What the data for this category looks like
The data sources for this category include customer surface treatment specifications, bath solution test results, rectifier operation data, online water quality monitoring data, and hazardous waste management ledgers. Data update frequencies vary significantly: bath solution parameters and fixture usage records are updated per tank batch, process execution records for each shift are submitted per shift, and hazardous waste and wastewater monitoring data are updated regularly to meet compliance requirements. Daily generated documents include bath solution analysis records, process parameter cards, fixture ledgers, film thickness and adhesion reports, hazardous waste disposal records, and wastewater monitoring reports. Most fields involve professional indicators such as bath solution concentration, current value, film thickness, and discharge limits, with units including g/L, A, μm, mg/L, and others.
What constraints these characteristics impose on database and operations
Heterogeneous data from multiple sources requires adaptation to different storage formats and write frequencies. High-frequency per-tank-batch writes will create sustained database connection pressure. Regularly updated compliance data requires strict field validation mechanisms. The field structures of different documents vary widely: some structured ledgers can be adapted to relational databases, while unstructured test reports are suitable for document databases. Failure to reasonably distinguish storage types will lead to increased data query and parsing costs. Additionally, the update rhythms of real-time online monitoring data and offline test data differ, so read-write separation or caching strategies must be configured specifically to avoid high-frequency requests slowing down overall business processes.
How to set the configurations
The following configurations are formulated based on the database node function of FastGPT v4.17.0.
| Config Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://username:secret@db-host:5432/ep_ledger | Adapts to structured fixed-field data such as hazardous waste ledgers and process parameter cards; the standard 5432 port ensures basic connection compatibility |
DB_MAX_LINK | 12–25 | Addresses high-frequency per-tank-batch writes and concurrent requests from multiple data sources, preventing service interruptions caused by connection pool exhaustion |
REDIS_URL | redis://default:auth@cache-host:6379/0 | Caches high-frequency read real-time bath solution data and fixture status, reducing repeated query pressure on relational databases |
MONGODB_URI | mongodb://user:pass@mongo-host:27017/ep_reports | Stores semi-structured data such as unstructured film thickness reports and offline test records, adapting to flexible field structures |
Three common mistakes
- The symptom is a database connection error
connect ETIMEDOUT. Local testing shows normal access, but deployment fails online. The cause is that the online environment’s security group or firewall has not opened access rules for the corresponding database port, preventing external requests from connecting to the database service. - The symptom is request queuing or timeout during API calls, and the concurrent volume cannot support peak business load. The cause is failure to adjust the
DB_MAX_LINKparameter for the high-frequency per-tank-batch write scenario; the connection pool size is insufficient to cover concurrent connection requirements. - The symptom is mixed result formats when querying from the database node, requiring additional code for parsing and conversion. The cause is failure to distinguish storage types for structured and unstructured data; mixed storage leads to return formats that do not meet business expectations.
How to confirm the configurations are correct
- Run the built-in database connection test node in FastGPT, verify that configurations such as
PG_URLandMONGODB_URIcan establish normal connections with no timeout or permission errors. - Simulate concurrent write requests for multiple tank batches, observe the occupancy of the database connection pool, and confirm that the
DB_MAX_LINKsetting adapts to the current business’s peak load. - Trigger real-time data queries, verify that the cache node can normally return high-frequency read bath solution data, with latency meeting business response requirements.
- Trigger queries for different types of data, confirm that the return format matches the field structure of the corresponding document, and can be used without additional code conversion.
The values provided are common starting points and should be measured against the reader's own samples.