What data in this category looks like
Cybersecurity investment research data primarily comes from public vulnerability intelligence databases, threat intelligence sources, enterprise asset mapping data, penetration test exercise logs, and other similar channels. Update frequencies fall into three categories: real-time (vulnerability POC/EXP releases), daily (threat intelligence summaries), and hourly (asset scan updates). Each data entry includes fields such as CVE ID, threat level, affected asset scope, POC code snippets, remediation plans, and associated APT organizations. Units include CVSS scores (range 0-10), asset counts, and timestamps. Some documents contain multi-line code and special characters.
What constraints do these characteristics place on database and operations work
The high-frequency, multi-type update rhythm requires the database to support high-concurrency writes and low-latency reads, to avoid delays in investment research data. Multi-field associated retrieval needs require building multi-dimensional indexes, otherwise query performance will decline. Large-volume code snippets and log documents require sufficient storage capacity and large field support to prevent data truncation. At the same time, the sensitivity of security data requires the database to have a high-availability architecture to prevent intelligence interruptions caused by single-point failures.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Cybersecurity documents often contain POC/EXP code and penetration test logs, so single-file size is generally large |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Parsing large-volume security documents takes a long time, to avoid task termination due to early timeout |
RECALL_TOP_K | Top 15-25 entries | Investment research requires associating multi-dimensional security intelligence, so the number of recalled entries needs to cover sufficient associated items |
MYSQL_CHARSET | utf8mb4 | Required to store POC code with special symbols and Chinese vulnerability descriptions to avoid character garbling |
MONGO_REPLICA_SET_NODES | At least 3 nodes | Security data requires a high-availability architecture to prevent intelligence retrieval interruptions caused by single-point failures |
DB_INDEX_FIELDS | ["cve_id", "threat_level", "update_time"] | Investment research frequently retrieves by vulnerability ID, threat level, and update time, so corresponding indexes need to be established |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Chinese vulnerability descriptions stored in a MySQL database display garbled characters, and unrecognizable characters appear in query result fields. Cause: The database connection character set was not set to
utf8mb4, and the corresponding character set parameter was not configured synchronously in the data source plugin. - Symptom: Parsing tasks for security documents larger than 10 MB remain in the "parsing" state for a long time, and finally return a timeout error. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted, and the time taken for single-threaded parsing of large files exceeded the default threshold. - Symptom: When multiple users initiate intelligence retrieval at the same time, the interface returns a
503 Service Unavailablestatus code. Cause: The database connection pool configurationMAX_POOL_SIZEvalue is too low to support high-frequency concurrent investment research query requests.
How to confirm the configuration is correct
- Upload a single large cybersecurity document and confirm that the parsing task completes within the preset timeout period.
- Run retrieval operations by vulnerability ID and threat level, and confirm that returned results have no character garbling and complete fields.
- Initiate multiple sets of concurrent intelligence query requests, and confirm that the interface has no abnormal errors and returned results meet expectations.
- Check the running status of the database cluster, and confirm that node synchronization and high-availability configurations are working properly.
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.