Notre avis
Fournit une référence complète pour le langage de requête APL d'Axiom, incluant syntaxe, opérateurs, fonctions et modèles d'utilisation.
Points forts
- Couvre la syntaxe de base et avancée d'APL
- Inclut la découverte de schéma et des exemples concrets
- Fournit des modèles de requêtes pour divers cas d'usage
- Explique la gestion du temps et les filtres efficaces
Limites
- Nécessite l'authentification CLI Axiom pour fonctionner
- N'est pas invocable directement par l'utilisateur
- Limitée à la plateforme Axiom
Lorsque vous devez écrire, déboguer ou optimiser une requête APL pour analyser des données d'observabilité dans Axiom.
Pour une simple recherche de champ, utilisez directement `getschema` ; pour des alertes en temps réel, préférez les monitors Axiom.
Analyse de sécurité
SûrThe skill only allows read-only and query operations on Axiom datasets via restricted Bash commands, and uses safe utilities like Read, Grep, Glob. No destructive, exfiltrating, or obfuscated actions are instructed.
Aucun point d'attention détecté
Exemples
Write an APL query to count the number of errors (status >= 500) per service from the 'logs' dataset in the last hour, sorted by count descending.Show me an APL query that uses bin_auto to bucket requests by time over the last 24 hours and count them.I'm writing an APL query that times out. Can you help me optimize it? Here's my query: ['logs'] | where _time >= ago(7d) | summarize count() by service, status ; it's too slow.name: axiom-apl description: APL query language reference for Axiom. Provides operators, functions, patterns, and CLI usage. Auto-invoked by specialized Axiom skills when writing or debugging APL queries. compatibility: Requires authenticated Axiom CLI (axiom) user-invocable: false context: fork allowed-tools: Bash(axiom query:), Bash(axiom dataset list:), Bash(axiom stream:*), Read, Grep, Glob
Axiom Processing Language (APL)
APL is Axiom's query language for analyzing observability data. This skill provides comprehensive guidance for writing, debugging, and optimizing APL queries.
Quick Reference
Documentation: https://axiom.co/docs/apl/introduction
CLI usage: See references/cli.md
Core Workflow
1. List Available Datasets
axiom dataset list -f json
2. Discover Schema (CRITICAL - Always Do First)
['<dataset>'] | getschema
Never guess field names. The schema shows all fields with their types.
3. Sample Data
['<dataset>'] | limit 10
4. Write Query
See references for operators, functions, and patterns.
APL Syntax Essentials
Dataset Reference
['dataset-name'] // Bracket notation (required for names with dots/dashes)
dataset_name // Plain identifier (only for simple names)
Field Reference
field_name // Plain field
['field.with.dots'] // Bracket notation for dotted fields
['service.name'] // OTel data (see references/otel.md for field mappings)
Basic Query Structure
['dataset']
| where <condition>
| extend <new_field> = <expression>
| summarize <aggregation> by <grouping>
| project <fields>
| sort by <field> desc
| limit 100
Time Handling
Always filter by time first - it's the most selective filter.
// Relative time
| where _time >= ago(1h)
| where _time >= ago(24h) and _time < ago(1h)
// Absolute time
| where _time >= datetime(2024-01-15T10:00:00Z)
| where _time between (datetime(2024-01-15) .. datetime(2024-01-16))
Time functions:
ago(timespan)- Relative past timenow()- Current timedatetime(string)- Parse datetimebin(_time, 5m)- Time bucketingbin_auto(_time)- Automatic bucketing
When NOT to Use
- Simple field lookup: Use
getschemadirectly instead of invoking the full skill - Known query patterns: If you already have a working query, don't re-invoke for syntax help
- Real-time alerting: Use Axiom Monitors for continuous alerting, not ad-hoc queries
References
- CLI Usage - Command flags and execution
- Operators - Tabular and scalar operators
- Functions - String, datetime, aggregation functions
- Patterns - Query patterns by use case
- Common Gotchas - Mistakes and fixes
- OpenTelemetry - OTel field mappings and trace patterns
Ingénierie de Prompts
Data & IA
Bonnes pratiques et templates de prompt engineering pour maximiser les résultats IA.
Visualisation de Données
Data & IA
Génère des visualisations de données et graphiques adaptés à vos données.
Architecture RAG
Data & IA
Guide de configuration d'architectures RAG (Retrieval-Augmented Generation).