Log Pre-aggregation: Aggregate fine-grained data into coarse-grained data.
Dashboard Acceleration: For example, if a chart on the dashboard times out when querying Log Topic A, the second-level raw data in Topic A can be pre-aggregated into minute-level data. The aggregated results are saved to another Log Topic B, which is then used for data queries and dashboard configuration.
Log-to-metrics conversion: It persistently stores critical data.
Metrics ecosystem: Use this feature if your business needs to extract metrics (Metric-type data) from logs.
Persistent storage of critical data: Compared to logs, metric storage is more cost-effective. You can save critical data to a metric topic for long-term storage of (3 - 5) years.
Overview
See Concepts for terms and concepts related to timed SQL analysis.
Features
Scheduled SQL analysis can be simply understood as crontab SQL. You can configure a scheduling policy, and the system will execute SQL queries on the source log topic regularly based on the policy and save query results to the specified target log topic.
Log Computing & AnalysisBased on raw logs, queries are executed periodically and the results are saved to a specified log topic. For example, if your business requires daily or weekly reports, you can use scheduled SQL analysis.
Log aggregation. It can significantly reduce costs for index storage and log storage. For instance, aggregating one minute of historical logs into one hour of logs for storage can effectively cut expenses.
Filter and save data to a new log topic. For example, filter logs where the log level is ERROR.
