What This Category’s Data Entails
Data sources include agrochemical product monitoring data from the National Agricultural Technology Extension Service Center, public transaction information from bulk commodity spot trading platforms, and ex-factory price ledgers from production enterprises. Daily market data updates finish before 16:30 on each trading day. Monthly yield summary data releases on the first working day of the following month. Each data document includes these fields: product unique code, product category (such as herbicides, compound fertilizers), production region, daily transaction average price, previous cycle reference average price, inventory turnover days. Price fields use yuan/ton as their unit. Turnover day fields use days as their unit.
Constraints for Database and Operations
Multi-source heterogeneous data requires a unified product code mapping table. This avoids cross-data source association errors. Fixed trading day update schedules require operation scripts to trigger data pulling and writing only during specified trading day windows. Pause tasks during non-trading hours to reduce server load. Rich product categories and growing data volume over time require partitioning database tables by product category or update time. This ensures efficient aggregate queries. Field validation rules must cover numerical validity for price fields and unit consistency. This prevents dirty data from entering the system. Monthly summary data batch processing requires the database to support efficient batch writing and aggregate computing capabilities.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DATA_SOURCE_SYNC_INTERVAL | 300 seconds | Agrochemical product data updates at fixed trading day windows. A 5-minute sync interval completes full data pulling within the update window and avoids delays |
DB_WRITE_BATCH_SIZE | 500 records per batch | Excessive single-batch writes cause database table locking. Insufficient batches increase IO overhead. 500 records matches the single-batch data volume for agrochemical products |
DATA_VALIDATION_RULES | Enable non-negative validation for price fields and mandatory unit matching | Agrochemical price fields have no negative values. Units must be unified as yuan/ton to prevent dirty data from entering the system |
DB_PARTITION_POLICY | Partition by product category | Agrochemical products have large category differences. Partitioning by category improves query and aggregate efficiency for similar data |
SQL_PARSE_TIMEOUT | 60 seconds | Complex multi-table join queries (such as cross-category yield summaries) require sufficient parsing time to avoid timeout failures |
ONEAPI_CONCURRENT_MAX | Calibrate based on actual testing | Adapt to hardware video memory configuration. Matches actual load capacity for hardware such as A800 80G |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When running data queries, generated SQL statements cannot be parsed by the database due to included punctuation. Queries return empty results or throw an
SQL syntax errorerror. Cause: Data fields are not escaped. Directly concatenating field values with punctuation when generating SQL causes syntax errors. - Phenomenon: When migrating agrochemical product data originally stored in MongoDB to a domestic database, a
connection refusederror occurs. Data cannot be imported. Cause: The connection protocol and permission configuration of the domestic database are not adapted. Original MongoDB connection string parameters cannot be reused directly. - Phenomenon: When deploying a model using A800 80G hardware, a
503 Service Unavailableerror occurs after concurrent requests exceed the threshold. Some user requests time out. Cause: The concurrency upper limit is not adjusted based on hardware video memory configuration. High model loading video memory usage prevents new requests from being allocated resources.
How to Verify Successful Configuration
- Run a manual data sync task. Check if daily agrochemical market data is generated in the database. Verify that field units and category classifications match preset rules.
- Write a test SQL statement. Execute a cross-category yield summary query. Confirm the query result has no syntax errors and returns data that aligns with business logic.
- Adjust the concurrency upper limit parameter. Simulate multi-user requests. Observe if the service experiences timeouts or errors. Calibrate a reasonable concurrency threshold based on hardware video memory conditions.
- Check the database monitoring dashboard. Confirm that query latency for partitioned tables meets expectations. No table locking or IO overload occurs.
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.