What the Data for This Category Looks Like
Feed yield rate data is sourced from raw material quotes from domestic commodity spot exchanges, production ledgers from feed manufacturers, and publicly available survey data from industry associations. Data is updated daily in the early morning, with full market data from the previous trading day. A full category data calibration and correction runs once per week. Each data entry includes fields such as raw material name, origin, pricing unit, current day’s transaction price, previous day’s transaction price, month-on-month change value, formula proportion for the corresponding finished feed, and statistical cycle. The formula proportion field marks the addition ratio of the raw material in the finished feed, and is the core associated field for feed yield rate calculations.
What Constraints Do These Characteristics Impose on Database and Operations Workflows?
The characteristics of feed yield rate data directly constrain database and operations workflow design. Daily scheduled bulk market data updates require the database to support low-latency bulk writes and scheduled task scheduling. The linked formula proportion field must be associated with the feed category main table, so the database must support foreign keys or joint indexes to ensure cross-table data consistency. Weekly full category data calibration requires retroactive modification of historical partitioned data, so the database must support flexible partition table modifications and transaction support. Synchronizing data from multiple sources requires configuring data validation rules to prevent data anomalies from quote deviations across channels. A data cleaning step must also be reserved to correct fields with inconsistent formatting.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_CONNECTION_POOL_SIZE | 10-15 | Adapts to the concurrent demand of daily bulk feed market data writes, avoids connection pool exhaustion while improving write efficiency |
DATA_SYNC_CRON_EXPRESSION | 0 0 7 * * * | Matches the daily early morning update schedule for feed market data, ensures that the same day’s data is fully stored in the database before 8:00 AM |
PARTITION_DATE_FIELD | stat_date | Stores feed data partitioned by statistical date, this field is the core partitioning basis for each data entry, facilitating historical data calibration and fast queries |
DATA_CLEAN_THRESHOLD | ±50 yuan/ton | Filters abnormal data where quote deviations across different data sources exceed this range, adapts to the reasonable fluctuation range of feed raw material quotes |
DB_BACKUP_CRON | 0 0 2 * * 0 | Matches the weekly full data calibration requirement, backup data can be used for rollback operations after calibration failures |
MAX_BATCH_INSERT_SIZE | 200 | Adapts to the scale of daily updated feed data per batch, balances write efficiency and database load |
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 Misconfigurations
- Symptom: A database connection timeout error is displayed when running
pnpm devin a local development environment, with theETIMEDOUTerror code in the logs. Cause: The correct MongoDB connection string was not configured, or the local development environment does not have outbound permissions for the required port, preventing a stable connection to the remote MongoDB instance. - Symptom: In feed data query scenarios, the database query results return a large number of redundant fields, leading to discrepancies between token consumption for model calls and actual API bills. Cause: No field filtering rules were configured, and all fields were pulled by default. Non-core fields such as formula proportion and origin code increase the token usage of the prompt.
- Symptom: The Oracle option is not displayed in the database connection module, making it impossible to directly configure an Oracle data source. Cause: The current version of the FastGPT database driver does not include built-in Oracle connection support, only built-in support for mainstream database types. If the feed scenario needs to connect to Oracle, a corresponding driver plugin must be installed manually.
How to Confirm Configurations Are Correctly Applied
- Run the scheduled sync task, check if daily feed market data entries are generated in the database, and verify that the
stat_datefield matches the current date. - Manually import a set of test data with quote deviations exceeding the preset abnormal threshold, confirm that the system automatically filters this type of data.
- Call the database query interface, verify that the returned fields only include the core fields specified in the configuration, with no redundant content.
- Perform a full backup and restore operation, confirm that the backup file can be properly restored to the test database instance.
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.