Database Systems Comparison
Table of Contents
- MySQL
- PostgreSQL
- DynamoDB
- MongoDB
- Cassandra
- Comparison Matrix
- Decision Framework
- Common Interview Questions
MySQL
What is MySQL?
MySQL is a relational database management system (RDBMS) based on SQL (Structured Query Language). It's one of the most popular open-source databases, known for its reliability, ease of use, and strong community support.
Key Characteristics
- ACID Compliance: Ensures data integrity through Atomicity, Consistency, Isolation, Durability
- SQL-based: Uses standard SQL for querying and data manipulation
- Storage Engines: Supports multiple storage engines (InnoDB, MyISAM, Memory, etc.)
- Replication: Master-slave and master-master replication support
- Partitioning: Horizontal partitioning capabilities
- Indexing: B-tree indexes, full-text indexes, spatial indexes
Capabilities
- Transactions: Full ACID transaction support with InnoDB
- Joins: Complex JOIN operations across multiple tables
- Foreign Keys: Referential integrity constraints
- Views: Virtual tables for data abstraction
- Stored Procedures: Server-side programming capabilities
- Triggers: Event-driven programming
When to Use MySQL
- Web Applications: Traditional web apps with structured data
- E-commerce: Order management, inventory, user accounts
- Content Management: Blogs, CMS systems
- Financial Applications: When ACID compliance is critical
- Read-Heavy Workloads: With proper read replicas
- Small to Medium Scale: Up to millions of records with proper optimization
Pros
- Mature and stable
- Large community and ecosystem
- Excellent documentation
- Cost-effective (open source)
- Good performance for read-heavy workloads
- Wide hosting support
Cons
- Limited scalability for very large datasets
- JSON support is basic compared to NoSQL
- Complex sharding requirements for horizontal scaling
- Performance can degrade with complex queries on large datasets
PostgreSQL
What is PostgreSQL?
PostgreSQL is an advanced open-source relational database that emphasizes extensibility and standards compliance. Often called "Postgres," it's known for its robustness, feature richness, and support for both relational and non-relational data.
Key Characteristics
- Object-Relational: Supports both relational and object-oriented features
- ACID Compliant: Strong consistency guarantees
- Extensible: Custom data types, operators, and functions
- Standards Compliant: Closely follows SQL standards
- Multi-Version Concurrency Control (MVCC): High concurrency without locking
- Advanced Data Types: JSON, Arrays, XML, Geographic data
Capabilities
- Complex Queries: Advanced SQL features, window functions, CTEs
- JSON/JSONB: Native JSON support with indexing
- Full-Text Search: Built-in text search capabilities
- Geospatial: PostGIS extension for geographic data
- Custom Functions: Support for multiple programming languages
- Parallel Queries: Parallel execution for large queries
- Logical Replication: Selective data replication
When to Use PostgreSQL
- Complex Applications: Applications requiring advanced SQL features
- Data Analytics: Complex reporting and analytical queries
- Geospatial Applications: Location-based services with PostGIS
- JSON-Heavy Workloads: When you need both relational and document features
- High Concurrency: Applications with many concurrent users
- Data Integrity Critical: Financial, healthcare, government systems
Pros
- Feature-rich and powerful
- Excellent JSON support
- Strong consistency and ACID compliance
- Highly extensible
- Great for complex queries
- Active development community
Cons
- Steeper learning curve
- Higher resource consumption than MySQL
- More complex to tune and optimize
- Slower for simple read operations compared to MySQL
DynamoDB
What is DynamoDB?
Amazon DynamoDB is a fully managed NoSQL database service provided by AWS. It's designed for applications that need consistent, single-digit millisecond latency at any scale.
Key Characteristics
- Managed Service: No server management required
- NoSQL: Key-value and document data model
- Serverless: Auto-scaling capabilities
- Eventually Consistent: Default consistency model (strongly consistent reads available)
- Global Tables: Multi-region replication
- Single-Digit Latency: Consistent performance at scale
Capabilities
- Automatic Scaling: Handles traffic spikes automatically
- Global Secondary Indexes (GSI): Query flexibility
- Local Secondary Indexes (LSI): Alternative sort keys
- DynamoDB Streams: Change data capture
- Point-in-Time Recovery: Backup and restore capabilities
- Encryption: At rest and in transit
- DAX: In-memory caching layer
When to Use DynamoDB
- Serverless Applications: AWS Lambda-based architectures
- Gaming: Leaderboards, player data, session management
- IoT Applications: High-volume sensor data ingestion
- Real-time Applications: Chat apps, live streaming
- Mobile Applications: User profiles, app data synchronization
- High Traffic Web Apps: When you need predictable performance
Pros
- Fully managed (no ops overhead)
- Seamless scaling
- Predictable performance
- Integration with AWS ecosystem
- Pay-per-use pricing model
- Built-in security features
Cons
- Vendor lock-in to AWS
- Limited query flexibility
- No complex joins or transactions across items
- Learning curve for data modeling
- Can be expensive at scale
- No SQL interface
MongoDB
What is MongoDB?
MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It's designed for scalability, performance, and developer productivity.
Key Characteristics
- Document-Based: Stores data as BSON documents
- Schema Flexible: Dynamic schemas allow easy evolution
- Horizontal Scaling: Built-in sharding support
- Rich Query Language: Powerful querying capabilities
- Aggregation Framework: Data processing and transformation
- Replica Sets: High availability and data redundancy
Capabilities
- Flexible Schema: No predefined schema required
- Rich Queries: Complex queries with filtering, sorting, projections
- Indexing: Compound, text, geospatial, and partial indexes
- Aggregation Pipeline: Data transformation and analytics
- GridFS: Store and retrieve large files
- Change Streams: Real-time data change notifications
- Transactions: ACID transactions (MongoDB 4.0+)
When to Use MongoDB
- Content Management: Blogs, news sites, catalogs
- Real-time Analytics: Event logging, user behavior tracking
- Product Catalogs: E-commerce with varying product attributes
- Content Delivery: Media, gaming, social platforms
- Rapid Prototyping: When schema requirements are unclear
- Microservices: Service-specific data storage
Pros
- Flexible schema design
- Developer-friendly JSON-like documents
- Excellent horizontal scaling
- Rich querying capabilities
- Strong community and ecosystem
- Good performance for read/write operations
Cons
- Memory intensive
- No joins between collections (limited)
- Eventual consistency in sharded environments
- Complex sharding setup and management
- Data duplication often required
Cassandra
What is Cassandra?
Apache Cassandra is a distributed NoSQL wide-column database designed for handling large amounts of data across many commodity servers. Originally developed by Facebook and later open-sourced, Cassandra provides high availability with no single point of failure.
Key Characteristics
- Wide-Column Store: Data organized in column families (similar to tables)
- Masterless Architecture: All nodes are equal, no single point of failure
- Distributed: Data automatically distributed across multiple nodes
- Eventually Consistent: Tunable consistency levels
- Linear Scalability: Performance increases linearly with added nodes
- Fault Tolerant: Continues operating even when nodes fail
Capabilities
- High Availability: 99.99% uptime with proper configuration
- Massive Scalability: Handle petabytes of data across thousands of nodes
- Tunable Consistency: Choose consistency level per query
- Multi-Data Center: Built-in multi-datacenter replication
- Time Series Data: Excellent for time-stamped data
- CQL: Cassandra Query Language (SQL-like syntax)
- Compaction: Automatic data optimization and cleanup
Architecture Concepts
- Keyspace: Top-level namespace (similar to database)
- Column Family/Table: Container for rows
- Partition Key: Determines data distribution across nodes
- Clustering Key: Determines data ordering within partition
- Replication Factor: Number of copies of each data piece
- Consistency Level: Trade-off between consistency and availability
When to Use Cassandra
- Time Series Data: IoT sensors, monitoring, logs, metrics
- High Write Throughput: Applications with heavy write loads
- Global Distribution: Multi-region applications
- Always-On Applications: Systems that cannot tolerate downtime
- Large Scale Analytics: Big data processing and analytics
- Event Logging: User activity tracking, audit trails
- Content Management: Social media, news feeds, catalogs
Pros
- Linear scalability (add nodes = more performance)
- No single point of failure
- Multi-datacenter support out of the box
- Excellent write performance
- Handles large datasets efficiently
- Tunable consistency
- Open source with enterprise support
Cons
- Limited query flexibility (no joins)
- Eventual consistency by default
- Complex data modeling requirements
- High operational complexity
- Memory intensive
- No ACID transactions across partitions
- Steep learning curve
Data Modeling Principles
- Query-First Design: Design tables based on queries, not entities
- Denormalization: Duplicate data to avoid joins
- Partition Key Design: Ensure even data distribution
- Clustering Key: Order data for efficient range queries
- Avoid Large Partitions: Keep partitions under 100MB
Comparison Matrix
| Feature | MySQL | PostgreSQL | DynamoDB | MongoDB | Cassandra |
|---|---|---|---|---|---|
| Type | Relational | Relational | NoSQL (Key-Value) | NoSQL (Document) | NoSQL (Wide-Column) |
| ACID | ✅ Full | ✅ Full | ❌ Limited | ✅ Limited* | ❌ No |
| Scaling | Vertical + Read Replicas | Vertical + Read Replicas | ✅ Auto Horizontal | ✅ Horizontal | ✅ Linear Horizontal |
| Schema | Fixed | Fixed | Schema-less | Flexible | Wide-Column |
| Queries | SQL | Advanced SQL | Key-based + GSI | Rich Query Language | CQL (Limited) |
| Joins | ✅ Complex | ✅ Complex | ❌ No | ❌ Limited | ❌ No |
| JSON Support | Basic | ✅ Excellent | ✅ Native | ✅ Native | Limited |
| Managed Options | Cloud providers | Cloud providers | ✅ Fully managed | Atlas (managed) | DataStax Astra |
| Cost | Low (open source) | Low (open source) | Pay-per-use | Medium | Medium (open source) |
| Learning Curve | Low | Medium | Medium | Low-Medium | High |
| Consistency | Strong | Strong | Eventual/Strong | Strong/Eventual | Tunable |
| Multi-DC | Manual setup | Manual setup | ✅ Global Tables | Manual setup | ✅ Built-in |
*MongoDB supports ACID transactions within replica sets and sharded clusters (4.0+)
Decision Framework
Choose Cassandra when:
- Need massive scale (petabytes of data)
- High write throughput requirements
- Multi-datacenter deployment
- Always-on availability critical
- Time series data or event logging
- Can accept eventual consistency
Choose MySQL when:
- Building traditional web applications
- Need strong ACID compliance
- Team is familiar with SQL
- Budget is constrained
- Read-heavy workloads with moderate write volume
- Data fits well in relational model
Choose PostgreSQL when:
- Need advanced SQL features
- Working with both relational and JSON data
- Require complex analytical queries
- Need geographic/spatial data support
- High concurrency requirements
- Data integrity is paramount
Choose DynamoDB when:
- Building on AWS ecosystem
- Need predictable performance at any scale
- Serverless architecture
- Simple access patterns (key-value lookups)
- Want fully managed solution
- Traffic patterns are unpredictable
Choose MongoDB when:
- Rapid application development
- Schema requirements change frequently
- Need horizontal scaling
- Working with semi-structured data
- Building content management systems
- Microservices architecture
Common Interview Questions
1. "How do you handle scaling in each database?"
MySQL: Vertical scaling, read replicas, sharding (complex) PostgreSQL: Similar to MySQL, plus logical replication DynamoDB: Automatic horizontal scaling, partition key design critical MongoDB: Horizontal sharding, replica sets for availability Cassandra: Linear horizontal scaling, add nodes for more capacity
2. "When would you choose NoSQL over SQL?"
Consider NoSQL when:
- Rapid development with changing requirements
- Need to scale horizontally
- Working with unstructured/semi-structured data
- Simple query patterns
- Need high availability over consistency
3. "How do you ensure data consistency?"
SQL Databases (MySQL/PostgreSQL): ACID transactions, foreign keys DynamoDB: Eventually consistent by default, strongly consistent reads available MongoDB: ACID transactions within replica sets, eventual consistency in sharded setups Cassandra: Tunable consistency (ONE, QUORUM, ALL), eventual consistency by default
4. "What are the trade-offs between these databases?"
Consistency vs Availability: SQL databases favor consistency, NoSQL often favors availability Flexibility vs Structure: NoSQL offers schema flexibility, SQL provides data structure Scalability vs Complexity: NoSQL scales easier but can be more complex to model Query Power vs Performance: SQL offers complex queries, NoSQL offers better performance for simple operations
5. "How would you design a system that needs both ACID compliance and horizontal scaling?"
- Use PostgreSQL with read replicas and careful partitioning
- Consider NewSQL databases (Google Spanner, CockroachDB)
- Implement application-level sharding with MySQL
- Use MongoDB with proper transaction design
- Consider microservices with database per service pattern
6. "Compare Cassandra with other NoSQL databases"
Cassandra vs MongoDB:
- Cassandra: Better for write-heavy, time series data, linear scaling
- MongoDB: Better for complex queries, flexible documents, easier development
Cassandra vs DynamoDB:
- Cassandra: Open source, multi-cloud, more control over infrastructure
- DynamoDB: Fully managed, simpler operations, tight AWS integration
7. "When would you choose Cassandra for a system design?"
Choose Cassandra when:
- Building IoT data ingestion system (millions of sensors)
- Time series analytics (metrics, monitoring, logs)
- Global social media platform (activity feeds)
- Financial trading systems (high write throughput)
- Always-on services (99.99% uptime requirement)
Example Architecture:
IoT Sensors → Load Balancer → Cassandra Cluster → Analytics Pipeline
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Multi-DC Replication