What data in this category looks like
IT service research data comes from IT asset operation and maintenance ledgers, performance monitoring logs, vendor technical white papers, industry compliance standard documents, and fault review records. Update cadences fall into three categories: real-time (performance monitoring metrics), daily (asset ledger updates), and irregular (vendor releases of new technical specifications).
Document structures include fields such as asset unique identifier, CPU usage rate, memory occupancy, response latency, version number, fault occurrence time, and repair measures. Units include percentage, millisecond, GB, count, and others. Data includes both structured monitoring metrics and unstructured technical documents, with significant format differences.
Constraints on database and operations workflows
Real-time performance monitoring data has a high write frequency, generating tens of thousands of records per hour. This requires configuring high-concurrency database connection pools to support batch writes. Mixed storage of multi-field structured data and unstructured documents requires tiered storage strategies to distinguish hot and cold data. Research scenarios require retrospective access to historical data, so databases must retain full backups for at least 30 days. Different data sources have widely varying update cadences, so scheduled synchronization tasks must be configured to adapt to different update cycles. Operations staff need to quickly locate historical data associated with faults, so indexes must be created for high-frequency query fields.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_CONNECTION_POOL_SIZE | 32–64 | Adapts to concurrent write and query demands of IT service research data, avoids connection exhaustion |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time for long-form vendor technical white papers, operation manuals and similar documents |
DB_WRITE_BATCH_SIZE | 500–1000 records | Reduces disk I/O overhead during batch writing of monitoring data, improves write efficiency |
KNOWLEDGE_BASE_BACKUP_RETENTION_DAYS | 30–90 days | Meets historical data retrospective needs of research scenarios, balances storage costs and data retention periods |
SQL_QUERY_TIMEOUT | 300 seconds | Adapts to query time for cross-data-source operation monitoring data, prevents long queries from being forcibly terminated |
RECALL_TOP_K | 10–15 | Accurately matches technical documents and operation data required for research, reduces invalid recall results |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Returns 503 Service Unavailable status code during concurrent calls, fails to adjust database connection pool size, leading to connection resource exhaustion and inability to process new requests.
- Returns empty results when querying operation data via SQL, fails to configure SQL data source access permissions or sets the
SQL_QUERY_TIMEOUTparameter value too small, cannot complete full data retrieval. - Knowledge base exports only support global dimensions and cannot export by business category, fails to enable sharding and indexing by business fields, only retains backup configurations for the global knowledge base.
How to confirm configurations are correct
- Run a concurrent test script, observe whether the active connection count of the database connection pool stays within the configured
DB_CONNECTION_POOL_SIZErange. - Initiate an SQL query request for operation monitoring data, verify that expected structured data can be retrieved.
- Trigger a knowledge base backup task, check whether the backup file includes associated data for the specified business category.
- View database slow query logs, confirm there are no timed-out query tasks, and that indexes for high-frequency query fields are active.
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.