# Stack Visualization & Diagnostics Reporting Specification

## 1. Executive Summary: Enhancing Observability with Toyota Way Principles

This specification details the design for `batuta`, a comprehensive stack visualization and diagnostics reporting tool. `batuta` aims to provide unparalleled observability into complex, multi-layered software systems by integrating advanced analytics with intuitive visualizations. The tool adheres to key Toyota Way principles:

-   **Mieruka (Visual Control)**: Transform complex system data into easily understandable ASCII dashboards and visualizations, making problems immediately apparent.
-   **Jidoka (Built-in Quality)**: Automate the detection of anomalies, predict potential failures, and correlate errors to their root causes, effectively "stopping the line" for critical issues.
-   **Genchi Genbutsu (Go and See)**: Enable deep dives into any layer of the stack, allowing engineers to "go to the source" of issues with precise dependency mapping and error correlation.

`batuta` will empower engineers to understand system behavior, proactively identify risks, and accelerate root cause analysis, thereby fostering a culture of continuous improvement (Kaizen) and built-in quality.

## 2. Architecture: Data Flow Pipeline

The `batuta` system operates as a data flow pipeline, ingesting raw operational data, processing it through various analytical stages, and outputting actionable insights and visualizations.

```ascii
+-----------------+     +-----------------+     +-------------------+     +---------------------+
| Data Sources    | --> | Data Ingestion  | --> | Data Processing   | --> | Analytics & ML      |
| (Logs, Metrics, |     | (Collectors,    |     | (Normalization,   |     | (Graph, Anomaly,    |
| Traces, Events) |     | Agents, Hooks)  |     | Filtering,        |     | Prediction, Error)  |
+-----------------+     +-----------------+     +-------------------+     +---------------------+
       |                                                                             |
       v                                                                             v
+-----------------+     +-------------------+     +---------------------+     +-----------------+
| Configuration   | <-- | Diagnostics Engine| <-- | Visualization Engine| <-- | Reporting       |
| (.batuta-diag.t)|     | (Correlator,     |     | (ASCII Dashboards,  |     | (CLI, API,       |
|                 |     | Root Cause)     |     | Dependency Maps)    |     | Notifications)  |
+-----------------+     +-------------------+     +---------------------+     +-----------------+
```

**Data Flow**:
1.  **Data Sources**: Raw data from various system components.
2.  **Data Ingestion**: Standardized collection of data via agents, hooks, or direct integrations.
3.  **Data Processing**: Cleansing, enrichment, and transformation of ingested data.
4.  **Analytics & ML**: Application of algorithms for insights.
5.  **Diagnostics Engine**: Correlation of analytical findings to pinpoint root causes.
6.  **Visualization Engine**: Renders data into human-readable formats.
7.  **Reporting**: Delivers insights through CLI, API, or notification systems.
8.  **Configuration**: Controls behavior of the entire pipeline.

## 3. Stack Layer Taxonomy

`batuta` defines a 6-layer hierarchy to categorize system components and facilitate cross-stack analysis. Each layer has specific metrics and diagnostic patterns.

1.  **Compute Layer**: Virtual Machines, Containers, Serverless Functions, Hardware.
    *   *Metrics*: CPU utilization, Memory, Disk I/O, Network Throughput, Latency.
    *   *Diagnostics Focus*: Resource contention, hardware failures, OS-level errors.
2.  **Data Layer**: Databases (SQL, NoSQL), Caches, Message Queues, Storage Systems.
    *   *Metrics*: Query latency, Throughput, Error rates, Cache hit ratios, Disk usage.
    *   *Diagnostics Focus*: Query performance, data consistency, storage bottlenecks.
3.  **ML Layer**: Training Systems, Inference Endpoints, Feature Stores, Model Registries.
    *   *Metrics*: Model accuracy, Prediction latency, Data drift, GPU utilization.
    *   *Diagnostics Focus*: Model performance degradation, data quality issues, resource allocation.
4.  **Transpiler Layer**: Build Systems, Compilers, Code Generators, Runtime VMs (e.g., WASM).
    *   *Metrics*: Build times, Compilation errors, Binary size, Execution performance.
    *   *Diagnostics Focus*: Build failures, performance regressions introduced by transpilation.
5.  **Quality Layer**: Test Suites, Linters, Static Analyzers, Coverage Tools, Security Scanners.
    *   *Metrics*: Test pass rates, Coverage%, Linting errors, Vulnerability counts.
    *   *Diagnostics Focus*: Regression detection, quality gate failures, security vulnerabilities.
6.  **Orchestration Layer**: Kubernetes, Nomad, Swarm, CI/CD Pipelines, Workflow Engines.
    *   *Metrics*: Deployment success rate, Rollback count, Pod status, Job execution time.
    *   *Diagnostics Focus*: Deployment failures, service outages, resource scheduling conflicts.

This taxonomy enables `batuta` to perform holistic diagnostics across heterogeneous technology stacks.

## 4. Dependency Graph Analysis

`batuta` constructs and analyzes a dynamic dependency graph of the entire system, identifying critical paths, potential bottlenecks, and architectural anomalies.

-   **Node Definition**: Services, microservices, databases, queues, ML models, CI jobs, etc.
-   **Edge Definition**: API calls, message passing, data dependencies, build-time dependencies.

Key graph analysis algorithms employed:
-   **PageRank**: Identifies the most "important" or central components in terms of dependency flow. High PageRank nodes are critical failure points.
-   **Betweenness Centrality**: Pinpoints components that act as bridges between different parts of the system. High betweenness centrality indicates potential communication bottlenecks or single points of failure.
-   **Louvain Community Detection**: Groups strongly interconnected components, revealing logical service boundaries and identifying unexpected coupling between seemingly disparate parts of the system.

```ascii
+-------+     +-------+     +-------+
| Svc A | --> | Svc B | <-- | DB 1  |
+-------+     +-------+     +-------+
   ^              |              ^
   |              v              |
+-------+     +-------+     +-------+
| Svc C | --- | Queue | --- | Svc D |
+-------+     +-------+     +-------+

PageRank: Svc B (High), Queue (Medium)
Betweenness Centrality: Queue (High)
Communities: {Svc A, Svc B, DB 1}, {Svc C, Queue, Svc D} (potential unexpected coupling)
```

## 5. ML-Driven Insights

`batuta` leverages machine learning models to provide predictive and proactive diagnostics.

-   **Anomaly Detection (Isolation Forest)**: Identifies unusual patterns in metrics and logs that deviate significantly from learned normal behavior, flagging potential problems before they escalate.
-   **Upgrade Risk Prediction (Random Forest)**: Forecasts the likelihood of new bugs or performance regressions after a component upgrade, based on historical data of similar changes and their outcomes.
-   **Error Forecasting**: Predicts future error rates or outages based on current system state and historical trends using time-series forecasting models (e.g., ARIMA, Prophet).

## 6. Error Correlation and Root Cause Analysis

`batuta` automatically correlates disparate error signals across the stack to pinpoint root causes, reducing mean time to resolution (MTTR).

-   **Event Grouping**: Clusters related events (logs, traces, metrics anomalies) based on temporal proximity, shared identifiers, and semantic similarity.
-   **Fishbone (Ishikawa) Visualization**: For a given incident, `batuta` constructs a Fishbone diagram, categorizing potential causes by:
    *   **People**: Misconfigurations, human errors.
    *   **Process**: Flawed deployment, testing gaps.
    *   **Environment**: Infrastructure issues, network problems.
    *   **Tools**: Software bugs, toolchain failures.
    *   **Measurement**: Monitoring gaps, incorrect alerts.
-   **Trace Analysis**: Uses distributed tracing to visualize the flow of requests across services and identify the exact component or operation causing a bottleneck or error.

## 7. Rich ASCII Dashboards

`batuta` provides customizable, interactive ASCII-based dashboards for real-time visualization directly in the terminal, adhering to the Mieruka principle.

-   **System Health Overview**: A high-level view of all 6 layers, highlighting critical alerts and performance indicators.
-   **Service-Specific Dashboards**: Detailed views for individual services, including logs, metrics, and dependency status.
-   **Dependency Maps**: Interactive ASCII graphs showing real-time service dependencies and their health.

```ascii
+-------------------------------------------------------------+
| System Health Overview              [batuta diagnose live]  |
+-------------------------------------------------------------+
| Compute [█████     ]  80% OK   | Data [███████   ]  90% OK   |
| ML      [███       ]  60% WARN | Transpiler [██████    ]  70% OK   |
| Quality [████████  ]  95% OK   | Orch. [███       ]  50% CRITICAL |
+-------------------------------------------------------------+
| ⚠️ Orchestration Layer Critical: Deployment Failure 'frontend-v2' (1/3 pods) |
|   See `batuta diagnose logs --layer orchestration` for details           |
+-------------------------------------------------------------+
```

## 8. CLI Interface

`batuta` is primarily controlled via a powerful command-line interface.

-   `batuta diagnose [layer]` : Provides real-time diagnostics for a specific layer or overall system health.
-   `batuta graph [service]` : Visualizes the dependency graph for a given service or the entire stack.
-   `batuta analyze [type] [options]` : Triggers ML-driven analysis (e.g., `batuta analyze anomalies`).
-   `batuta report [incident-id]` : Generates a comprehensive incident report, including Fishbone diagrams.
-   `batuta config validate` : Validates the `.batuta-diagnostics.toml` configuration.
-   `batuta history [query]` : Reviews historical diagnostic reports and trends.

## 9. Configuration Format: `.batuta-diagnostics.toml`

The behavior and integrations of `batuta` are controlled by a `.batuta-diagnostics.toml` file located at the project root.

```toml
# .batuta-diagnostics.toml

[general]
  data_retention_days = 30
  dashboard_refresh_interval_sec = 5

[data_sources]
  [data_sources.logs]
    type = "file"
    path = "/var/log/**/*.log"
    parser = "regex:.*(INFO|WARN|ERROR).*"
  [data_sources.metrics]
    type = "prometheus"
    endpoint = "http://localhost:9090"
  [data_sources.traces]
    type = "jaeger"
    endpoint = "http://localhost:14268"

[ml_insights]
  anomaly_detection_threshold = 0.95
  upgrade_risk_model_path = "/models/upgrade_risk.rf"

[visualization]
  default_layer_view = "orchestration"
  ansi_colors_enabled = true

[reporting]
  notification_webhook_url = "https://hooks.slack.com/services/..."
  report_format = "markdown"
```

## 10. Implementation Phases

`batuta` will be developed in several phases:

**Phase 1: Core Data Ingestion & Basic Visualization (Q1 2026)**
-   Initial data ingestion from common log/metric formats.
-   CLI for basic `batuta diagnose` and `batuta graph` functionality.
-   Simple ASCII dashboards for system health.

**Phase 2: Dependency Graph & ML Anomaly Detection (Q2 2026)**
-   Advanced dependency graph construction.
-   Integration of Isolation Forest for anomaly detection.
-   Enhanced ASCII visualizations for graph analysis.

**Phase 3: Root Cause Analysis & Predictive Insights (Q3 2026)**
-   Error correlation and automated Fishbone diagram generation.
-   Implementation of upgrade risk prediction and error forecasting.
-   Comprehensive incident reporting.

**Phase 4: Extensibility & Advanced Integrations (Q4 2026)**
-   Plugin architecture for custom data sources and ML models.
-   Integration with CI/CD platforms for pre-deployment diagnostics.

## 11. Academic References

1.  **Ishikawa, K. (1985)**. *What Is Total Quality Control?: The Japanese Way*. Prentice-Hall. (Introduced Fishbone diagrams)
2.  **Page, L., Brin, S., Motwani, R., & Winograd, T. (1999)**. *The PageRank Citation Ranking: Bringing Order to the Web*. (Foundation for PageRank algorithm)
3.  **Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008)**. *Fast unfolding of communities in large networks*. Journal of Statistical Mechanics: Theory and Experiment. (Louvain method for community detection)
4.  **Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008)**. *Isolation Forest*. 2008 Eighth IEEE International Conference on Data Mining. (Foundational paper for Isolation Forest)
5.  **Breiman, L. (2001)**. *Random Forests*. Machine Learning, 45(1), 5-32. (Foundational paper for Random Forests)
6.  **Kim, M., & Kim, Y. (2020)**. *A Survey on Root Cause Analysis in Microservices*. IEEE Access, 8, 203875-203890.
7.  **Toyota Production System (TPS)**: Concepts of Mieruka, Jidoka, Genchi Genbutsu, and Kaizen.

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**Document Version**: 1.0.0 (Draft)
**Last Updated**: December 7, 2025
**Author**: Paiml-MCP-Agent
**Status**: DRAFT - Awaiting Review and Feedback
