What Data for This Category Looks Like
Data for this category comes primarily from store POS systems, CRM customer management systems, supply chain inventory management systems, and third-party auto aftermarket market data sources. Data updates run once daily. A fixed early morning window completes aggregation of the previous day’s revenue and cost, plus revenue yield calculations. Each data record includes store unique code, service category identifier, total daily revenue, total daily cost, core calculation fields, corresponding calculation date, and other fields. All fields use structured numerical or enumeration types, with no nested complex hierarchies.
Constraints Imposed on Database and Operations Workflows
Multi-source heterogeneous data sources require pre-deployment of field standardization cleaning. Without this, field missing or format exceptions occur, disrupting subsequent calculations. Daily fixed-time batch data aggregation and writing create database write peak pressure. Configure read-write separation to share load and prevent single-node overload. Core calculation fields depend on full daily revenue and cost data. Configure data integrity verification rules to ensure no missing data. Store data partitioned by calculation date. This reduces scan range for historical data queries and improves query efficiency.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
batch_write_size | 500-800 records/batch | Adapts to the scale of single-batch data for daily batch synchronization, avoiding excessive database connection resource occupation from a single write |
read_replica_count | 2-3 replicas | Shares query pressure from daily early morning write peaks, avoiding main node overload |
data_ttl_days | 90 days | Matches industry data retention cycle requirements, automatically cleaning expired data to free storage capacity |
field_validation_rule | Calibrated via actual testing | Adapts to format differences of heterogeneous data sources accessed by different stores, ensuring field standardization verification takes effect |
connection_timeout | 30 seconds | Covers network latency fluctuations during multi-source data synchronization, avoiding synchronization interruptions from brief network anomalies |
partition_strategy | Partition by data_date | Adapts to business requirements for high-frequency queries by calculation date, reducing historical data scan range |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Each scenario requires individual analysis, and it is recommended to test using your own samples before finalizing settings.
Three Common Misconfigurations
- A
connection timed outerror appears. mongosh connects normally, but the business service cannot establish a database connection. The root cause is that the business service’s database connection pool maximum connection count is set too low. This cannot support peak traffic from daily early morning batch synchronization, exhausting the connection pool. - Some historical data records lack the
tmbIdfield. This causes empty results for cross-table association queries. The root cause is that early data synchronization tasks did not configure mandatory verification rules for this field. Stock data was not fully completed. - Some store data fails to import into the database during daily batch synchronization tasks. The root cause is that no automatic retry mechanism for batch writes was configured. A single write failure does not trigger supplementary transmission, leading to data omission.
How to Verify Successful Configuration
- Run a simulated batch write test. Observe the database connection pool usage status. Adjust related configurations to match the range of business peak load.
- Randomly select multiple historical data sets. Verify that all required fields are present. Confirm field verification rules are working correctly.
- Check daily batch synchronization task execution logs. Confirm there are no database connection timeout errors. Verify connection configuration effectiveness.
- Run a query test by calculation date. Compare query latency across different partition strategies. Confirm the partition configuration is active.
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.