What the data for this category looks like
Data related to water utility yield rates comes from local public utility regulatory platforms and internal operation management systems of water enterprises. Updates are made daily for statistical data from the previous calendar day. Each entry is a structured item that includes project ID, statistical date, water supply service revenue, sewage treatment service revenue, total operating costs, core revenue metrics, and region code. Revenue and cost fields use RMB yuan as their unit. Core revenue metrics are dimensionless calculated values. All fields have fixed formats with no dynamic expansion options.
What constraints do these characteristics impose on the "database and operations" workflow
Daily batch data writes at fixed times require the database to have sufficient write concurrency buffers to avoid write queue blocking. Structured fixed fields need joint indexes covering common query dimensions to shorten response times for yield rate and market trend reports. Data volume grows linearly with the number of connected water utility projects, so partition storage by statistical date is required to reduce query overhead. Historical daily report data is rarely updated, making a hot-cold data tiered storage strategy suitable to reduce long-term storage costs. The multi-source data splicing feature requires adding data validation steps during operations to prevent missing fields or abnormal values from entering the database.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGO_WRITE_CONCURRENCY | 8–12 | Matches the concurrency requirements of daily batch writes to avoid write queue backlog |
DATA_PARTITION_INTERVAL | 1 day | Partitions by statistical date, adapts to the daily updated water utility daily report data structure |
VECTOR_EMBEDDING_MODEL | shaw/dmeta-embedding-zh | Adapts to vector generation requirements for Chinese water industry professional terminology |
MAX_DISK_USAGE_PERCENT | 70% | Reserves sufficient disk space for batch data writes to avoid disk full errors |
SYSTEM_RESOURCE_LIMIT | CPU 6–7 cores, memory 48–56 GB | Adapts to 8-core 64g hardware configurations, prevents resource exhaustion that causes service lag |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Adapts to the parsing duration of water utility daily report documents, avoids timeout interruptions |
The parameter values provided on this page are general recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Disk full errors are triggered during batch data writes, and the "ENOSPC: no space left on device" prompt appears even after restarting the service. Cause: The
MAX_DISK_USAGE_PERCENTparameter was not configured to limit disk usage thresholds, and no automatic expiration data cleanup rules were set, resulting in continuous accumulation of water utility daily report data. - Symptom: Knowledge base search interface calls return timeouts, and logs show excessive time spent in the vector generation phase. Cause: Batch processing parameters for the vector model were not adjusted based on hardware configurations, and joint indexes were not established for the core fields of water utility data, resulting in an overly large query scan range.
- Symptom: After cross-version upgrade, the container cannot connect to the MongoDB database, with the error "getaddrinfo EAI_AGAIN mongo". Cause: The container's MongoDB connection configuration was not updated synchronously when migrating the database directory, and the upgrade span from v4.6.7 to v4.8.10 was too large, with no intermediate version compatibility checks performed.
How to Verify Successful Configuration
- Execute the daily batch data write script, check that there is no backlog in the database write queue, and verify the configuration effect of the
MONGO_WRITE_CONCURRENCYparameter. - Call the knowledge base search interface, confirm that the returned fields match the water utility daily report data structure, and verify that the index configuration is effective.
- Check disk usage monitoring, confirm that the threshold set by
MAX_DISK_USAGE_PERCENThas not been triggered, and verify that disk space management is effective. - Restart the deployment service, check that there are no errors in the container's MongoDB connection, and confirm that configuration synchronization after cross-version upgrades is complete.
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.