What the data for this category looks like
Consumer building materials market data comes from daily sampling of regional building material markets, manufacturer shipping ledgers, and public industry quotation platforms. Data is updated once per day. Full collection is completed after the day’s trading ends. Each data record includes category identifier, collection area code, quotation unit, prices across channels, and collection timestamp. Quotation units include yuan per square meter, yuan per ton, yuan per piece, and other formats. There is no unified fixed format, requiring dynamic adaptation based on the category.
Constraints on Database and Operations Workflows
The daily centralized update schedule creates fixed-period write peaks. Configure reasonable batch write parameters to avoid database blocking. The multi-category, multi-region field structure requires joint indexes to cover high-frequency query scenarios. Without this, query latency will increase. Multi-source data collection requires a pre-validation link. The database must support field validation rules to prevent invalid data from being stored. Market data has high timeliness requirements. Configure low-latency write links. Regularly archive historical data to control storage costs.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb_write_batch_size | 500-800 records/batch | Consumer building materials market data has large per-batch collection volume; this configuration balances write performance and memory usage |
query_index_ttl_days | 365 days | Daily market report data must be retained for more than 1 year for trend analysis; expired indexes are automatically cleaned to save storage space |
data_sync_timeout | 600 seconds | When collecting multi-region building material data across platforms, the single sync data volume is large; sufficient timeout must be reserved to avoid interruptions |
connection_pool_max_size | 20-30 | Consumer building materials data query concurrency fluctuates with working day time periods; this configuration adapts to connection demands during peak hours |
field_validation_level | strict | Consumer building materials data has many fields with close relationships; strict validation prevents invalid data from being written to the database |
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 case-by-case analysis. Testing on local samples is recommended before finalizing settings.
Three Common Misconfigurations
- Symptom: MongoDB connection timeout, normal connection via mongosh. Cause: The
authSourceparameter ofmongodb_connection_stringis not configured, causing the default authentication database to mismatch the target database. - Symptom: Obvious workflow canvas drag delay, with lag when the number of nodes exceeds 50. Cause: Using version 4.6.5 or earlier. This version does not optimize the workflow node rendering queue, causing main thread blocking with large numbers of nodes.
- Symptom: Some fields are empty when batch writing market data. Cause:
field_validation_levelis not set to strict mode, causing undefined fields to be filtered or missing values to remain uncompleted.
How to Verify Configurations
- Run a full data sync task, check that there are no timeout errors in the task log, confirming that the
data_sync_timeoutconfiguration meets actual data volume requirements. - Initiate multiple concurrent query requests, monitor that the database connection count does not exceed the
connection_pool_max_sizesetting, confirming that the connection pool configuration adapts to current business concurrency. - Randomly select 10 written database records, check that all required fields exist and have correct formats, confirming that the field validation configuration is active.
- View database index statistics, confirm that the joint index of
product_idandupdate_timehas been hit by high-frequency queries, confirming that the index configuration is reasonable.
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.