Visualization¶

Explore the Azure App Service Practical Guide through interactive knowledge graphs and visual maps.

flowchart TD
    subgraph Graphs["Available Visualizations"]
        KG["Core Knowledge Graph"]
        TM["Troubleshooting Map"]
        LP["Learning Paths"]
    end
    subgraph Nodes["Node Types"]
        CON["Concept (Blue)"]
        BP["Best Practice (Green)"]
        PB["Playbook (Orange)"]
        LAB["Lab (Red)"]
        KQL["KQL (Purple)"]
    end
    KG --> CON
    KG --> BP
    TM --> PB
    TM --> LAB
    TM --> KQL
    LP --> CON
    LP --> BP

Traditional documentation navigation relies on hierarchical menus and search. Visual graphs offer:

Relationship Discovery: See how concepts connect to playbooks, labs, and KQL queries
Learning Path Clarity: Understand prerequisites and progression at a glance
Troubleshooting Context: Navigate from symptoms to solutions through evidence chains

Available Visualizations¶

Core Knowledge Graph

The complete site structure showing how Platform concepts, Best Practices, and Troubleshooting documents interconnect.

Explore the Knowledge Graph
Troubleshooting Map

Navigate troubleshooting workflows visually. See how playbooks connect to labs, KQL queries, and evidence patterns.

Open Troubleshooting Map
Learning Paths

Visual learning progressions for Python, Node.js, Java, and .NET development on App Service.

View Learning Paths

Graph Legend¶

Understanding the node types and edge relationships used across all visualizations:

Node Types¶

Type	Color	Description
`concept`	Blue	Platform concepts and architecture (How App Service Works, Request Lifecycle)
`best_practice`	Green	Operational guidance (Production Baseline, Security Best Practices)
`playbook`	Orange	Troubleshooting procedures (Intermittent 5xx, Memory Pressure)
`lab`	Red	Hands-on reproducible experiments
`kql`	Purple	KQL query patterns for diagnostics
`map`	Teal	Decision trees, evidence maps, mental models
`reference`	Gray	CLI cheatsheets, platform limits

Edge Types¶

Relationship	Meaning
`prerequisite`	Must understand A before B
`related`	Conceptually connected
`used_in`	A is used within B
`deep_dive_for`	A provides detailed coverage of B
`troubleshooting_for`	Playbook addresses issues in concept
`validated_by_lab`	Playbook hypothesis tested by lab
`investigated_with_kql`	Playbook uses this KQL query

How Graphs Are Built¶

The knowledge graphs are automatically generated from document frontmatter:

---
title: Memory Pressure and Worker Degradation
slug: memory-pressure-and-worker-degradation
doc_type: playbook
section: troubleshooting
topics:
  - performance
  - memory
  - worker
products:
  - azure-app-service
related:
  - intermittent-5xx-under-load
  - slow-response-but-low-cpu
prerequisites:
  - how-app-service-works
  - request-lifecycle
used_in:
  - first-10-minutes-performance
evidence:
  - kql
  - detector
  - lab
---

The build pipeline (tools/build_doc_graph.py) extracts these relationships to generate the graph JSON.

Technical Implementation¶

The visualizations use Cytoscape.js for interactive 2D graph rendering:

Click to Navigate: Click any node to open the corresponding documentation page
Search/Filter: Use the search box to highlight matching nodes
Zoom/Pan: Mouse wheel to zoom, drag to pan
Layout: Automatic force-directed layout with manual adjustment support

Contributing to Visualizations¶

To add a document to the knowledge graph:

Add standardized frontmatter to your markdown file
Include related, prerequisites, or used_in fields as appropriate
Run python tools/build_doc_graph.py to regenerate the graph
Run python tools/validate_frontmatter.py to check for broken links

See the Taxonomy for complete frontmatter schema documentation.