aggregating-event-datasets

To aggregate event logs by multiple dimensions in OPAL, you use the statsby command to group records and compute metrics like count, sum, or average across a defined time window, producing one summary row per group.

Counting errors per namespace from large log datasets is handled by using make_col to extract the namespace dimension, then applying statsby to aggregate error counts and produce a top-N summary across the chosen time range.

Yes, you can compute averages and percentiles grouped by service in OPAL by applying the statsby aggregation command, which supports multiple aggregation functions like avg and percentile within a single pass over the event logs.

You extract grouping fields before aggregating logs with statsby by using the make_col command to derive new dimensions, such as namespace or pod, from existing event data for subsequent grouping operations.

OPAL statsby supports count, sum, average, percentile, and topk aggregations for log analysis, enabling efficient computation of multiple metrics per group across a defined time window in a single pass.

Yes, you can use statsby to produce top-N summaries suitable for dashboards by applying the topk aggregation function to rank groups within your event datasets across a specified time window.