GitHub
working-with-resources
Work with Resource datasets (mutable state tracking) using OPAL temporal joins. Use when you need to enrich Events/Intervals with contextual state information, track resource state changes over time, or navigat...
rustomax AI Skills
GitHub ↗Explorez les skills IA publiés de rustomax et consultez leurs instructions source.
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GitHub
aggregating-event-datasets
Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_...
GitHub
window-functions-deep-dive
Master OPAL window functions for row-relative calculations, rankings, and moving aggregates. Covers lag(), lead(), row_number(), rank(), dense_rank(), moving averages, first(), and last(). Use when comparing ro...
GitHub
detecting-anomalies
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier dete...
GitHub
field-extraction-parsing
Extract structured fields from unstructured log data using OPAL parsing functions. Covers extract_regex() for pattern matching with type casting, split() for delimited data, parse_json() for JSON logs, and JSON...
GitHub
analyzing-apm-data
Monitor application performance using the RED methodology (Rate, Errors, Duration) with Observe. Use when analyzing service health, investigating errors, tracking latency, or building APM dashboards. Covers whe...
GitHub
analyzing-tdigest-metrics
Analyze percentile metrics (tdigest type) using OPAL for latency analysis and SLO tracking. Use when calculating p50, p95, p99 from pre-aggregated duration or latency metrics. Covers the critical double-combine...
GitHub
filtering-event-datasets
Filter and search event datasets (logs) using OPAL. Use when you need to find specific log events by text search, regex patterns, or field values. Covers contains(), tilda operator ~, field comparisons, boolean...
GitHub
working-with-intervals
Work with Interval datasets (time-bounded data) using OPAL. Use when analyzing data with start and end timestamps like distributed traces, batch jobs, or CI/CD pipeline runs. Covers duration calculations, tempo...
GitHub
working-with-reference-tables
Work with Reference Tables (static CSV lookup data) using OPAL to enrich datasets with descriptive information. Use when you need to map IDs to human-readable names, add static metadata from CSV uploads, or per...
GitHub
aggregating-gauge-metrics
Aggregate pre-computed metrics (gauge, counter, delta types) using OPAL. Use when analyzing request counts, error rates, resource utilization, or any numeric metrics over time. Covers align + m() + aggregate pa...
GitHub
time-series-analysis
Analyze event datasets (logs) and intervals over time using OPAL timechart. Use when you need to visualize trends, track metrics over time, or create time-series charts. Covers timechart for temporal binning, b...
GitHub
analyzing-text-patterns
Extract and analyze recurring patterns from log messages, span names, and event names using punctuation-based template discovery. Use when you need to understand log diversity, identify common message structure...
GitHub
subquery-patterns-and-union
Use OPAL subquery syntax (@labels) and union operations to combine multiple datasets or time periods. Essential for period-over-period comparisons, multi-dataset analysis, and complex data transformations. Cove...
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