What data looks like for this category
Data for warehousing, lot tracking and in-plant logistics scenarios mainly comes from ERP/WMS systems, barcode/RFID identification devices, purchase work orders, storage location management modules, AGV transport systems and weighing equipment. Daily generated documents include receiving slips, putaway slips, material picking and returning slips, warehouse transfer slips, inventory counts, lot ledgers, first-in first-out records and expiration date records. Data update frequencies include real-time (AGV position, barcode scanning), daily (storage location changes) and monthly inventory (book vs physical reconciliation). Core fields include material codes, lot numbers, storage location numbers, expiration dates, inspection status and circulation records. Units are mostly string codes, date formats and weight values.
What constraints these characteristics impose on database and operations
Real-time data’s high-frequency writes create concurrent connection pressure. Reasonable scaling capacity for database connection pools must be ensured. Multi-dimensional query requirements for lot ledgers and expiration date records require targeted indexes. This avoids performance loss from full table scans. Cross-process material flow correlation uses multi-table join operations. Database associated query performance must be optimized. Batch processing of book-physical discrepancies and stagnant materials generates high-volume data writes. Storage partitions must be planned appropriately to avoid disk overload. Data traceability requires retention of complete historical change records. This increases database storage overhead.
How to configure settings
This document applies to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://wms_user:SecurePass123@db-host1:27017,db-host2:27017/admin?replicaSet=wms_batch_rs&readPreference=secondaryPreferred | This category includes real-time AGV position and barcode scanning data. Replica set configuration ensures high availability. Read-write separation reduces primary node write pressure |
PG_URL | postgresql://batch_user:SecurePass456@db-host:5432/fastgpt_wms?search_path=batch_trace | Lot ledgers and expiration date records require vector retrieval for similar lot traceability. The pgvector extension supports this requirement |
DB_MAX_LINK | 80–120 | This category has real-time, daily and monthly batch data import scenarios. This connection number range matches concurrent request volume and avoids connection exhaustion |
REDIS_URL | redis://redis-cluster:6379?password=SecurePass789&cluster=yes | Real-time storage location status and AGV position data require high-frequency caching. Cluster configuration ensures high availability of the cache service |
Three common configuration mistakes
- The symptom is sustained database disk IO usage above 90%, with daily disk read/write volume exceeding several terabytes. The cause is failure to create partition indexes for high-frequency writes of warehousing lot data, and failure to enable database read-write separation configuration.
- The symptom is service startup failure due to vector database dependency configuration conflicts during deployment. The cause is mixing redundant configurations of pgvector, ob and milvus in the compose file, without pruning dependency items based on category data requirements.
- The symptom is connection interruption that cannot automatically recover after the MongoDB replica set primary node drifts. The cause is failure to configure replica set automatic reconnection parameters, leading to interruption of lot flow data synchronization.
How to confirm proper configuration
- Run the
mongo --eval "rs.status()"command to check the MongoDB replica set status. Confirm that read-write separation configuration is active, and all replica set nodes have normal status. - Log in to the PostgreSQL database, run the
SELECT extname FROM pg_extension WHERE extname='vector';statement. Confirm that the pgvector extension has loaded normally. - Run a batch import test data script. Check database connection pool usage. Confirm that the
DB_MAX_LINKconfiguration’s connection exhaustion threshold is not triggered. - View the Redis cluster monitoring dashboard. Confirm that cache nodes have no connection timeout errors, and real-time data cache hit rate meets standards.
The values provided are common starting points, and should be evaluated against the reader’s own samples.