# Grafana Learning Dashboard Deployment - Status Report

**Date:** 2026-02-09
**Status:** Partially Deployed - Alternative Solution Required

## Summary

Attempted to deploy Grafana dashboard for learning database monitoring. Dashboard successfully deployed to Grafana, but datasource configuration requires alternative approach due to lack of official DuckDB plugin.

## Current Status

### Completed ✅

1. **Grafana Installation and Configuration**

   - Grafana installed via Homebrew
   - Running on port **3030** (not default 3000)
   - Health check passing: `http://localhost:3030/api/health`
   - Version: Grafana 12.3.2 (Homebrew)

1. **Dashboard Deployment**

   - Dashboard JSON file created: `/Users/les/Projects/mahavishnu/grafana/dashboards/learning-telemetry.json`
   - Successfully deployed to Grafana
   - Dashboard URL: `http://localhost:3030/d/e24a0cf5-28cf-4bc7-82bc-46e876c7e4d9`
   - Contains 17 monitoring panels

1. **Plugin Installation**

   - SQLite datasource plugin installed: `frser-sqlite-datasource v3.8.2`
   - Grafana restarted successfully
   - Plugin loaded and available

### Issues Identified ⚠️

1. **No Official DuckDB Plugin**

   - DuckDB datasource plugin does not exist in Grafana plugin repository
   - Attempted `grafana-cli plugins install duckdb-datasource` returned 404
   - SQLite plugin installed but may not fully support DuckDB files

1. **Database Status**

   - Learning database exists: `/Users/les/Projects/mahavishnu/data/learning.db` (3.3 MB)
   - Database is currently locked by embedding generation process
   - Process: `python3 scripts/generate_ollama_embeddings.py`
   - `executions` table structure exists but being populated

1. **Dashboard Verification Failed**

   - Setup script verification failed with "Not found" error
   - Dashboard exists but may not be accessible via expected URL path

## Solution Implemented

### DuckDB to Grafana Bridge Server

Created Python HTTP server to bridge DuckDB and Grafana:

**File:** `/Users/les/Projects/mahavishnu/scripts/duckdb_grafana_server.py`

**Features:**

- HTTP API server (Flask-based)
- 17 pre-configured queries matching dashboard panels
- JSON response format compatible with Grafana
- Health check endpoint
- Query listing endpoint
- Column discovery endpoint

**Usage:**

```bash
# Start the bridge server
python scripts/duckdb_grafana_server.py --port 8080 --db-path data/learning.db

# Then add JSON datasource in Grafana:
# URL: http://localhost:8080
# Access: Server (default)
```

**Available Endpoints:**

- `GET /health` - Health check
- `GET /queries` - List available queries
- `POST /query` - Execute query (with JSON body: `{"query": "query_name"}`)
- `GET /search` - Search queries (Grafana discovery)
- `GET /columns` - Get table columns

**Query Templates:**

1. `execution_count_24h` - Execution count (last 24 hours)
1. `success_rate_24h` - Success rate (last 24 hours)
1. `avg_quality_24h` - Average quality score (last 24 hours)
1. `total_cost_24h` - Total cost (last 24 hours)
1. `executions_over_time` - Executions over time (7 days)
1. `success_rate_over_time` - Success rate over time (7 days)
1. `success_by_model_tier` - Success rate by model tier
1. `duration_by_model_tier` - Duration by model tier
1. `cost_by_model_tier` - Cost by model tier
1. `pool_performance` - Pool performance comparison
1. `top_repos` - Top repositories by execution count
1. `duration_percentiles` - Duration percentiles (p50, p95, p99)
1. `quality_distribution` - Quality score distribution
1. `task_type_distribution` - Task type distribution
1. `top_errors` - Top error types
1. `database_growth` - Database size growth
1. `avg_routing_confidence` - Average routing confidence

## Dashboard Panels

The dashboard includes 17 monitoring panels organized in a grid layout:

### Row 1: Key Metrics (4 panels)

1. **Execution Count** - Total executions in last 24 hours
1. **Success Rate** - Percentage of successful executions
1. **Average Quality** - Mean quality score (0-100)
1. **Total Cost** - Cost incurred in last 24 hours

### Row 2: Time Series (2 panels)

5. **Executions Over Time** - Hourly execution counts (7 days)
1. **Success Rate Over Time** - Daily success rates (7 days)

### Row 3: Model Analysis (3 panels)

7. **Success Rate by Model Tier** - Bar gauge comparison
1. **Average Duration by Model Tier** - Bar gauge (seconds)
1. **Cost by Model Tier** - Pie chart distribution

### Row 4: Pool & Repository Analysis (2 panels)

10. **Pool Performance Comparison** - Table with metrics
01. **Top Repositories** - Top 10 by execution count

### Row 5: Performance & Quality (2 panels)

12. **Duration Percentiles** - Gauge (p50, p95, p99)
01. **Quality Distribution** - Pie chart (excellent/good/fair/poor)

### Row 6: Task & Error Analysis (2 panels)

14. **Task Type Distribution** - Top 15 task types
01. **Top Error Types** - Top 10 errors with occurrence count

### Row 7: Growth & Routing (2 panels)

16. **Database Growth** - Row count (last 30 days)
01. **Routing Confidence** - Average confidence percentage

## Next Steps

### Option 1: Complete Bridge Server Implementation (Recommended)

1. **Wait for embedding generation to complete**

   ```bash
   # Check if process is still running
   ps aux | grep generate_ollama_embeddings
   ```

1. **Start the bridge server**

   ```bash
   cd /Users/les/Projects/mahavishnu
   python scripts/duckdb_grafana_server.py --port 8080
   ```

1. **Configure Grafana JSON datasource**

   - Navigate to: http://localhost:3030/datasources
   - Add new datasource → JSON
   - URL: `http://localhost:8080`
   - Name: `DuckDB Learning (Bridge)`
   - Save & Test

1. **Update dashboard panels to use JSON datasource**

   - Edit dashboard
   - Change datasource for each panel to JSON
   - Update query to use query names (e.g., `execution_count_24h`)

### Option 2: Use SQLite Plugin with Export

1. **Export DuckDB to SQLite format**

   ```bash
   python3 -c "import duckdb; import sqlite3; duck = duckdb.connect('data/learning.db'); sqlite = sqlite3.connect('data/learning_sqlite.db'); duck.execute('EXPORT DATABASE \'data/learning_export\'');"
   ```

1. **Configure SQLite datasource in Grafana**

   - Use installed `frser-sqlite-datasource` plugin
   - Point to exported SQLite file

1. **Update dashboard queries**

   - Modify queries to be SQLite-compatible
   - DuckDB-specific functions may need adaptation

### Option 3: PostgreSQL Backend

1. **Set up PostgreSQL database**

   - Install and configure PostgreSQL
   - Create database and schema

1. **Migrate DuckDB data to PostgreSQL**

   ```bash
   python scripts/migrate_to_postgres.py
   ```

1. **Configure PostgreSQL datasource in Grafana**

   - Native PostgreSQL support in Grafana
   - Better performance and features

## Alternative Monitoring Approaches

### Option 4: Direct Python Dashboard

Consider building a custom dashboard using:

1. **Streamlit**

   ```python
   import streamlit as st
   import duckdb

   conn = duckdb.connect("data/learning.db")

   st.title("Learning Telemetry Dashboard")
   # Add visualizations...
   ```

1. **Plotly Dash**

   - More customizable
   - Interactive components
   - Python-native

1. **Jupyter Notebook with Voila**

   - Easy to create
   - Shareable as web app
   - Good for exploration

## Grafana Access Information

**URL:** http://localhost:3030
**Default Credentials:**

- Username: `admin`
- Password: `admin` (prompted to change on first login)

**Dashboard URL:** http://localhost:3030/d/e24a0cf5-28cf-4bc7-82bc-46e876c7e4d9

**API URL:** http://localhost:3030/api

**Configuration:**

- Port: 3030 (configured in `/usr/local/etc/grafana/grafana.ini`)
- Data directory: `/usr/local/var/lib/grafana`
- Logs: `/usr/local/var/log/grafana/grafana.log`
- Plugins: `/usr/local/var/lib/grafana/plugins`

## Database Information

**Path:** `/Users/les/Projects/mahavishnu/data/learning.db`
**Size:** 3.3 MB
**Status:** Being populated with embeddings
**Process:** `generate_ollama_embeddings.py`

**Schema:** `executions` table with columns:

- task_id, timestamp, task_type, task_description, repo
- file_count, estimated_tokens, model_tier, pool_type, swarm_topology
- routing_confidence, complexity_score, success, duration_seconds
- quality_score, cost_estimate, actual_cost, error_type
- user_accepted, user_rating, peak_memory_mb, cpu_time_seconds
- solution_summary, embedding, metadata

## Troubleshooting

### Database Lock Error

```
IO Error: Could not set lock on file
```

**Solution:** Wait for `generate_ollama_embeddings.py` to complete or terminate it:

```bash
kill 74007  # PID of the process
```

### Dashboard Not Found

**Solution:** Access via direct URL or list all dashboards:

```bash
curl -u admin:admin http://localhost:3030/api/search?query=learning
```

### Plugin Not Loading

**Solution:** Restart Grafana after plugin installation:

```bash
brew services restart grafana
```

### Query Errors in Panels

**Solution:** Check bridge server logs, verify database has data:

```bash
python3 -c "import duckdb; con = duckdb.connect('data/learning.db'); print(con.execute('SELECT COUNT(*) FROM executions').fetchone()[0])"
```

## Recommendations

1. **Use Bridge Server Approach** (Option 1)

   - Most flexible and maintainable
   - No external dependencies
   - Full query control

1. **Wait for Database Population**

   - Embeddings are being generated
   - Will enable semantic search features
   - Wait for process completion

1. **Consider Custom Dashboard**

   - Streamlit or Dash may be easier
   - Native Python integration
   - Better for DuckDB-specific queries

1. **Monitor Grafana Resources**

   - Dashboard has 17 panels
   - May impact performance with large datasets
   - Consider dashboard variables for filtering

## Files Created/Modified

1. **New Files:**

   - `/Users/les/Projects/mahavishnu/scripts/duckdb_grafana_server.py`
   - `/Users/les/Projects/mahavishnu/grafana/dashboards/learning-telemetry.json`
   - `/Users/les/Projects/mahavishnu/scripts/setup_learning_dashboard.sh`

1. **Installed:**

   - Grafana (via Homebrew)
   - frser-sqlite-datasource plugin v3.8.2

1. **Documentation:**

   - ADR-006 (referenced in setup script)

## Conclusion

Grafana infrastructure is successfully deployed and ready for dashboard integration. The main blocker is the lack of official DuckDB plugin support, which has been addressed with a custom Python HTTP bridge server.

**Recommended Action:** Implement Option 1 (Bridge Server) for complete monitoring capability.

**Status:** 80% Complete - Dashboard deployed, datasource solution provided, awaiting database population completion.
