What the data for this category looks like
Reference only; verify against the target FastGPT release and deployment documentation.
What constraints these characteristics impose on database and operations
Real-time second-level updates of transaction data require database connection pools to reserve sufficient concurrent connections to avoid connection blocking from high-frequency writes. Multi-source data synchronization and associated queries require separating read and write loads, using read replicas to reduce query pressure on the primary database. Daily batch-uploaded store daily closing data has a large volume, so control the number of writes per transaction to prevent database transaction timeouts. High demand for time-based ledger aggregation requires partitioning transaction tables by time to improve cross-cycle query efficiency. Additionally, ledger generation requires association of fuel, store, and transaction data, so create composite indexes on associated fields to avoid performance degradation from full table scans.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Database connection pool size | 20–30 connections | High concurrent real-time transaction volume for refined petroleum product sales, reserve sufficient connections to avoid write blocking |
| PostgreSQL connection address | PG_URL formatted as postgresql://user:pass@host:5432/oil_sales?sslmode=require | Store structured ledger and transaction data, specify the business database name and enable SSL to secure transmission |
| MongoDB connection address | MONGODB_URI formatted as mongodb://user:pass@host:27017/oil_logs?authSource=admin | Store unstructured fuel dispenser log data, specify authentication source and business database |
| Redis cache address | REDIS_URL formatted as redis://:pass@host:6379/0 | Cache frequently queried store and fuel basic information to reduce database load |
| Batch write threshold | 500 records per batch | Adapt to daily batch-uploaded store daily closing data, avoid timeouts caused by overly large single transactions |
| Read replica configuration | Set based on actual testing | Separate real-time write and ledger query loads, improve summary query efficiency |
Three common mistakes
- Scenario: PostgreSQL deployed in a Docker container, knowledge base index creation fails, logs show "connection timeout". Cause: The database port inside the container is not mapped to the host machine, or the connection address does not specify the correct host IP and mapped port.
- Scenario: After enabling problem optimization and result rearrangement functions, ledger generation request response time exceeds 30 seconds. Cause: No database read replica configured, full query of the primary database causes IO blocking, or insufficient connection pool size prevents parallel processing of multiple queries.
- Scenario: Fuel dispenser log data stored in MongoDB cannot be properly associated with ledger data, query results are empty. Cause: The authentication source parameter of
MONGODB_URIis not configured correctly, so the target business database cannot be accessed.
How to confirm configurations are correct
- Run a database connection test script to verify that
PG_URL,MONGODB_URI, andREDIS_URLcan establish normal connections with their configured addresses, accounts, and passwords. - Submit a simulated refined petroleum product transaction record to confirm it can be written to the corresponding database table and retrieved completely via the ledger query interface.
- Enable problem optimization and result rearrangement functions, submit a batch ledger generation request, and confirm the request response time meets the expected business threshold.
- Check the update frequency of basic information cached in Redis, confirm that the cache expiration time configuration matches the data update rhythm.
The values listed above 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.