Relational databases like PostgreSQL and MySQL have become essential data management technologies powering modern applications. Both are mature, feature-rich open source databases with overlapping capabilities. However, important differences make each database better suited for certain applications.
In this in-depth guide, we will compare PostgreSQL and MySQL across various technical capabilities and use cases to help you choose the right database for your next project.
Brief Background
First released in 1996, PostgreSQL is an open source object-relational database known for its strong data integrity, standards compliance, extensibility, and support for complex workloads. It powers a wide variety of applications ranging from enterprise business systems to scientific data analysis.
MySQL, first released in 1995, is a popular open source database designed for high performance, ease of use, scalability, and integration into the LAMP web development stack. It powers many well-known web applications and services.
While both databases share similarities by being open source with SQL interfaces, important differences make each excel in distinct use cases.
Key Differences and Similarities
PostgreSQL and MySQL share foundational database features, while differing in areas like advanced functionality, performance tuning, and use case specialization.
Similarities:
- Both support SQL with standard data types, querying etc
- Available under open source licenses, allowing free usage and modification
- Cross platform support across Windows, Linux, macOS etc
- Can be extended through internal languages and external apps
- Options for high availability clusters and geographic replication
Key Differences:
- Standards compliance – PostgreSQL aims for maximum SQL standards compliance, MySQL only partial
- Advanced features – PostgreSQL has more advanced features like enforced data integrity checks, cross-database federation etc
- Performance optimization – PostgreSQL is optimized for data integrity and stability, MySQL for speed and simplicity
- Use case specialization – PostgreSQL for analytics and complex workloads, MySQL for simple web apps and scalability
Understanding these trade-offs allows matching each database to use cases that play to their respective strengths.
PostgreSQL Overview and Key Capabilities
PostgreSQL descends from the POSTGRES research database project at UC Berkeley and has an active open source community governing ongoing development. Some key capabilities include:
- Full ACID compliance for maximum data integrity protection
- MVCC architecture for performance and consistency in high concurrence environments
- User extensibility via stored procedures in languages like Python, Perl etc
- GIS support and spatial data types through PostGIS extension
- Sophisticated indexing like partial, expression, and generalized inverted indexes
- Replication options for scaling reads and disaster resilience
With these features, PostgreSQL can provide strong data correctness guarantees while efficiently handling heavy analytical workloads, though at the cost of additional complexity.
When to Use PostgreSQL
PostgreSQL is optimized for use cases that demand maximum data integrity protection, extensibility, and support for complex querying.
Typical applications include:
- Transactional business systems needing guaranteed ACID compliance
- Analytics databases for business intelligence or scientific analysis
- Applications using geographic data via integration with PostGIS
- JSON-powered systems leveraging PostgreSQL‘s native JSONB support
- Heterogeneous data federation hubs connecting disparate databases
The additional assurances, advanced features, and specialty data types enable PostgreSQL to excel with these workloads.
MySQL Overview and Key Capabilities
MySQL became popular as a lightweight, high performance database for the LAMP web development stack. Under Oracle‘s stewardship, MySQL continues to focus on speed, ease of use, scalability, and integration with programming languages. Some key features include:
- Integration with languages like PHP, Python, Perl, Java etc
- High performance architecture optimized for simplicity and speed rather than advanced features
- Scalability via sharding and cluster support
- SQL and NoSQL access methods
Together these allow MySQL to work well for web and cloud-native applications needing maximum throughput.
When to Use MySQL
MySQL is a great default choice for web-based or cloud applications where developers value simplicity, performance and scalability above all else.
Common use cases include:
- LAMP/LEMP powered websites using PHP, Python etc
- High traffic web apps like social networks or ecommerce sites
- Mobile and embedded databases needing a small footprint
- Distributed data stores leveraging MySQL‘s clustering
- Systems requiring maximum scalability via large clusters
For these applications, MySQL provides an easy to use, fast performing relational data store well suited for simplifying development.
Side-by-Side Comparisons
We will now drill deeper across various technical criteria to highlight areas where PostgreSQL and MySQL differ. Understanding these subtleties helps match each database to appropriate applications.
SQL Standards Support
PostgreSQL aims to conform closely to SQL standards, achieving near complete coverage of mandatory features for an RDBMS. This ensures maximum compatibility and portability.
In contrast, MySQL adopts a more practical approach focused on developers‘ needs rather than theoretical purity. So it implements only a subset of SQL standardized functionality.
This means PostgreSQL provides more assurance as environments grow complex. But MySQL is simpler for basic use cases.
Indexing and Partitioning
Sophisticated indexing allows optimizing data storage and retrieval for particular access patterns. Relatedly, partitioning divides tables across multiple underlying file groups.
PostgreSQL supports expressing indexing schemes like partial (covering specific columns), bitmap (bit array based), and expression (applying functions) indexes for greater tuning flexibility. Table partitioning methods like list, range, and hash are also available.
MySQL provides basic B-Tree indexes plus some more advanced ones like inverted indexes in the InnoDB storage engine. Partitioning support includes list, range, and key variants.
So PostgreSQL has greater overall indexing flexibility while MySQL covers common requirements. Partitioning capabilities are broadly similar.
High Availability, Replication
For resilience and scalability, databases offer live replication features plus failover capable clusters for high availability across machines.
PostgreSQL supports numeric forms of replication including asynchronous streaming, synchronous cascading, and logical decoding. Clustered configurations are available through third parties or community tools like Patroni.
MySQL also provides asynchronous and semisynchronous replication methods, along with multi-master clusters integrated via InnoDB cluster.
Overall both databases can achieve comparable levels of resilience and scalability, with MySQL offering marginally simpler clustering.
Data Types and Functionality
The data types and expandability options that databases support determine an application‘s expressiveness.
PostgreSQL offers a vast array of standard, specialized (geometric, textual, numeric etc), and user defined types. These enable cleanly modeling complex data. Functions can be created in a variety of languages.
MySQL has a reasonable type catalog covering common cases but lacks certain specialized ones for requirements like geospatial analysis. Functions have more limited support through internal languages only.
So PostgreSQL provides greater modeling flexibility where MySQL opts for simplicity.
Query Performance Benchmarking
Independent benchmarking helps quantify PostgreSQL and MySQL‘s performance across typical online transaction processing (OLTP) workloads.
Tests by 3rd party analysts show PostgreSQL trailing MySQL in simple read/write tasks but close or exceeding performance for complex analytical querying. This aligns with their differing architectural priorities.
So applications wanting maximum throughput on basic CRUD operations are better served by MySQL. But PostgreSQL shines once advanced SQL features or concurrent workloads come into play.
When Should You Use Each Database?
Weighing up the detailed technical comparison, we can summarize guidelines on matching applications to either database.
Ideal Use Cases for PostgreSQL
- Analytics & Business Intelligence – querying complexity and correcteness prioritized over raw speed
- Data Warehouses – leveraging PostgreSQL‘s analytics features
- Scientific Databases – custom data types, integrity critical
- Geospatial Applications – integration with PostGIS location intelligence
- JSON Document Stores – storing schema-less data alongside relational
- CockroachDB – cloud-native SQL database built fully PostgreSQL compatible
Ideal Use Cases for MySQL
- High Traffic Web Applications – optimizing simplicity and throughput
- eCommerce Sites – simpler requirements than enterprise databases
- Mobile & Embedded Databases – small footprint and efficiency advantages
- Digital Media Platforms – optimizing for large media storage
- Software-as-a-Service – scaling horizontally over shards
- WordPress – popular content management system using MySQL by default
When to Use Both Together
If aspects of your application fit both databases‘ strengths, a polyglot persistence architecture using each for appropriate subtasks can be highly effective. This is a popular technique that leverages PostgreSQL for analytics alongside MySQL powering web/app frontends.
Conclusion
PostgreSQL and MySQL are mature open source databases powering many mission critical applications today. Both continue to improve through open source community innovation.
However, PostgreSQL prioritizes capabilities allowing more precise data modeling, integrity protection, standards conformance and analytical complexity – but at the cost of additional complexity.
MySQL focuses instead on simpler application building prioritizing speed, ease of use and scalability over advanced functionality.
Understanding these technical and philosophical differences helps explain why PostgreSQL underpins data infrastructures needing air tight data governance, while MySQL provides no-fuss scalability for web and cloud-native apps.
Both have merits for the right use cases – understanding their respective strengths as outlined here provides guidance on choosing either database for your next application.
