Database and Operations for Glass Yield Rates

What this category of data looks like

Glass yield rate data comes from three main sources: public quotes from the National Building Materials Circulation Association, factory quote systems of glass manufacturers, and real-time interfaces of commodity trading markets. There are two update frequency categories: spot data updates every working day, futures data updates after each trading day closes. Some third-party data sources support hourly refreshes. Each data entry is a structured market record. It includes category identifier, origin information, benchmark quote, daily relative change value, statistical date, and data source identifier. The change magnitude field uses permille units, not percentage format.

Constraints on Database and Operations Workflows

Multi-data source access requires the database to support format verification and deduplication for multi-source data. This prevents dirty data from being written. Data sources with different update frequencies need differentiated synchronization task configurations. Real-time data sources require paired lightweight pull triggers. Daily update data sources can use scheduled task scheduling. Most glass market query dimensions cover origin and category. Create a joint index for the production_area and product_type fields. Full table scans will cause excessive query latency without this index. Data timeliness requirements are high. Implement a hot data archiving strategy. Migrate historical data older than 7 days to cold storage. This prevents the hot database from expanding excessively and affecting response speed.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`MONGO_CONNECTION_POOL_SIZE`	`10–15`	Glass market data has moderate concurrent query volume. This value balances connection resource usage and request response speed
`DATA_SYNC_CRON_EXPR`	`0 0-23/4 * * *`	Glass spot data syncs every 4 hours. This balances data timeliness and server load
`INDEX_EXPIRE_AFTER_SECONDS`	`604800`	Corresponds to a 7-day hot data retention period. Data older than this is automatically archived to cold storage to reduce hot database pressure
`QUERY_MAX_RETURN_COUNT`	`200`	Returns a maximum of 200 entries per query. This prevents memory overflow and transmission delays caused by large result sets
`MONGOOSE_VALIDATION_LEVEL`	`strict`	Enforces data field format verification. This matches the fixed field structure of glass market data and reduces dirty data writes

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Analyze specific issues individually. Test on your own samples before finalizing settings.

Three Common Misconfigurations

Symptom: Running npm list inside the container fails to find the mongoose dependency, but the project starts normally. Cause: The mongoose dependency is packaged in the local node_modules of the FastGPT core package, and is not installed globally. Use the docker exec <container name> npm list mongoose command to view the version inside the container.
Symptom: Some fields return empty when querying glass market data. Cause: No joint index created for production_area and product_type. Some concurrent queries are truncated due to timeout, returning incomplete results.
Symptom: Connection timeout occurs for subsequent query requests when batch syncing glass historical data. Cause: No reasonable connection pool size configured. Concurrent sync tasks exhaust database connections, preventing new requests from acquiring connections.

How to Verify Successful Configuration

Run the docker exec <container name> npm list mongoose command. Confirm the returned mongoose version matches the version declared in the project's package.json file.
Call the market query interface. Verify that the returned results include preset business fields such as production_area, product_type, and daily_change, with no missing fields or format errors.
Simulate concurrent query requests. Observe that database connection count does not exceed the configured MONGO_CONNECTION_POOL_SIZE limit.
Check the data synchronization logs. Confirm that the most recent synchronization task completed within the time specified by DATA_SYNC_CRON_EXPR, with no abnormal errors.

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.