What This Category’s Data Looks Like
Profit-related data for e-commerce services primarily comes from e-commerce ERP systems, payment reconciliation APIs, supply chain cost ledgers, and third-party market trend APIs. The primary data update method is T+1 full updates, while real-time profit monitoring for core SKUs supports hourly incremental updates. Each individual data record uses a standardized JSON structure with fixed fields including shop_id, stat_date, total_revenue, operation_cost, daily_profit, category_code, and others. category_code is an enum type corresponding to 23 first-level e-commerce product categories. stat_date is an ISO-formatted date string. daily_profit is a numeric field with the unit of Chinese Yuan. Some associated data requires using source_id to link corresponding records from external market trend data sources.
Constraints on Database and Operations Imposed by These Data Characteristics
The data characteristics of this category impose multiple constraints on database and operations workflows: The coexistence of T+1 full updates and hourly incremental updates requires the database to support efficient batch writing and incremental synchronization mechanisms. Joint indexes for stat_date and shop_id — used in high-frequency queries — must be optimized regularly to avoid query delays. The multi-value nature of the enum field category_code requires limiting the scope of secondary index creation to reduce storage overhead. The source_id validation logic for linking external data sources requires configuring real-time data consistency check rules to prevent empty or mismatched association fields. Data volume fluctuations are significant during e-commerce peak promotion periods, so sufficient database connection pool capacity and concurrent processing capacity must be reserved. Additionally, field change requirements from temporary campaigns require database table structures to support flexible expansion.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_MAX_POOL_SIZE | 200–300 | Adapts to concurrent connection requirements during e-commerce peak promotions, preventing service interruptions caused by exhausted connections |
DATA_INCREMENT_SYNC_CRON | 0 * * * * | Matches the hourly incremental update business rhythm for core SKUs, ensuring real-time data timeliness |
QUERY_INDEX_FIELDS | ["stat_date", "shop_id", "category_code"] | Covers multi-dimensional conditions for high-frequency queries, reducing the probability of full table scans |
SOURCE_ID_FIELD_NAME | source_id | Matches the field naming convention for linking external market trend data sources in e-commerce data, simplifying association logic |
DB_BACKUP_CRON | 0 3 * * * | Executes database backups at 3 AM daily, avoiding business peak hours and reducing impact on normal services |
DATA_VALIDATION_TIMEOUT | 60 seconds | Sets a reasonable timeout duration for full data validation, preventing update task failures caused by validation timeouts |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by data format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Scenario: After upgrading from v4.6.7 to v4.6.8, modifying the MongoDB configuration file and restarting the service results in a
502 Bad Gatewayerror, and the platform cannot be accessed for login. Cause: After the upgrade, the MongoDB authentication configuration itemMONGO_AUTH_SOURCEis reset to default, and the authentication source parameter from the original configuration file is not synchronized, causing a database connection failure. - Scenario: The number of returned results when querying profit data is less than expected, and some
daily_profitfields are empty. Cause: The association validation rule forSOURCE_ID_FIELD_NAMEis not configured, causing the associated records from external market trend data sources to not be matched correctly, and some data fails to complete writing. - Scenario: Database connection timeouts occur during peak promotions, with
ETIMEDOUTerrors appearing. Cause: TheMONGO_MAX_POOL_SIZEconfiguration value is not adjusted, and the default connection pool capacity is insufficient to support peak concurrency, causing connection requests to queue and time out.
How to Verify Proper Configuration
- Access the database management tool, view the created index list, and confirm that all fields specified in the
QUERY_INDEX_FIELDSconfiguration have been created as indexes. - Manually trigger the incremental data synchronization task, check whether the time interval for completed synchronization aligns with the business update rhythm, with no abnormal delays.
- Initiate multiple concurrent profit data query requests, observe the database connection logs, and confirm that no connection exhaustion or timeout errors occur.
- Import test data containing the
source_idfield, verify that the external data source association logic functions normally, with no field mismatch issues.
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.