What the data for this category looks like
Data sources include domestic food and beverage industry monitoring platforms, offline supermarket POS terminals, online e-commerce transaction data, and supply chain factory quotation data. Update rhythms fall into three categories:
- Offline supermarket retail data updates daily
- Online e-commerce real-time transaction data updates hourly
- Supply chain factory quotation data updates weekly
Each data document includes these standard, non-redundant fields: product_category (category, such as light soy sauce, cooking wine) specification (product specification, such as 500ml bottle, 1kg bag) origin (production origin) price_unit (pricing unit, such as CNY/500g, CNY/bottle) retail_price wholesale_price stat_date (statistical date)
What constraints these data characteristics impose on database and operations
Multiple data source types and varied update rhythms require differentiated synchronization scheduling rules. These rules prevent ineffective synchronization and data lag. Differences in how specifications and pricing units are described across data sources require standardized mapping before data is imported. Without this mapping, queries will face unit confusion and data alignment errors. High-frequency multi-source data generates large volumes of incremental records. A reasonable database sharding strategy and data retention rules must be configured to balance read-write performance and storage costs. Batch synchronization task trigger timing must align with each data source's update window. Misalignment leads to empty data sets being pulled or outdated data being used.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
multi_source_sync_strategy | Configure differentiated synchronization cycles by data source type | Match the different update rhythms of offline supermarkets, online e-commerce, and factory quotations to avoid ineffective synchronization |
field_normalization_rule | Unify specifications and pricing units into standard formats | Resolve differences in field descriptions across data sources to ensure data consistency |
MONGO_SHARD_KEY | stat_date + product_category | Shard by statistical date and category to optimize query performance for time ranges and categories |
DATA_RETENTION_PERIOD | 365 days for retail data, 730 days for factory quotation data | Adapt to business retention requirements for different data and reduce storage costs |
BATCH_SYNC_TIMEOUT | 1800 seconds | Cover the maximum duration of multi-source batch synchronization to prevent task timeout interruptions |
MONGO_USER_AUTH_MODE | Role-based access control | Differentiate access permissions between data operations personnel and business query personnel to safeguard data security |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material forms, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on one's own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Batch synchronization tasks return no results after execution, and logs show an empty data list was pulled. Cause: The synchronization cycle is not configured to match the actual update time of each data source. The task triggers before the data source finishes that day's data update, so no valid data set can be retrieved.
- Phenomenon: Database queries return mixed price field units, such as CNY/500g and CNY/bottle appearing together. Cause: The
field_normalization_ruleis not configured. No standardized mapping is performed for specifications and pricing units across different data sources, leading to ununified formats when data is imported. - Phenomenon: Attempts to log in to the database with a custom MongoDB user result in an authentication failure prompt. Cause: The MongoDB user creation command is not executed correctly, and the correct database role or connection parameters are not specified. This leads to ineffective permission configuration.
How to confirm the configuration is complete
- Execute
db.getCollection('condiment_yield').find({stat_date: ISODate("2024-05-20")}).limit(1)and check if the returned document fields include standardized fields such asproduct_category,specification, andprice_unit. - View synchronization task execution logs to confirm that multi-source data synchronization cycles match the configured
multi_source_sync_strategy, with no abnormal interruption records. - Use the created non-root user to execute
db.auth("fastgpt_user", "your_password")to confirm that authentication succeeds and read-write permissions for the corresponding collection are available. - Trigger a batch synchronization task, then check that the number of task execution results matches the actual data volume of each data source, with no empty data or timeout 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.