Database Comparison
Comparison of Database Loads
- PostgreSQL
- DynamoDB
- Cassandra
- MongoDB
Criteria |
PostgreSQL |
DynamoDB |
Cassandra |
MongoDB |
Data Model |
Relational (SQL) |
Key-Value / Document |
Wide-Column (Keyspace/Table) |
Document (BSON) |
Consistency Model |
Strong Consistency (ACID) |
Eventual or Strong (Configurable) |
Eventual Consistency (Tunable) |
Eventual or Strong (Configurable) |
Write Load Handling |
Moderate to High |
Very High |
Very High |
High |
Write Throughput |
~500 to 5,000 writes per second |
50,000+ WPS, can scale to millions |
50,000+ WPS, can scale to millions |
~5,000 to 50,000 writes per second |
Read Load Handling |
Moderate to High |
Very High |
Very High |
High |
Read Throughput |
~1,000 to 10,000 queries per second |
100,000+ QPS, can scale to millions |
100,000+ QPS, can scale to millions |
~10,000 to 100,000 queries per second |
Scalability |
Vertical Scaling (some Horizontal) |
Horizontal (Auto-scaling, Serverless) |
Horizontal (Manual Sharding, Scaling) |
Horizontal (Sharding, Replica Sets) |
Latency |
Low to Moderate (10-100 ms) |
Low Latency (Single-digit milliseconds) |
Low Latency (2-10 ms) |
Low to Moderate Latency (5-50 ms) |
Query Complexity |
High (SQL, Complex Joins, ACID) |
Low (Simple Queries, Key-based Access) |
Moderate (No Joins, Focus on Partition) |
High (Rich Query Language, Aggregation) |
Use Case Focus |
Transactional Workloads, Analytics |
Key-Value Operations, High Throughput |
Write-Intensive, Time-Series, Logging |
Flexible Schema, Semi-Structured Data |
Quantified Load Summary Table
Database |
Write Load Handling |
Read Load Handling |
Latency |
Best Use Cases |
PostgreSQL |
Moderate to High (500-5,000 WPS) |
Moderate to High (1,000-10,000 QPS) |
Low to Moderate (10-100 ms) |
Transactional systems, complex queries, analytics |
DynamoDB |
Very High (50,000+ WPS, scalable) |
Very High (100,000+ QPS, scalable) |
Low (Single-digit ms) |
High-throughput web apps, IoT, serverless apps |
Cassandra |
Very High (50,000+ WPS, scalable) |
Very High (100,000+ QPS, scalable) |
Low (2-10 ms) |
Real-time data processing, time-series, logging |
MongoDB |
High (5,000-50,000 WPS) |
High (10,000-100,000 QPS) |
Low to Moderate (5-50 ms) |
Flexible schemas, semi-structured data, IoT |
Key Takeaways
PostgreSQL: Moderate to high throughput with low to moderate latency, ideal for ACID-compliant, relational workloads.DynamoDB: Exceptional for extreme write and read loads, with low latency and auto-scaling, making it perfect for serverless, real-time applications.Cassandra: High write and read loads with low latency, designed for distributed systems needing high availability and large-scale data handling.MongoDB: High throughput with moderate scalability, well-suited for flexible, document-based data models requiring rich queries.
This breakdown helps in deciding the right database based on quantified load and latency needs for various system design scenarios.