Zero-Config Detection

Auto-Facet
Detection

No manual mapping. No configuration files. Just pipe your logs and watch facets get detected automatically from any format.

How It Works

Multiple parsers run in parallel to detect patterns and extract facets automatically

Logs Arrive

You pipe logs to vibex.sh via CLI, SDK, or API. Any format works—JSON, Nginx, Syslog, Docker, Kubernetes, or custom formats.

echo '{"cpu": 45, "memory": 78}' | npx vibex-sh

Parallel Parsing

Multiple parsers run simultaneously to detect patterns. JSON fields, web server logs, syslog formats, container metadata—all detected in parallel.

JSON, Nginx, Syslog, Docker, Kubernetes, Key-Value, Stack Traces

Facet Extraction

Fields are automatically extracted and indexed. Numeric values become metrics, strings become categories, timestamps are filtered out automatically.

cpu, memory, ip, method, path, status, level, error, user_id, etc.

Smart Chart Selection

Charts are automatically created based on data type. Time series for numbers, pie charts for small categories, bar charts for larger sets.

Time series, pie charts, bar charts, gauges, histograms, scatter plots

Composite Facet Detection

Context-aware logic automatically detects and combines related fields to create composite facets. For example, method+path combinations, status+duration correlations, or user+action pairs.

method+path, status+duration, user_id+action, ip+country

Supported Formats

Automatic detection works with 11 parsers covering web, system, and application logs

Web Server

Nginx Access Logs
Apache Access Logs
HAProxy Logs
AWS ALB Logs

Application

JSON Logs
Key-Value Pairs
Smart Pattern Detection
Stack Traces

System & Containers

Syslog
Docker Container Logs
Kubernetes Pod Logs
Firewall Logs

Context-Aware Composite Facets

Beyond single fields, vibex.sh automatically detects and combines related fields to create meaningful composite facets for deeper insights

HTTP Request Patterns

Automatically combines method and path to show request patterns

Composite Facet:

method + path

GET /api/users, POST /api/auth, DELETE /api/sessions

Performance Correlations

Links status codes with response times to identify slow endpoints

Composite Facet:

status + duration

200 + duration_ms, 500 + duration_ms, 404 + duration_ms

User Activity Tracking

Combines user identifiers with actions for behavioral analysis

Composite Facet:

user_id + action

user_id + action, ip + country, session_id + event

Error Context

Groups error codes with error messages for better debugging

Composite Facet:

error_code + message

error_code + error_message, level + message, exception + stack

Example Detection

See how different log formats are automatically parsed and facets extracted

JSON Log

{"cpu": 45, "memory": 78, "status": "healthy", "timestamp": "2024-01-01T12:00:00Z"}

Detected Facets

cpu (number)memory (number)status (category)timestamp (filtered)

Nginx Access Log

192.168.1.1 - - [01/Jan/2024:12:00:00 +0000] "GET /api/users HTTP/1.1" 200 1234

Detected Facets

ip (192.168.1.1)method (GET)path (/api/users)status (200)bytes (1234)

Application Log

2024-01-01 12:00:00 [ERROR] User 12345 failed to authenticate from IP 10.0.0.1

Detected Facets

level (ERROR)user_id (12345)ip (10.0.0.1)timestamp (filtered)

Time Saved

Compare manual configuration vs automatic detection

Manual Configuration

✗Hours mapping fields to chart types
✗YAML/JSON configuration files to maintain
✗Updates needed when log format changes
✗Manual chart type selection for each field