What this category’s data looks like
Data for this category comes from ticketing systems, real-time chat sessions, product operation and maintenance logs, and customer SLA agreement documents. Two update cycles are used: real-time updates correspond to new ticket creation, status changes, and processing progress synchronization. Daily updates correspond to summary statistics for full-service reports.
Daily documents include customer tickets, problem tiering tables, processing records, and service reports. Fields include ticket unique identifier, customer identity identifier, problem category, urgency level, assigned team, processing duration, and SLA compliance records. Ticket ID uses a string format. Processing duration is measured in seconds. SLA time limits are measured in days.
Constraints imposed by these characteristics on database and operations workflows
Real-time ticket data requires databases to support high-concurrency writes, and avoid blocking subsequent requests with single-item write operations. Multi-source heterogeneous data — including unstructured ticket text and structured SLA statistics — requires a unified data access layer. This prevents query exceptions caused by field naming conflicts.
Daily aggregated service reports require scheduled batch reads of historical data. Configure a reasonable connection pool size to prevent connection exhaustion from occupying server resources.
Dynamic adjustments to problem tiering require frequent updates of field values. This requires databases to support flexible schema changes. At the same time, create indexes for high-frequency query fields to shorten response times.
Storage of SLA-related data must ensure traceability, and prevent unauthorized modification operations.
Configuration settings
The following configuration items apply to FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://[username]:[password]@host:port/fastgpt_tickets?authSource=admin | Ticket data is mostly unstructured text. MongoDB is suitable for storing ticket records with dynamic fields, and supports fast index creation by ticket ID and problem category |
DB_MAX_LINK | 20–30 | Matches daily ticket concurrency levels. Avoids occupying server memory with an overly large connection pool, and prevents connection wait timeouts during high concurrency with an overly small pool |
REDIS_URL | redis://:auth_password@host:port/0 | Used to cache high-frequency query problem tiering rules and SLA thresholds, reducing read pressure on relational databases and shortening tool call response times |
PG_URL | postgresql://[username]:[password]@host:port/fastgpt_report | Structured service reports and SLA compliance statistics are suitable for aggregate queries in relational databases, ensuring the accuracy of statistical results |
Three common configuration errors
- Symptom: An error "connection timeout" is returned when calling a database tool in a workflow, or an empty result is returned. Cause: Authentication parameters for
MONGODB_URIare not configured correctly, orDB_MAX_LINKis set too low, causing the connection pool to be exhausted and unable to establish new database connections. - Symptom: Statistical data in service reports is not updated synchronously after ticket data is updated. Cause: A scheduled task for data aggregation is not configured, or Redis cached problem tiering rules are not invalidated in time, causing old tiered data to be read.
- Symptom: The urgency field of a ticket cannot be modified normally. Cause: The database connection has insufficient schema change permissions, or no index is created for this field, causing the update operation to time out and fail.
How to verify a successful configuration
- Run the built-in FastGPT database connection test script to verify that
MONGODB_URIandPG_URLcan establish normal connections, and check the returned connection status code. - Submit a test ticket, observe whether a new record appears in the FastGPT interface ticket list, and confirm whether complete data for this ticket exists in the corresponding database collection or table.
- Manually trigger a daily aggregation task, check whether a new summary document appears on the service report generation page, and confirm that there are no error messages in the task logs.
- Adjust the problem tiering of a test ticket, verify whether the corresponding field in the database is updated successfully, and confirm that the new tiered value can be read when the Redis cache has not expired.
The values provided 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 2026-10-19.