aggregating-event-datasets

Aggregate event datasets with OPAL statsby across a defined time window.

Updated Aug 23, 2026

Search Tags:#logs #data-analysis #aggregate #OPAL #statsby #group_by #topk

Ready to run

npx skills add https://github.com/rustomax/observe-community-mcp --skill aggregating-event-datasets

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aggregating-event-datasets
Source: https://github.com/rustomax/observe-community-mcp/tree/main/skills/aggregating-event-datasets
Command: npx skills add https://github.com/rustomax/observe-community-mcp --skill aggregating-event-datasets

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables analysts to quickly derive meaningful summaries from large volumes of event logs by applying OPAL's statsby aggregation.

Core Features & Use Cases

Derived columns: Use make_col to extract dimensions (e.g., namespace, pod) for grouping.
Efficient aggregation: Compute counts, sums, averages, percentiles, and tops per group with a single pass over the time window.
Use Case: Identify the top namespaces by error count across the last 24 hours, or produce a per-service average latency by group.

Quick Start

Start by discovering a dataset, create derived grouping columns with make_col, then apply statsby to produce one row per group across the chosen time range.

Frequently Asked Questions about aggregating-event-datasets

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I aggregate event logs by multiple dimensions using OPAL?▼

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.

What is the best way to count errors per namespace from large log datasets?▼

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.

Can I compute averages and percentiles grouped by service in OPAL?▼

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.

How do I extract grouping fields before aggregating logs with statsby?▼

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.

What types of aggregations does OPAL statsby support for log analysis?▼

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.

Can I use statsby to produce top-N summaries for dashboards?▼

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.