What the data for this category looks like
This category's data primarily comes from raw fiber inspection, ERP systems, real-time collected data from spinning and looms, evenness testers, fabric inspection machines, and order specification documents. Daily generated documents include cotton blending formulas, process setup sheets, equipment operation records, yarn inspection reports, grey fabric inspection records, defect ledgers, and more. Data updates follow the production workflow: new data is generated for each batch of raw materials, each shift’s production, and each roll of fabric produced. Data fields include raw material batch numbers, process parameters, equipment runtime, inspection results, defect types and locations, order specifications, and more. Data must be split and stored by production stage, with no unified fixed multi-field aggregation format.
What constraints these characteristics impose on the database and operations maintenance link
Heterogeneous data from multiple sources requires compatibility with different format write interfaces, which places demands on the database’s compatibility capabilities. High-frequency updates generate high-concurrency write requests, so reasonable connection pool upper limits must be configured to avoid connection exhaustion. The complex association logic of data across different production stages requires clear association indexes to ensure the efficiency of associated queries during ledger summary. The unstructured nature of defect records and inspection data requires reserved flexible field expansion space, to avoid frequent table structure modifications when new inspection items are added later. Additionally, data must be quickly retrievable by production batch and time range, so secondary indexes must be established for commonly used query dimensions.
How to set the configurations
The following configurations are written based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
PG_URL | postgresql://dbuser:dbpass@dbhost:5432/spinning_weaving_db?sslmode=disable | Adapts to the structured storage needs of spinning and weaving ledgers, supports complex associated queries and vector extensions |
DB_MAX_LINK | 20–30 | Matches the high-frequency write concurrency from each batch of raw materials and each shift’s production, avoids connection pool exhaustion |
REDIS_URL | redis://redispass@redishost:6379/0 | Caches frequently queried process parameters and defect classification data, reduces load on the primary database |
MONGODB_URI | mongodb://mongouser:mongopass@mongohost:27017/weaving_logs?authSource=admin | Stores unstructured defect photos and original inspection data, adapts to flexible field expansion |
Three common configuration mistakes
- After configuring the database connection, SQL extraction results cannot be executed. The cause is failure to adapt to the multi-data source format of spinning and weaving ledgers, the connection string does not specify the instance path of the corresponding production database, or the user has not been granted permissions to access core tables such as inspection data and equipment records.
- Database connections frequently time out. The cause is that the
DB_MAX_LINKconfiguration value is too low, which cannot match the high-concurrency write requests brought by each batch of raw materials and each shift’s production, leading to connection pool exhaustion. - SQL commands cannot be executed after entering the pgvector image. The cause is that the vector extension is not enabled in the connection parameters, or the correct database port and access whitelist have not been configured, preventing the image from accessing external database instances.
How to confirm the configuration is complete
- Run the built-in database connectivity test tool in FastGPT, verify the connectivity of configurations such as
PG_URLandMONGODB_URI, and confirm there are no authentication or network errors. - Insert a simulated cotton blending formula data record, then query it to confirm that data writing and reading work normally, verifying that read/write permission configurations are correct.
- After adjusting the
DB_MAX_LINKconfiguration, simulate multiple concurrent write requests, confirm that there are no connection timeout or rejection errors. - Check the database operation logs, confirm that there are no frequent connection disconnections or permission error reports, verifying the stability of the operation and maintenance configuration.
The values provided are common starting points and should be measured against the reader's own samples.