What the data for this category looks like
Data related to white appliance profit yields comes from three main sources: brand-side ERP systems, offline retail terminal POS devices, and online e-commerce sales application programming interfaces (APIs). Data updates once daily, with full historical data for the previous day aggregated overnight. Each data document corresponds to daily profit-related metrics for a single model, with a flat document structure. Core fields include model_id (model code string), manufacturer_price (factory settlement price, unit: yuan), terminal_price (terminal selling price, unit: yuan), energy_cost (daily energy conversion cost, unit: yuan), daily_sales (daily sales volume, unit: units), daily_profit (daily total profit, unit: yuan). No nested data fields are present, all metrics are directly tied to profit calculations, and no redundant fields are included.
What constraints do these characteristics impose on database and operations work
Alignment requirements for multi-source data create strict validation constraints for extract-transform-load (ETL) workflows. Data sources from different channels have format differences, requiring field mapping and data normalization during synchronization. Failure to complete these steps will lead to profit calculation deviations. The fixed daily full update requirement demands precise scheduled task scheduling in operations, to avoid task backlog or missed update windows. A timeout fault-tolerance mechanism must also be set up, to prevent the overall synchronization task from failing due to abnormal data for a single model. The feature that core fields are directly tied to profit requires mandatory field validation rules in database operations, to ensure documents missing key metrics are not included in calculation workflows. The number of white appliance models continues to grow with product line iterations, so a composite index on model_id and update_date must be created to optimize high-frequency query performance by model and date. Additionally, the database connection pool size must be controlled, to avoid exhausting system resources from concurrent connections of multi-source synchronization tasks.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_CONNECTION_STRING | mongodb://fastgpt:your_password@localhost:27017/white_appliance_profit?authSource=admin | Standard MongoDB connection format adapted for local development and online deployment, including authentication information and target database |
DATA_SYNC_CRON | 0 0 2 * * * | Triggers data synchronization at 2:00 AM daily, avoiding peak retail and operations business hours to ensure data update timeliness |
DB_CONNECTION_POOL_SIZE | 20-30 | Matches concurrent connection requirements for multi-source data synchronization, preventing task interruptions caused by exhausted connection pools |
DB_QUERY_TIMEOUT | 600 seconds | Adapts to the time required for associated queries of multi-dimensional data for a single model, preventing long queries from being forcibly terminated |
DATA_VALIDATION_REQUIRED_FIELDS | model_id, update_date, terminal_price, daily_profit | Lists mandatory fields required for core profit calculations, ensuring data validity |
INDEX_EXPIRE_AFTER_SECONDS | 15552000 seconds | Automatically archives historical data older than 180 days, controlling storage costs and query performance |
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. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common errors
- Phenomenon: A database connection timeout is displayed when
pnpm devis run in the local development environment, and the log returns anETIMEDOUTerror. Cause: The host address and port in the configuredMONGODB_CONNECTION_STRINGare not set correctly, or the local MongoDB service is not started. - Phenomenon: The token consumption returned after calling the model to query the database does not match the actual API call records. Cause: Long text data returned by the database is not truncated, causing additional token consumption when the model generates responses, or the
DB_QUERY_TOKEN_LIMITparameter is not configured to limit the returned data length. - Phenomenon: The Oracle type cannot be selected in the database connection module. Cause: The built-in database driver of FastGPT does not include the Oracle adaptation package. The corresponding dependency package must be manually installed and the system configuration updated.
How to confirm the configuration is complete
- The local MongoDB command-line tool is run, the configured
MONGODB_CONNECTION_STRINGis entered, and successful login to the target database is verified. - A data synchronization task is manually triggered, and the task log is checked for data validation failure or connection timeout error messages, to confirm that the filling status of mandatory fields meets expectations.
- A database query filtered by
model_idandupdate_dateis initiated, to verify that the returned fields include core profit metrics, and that the query duration meets business expectations. - All database-related parameters are checked for correct configuration in the system environment variables, to avoid missing authentication information or port configurations.
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.