What This Category of Data Looks Like
Carbon steel yield rate data primarily comes from industry spot trading platforms, public market APIs of futures exchanges, and publicly announced factory price channels from steel mills. Data refreshes once daily at fixed times. It covers major carbon steel product categories including hot-rolled coils and rebar. Each data entry uses structured JSON format, with fields including product code, origin, specification model, daily settlement price, previous day’s settlement price, change benchmark value, daily trading volume, and more. Price fields use yuan per ton as the unit. Change fields use dimensionless percentage benchmark values. No cross-category aggregate statistics are included.
Constraints for Database and Operations Workflows
The daily fixed-time refresh requirement means scheduled pull tasks must be configured in operations, to avoid high-frequency calls that trigger API rate limits. The structured fixed-field feature means database table schemas must predefine required fields, to avoid unnecessary field redundancy. The fixed units for price and change values require unified storage formatting, with no extra conversion steps needed. The multi-source data feature means a pre-validation step must be added, to perform consistency checks on same-category data from different channels and filter out abnormal values. Additionally, carbon steel data for different detailed specifications must be stored separately, to avoid query errors caused by field conflicts.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DATA_SYNC_CRON | 0 15 16 * * ? | Carbon steel data is typically updated around 16:00 daily. Triggering the task 15 minutes early ensures access to the latest complete dataset |
DB_BATCH_INSERT_SIZE | 500 | Inserting 500 carbon steel data entries per batch balances write performance and transaction stability, avoiding timeouts caused by too large a single insert batch |
DATA_VALIDATE_THRESHOLD | 0.9 | Perform consistency checks on multi-source carbon steel price data. Data is deemed valid when cross-source deviation is less than 0.9 times |
DB_CONNECTION_POOL_SIZE | 10-15 | The concurrency of carbon steel data pulling and querying is moderate. A 10-15 connection pool meets daily operations and business query needs, avoiding connection exhaustion |
PARSE_DATA_TIMEOUT | 60 seconds | Parsing and validating 500 carbon steel data entries typically takes 30-45 seconds. Setting a 60-second timeout covers normal processing durations and avoids task interruptions |
LOG_RETENTION_DAYS | 90 days | Operations logs for carbon steel market data must be retained for at least 90 days for troubleshooting, complying with industry data retention regulations |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material forms, data volume, and business rules. Specific issues require individual analysis. Testing on internal samples is recommended before finalizing settings.
Three Common Misconfigurations
- Issue: After querying carbon steel market data via a database node, the returned result is a raw JSON array, and field values cannot be extracted directly. Cause: No result parsing rules are configured for the database node, so the default unprocessed query result is returned.
- Issue: Scheduled pull tasks return empty results or 503 status codes when triggered during daily peak hours. Cause: No rate limit protection is set for the data source API, and high-frequency calls trigger the third-party interface's rate limiting mechanism.
- Issue: When workflows are called concurrently, the MCP node returns a none value with no valid data. Cause: No retry mechanism is configured for the MCP node. When concurrent requests exceed the interface's carrying capacity, the first failed request does not trigger automatic retries.
How to Confirm Proper Configuration
- Check the scheduled task logs to confirm that the data pull task is triggered at the specified daily time, with no failed records.
- Randomly select multiple carbon steel data records, and verify that the fields stored in the database match the fields returned by the data source, with matching units.
- Simulate daily concurrent requests, and check that the database query interface's connection count does not exceed the configured connection pool limit.
- Trigger a data validation task once, and confirm that abnormal data is filtered out, with no dirty data written to the database.
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.