What the data for this category looks like
Data sources include application invocation logs, vector retrieval hit records, and resource usage reporting APIs from the all-in-one AI platform. Data update cadence falls into two categories: real-time details and scheduled aggregations. Real-time detail data is written immediately upon each application invocation. Aggregated data is generated on an hourly or daily cycle.
Single data document fields include unique application identifier, anonymized caller identity hash, invocation timestamp, consumed token count, vector retrieval count, and associated storage resource usage, among others. Field units follow standard measurement formats: timestamps are in milliseconds, token counts and retrieval counts are measured in units of occurrences, and storage usage is measured in gigabytes (GB). Anonymized caller identity is processed via hashing to avoid storing real user identity information.
What constraints these characteristics impose on database and operations workflows
High-frequency writes of real-time detail data require databases to support low-latency, high-concurrency write operations. Use time-series storage engines or sharded database architectures to reduce write bottlenecks.
For scheduled aggregated data generation, configure periodic scheduling tasks, and pair these tasks with data expiration and cleanup rules to meet data retention compliance requirements for financial scenarios.
Combined queries across multiple field types — such as filtering usage data by application ID and time range — require joint indexes to optimize query performance.
Anonymized caller identity storage must use hashing for anonymization to prevent sensitive information leaks.
Real-time monitoring of storage usage fields requires threshold alerts to avoid disk space exhaustion disrupting platform operations.
Additionally, multi-dimensional query demands for usage statistics data require deliberate database partitioning strategies. Partitioning by time significantly improves query efficiency for historical data.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
STAT_DATA_RETENTION_DAYS | 30–90 days | Meets financial industry data retention compliance requirements while controlling long-term storage costs |
STAT_WRITE_BATCH_SIZE | 50–100 entries | Balances write concurrency and single I/O overhead, avoids database blocking caused by overly large single writes |
STAT_AGGREGATE_INTERVAL | 3600 seconds | Balances data timeliness and aggregation computing overhead, meets statistical needs of most business scenarios |
STAT_INDEX_APPID_TIME | Enabled | Covers high-frequency query scenarios based on application ID + time range, improves query response speed |
STAT_STORAGE_ALARM_THRESHOLD | 85% disk usage | Reserves sufficient space to handle sudden writes, avoids service exceptions caused by full disks |
STAT_ANONYMIZE_HASH_SALT | Randomly generated 16-character string | Improves the security of anonymized identifiers, prevents reverse deduction of sensitive information |
The parameter values provided on this page are general recommendations for use as a starting point for configuration. Actual values are affected by data format, data volume, and business rules. Specific issues require individual analysis. Testing on self-managed samples is recommended before finalizing settings.
Three common mistakes
- Symptom: A
column vector does not existerror appears after restarting the database, preventing loading of vector hit statistics data for usage statistics. Cause: The installation statement for the vector extension was not retained in the database initialization script, and the associated field rebuild script was not executed after reinstalling the extension. - Symptom: After containerized deployment, the usage statistics service cannot connect to the database. Logs show a
Connection refusedstatus code. Cause: The database connection configuration inside the container was not mapped to the host port, or container network policies restrict port access. - Symptom: Querying usage statistics data by time range returns no results, or the number of results is far lower than expected. Cause: Joint indexes were not configured correctly, or the query time range exceeds the set data retention period.
How to confirm the configuration is properly set
- Run the database's built-in index viewing command to confirm that a joint index for
app_idandcall_timestamphas been created. - Trigger a simulated application invocation, and use a database query tool to verify that real-time usage detail data has been properly written to the corresponding table.
- Check the scheduled task scheduling logs to confirm that aggregated statistics data has been generated according to the configured cycle. Verify data integrity via the database aggregation table.
- Check the database alert rules in the operations and maintenance monitoring platform to confirm that thresholds have been set in line with business compliance and storage requirements.
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.