What Data for This Category Looks Like
Data for this category primarily comes from customer ITSM systems, monitoring platforms, asset inventories, and on-site duty records. Four types of documents are generated daily: on-site daily reports, service desk tickets, inspection reports, and SLA monthly reports. On-site daily reports include fields for on-site items that day and device status. Service desk tickets include fields for ticket number and processing duration. Inspection reports include fields for inspection points and abnormal items. SLA monthly reports include fields for fulfillment rate statistics. Data update cadence is divided into daily and monthly levels. Tickets and duty records update daily. SLA monthly reports are compiled and generated monthly.
Constraints on Database and Operations Workflows
Data sources for this category are scattered and have significant format differences. Multi-source data access adaptation logic must be configured to unify data formats across disparate systems. Daily and monthly update cadences require separate configuration of incremental synchronization and full scheduled tasks to avoid resource waste or data lag. Document structures with multiple types of custom fields require support for flexible field mapping rules to prevent data storage confusion. The most time-consuming SLA statistics and ticket aggregation processes require targeted database indexes to improve query efficiency. Data anomalies may lead to customer penalties, contract impact and other negative outcomes. Data backup and anomaly alert mechanisms must be configured. FastGPT v4.17.0 supports multi-source data synchronization for this scenario, simplifying access workflows.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://oms_user:oms_pass@db-host:27017/fastgpt_oms?authSource=admin | Adapts to unstructured data storage such as on-site service tickets and inspection reports, supports flexible field expansion, and is natively supported in FastGPT v4.17.0 |
PG_URL | postgresql://sla_user:sla_pass@db-host:5432/fastgpt_sla?sslmode=require | Stores structured SLA monthly reports and ticket statistics data, supports complex queries and transaction operations, and adapts to bulk write requirements for monthly aggregation |
DB_MAX_LINK | 20–30 | Adapts to concurrent requirements for multi-source data synchronization, prevents database connection exhaustion, and ensures stable task execution |
REDIS_URL | redis://cache_pass@cache-host:6379/0 | Caches frequently queried inspection point and on-site staff information, reduces database load, and improves aggregation task response speed |
| Sync Task Cadence | Daily sync tasks at 300 second intervals, monthly sync tasks at 86400 second intervals | Matches the cadence of daily ticket updates and monthly SLA report updates, balances data timeliness and resource usage |
Three Common Misconfigurations
- Phenomenon: A
connection refusederror occurs when starting a data sync task. Logs show the database connection pool is exhausted. Cause: TheDB_MAX_LINKparameter was not adjusted based on the data volume of the on-site service, causing concurrent sync tasks to fail to obtain available connections. - Phenomenon: Some inspection data is missing when generating SLA monthly reports, and the number of query results is abnormal. Cause:
REDIS_URLwas not configured as a cache middleware. Frequently queried inspection point information is not cached, causing excessive database pressure and packet loss. - Phenomenon: Existing on-site ticket data cannot be read after private deployment, and empty results are returned after migration queries. Cause: Authentication parameters or database name in
MONGODB_URIwere not configured correctly, preventing access to the target dataset.
How to Verify Successful Configuration
- Run a full data sync task, check if corresponding categories of on-site daily reports and service desk ticket records are generated in the database.
- Access the FastGPT database monitoring panel, confirm that the current usage of
DB_MAX_LINKdoes not reach the configured upper limit. - Test generating a monthly SLA report, check if inspection and ticket aggregation data for the corresponding period can be pulled normally.
- Check the Redis cache key-value pair list, confirm that frequently queried on-site staff information has been cached normally.
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.