schema-travels 1.2.0

Intelligent SQL to NoSQL schema migration - analyzes query patterns to recommend optimal MongoDB schema design

🧭 Schema Travels

Intelligent SQL → MongoDB Schema Migration

"Stop guessing embed vs. reference. Let your query patterns decide."

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The Problem

Migrating from PostgreSQL/MySQL to MongoDB? You'll face this question hundreds of times:

"Should I embed this data or reference it?"

Most migration tools do 1:1 table-to-collection mapping — which completely ignores why you're moving to NoSQL in the first place.

The right answer depends on how you actually access your data. But manually analyzing thousands of queries? Nobody has time for that.

The Solution

Schema Travels analyzes your real query patterns and recommends an optimal MongoDB schema:

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Query Logs     │────▶│  Pattern Analysis │────▶│  AI-Powered     │
│  + SQL Schema   │     │  • Hot joins      │     │  Recommendations│
│                 │     │  • Co-access %    │     │  • EMBED        │
│                 │     │  • Write ratios   │     │  • REFERENCE    │
└─────────────────┘     └──────────────────┘     └─────────────────┘

Result: A MongoDB schema optimized for your access patterns — not generic "best practices."

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What's New in v1.2.0

🎛️ Cache Modes

Control how sensitive the cache is to log changes:

# Relaxed (default): Ignores small log changes
# Cache invalidates only on significant pattern changes
schema-travels analyze --cache-mode relaxed ...

# Strict: Any change in query counts = fresh recommendations  
schema-travels analyze --cache-mode strict ...

Scenario	Relaxed	Strict
2 extra log lines	✅ Cache hit	❌ Fresh AI call
New join pair found	❌ Fresh AI call	❌ Fresh AI call
Schema DDL changed	❌ Fresh AI call	❌ Fresh AI call

🔄 Reproducible Results with Caching (v1.1.0)

Same inputs now produce the same recommendations every time:

# First run - calls Claude API
schema-travels analyze --logs-dir ./logs --schema-file ./schema.sql
# → Cached to ~/.schema-travels/cache/

# Second run - uses cache (instant, consistent)
schema-travels analyze --logs-dir ./logs --schema-file ./schema.sql
# → Same recommendations, no API call

# Force fresh analysis
schema-travels analyze --logs-dir ./logs --schema-file ./schema.sql --no-cache

🔑 Better API Key Errors (v1.1.0)

Clear, actionable error when API key is missing:

╭─────────────────────────────────────────────────────────────────────╮
│                    ⚠️  API KEY NOT CONFIGURED                        │
├─────────────────────────────────────────────────────────────────────┤
│  Schema Travels requires an Anthropic API key for AI-powered       │
│  schema recommendations.                                            │
│                                                                     │
│  Option 1: export ANTHROPIC_API_KEY=sk-ant-xxxxx                   │
│  Option 2: echo "ANTHROPIC_API_KEY=sk-ant-xxxxx" > .env            │
│                                                                     │
│  Get your API key at: https://console.anthropic.com/settings/keys  │
╰─────────────────────────────────────────────────────────────────────╯

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Quick Start

Installation

pip install schema-travels

Basic Usage

# Set your API key
export ANTHROPIC_API_KEY=sk-ant-xxxxx

# Analyze your database
schema-travels analyze \
    --logs-dir ./postgresql-logs \
    --schema-file ./schema.sql \
    --target mongodb \
    --output results.json

What You Get

📊 Analysis Summary
═══════════════════════════════════════════════════

Hot Joins (Top 5):
  users ⟷ orders      : 12,847 calls, 8.3ms avg
  orders ⟷ order_items: 11,203 calls, 5.1ms avg  
  products ⟷ reviews  : 8,456 calls, 12.7ms avg

Recommendations:
┌─────────────┬─────────────┬───────────┬────────────┐
│ Parent      │ Child       │ Decision  │ Confidence │
├─────────────┼─────────────┼───────────┼────────────┤
│ users       │ addresses   │ EMBED     │ 92%        │
│ orders      │ order_items │ EMBED     │ 87%        │
│ users       │ orders      │ REFERENCE │ 85%        │
│ products    │ reviews     │ REFERENCE │ 78%        │
└─────────────┴─────────────┴───────────┴────────────┘

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Why Schema Travels?

❌ What Other Tools Do

SQL Table          →    MongoDB Collection
─────────────────────────────────────────
users              →    users
addresses          →    addresses  
orders             →    orders
order_items        →    order_items

This is just PostgreSQL with different syntax.

✅ What Schema Travels Does

// users collection — addresses embedded (92% co-accessed)
{
  _id: ObjectId("..."),
  email: "[e-mail]",
  name: "John Doe",
  addresses: [                    // ← EMBEDDED (bounded, rarely updated)
    { street: "123 Main", city: "NYC", is_default: true }
  ]
}

// orders collection — items embedded, user referenced
{
  _id: ObjectId("..."),
  user_id: ObjectId("..."),       // ← REFERENCED (accessed independently)
  status: "shipped",
  items: [                        // ← EMBEDDED (always fetched together)
    { product_id: "...", quantity: 2, price: 29.99 }
  ]
}

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Features

📊 Access Pattern Analysis

• Hot Join Detection — Find frequently co-accessed tables
• Co-access Ratios — Measure how often tables are queried together
• Write Ratio Tracking — Identify update-heavy tables (bad embed candidates)
• Solo Access Detection — Find independently accessed entities

🤖 AI-Powered Recommendations

• Claude Integration — Intelligent embed/reference decisions
• Confidence Scores — Know how certain each recommendation is
• Detailed Reasoning — Understand why each decision was made
• Warning Detection — Get alerts for potential issues

🔄 Reproducible Results (v1.1.0+)

• Recommendation Caching — Same inputs = same outputs
• Cache Modes — relaxed ignores small log changes, strict for precision (v1.2.0)
• Version Tracking — Cache auto-invalidates when logic changes
• Comparison Tools — Diff recommendations between runs
• Cache Control — --no-cache for fresh analysis

⚡ Migration Simulation

• Storage Impact — Estimate size changes from embedding
• Latency Projection — Predict query performance improvements
• Cost Estimation — Calculate infrastructure cost differences

📈 Visualization

• HTML Reports — Interactive schema visualization
• Mermaid Diagrams — ER diagrams for documentation
• Console Output — Rich terminal formatting

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How It Works

1. Collect Access Patterns

Schema Travels parses your PostgreSQL/MySQL query logs:

2024-01-15 10:30:45 LOG: statement: SELECT u.*, a.* FROM users u 
    JOIN addresses a ON u.id = a.user_id WHERE u.id = 123
2024-01-15 10:30:45 LOG: duration: 3.45 ms

2. Analyze Patterns

For each table relationship, it calculates:

Metric	Description	Embed Signal
Co-access ratio	% of queries accessing both tables	High = Embed
Child independence	% of queries accessing child alone	High = Reference
Write ratio	% of operations that are writes	High = Reference
Cardinality	Avg/max children per parent	High = Reference

3. Apply Decision Rules

# Simplified decision logic
if max_children > 1000:
    return REFERENCE  # Unbounded = always reference

if co_access > 70% and write_ratio < 30% and max_children < 100:
    return EMBED      # High co-access, low writes, bounded

if child_independence > 40%:
    return REFERENCE  # Accessed alone too often

if write_ratio > 50%:
    return REFERENCE  # Too many updates

4. Generate Schema

Output includes:

• MongoDB collection definitions
• Embedded document structures
• Reference relationships
• Sample documents

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Configuration

Environment Variables

# Required for AI recommendations
export ANTHROPIC_API_KEY=sk-ant-xxxxx

# Optional
export ANTHROPIC_MODEL=claude-sonnet-4-20250514  # or claude-opus-4-5-20250514
export LOG_LEVEL=INFO

Or create a .env file:

cp .env.example .env
# Edit .env with your API key

CLI Options

schema-travels analyze \
    --logs-dir ./logs           # Directory with query logs
    --schema-file ./schema.sql  # SQL DDL file
    --db-type postgres          # postgres or mysql
    --target mongodb            # Target database
    --output results.json       # Output file
    --use-ai                    # Enable AI recommendations (default)
    --no-ai                     # Use rule-based only
    --cache-mode relaxed        # relaxed (default) or strict
    --no-cache                  # Bypass recommendation cache
    --clear-cache               # Clear all cached recommendations

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Commands

Command	Description
schema-travels analyze	Run full analysis
schema-travels report --analysis-id <id>	View previous analysis
schema-travels history	List all analyses
schema-travels simulate --analysis-id <id>	Run migration simulation
schema-travels config	Show current configuration

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Input Requirements

Query Logs

PostgreSQL — Enable in postgresql.conf:

log_statement = 'all'
log_duration = on
log_line_prefix = '%t [%p] %u@%d '

MySQL — Enable slow query log:

slow_query_log = 1
long_query_time = 0
log_queries_not_using_indexes = 1

Schema File

Standard SQL DDL:

CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) NOT NULL
);

CREATE TABLE orders (
    id SERIAL PRIMARY KEY,
    user_id INTEGER REFERENCES users(id),
    total DECIMAL(10,2)
);

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Storage Locations

~/.schema-travels/
├── schema_travels.db     # Analysis history (SQLite)
└── cache/
    ├── index.json        # Cache index with metadata
    └── <hash>.json       # Cached recommendations

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Example Output

Recommendations JSON

{
  "recommendations": [
    {
      "parent_table": "users",
      "child_table": "addresses",
      "decision": "EMBED",
      "confidence": 0.92,
      "reasoning": [
        "92% co-access ratio",
        "Low write frequency (8%)",
        "Bounded cardinality (avg 2.1, max 5)"
      ],
      "warnings": []
    }
  ]
}

Generated MongoDB Schema

// users collection
{
  $jsonSchema: {
    bsonType: "object",
    required: ["email"],
    properties: {
      email: { bsonType: "string" },
      name: { bsonType: "string" },
      addresses: {
        bsonType: "array",
        items: {
          bsonType: "object",
          properties: {
            street: { bsonType: "string" },
            city: { bsonType: "string" },
            zip: { bsonType: "string" }
          }
        }
      }
    }
  }
}

---

Architecture

schema-travels/
├── src/schema_travels/
│   ├── collector/      # Log parsing, schema extraction
│   ├── analyzer/       # Pattern detection (hot joins, mutations)
│   ├── recommender/    # AI recommendations, schema generation, caching
│   ├── simulator/      # Migration impact estimation
│   ├── persistence/    # SQLite storage for history
│   └── cli/            # Command-line interface
├── tools/              # Workload generator, visualizer
├── examples/           # Sample schemas and logs
└── tests/              # Test suite

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Development

# Clone
git clone https://github.com/kraghavan/schema-travels.git
cd schema-travels

# Setup
python -m venv venv
source venv/bin/activate
pip install -e ".[dev]"

# Test
pytest --cov=schema_travels

# Lint
ruff check src/
ruff format src/

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Roadmap

• PostgreSQL log parsing
• MySQL log parsing
• MongoDB schema generation
• Claude AI integration
• Migration simulation
• Recommendation caching (v1.1.0)
• Cache modes - relaxed/strict (v1.2.0)
• DynamoDB support
• Web UI dashboard
• Real-time log streaming
• Multi-database analysis

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Contributing

Contributions welcome! Please read CONTRIBUTING.md first.

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License

MIT License — see LICENSE for details.

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Acknowledgments

• Built with Claude by Anthropic
• SQL parsing by sqlglot
• CLI powered by Click and Rich

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Stop guessing. Start measuring.
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