Benchmarking Database Architectures for Network Analytics
Comprehensive performance analysis of MySQL, MongoDB, and Neo4j for graph operations
Overview
This project addresses the critical challenge of selecting the optimal database for large-scale network analytics. With the proliferation of connected data in social networks, transportation systems, and supply chains, choosing the right database architecture can mean the difference between millisecond and multi-second query responses.
We provide a rigorous, reproducible benchmarking framework comparing three distinct database paradigms for common graph-related operations.
The Problem
Organizations working with network data face a fundamental question: Which database should I use?
- Relational databases (MySQL) offer ACID guarantees and SQL familiarity
- Document stores (MongoDB) provide flexibility and horizontal scalability
- Graph databases (Neo4j) are purpose-built for relationship traversal
Without empirical evidence, teams risk choosing architectures that lead to poor performance, costly migrations, or over-engineered solutions.
Our Approach
Rigorous Testing Methodology
We implemented a fair, apples-to-apples comparison using:
- Unified Dataset: Synthetic road network with 500 cities and 250,000 directed connections
- Equivalent Queries: Four semantically identical queries testing different operation types
- Comprehensive Metrics: Execution time, CPU usage, memory consumption
- Realistic Scale: Dataset size representative of real-world network analytics
Query Test Suite
| Query | Operation Type | Purpose |
|---|---|---|
| Q1 | Filtering | Find roads Richmond → Atlanta (≥500 miles) |
| Q2 | Aggregation | Top 5 city pairs by average distance |
| Q3 | Multi-Hop Traversal | Cities reachable within 2 hops from Richmond |
| Q4 | Shortest Path | Shortest route Richmond → Amman (max 2 hops) |
Tech Stack
- Languages: Python 3.8+, SQL (MySQL), Cypher (Neo4j)
- Databases:
- MySQL 8.0 (Relational)
- MongoDB 5.0 (Document-Oriented)
- Neo4j 5.0 (Graph)
- Libraries:
-
mysql-connector-python,pymongo,neo4j(Database drivers) -
psutil(Real-time performance monitoring) -
Faker(Synthetic data generation) -
matplotlib(Performance visualization)
-
Key Features
🔄 Automated Data Generation
Script generates large-scale synthetic road network dataset (500 cities, 250K edges) ensuring reproducibility
📊 Multi-Database Population
Optimized batch insertion scripts populate identical dataset efficiently across all three databases
🎯 Unified Query Suite
Four distinct, equivalent queries designed to test common network analytics operations across paradigms
📈 Real-Time Metrics Monitoring
psutil library captures detailed CPU usage and memory consumption patterns during query execution
📉 Comprehensive Visualizations
Automatically generates 15+ comparative performance charts clearly illustrating trade-offs between databases
Performance Results
🏆 Winner by Query Type
| Query Type | Winner | Execution Time | Runner-Up |
|---|---|---|---|
| Shortest Path (Q4) | Neo4j | 2.5s | MySQL (11.5s) |
| Multi-Hop Traversal (Q3) | MySQL | 13.5s | Neo4j (25.5s) |
| Aggregation (Q2) | MongoDB | 1.5s | MySQL (2.25s) |
| Simple Filtering (Q1) | MySQL | 0.25s | MongoDB (0.35s) |
📊 Performance Highlights
- Neo4j: 19.2x faster than MongoDB for shortest path queries
- MySQL: 5.4x faster than MongoDB for multi-hop traversals
- MongoDB: Most efficient for aggregation pipelines
- MySQL: Lowest latency for simple filtering (8.6x faster than Neo4j)
Key Insights
🎯 Specialized Tools for Specialized Jobs
Neo4j’s 19.2x performance gain for shortest path queries proves the immense value of native graph databases for pathfinding algorithms.
📈 Query Type Matters Most
The benchmark demonstrates that operation type (pathfinding vs. aggregation) is more critical than database model itself.
⚡ Modern SQL is Graph-Capable
MySQL’s strong traversal performance shows that Recursive CTEs make relational databases viable for moderate graph workloads.
🔗 Document Stores Struggle with Relationships
MongoDB’s reliance on application-side logic (custom BFS) or complex $lookup pipelines for graph operations resulted in significant performance penalties.
Decision Framework
Based on our findings, we recommend:
| Use Case | Database | Reason |
|---|---|---|
| Pathfinding at scale | Neo4j | Native graph algorithms |
| Mixed workloads | MySQL | Best all-rounder |
| Aggregations | MongoDB | Powerful aggregation pipeline |
| Existing SQL infrastructure | MySQL | Strong graph support via CTEs |
Impact & Applications
This research provides:
✅ Evidence-based decision making for database architecture selection
✅ Reproducible methodology for custom benchmarking scenarios
✅ Performance baselines for network analytics applications
✅ Cost optimization guidance for cloud database deployments
Real-World Applications
- Transportation/routing systems
- Social network analytics
- Supply chain optimization
- Fraud detection networks
- Knowledge graphs
Future Work
- Extend to distributed graph databases (e.g., Apache Giraph)
- Test on billion-scale datasets
- Include write performance benchmarks
- Evaluate cloud-managed database services
Links
This project demonstrates that choosing the right database requires understanding your specific workload characteristics—there is no one-size-fits-all solution.