What Data for This Category Looks Like
Data sources include alternative composite asset valuation reports submitted by custodian institutions, and interfaces from third-party alternative investment data service providers. Updates follow a batch update schedule aligned with quarterly or semi-annual accounting cycles. Some non-standard assets are disclosed and updated on a monthly basis. Each data document contains full accounting information for a single composite financial product. The structure includes fields such as unique asset code, full product name, accounting cycle, unit net value, cumulative return value, annualized return reference value, disclosure date, and custodian identifier. For units, net value is measured in yuan per share, while return metrics are dimensionless base values, with no percentage notation used.
Constraints Imposed on Database and Operations
The multi-source heterogeneous nature of alternative composite yield rate data requires the database to support flexible schema expansion. This accommodates imports of data sources in varying formats, including custodian bank reports and third-party interfaces. Periodic batch update patterns generate periodic high-volume write requests. Configure the database connection pool concurrency upper limit to prevent a single batch write from exhausting resources. Frequent queries center on asset code, accounting cycle, and disclosure date. Create a joint index for these fields to reduce query latency. The diversity of non-standard asset fields requires retaining data validation rules during operations. This promptly identifies records with abnormal formats or missing values. Some assets have delayed update characteristics. Configure data synchronization retry mechanisms and timeout thresholds to ensure data integrity.
How to Configure Parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_MAX_POOL_SIZE | 10-15 connections | Adapts to concurrent demands of batch writes and frequent queries, avoids connection exhaustion or resource idle |
PARSE_BATCH_SIZE | 200-500 records per batch | Matches the performance limit of single database writes, prevents timeout of single batch requests |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing and import duration of non-standard batch reports, prevents task termination mid-execution |
DATA_SYNC_RETRY_TIMES | 3 retries | Addresses temporary fluctuations or delays in data source interfaces, ensures data synchronization integrity |
INDEX_CREATION_BATCH | 500 records per batch | Builds joint indexes in batches, reduces database CPU usage and prevents service freezes |
QUERY_CONCURRENCY_LIMIT | 800 concurrent requests | Matches daily query peak loads of the business scenario, ensures stable response |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test against available local samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When configuring 1000 concurrent queries, the success rate is only 22%, with a large number of requests returning timeout or connection refused errors. Cause: The
QUERY_CONCURRENCY_LIMITparameter is set lower than the actual concurrent peak demand, and the database connection pool cannot handle the traffic, resulting in failed request processing. - Symptom: In a Docker deployment environment, when uploading knowledge base parsing files, logs repeatedly report
slow operation xxxxms, and MongoDB response latency exceeds thresholds. Cause: TheMONGODB_MAX_POOL_SIZEconfiguration is too low, and concurrent write requests exhaust connection pool resources, leading to excessively high database operation latency. - Symptom: Only partial data is captured when importing batch custodian reports, and subsequent queries cannot obtain complete asset information. Cause: The
PARSE_BATCH_SIZEparameter is set too large, and the number of records in a single batch write exceeds the database's single-processing limit, resulting in failure to write some records to the database.
How to Verify Proper Configuration
- Run a single batch data import test, import the test dataset for this category, verify that the number of imported records in the database matches the source data, and adjust batch import-related parameters based on import success rates.
- Initiate multiple concurrent query requests, monitor real-time occupancy of the database connection pool, and adjust concurrency upper limits and connection pool size parameters based on connection pool load status.
- Export the dataset files generated by the knowledge base, verify the field coverage in the files, and adjust data mapping and validation rules based on missing business fields.
- View MongoDB slow query logs, confirm that indexes have been created for frequently queried fields, and adjust index building batch parameters based on query latency.
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.