PostgreSQL vs. MySQL: 10 Key Differences
PostgreSQL and MySQL are among the most popular open-source relational database management systems (RDMS) worldwide. Both RDMS enable businesses to organize and interlink large amounts of data, allowing for effective data management. For all of their similarities, PostgreSQL and MySQL differ from one another in many ways. In this PostgreSQL vs. MySQL comparison, we analyze crucial differences between the two database management systems to discover how they work and when to use them.
PostgreSQL vs. MySQL Comparison
PostgreSQL and MySQL are open-source relational database systems that allow users to create, modify and delete data. Since data typically exists across several tables, users rely on shared data points to connect tables. When data points connect and establish relationships across several table forms, users can search across multiple tables by entering a single query. Both PostgreSQL and MySQL provide insights into all data, but knowing their differences is crucial for choosing the right database management system. We will compare the database systems across the following key touchpoints based on Sept 2022 versions:
- Languages Supported
- Data Types
- Case Sensitivity
- Security Features
PostgreSQL and MySQL both support a wide variety of computer languages. It’s possible to work in both RDMS with:
But PostgreSQL has a slight advantage over MySQL, because it supports more procedural languages, such as:
PostgreSQL and MySQL rely on Structured Query Language (SQL), which is a standard language used to store and manipulate data in a database management system.
Since the two platforms are SQL-based, they have a lot in common in terms of syntax.
Here’s an example of what a query in both database management systems looks like:
SELECT * FROM customers;
MySQL doesn’t support all SQL features, as PostgreSQL does. That’s because MySQL isn’t fully SQL-compliant, while PostgreSQL is.
PostgreSQL supports complex SQLs such as
FULL OUTER JOINS and
It also supports the
INSTEAD OF trigger.
MySQL doesn’t support any of the above, but it does support
ISNULL functions. PostgreSQL doesn’t support either, but it does include the
COALESECE function that returns the first argument that is null.
Using indexes in a database management system helps increase the speed at which a query is performed.
Indexes are especially valuable for businesses that store large amounts of data, because tables aren’t sorted by default. Finding a specific entry can be difficult and time-consuming, since database servers then need to examine every row and column in a table.
Indexes are optional structures that copy data from tables, and help logically arrange that data.
For example, in a customer table, indexes usually arrange data using
last_name criteria. Indexes help businesses find a specific customer by their first and last name, as if they were using a phone book.
MySQL supports full-text and partial text indexing and includes four types of indexes:
- Balanced Search Tree, i.e., B-Tree indexes: Users can move through a database using B-tree indexes that arrange the database into a multi-tier tree. To find a requested entry, they need to enter a specific index key. Some of the B-Tree indexes are INDEX, UNIQUE and PRIMARY KEY.
- R-Tree indexes: This tree structure is mostly used for storing multi-dimensional spatial data, such as coordinates and regions.
- Hash indexes: These are single-column results that allow users to find specific entries using short hashed keys instead of their original values. They are especially useful for dictionaries and are available for full-text indexing.
- Inverted indexes: This is another type of index that is available only for full-text indexing. Unlike the standard forward index, which maps rows to keywords, an inverted index lists words and maps every word to its respective row.
PostgreSQL includes full-text, partial and expression indexes. It supports B-tree and Hash just like MySQL, but in addition to that, it also covers:
Generalized Search Tree or GiST indexes: GiST is another balanced type of tree in which users can implement B-tree and R-tree indexing schemes. These indexes are excellent for large textual documents, images and geodata or whenever there’s a risk that values from the same column but different rows may overlap.
Space-partitioned GiST or SP-GiST indexes: This type of index is useful when analyzing large amount of data that is clustered so the tree appears unbalanced. When applied, SP-GiST allow for partitioned tree search, facilitating the search for a specific query.
Generalized Inverted Index or GIN indexes: These indexes are useful when dealing with single columns that have multiple values.
Block Range INdex or BRIN: When handling large data, BRIN can be of help just like SP-GiST. These indexes are fairly new — they were introduced in 2019 in PostgreSQL 9.5. They help users eliminate unnecessary data quickly, by forming blocks of adjacent pages, while the page summary is placed in Index.
When it comes to data types, PostgreSQL covers a wider variety than MySQL.
PostgreSQL supports the following native data types:
- Date and time
- Bit String
- Text search
- Object identifier
MySQL, on the other hand, supports five data types:
- Date and time
PostgreSQL not only supports more native data types than MySQL, but it also allows users to create and add their own types using the
CREATE TYPE command.
In terms of search, the two RDMS are slightly different.
MySQL is not case sensitive on macOs and Windows, but it is case sensitive on Linux.
On the other hand, PostgreSQL is always case sensitive, regardless of the operating system it is used on. This means that users need to be mindful of capitalizing strings exactly as in the database to find desired results.
In the past, PostgreSQL was known as the slower system because of its complexity, but the disparity between the two is no longer as noticeable.
Still, the speed at which PostgreSQL and MySQL perform depends on the commands they need to execute.
MySQL is a better choice for read-only commands. However, when it’s necessary to process large data and deal with complex analytical queries, PostgreSQL performs faster because it supports more indexes that help users narrow down the search.
Replication is the process of copying data from the source database to one or more database replicas.
The reason replication is important is that it enables several users to access the same information.
PostgreSQL and MySQL both support database replication.
The difference between the two is in the type of replication they offer.
MySQL supports asynchronous replication. This means that data is copied to the primary source first and then to the replica(s).
PostgreSQL offers several types of replication, including:
- Asynchronous Multimaster replication: Like MySQL’s asynchronous replication, it allows users to copy data on several servers by writing data on the primary source and then on replicas. Every server works independently from others.
- Synchronous Multimaster replication: This is the type of replication in which data is simultaneously written to the primary source and the replica(s). If someone modifies data in the source, the change appears on replicas.
- Shared Disk Failover: When user wants to create just one copy of the database to prevent synchronization overhead, i.e., one task waiting for another task, shared disk failover is the best option. Multiple servers share a single disk array, so if something goes wrong with the main database server, other servers can launch the database.
- File System replication: When the file system is the same on the source server and standby servers, the changes to a file system on the source also appear on other servers.
- Write-Ahead Log Shipping: This type of replication is typical of warm and hot servers. Warm servers are turned on occasionally to get updates from the source server. Hot servers regularly receive updates from the main server. If there’s a failover of the main server, backup sources can easily replace it because they’re up-to-date with the latest data, thanks to write-ahead log (WAL) records.
- Logical replication: When users opt for logical replication, they send modified data one by one from one server to the other.
Trigger-Based Primary-Standby replication: In this case, data changes are directed to a designated primary server. The primary server then asynchronously sends changed data to standby servers.
SQL-Based Replication Middleware: When this type of replication is active, software sends SQL queries to all stand-by servers.
PostgreSQL and MySQL are safe and secure database management systems because they rely on strict authorization and authentication processes.
- Trust authentication: This allows everyone that’s connected to a server to access a database.
- Password authentication: Users need to enter a password to access a database.
- Kerberos authentication: This type of verification checks server requests.
- Ident-based authentication: This retrieves the name of a user’s operating system, then checks for allowed database usernames.
- Privileged Access Management (PAM): This allows administrators to manage and monitor privileged accounts that have access to a database. It can be applied to both human and non-human users.
MySQL relies on:
- Access Control Lists (ACLs): This can include not only connections, but also queries a user can perform.
- Lightweight directory access protocol (LDAP): A plugin included with MySQL Enterprise Edition that allows for user authentication and authorization.
PAM: In MySQL, PAM is available as a plugin with the MySQL Enterprise Edition.
Aside from providing users with extensive documentation, both have large communities of users willing to offer advice to others.
PostgreSQL has the option for users to join their mailing list, IRC or Slack and chat with their community members if they need help.
Additionally, they offer geo-tagging and commercial support for particular regions.
As for MySQL, it’s possible to purchase the 24/7 support package and contact experienced developers for assistance.
PostgreSQL is released under the Open-Source Initiative-approved PostgreSQL license.
The system is free to use, even for commercial purposes.
However, using CloudSQL for PostgreSQL comes with a price tag.
MySQL has the GNU General Public License and it’s free for personal use. But, for commercial use, a commercial license needs to be purchased.
MySQL offers three packages:
- Standard Edition (from $4,280 a year)
- Enterprise Edition (from $10,700 a year)
Cluster Carrier Grade Edition Subscription (from $10,700 per year)
When To Use PostgreSQL
PostgreSQL is the right choice for large enterprises that perform complex queries on a day-to-day basis.
The system packs a wide variety of indexes and data types that allow users to quickly find, add and manage data.
Moreover, PostgreSQL offers a strong and secure system with multiple authentication measures.
In a fast-paced business environment, a feature-packed and fast database management system such as PostgreSQL is an ideal solution.
When To Use MySQL
MySQL is the second most popular database management system.
Much of its popularity lies in its high speed and the fact that its large community continuously improves it.
However, given the limitations MySQL has in terms of indexes, data types and security options, this system is more suitable for small businesses that don’t deal with a large amount of data daily.
Because of its speed, MySQL is a good choice when processing web application data and transactional data.
How To Use PostgreSQL And MySQL With Meltano
Meltano is an open-source DataOps platform developed to facilitate the process of data management.
As an open-source system, it can be modified to meet the requirements of any business, regardless of the niche, and help with data integration, orchestration and containerization.
Meltano relies on the Extract, Load, Transform (ELT) principle; it extracts data from multiple sources and loads it to a database warehouse. Once the data loads, users can apply the structured query language (SQL) tool to transform data from one format to another.
The platform can extract data from multiple sources and load it to databases, SaaS APIs and file formats.
Extractors can pull data from either PostgreSQL or MySQL, and send it to the desired destination using loaders.
A Recap On MySQL vs. PostgreSQL
MySQL and PostgreSQL allow for effective data management, but there are significant differences between the two.
- Supports many languages
- It isn’t fully SQL-compliant
- Has four types of indexes
- Supports five data types
- Is fast
- Supports asynchronous replication
- Has one type of default security authentication and two available as plugins in premium versions
- Has a huge community
- Is free for personal use, but can be pricey for commercial use
- Supports the same languages as MySQL in addition to more procedural languages
- Is fully SQL-compliant
- Supports six types of indexes
- Supports 21 data types
- Is fast
- Supports eight types of replication
- Has a wide variety of authentication and authorization options
- Has a large community and offers the possibility of contacting support via email, IRC and Slack
- Is free to use
With Meltano, you can easily manage, extract, integrate and orchestrate data, with either database system.
From an ever-growing library of 330+ connectors and data tools to complete transformation control, Meltano allows you to build your ideal data platform.