What this category's data looks like
Steel trade market and profitability data primarily comes from public market APIs of domestic steel spot trading platforms, daily quotation data from steel mills, and internal inventory and sales systems of trading enterprises. Data update rhythms fall into two categories. Full datasets sync once daily after market close. Popular steel products with intraday price adjustments receive incremental updates every 2 hours. Each data entry includes trading entity code, steel product code, specification model, purchase storage time, outbound sales time, purchase cost, sales transaction price, and purchase-sale price difference fields. Price-related fields use yuan/ton as their unit. No percentage-based statistical values are included.
Constraints on database and operations from these characteristics
Multi-source data access requires establishing unified data validation and cleaning rules. This prevents storage anomalies caused by format differences across data sources. Coexistence of daily full datasets and real-time incremental updates requires designing partitioned storage and differentiated synchronization scheduling mechanisms. This improves query efficiency. Multi-dimensional business fields including product, specification, and trading entity need joint indexes. This reduces latency for complex queries. Data must be archived according to business cycles. A tiered cold and hot storage strategy must match the access frequency of different datasets.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
db_sync_batch_size | 500–1000 records | Steel trade daily data volume is moderate. Batch synchronization avoids overload from single requests, balancing synchronization efficiency and resource usage |
db_partition_strategy | Partition by date | Data is updated daily. Partitioned queries significantly reduce retrieval latency across date ranges and lower database load |
api_concurrency_limit | 3–5 | External steel market APIs typically have concurrency limits below 5. This configuration prevents 429 Too Many Requests errors |
data_clean_rule | Standardized mapping by specification model | Steel product specifications have inconsistent descriptions. Standardization unifies field matching logic and improves data query accuracy |
db_connection_pool_size | 10–15 | Steel trade data query concurrency is moderate. This range avoids resource waste from oversized pools and request queuing from undersized pools |
workflow_timeout | 600 seconds | Full data synchronization and cleaning typically take less than 10 minutes. This configuration prevents task interruption due to timeout |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: Calls to external steel market APIs return
429 Too Many Requestserrors. Cause:api_concurrency_limitis not configured, and concurrent request count exceeds the external API's limit threshold. - Phenomenon: Workflow execution fails, with
MongoConnectionErrorshown in logs. Cause: Authentication parameters fordb_connection_stringare not configured correctly, or the database service port is not open, leading to connection timeout. - Phenomenon: Attempting to import PPT-format market data into the database fails. Cause: Database synchronization tasks only support structured data formats such as CSV and JSON, and cannot directly parse unstructured PPT content.
How to confirm configuration is complete
- Run a full data synchronization task, check for abnormal errors in the synchronization log, and adjust the value of the corresponding configuration item based on the log content.
- Randomly select one daily business data entry, verify whether the fields stored in the database match those from the original data source, and adjust the mapping rules of
data_clean_rulebased on the matching results. - Run a cross-date range query, check whether the query latency meets business expectations, and adjust the partition granularity of
db_partition_strategybased on the latency. - Check database connection pool monitoring metrics, confirm that the current connection count does not exceed the configured threshold, and avoid business disruption due to exhausted connections.
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.