What the data for this category looks like
Water utility ledger and monitoring data originates from pipe network pressure and flow monitoring terminals, water plant water purification process central control systems, drainage pump station PLC equipment, and user water usage ledger systems. Data update frequencies fall into three categories:
- Real-time monitoring data updates every 10 seconds
- Monthly user water ledgers sync daily
- Annual operation ledgers are archived quarterly
Data documents are split into three types: real-time monitoring tables, monthly summary tables, and annual report tables. Fields include unique device code, monitoring timestamp, instantaneous flow (cubic meters per hour), pipe network pressure (megapascals), water turbidity (NTU), operation work order number, payment amount (yuan), and others. Units are fixed per field type.
What constraints these characteristics impose on database and operations
Multi-source data has widely varying update frequencies. Real-time data requires low-latency writing. Bulk ledger data requires high-throughput processing. Storage media must be split to avoid resource preemption. Field types and units are diverse. Unified field mapping rules must be established to prevent format conflicts during data ingestion. Device codes and work order numbers are high-frequency query fields. Indexes must be created to improve retrieval efficiency. Long-term archived historical ledgers require regular cold backups to avoid excessive hot storage resource usage, which can negatively impact real-time data writing performance.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://water_user:secure_pass@db-host:27017/water_real_time?authSource=admin | Stores unstructured real-time monitoring logs and temporary summary data, adapted for high-concurrency write scenarios |
PG_URL | postgresql://water_report_user:secure_pass@db-host:5432/water_structured?sslmode=require | Stores structured monthly ledgers, annual reports, and user payment data, supports complex associated queries |
DB_MAX_LINK | 20–30 connections | Balances resource usage between real-time data writes and bulk report generation, avoids connection pool exhaustion |
REDIS_URL | redis://redis-host:6379/0?password=secure_pass | Caches frequently queried device statuses, ledger templates, and cached report results to reduce database load |
Three common errors
- Issue: Timeout errors occur during bulk water utility report generation, with a 504 status code returned. Cause: The
DB_MAX_LINKconfiguration value is set too low. Concurrent ledger query requests exhaust the database connection pool, preventing subsequent requests from obtaining connections in a timely manner. - Issue: Real-time monitoring data from some devices cannot be properly written to the database, and unit mismatches appear in ledger fields. Cause: No multi-source data field mapping rules are configured. Flow fields reported by different devices mix cubic meters per hour and liters per second, causing data format conflicts.
- Issue: Fewer results than expected are returned when searching historical water utility ledgers. Cause: No database index is created for the device code field, leading to excessively long full table scan time. Query timeouts result in truncated returned results.
How to confirm the configuration is complete
Reference only; verify against the target FastGPT release and deployment documentation.
- Monthly ledger aggregation tasks can be run to check the field integrity of generated reports. The threshold is calibrated based on the required field list for water utility ledgers.
- 10 concurrent real-time data upload requests can be simulated to check system response time. The threshold is calibrated based on business real-time requirements.
- Redis cache hit statistics can be reviewed to confirm frequent queries for device status data hit the cache. The threshold is calibrated based on cache hit rate requirements.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.