What data for this category looks like
Data sources include public quotes from commodity spot trading platforms, sampled statistical data from industry associations, and published ex-factory quotes from manufacturing enterprises. Updates are completed at a fixed daily time for full category market data. Some high-frequency circulating raw material data is synced hourly. Each data document corresponds to a single specification model of paint and ink product or core raw material. It includes fields such as product name, origin identifier, specification parameters, pricing unit, latest transaction quote, quote difference from the last statistical cycle, and statistical coverage scope.
Constraints on database and operations
Multi-source heterogeneous data sources require the database layer to support format alignment and conflict verification for multi-source data. Standardized conversion rules must be configured for fields including pricing unit and statistical cycle. Hierarchical update schedules require the operations layer to set up differentiated scheduled tasks. High-frequency data uses hourly incremental synchronization. Full daily report data uses batch writes at a fixed daily time. Combined queries for multiple dimensional fields of a single product require the database to create joint indexes covering product name, origin and specification model. This avoids full table scans. Immutable market historical data requires write operations to use append mode. Direct modification of stored records is prohibited. Data errors are resolved by adding new correction records. Daily summary broadcast requirements demand configuring scheduled aggregation tasks to generate standardized broadcast data sets. This reduces computational overhead from real-time queries.
How to configure settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_CONNECTION_POOL_SIZE | 100–150 | Adapts to concurrent requests from multi-source data synchronization and daily batch aggregation, avoids connection exhaustion |
DATA_SYNC_JOB_CRON | 0 2 * * * Full sync, 0 * * * * Incremental sync | Matches the hierarchical update schedule for paint and ink market trends, balances data freshness and system load |
DB_INDEX_CONFIG | ["product_name", "origin", "spec"] | Covers core query dimensions, improves response speed for combined queries |
DATA_VALIDATION_THRESHOLD | Calibrated based on actual measurements | Verifies reasonable range for quote differences, filters erroneous data from abnormal data sources |
MONGO_REPLICA_SET_ENABLED | true | Ensures high availability for data writing, avoids market data interruption caused by single-node failure |
BACKUP_CRON_EXPRESSION | 0 3 * * * | Runs full backup at 3 AM daily, avoids peak business hours |
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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration errors
- Phenomenon: MongoDB connection errors prompt "Connection refused" or "max open connections reached". The interface shows data synchronization failure. Cause: No reasonable connection pool size is configured, or no persistent storage volume is mounted for the MongoDB instance. This leads to data loss after restart and inability to establish a connection.
- Phenomenon: Empty results or missing fields are returned when querying market data. Cause: No correct joint index is configured, or no standardized conversion is performed for pricing units of multi-source data. This causes data to fail to match query conditions.
- Phenomenon: TPS fails to meet standards when calling market data via workflow. Cause: Database read preference is not configured as
secondaryPreferred. All requests hit the primary node, leading to excessive load on the primary node and response delay.
How to confirm correct configuration
- Execute the database connection test script. Verify that concurrent requests configured for the connection pool can be processed normally, with no connection exhaustion errors.
- Trigger the full synchronization task. Check if the synced database contains complete fields for all configured product categories, with no abnormal data with inconsistent units.
- Check the scheduled task logs. Confirm that hierarchical synchronization tasks and backup tasks are executed normally at preset times, with no failed records.
- Initiate a combined query request. Verify that the response time of returned results meets business requirements, with no full table scan log records.
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.