What data for this category looks like
Data sources for baijiu yield rate-related data include publicly available statistical documents from alcohol circulation associations, official channel quote disclosures from leading baijiu brands, and baijiu category transaction data from third-party bulk commodity trading platforms. Update frequency follows two schedules: daily updates for same-day transaction and channel monitoring data, and monthly updates for full-category channel inventory and average price sampling data. Each data entry includes these fields: target name, data collection date, data category (wholesale/retail/manufacturer), pricing unit, collected price, and price change range. Most pricing units are yuan per bottle, yuan per case, or yuan per ton. Price change range is expressed as the relative change from a baseline value.
Constraints on database and operations
Baijiu category data is scattered across multiple sources, uses layered update schedules, and has diverse units. These factors create multiple constraints for the database and operations link. Multi-source data docking requires unified data format conversion rules to avoid field naming conflicts across different sources. Layered update schedules need differentiated scheduled task scheduling strategies. Separate daily high-frequency tasks and monthly low-frequency tasks to reasonably allocate computing resources. Diverse pricing units require a unit mapping rule library to ensure stored data uses unified units. Data update timeliness requires timeout retry and exception alert mechanisms to ensure data is stored on schedule. Fluctuations in monthly sampling data volume require database elastic expansion thresholds to handle sudden data write pressure.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
database_connect_timeout | 30 seconds | Single query data volume for baijiu-related data is moderate. 30 seconds covers response times for most public data sources and avoids unnecessary waiting |
max_concurrent_tools | 8–12 | Most baijiu data sources are public APIs. Excessive concurrency triggers rate limits, while insufficient concurrency reduces update efficiency. Adjust based on actual measurements |
sync_cron_expression | Execute daily sync at 22:00, execute monthly sync at 00:00 on the 1st of each month | Matches the daily and monthly update schedules of baijiu data, avoiding peak business hours |
unit_mapping_rules | Establish unified mapping based on "yuan per bottle", "yuan per case", "yuan per ton" | Pricing units for baijiu data are primarily these three types. Unified conversion prevents inconsistent stored data units |
tool_call_retry_times | 2 times | Public data sources may have temporary fluctuations. 2 retries cover most temporary exceptions and avoid resource occupation from repeated execution |
batch_write_size | 500 records per batch | Single batch data volume for baijiu data is moderate. 500 records balances write efficiency and memory usage |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: A
429 Too Many Requestserror occurs during tool calls, or task queues pile up without execution. Cause: Themax_concurrent_toolsparameter is not set appropriately, with concurrency exceeding the data source's rate limit threshold or the platform's built-in concurrency upper limit. - Phenomenon: Database query tool calls fail, returning
tool_call failedor400 Bad Requesterrors, with theinvalid rolefield prompt in logs. Cause:unit_mapping_rulesare not configured correctly, leading to abnormal field formats for stored data, or special characters are not handled when splicing query statements, triggering interface verification failures. - Phenomenon: No results are returned when calling database tools in a local deployment environment, and the task status shows
pendingand is not completed. Cause:database_connect_timeoutis not configured correctly, or local environment firewall rules restrict access to database ports, leading to failed connection establishment without triggering a timeout alert.
How to Confirm Configuration is Correct
- Execute a single daily data sync task, check if baijiu data records for the corresponding date are generated in the database, and verify field formats and units are unified.
- Adjust the
max_concurrent_toolsparameter to different values, observe task queue execution efficiency and error rates, and determine the concurrency threshold that meets business needs. - Simulate a scenario of temporary fluctuations in data sources, call the tool and check if the retry mechanism is triggered, confirming exception handling logic takes effect.
- Check firewall and network configurations, confirm the database port can be accessed normally, and verify the actual effectiveness of the connection timeout parameter.
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.