What Data for This Category Looks Like
Data sources for this category typically include WAF, IDS, firewalls, and threat intelligence platforms. Daily output documents include rule change tickets, blocking logs, false positive analysis reports, and policy rollback records. Data updates occur either via daily batch synchronization or real-time triggers tied to security events. Common data fields include rule ID, event occurrence time, associated access IP, false positive judgment flag, and policy version number. No unified fixed statistical caliber exists, and work focuses solely on protection rules and security events.
Constraints on Database and Operations Workflows
Multi-source real-time data input requires databases to support high-concurrency writes and low-latency queries. This avoids log loss or delayed rule updates caused by single-node performance bottlenecks. Data updated by event or time dimension requires sharding and database partitioning mechanisms to support historical data retrieval and archiving. Structured rule change and rollback records require versioned storage to enable fast rollback operations. Multi-dimensional query demands for false positive analysis require databases to implement reasonable indexing strategies to improve filtering and statistical efficiency. This category directly maintains protection policy sets, so database read-write separation is required to avoid query operations interfering with rule write processes.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://admin:securepass@mongo1:27017,mongo2:27017,mongo3:27017/security?replicaSet=rs0 | This category stores large volumes of real-time blocking logs. Replica set deployment ensures high availability and prevents data write interruptions caused by single-node failures |
PG_URL | postgresql://secops:dbpass@pg-host:5432/security_ops?sslmode=require | Stores structured data such as rule change tickets and policy rollback records. SSL encryption ensures transmission security and prevents sensitive configuration leaks |
DB_MAX_LINK | 100–150 | This category generates large volumes of logs and query requests daily or per event. Sufficient connection counts support concurrent read-write operations and prevent service interruptions caused by exhausted connections |
REDIS_URL | redis://:redispass@redis-host:6379/0 | Caches frequently accessed rule sets and false positive analysis results to reduce direct database query load and improve response speed |
| Log data retention period | 30–90 days | This category requires historical log retrieval for false positive analysis. Excessively long storage occupies excessive disk resources, while excessively short storage fails to meet analysis requirements |
Three Common Misconfigurations
- Symptom: After upgrading FastGPT from v4.6.7 to v4.8.10 (compatible with FastGPT v4.17.0), access to existing blocking log data fails. Cause: The
MONGODB_URIorPG_URLconfiguration was not updated synchronously when migrating the database directory, causing the system to fail to locate the original data storage path. - Symptom: Knowledge base search response times out, and single query execution takes longer than expected. Cause: The
REDIS_URLcache for high-frequency rule set data is not configured. Each query directly accesses the database, and large volumes of concurrent requests cause excessive load. - Symptom: Unable to view database table model details, returning permission errors or empty data. Cause: Correct access permission parameters are not added to the
PG_URLconfiguration, or relevant permissions for database metadata query are not enabled.
How to Verify Proper Configuration
- Run the built-in FastGPT database connection test tool to verify that the
MONGODB_URIandPG_URLconfigurations can establish normal read-write connections. - Generate simulated blocking logs and write them to the database, then query the corresponding records to confirm that data write and query processes function correctly.
- View Redis monitoring metrics to confirm that cache hit rate meets expected levels, with no large volumes of missed cache query requests.
- Trigger a single rule change operation to verify that policy rollback records can be stored normally and displayed on the query page.
The provided values represent common starting points, and should be evaluated against individual deployment 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.