Database and Operations for Cybersecurity Yield Reporting

Data for cybersecurity yield reporting comes from enterprise security device operation logs, third-party threat intelligence interfaces, internal

What data for this category looks like

Data for cybersecurity yield reporting comes from enterprise security device operation logs, third-party threat intelligence interfaces, internal compliance audit reports, and asset vulnerability scan results. There are two update schedules: real-time alert data updates every 5 minutes, and full daily report summary data updates daily at midnight. The data uses a structured format, with fields including asset unique identifier, risk event type, disposal duration, compliance deduction score, security investment cost, and revenue conversion value. Asset unique identifier is a string type. Disposal duration uses minutes as the unit. Compliance deduction score is an integer. Security investment cost and revenue conversion value use generic numerical units.

What constraints these characteristics impose on database and operations workflows

The dataset uses mixed structured multi-field data, and supports two concurrent scenarios: high-frequency real-time writes and batch full updates. This requires the database to support mixed read-write loads, and cannot use only read-only storage engines. Fields include cost and conversion values, so multi-field aggregation calculations are needed to generate daily report summaries. The database must therefore support associative queries and group statistics. Asset unique identifier is a high-frequency query field, so a unique index must be configured to avoid duplicate data. The high-frequency real-time write feature requires controlling the number of records per batch write, to avoid excessive IO load affecting system stability.

How to set configurations

Configuration ItemRecommended ValueRationale
DB_WRITE_BATCH_SIZE200-500 records/batchBalances write throughput and IO load, adapts to the high-frequency write scenario of real-time alerts
DAILY_SYNC_TIMEOUT3600 secondsAdapts to the maximum synchronization time limit for full daily report data, avoids timeout interrupting full update tasks
INDEX_ASSET_IDEnabledAccelerates high-frequency queries based on asset unique identifiers, and avoids duplicate data writes
MONGO_CONNECTION_POOL_SIZE10-15 connectionsBalances database connection count and server resource usage, avoids service exceptions caused by exhausted connections
PARSE_SAFE_LOG_TIMEOUT60 secondsLimits the maximum parsing time for a single security log, avoids data discarding due to timeout
DB_TENANT_AUTO_MAPPINGEnabled per tenant configurationAutomatically generates the mapping relationship between tenants and database tables, adapts to multi-tenant deployment scenarios

The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing settings.

Three common configuration mistakes

  • Phenomenon: Database connection plugin installation fails, with an internal server error prompt. Cause: The access whitelist for the target database is not configured, or tenant information in the connection parameters is filled incorrectly.
  • Phenomenon: A MongoDB error is prompted when starting a new version, and the service cannot start normally. Cause: The MONGO_CONNECTION_POOL_SIZE configuration value exceeds the maximum connection limit allowed by the server, or the MongoDB service is not running properly.
  • Phenomenon: Unable to locate the storage database, tenant, and table information of the trained knowledge base. Cause: DB_TENANT_AUTO_MAPPING is not enabled, resulting in no mapping relationship between tenants and tables being generated, or the DB_TABLE_PREFIX parameter is not configured, causing table name prefix mismatches.

How to verify successful configuration

  • Execute the database connection test script to check whether a connection to the target database can be established successfully, and verify that the tenant and table names match the configuration parameters.
  • Submit a simulated security event data record to check whether the corresponding record is generated in the database, and verify that the write configuration takes effect.
  • Run the scheduled daily summary report task to check whether aggregation calculations can be completed and results generated within the preset time.
  • Check database connection pool monitoring metrics to confirm that the current number of connections does not exceed the MONGO_CONNECTION_POOL_SIZE configuration limit.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.