What Data for This Category Looks Like
Data for this category is generated around collection tasks. Sources include customer requirements, public data sources, collection tool outputs, and internal sample libraries. Four types of documents are routinely produced: collection plans, sample specifications, authorization statements, and task schedules. Data updates follow individual project cycles or weekly syncs. Data fields include sample scope, field specifications, collection legitimacy identifiers, and task schedule nodes. Units are mostly counts, characters, days, and similar. The core focuses on original data acquisition rules and execution plans.
What Constraints These Characteristics Impose on Database and Operations
Data for this category is multi-source, structurally flexible, and has unpredictable update cycles. This requires the database and operations link to support dynamic adaptation to storage needs for different collection tasks. Multi-source data access needs to support metadata and raw data in different formats. A storage architecture supporting multi-data source linkage must be configured. The feature of updating per project or weekly requires the database to have flexible table structure adjustment capabilities. This avoids frequent schema changes that disrupt task execution. Metadata such as collection legitimacy and sample specifications must be stored bound to tasks. Associated indexes must be configured to ensure query efficiency. Regular data consistency checks are also required to avoid task rework risks.
How to Set Configurations
The following configurations are designed for the database module of FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:pass@127.0.0.1:27017/fastgpt?authSource=admin | Adapts to FastGPT's MongoDB storage logic, supports account verification and specified business databases, and meets storage requirements for multi-source collection data |
PG_URL | postgresql://postgres:pass@127.0.0.1:5432/fastgpt?sslmode=prefer | Ensures PostgreSQL connection stability, used for storing structured metadata such as collection plans and task schedules |
DB_MAX_LINK | 15–30 | Balances database connection count and system memory usage, prevents connection exhaustion from interrupting collection tasks |
REDIS_URL | redis://:pass@127.0.0.1:6379/0 | Used for caching temporary collection task status and field specification data, reduces repeated database query overhead |
Three Common Misconfigurations
- Symptom: After a power outage and restart during Docker deployment, FastGPT fails to start, and PostgreSQL or MongoDB rejects connections. Cause: No persistent storage directory is configured for database files. Database files inside the container are lost when the container is destroyed, so existing data cannot be read after restart.
- Symptom: When using a database connection plugin to connect to PostgreSQL, workflow validation fails with a missing or null value prompt. Cause: The parameter format of
PG_URLis not correctly configured, and necessary verification parameters are omitted, causing connection verification to fail. - Symptom: MongoDB reports a connection timeout error on startup. Cause: The host or port configuration in
MONGODB_URIis incorrect, or network policies restrict access to database ports, preventing a valid connection from being established.
How to Verify Configurations Are Correct
- Run database connection test commands to verify that connections configured via
MONGODB_URIandPG_URLwork normally. Adjust connection parameters based on test results. - Check system runtime logs to confirm that the database connection count configuration does not exceed the maximum connection threshold allowed by the database. Adjust the connection count value based on actual database performance.
- Start a temporary collection task, check if Redis cache writes temporary status data normally, and verify that the
REDIS_URLconfiguration takes effect. - Simulate a power outage and restart scenario, check if database files are persistently stored, and confirm that the FastGPT service starts normally after restart.
The values provided are common starting points and should be evaluated 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 2026-10-19.