What data for this category looks like
Yield rate and market data for automated equipment originates from three sources: built-in operating parameter collection APIs, connected financial market data source APIs, and the revenue accounting module run after the device completes trades. Most data updates use full daily batch updates in the early morning. Some real-time monitoring scenarios push temporary fluctuation data per minute. Documents use structured JSON or CSV formats, with fields including device ID, collection timestamp, device operating status code, associated trading target code, daily yield rate, cumulative yield rate, position size, number of trades, and additional relevant fields. Timestamps use Unix millisecond format. Yield rates are dimensionless decimals. Position sizes use integer units. Number of trades is a positive integer.
What constraints this pattern imposes on database and operations
The mixed update model of daily batch updates and minute-level temporary data requires the database to support time-based partitioned storage, preventing continuous growth of single-table data volume. Fields include associated dimensions such as device ID and trading target code. Joint indexes must be created to improve query efficiency for multi-condition queries. Minute-level real-time write traffic requires the database connection pool to have sufficient concurrent processing capacity, preventing write blocking. Fixed-structure fields require pre-reserved extension fields, avoiding frequent table structure modifications during subsequent data iterations. The timeliness of temporary fluctuation data requires operation and maintenance monitoring to be configured with minute-level write delay alerts, ensuring data is stored in the database promptly.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_POOL_MAX_CONNECTIONS | 20–30 connections | Adapts to the concurrent load of minute-level real-time writes and daily batch imports for automated equipment, avoiding connection pool exhaustion |
DB_TABLE_PARTITION | Partition by natural day | Matches the daily update cycle of yield rate daily report data, optimizing single-table query and archiving efficiency |
BATCH_INSERT_SIZE | 500–1000 records per batch | Balances network overhead and data processing delay for single writes, adapting to the batch data output of automated equipment |
WRITE_TIMEOUT_MS | 30000 milliseconds | Handles large-volume daily batch data imports, preventing timeout interruptions to the write process |
INDEX_CREATION_DELAY | 600 seconds | Creates joint indexes only after batch data writes are completed, avoiding impact on real-time write performance |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: SQL queries occasionally return data successfully, while other identical query attempts fail, returning
504 Gateway Timeoutor empty fields. Cause: The database connection pool concurrency limit is set too low. Batch write tasks occupy all available connections, causing query requests to fail to acquire usable connections. - Phenomenon: Tool call nodes stall after execution, with no error logs or only
ETIMEDOUTdisplayed. Cause: The database write timeout configuration is shorter than the processing duration of batch data. A timeout is triggered, and no automatic retry is configured, causing the process to hang. - Phenomenon: Running
npm list mongoosein a container returns no output, but the dependency declaration exists inpackage.json. Cause: npm dependency packages are not installed globally in the container. Dependencies are only installed in the project directory, causing the global npm command to fail to recognize the specified package version.
How to confirm configurations are correct
- Run a single device data import script, check database connection logs, confirm that active connections do not exceed the configured maximum connection count.
- Run a batch data import task, verify that the import completion duration matches the configured write timeout setting.
- Check the database table structure, confirm that joint indexes for the associated fields have been created.
- Review operation and maintenance monitoring metrics, confirm that data write delay is within the preset acceptable range.
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.