, product full name, product type, statistical cycle start date, statistical cycle end date, return calculation value, custody institution code. Units are annualized return basis points or unit net asset value change values.
What constraints do these characteristics impose on database and operations workflows
Multi-data source synchronization requires configuring priority rules and conflict resolution policies to prevent data discrepancies across APIs from impacting accuracy. The tight T+1 update time window requires optimizing batch import concurrency and timeout settings, and configuring scheduled task scheduling to avoid financial market trading hours. The large number of structured fields and need to adapt to new product categories require database tables to reserve extended columns or support dynamic fields. The low-latency write requirement for real-time sub-items requires configuring a message queue to buffer write requests and avoid exhausting database connections. A data integrity check and alert mechanism must also be configured to ensure daily report data has no missing or incorrect entries.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_BATCH_INSERT_SIZE | 500-800 records/batch | Adapts to the volume of market data for T+1 batch updates, prevents single writes from triggering database table locks |
DATA_SYNC_TIMEOUT | 1800 seconds | Covers the full time window for full data synchronization, prevents task termination due to mid-run timeout |
MULTI_SOURCE_CONFLICT_POLICY | Prioritize official public API data | Follows data authority requirements for professional financial services, resolves numerical discrepancies across data sources |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for large structured market data tables, prevents import failure for large files |
DB_CONNECTION_POOL_SIZE | 20-30 connections | Balances connection demands from real-time sub-item writes and batch tasks, prevents connection pool exhaustion |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports batch import of full market data files, adapts to large daily report datasets |
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, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Scenario: Docker deployment throws
Access denied for user 'root'@'localhost'error after startup. Cause: Failed to modify environment variables such asMG_DB_CONN_STRandMG_DB_PASSWORDto point to an external compliant market data database, and still uses the built-in test database. - Scenario: Batch synchronization of T+1 daily reports terminates due to timeout. Cause: Failed to adjust the
DATA_SYNC_TIMEOUTparameter to a duration suitable for full data synchronization; the default value is insufficient to cover the complete synchronization process. - Scenario: Real-time market data sub-item pushes return
request timeoutstatus code 504, and database monitoring shows connection count has reached the limit. Cause: Failed to lower theDB_BATCH_INSERT_SIZEparameter; excessive single write data volume causes database blocking.
How to confirm configurations are correct
- Run the preset T+1 batch synchronization task, check the successful record count in the task execution log, and adjust verification rules to match business data volume requirements.
- Send a single real-time market data write request, check database connection status monitoring, confirm the connection pool is not exhausted, and adjust connection pool parameters as needed.
- Import a standard-format market data test file, check the integrity of parsed fields, and adjust file parsing-related parameters to match the data structure.
- Trigger the multi-source data synchronization process, check conflict handling logs, confirm data is merged according to preset rules, and no abnormal data remains.
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.