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PostgreSQL vs. MySQL: What You Need to Know
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PostgreSQL vs. MySQL: What You Need to Know

PostgreSQL vs. MySQL: What You Need to Know

Here’s how to choose a relational database for your next project. 

By Brandon Chen, March 10, 2021

Relational database management systems (RDBMS) have been around for several decades, powering transactional applications ranging from ecommerce shopping carts to banking portals. With many different options to choose from, PostgreSQL and MySQL have stood out as top contenders in the open-source relational database category.

However, having to pick one over the other is often a source of confusion for both newcomers and experienced database administrators. This blog will demystify relational databases, highlight some key differences between PostgreSQL and MySQL, and help you determine the best fit for your use case. The data management landscape consists of two main categories: systems that provide operational features for real-time, interactive workloads, called operational (or transactional) data systems; and systems that provide analytical features for retrospective and detailed analysis over large amounts of stored data, called analytical data systems. Relational databases like MySQL and PostgreSQL are operational databases. They are built for quicker updates. Analytical databases are built for more efficient analysis to power business intelligence and data-informed decisions. These data systems complement each other, and are typically deployed separately.

Comparing PostgreSQL and MySQL

Both systems have their benefits. Which is the better choice for your organization? The answer depends on your application-specific needs from the back-end data infrastructure. Let’s compare the two most popular databases — MySQL and PostgreSQL — across a range of different application needs:

Analytics

MySQL

In MySQL, read-only query latency increases as the number of concurrent connections grows. Additionally, if data is partitioned across multiple nodes, the query processor needs to scatter the query across multiple nodes, wait for each node to respond, and gather the responses from each node. This can be time-consuming at scale.

PostgreSQL

PostgreSQL provides simple aggregation and windowing functions but is not designed for large-scale analytics or live reporting. Lack of columnar storage and data compression features limit data ingestion rates and large-scale analytical operations.

Why it matters

Whether we’re considering small- or large-scale analytics, neither MySQL nor PostgreSQL is designed with analytical use cases in mind. If your application use case involves analytics — or is likely to involve more complex analytical queries in the future — you should pick an analytical data system that is built for analytics from the ground up. In an analytical database, data can be ingested at high speeds, and it is stored in a format that is optimized for analytical queries. Additionally, to return results fast for commonly asked queries, data can be pre-aggregated. 

Richer data types

MySQL

MySQL has support for primitive data types, and some unstructured data types such as JSON and spatial.

PostgreSQL

PostgreSQL has support for primitive data types (numeric, string, data, decimal, etc.) and unstructured data types (including JSON, XML, HSTORE, arrays, network and spatial).

Why it matters

With the explosion of unstructured data, the need to model and store richer data types without any lossy data transformations has become more important than ever. For example, your application might want to store a list of some sort. In that case, an array data type might be perfect to use. If your application has a lot of unstructured data to deal with, then PostgreSQL may be a better pick. 

ACID compliance

MySQL

MySQL has support for multiple storage engines, for example InnoDB (provides index-organized tables) and ISAM (fast storage engine without transactions and recovery).

PostgreSQL

PostgreSQL has a single, ACID-compliant storage engine.

Why it matters

ACID is a set of database properties, which guarantee consistent, safe and robust changes to application data. These properties are extremely important for building user-facing applications in verticals such as financial services, retail and SaaS, where data quality and integrity is essential. For example, in a simple balance transfer financial use case, you have to check the account balance, debit one account, and then credit the other account. Without ACID, the transfer amount could be lost in transit between the accounts, or credited without being debited. 

Highly concurrent mixed workloads

MySQL

MySQL is excellent at highly concurrent read-only functions.

PostgreSQL

PostgreSQL performs complicated read-write queries, while using data that requires validation.

Why it matters

Choosing the right database might depend on your application workload. In the case of mixed workloads, picking either option works.

Faster query results

MySQL

MySQL supports B-tree, R-tree, hash and inverted indexes.

PostgreSQL

PostgreSQL offers a wider range of indexes such as partial, bitmap and expression indexes. 

Why it matters

Databases use indexes to speed up queries when dealing with large volumes of data. With indexes, the database can directly fetch the data of interest without cycling through all the data. With multiple indexing options to pick from, there is additional room to fine-tune your database performance as your data grows. This means faster query responses from your database, and improved user experience for your application users.

Better security

MySQL

MySQL supports user and group management and granting SQL privileges to roles. It supports PAM, native windows services and LDAP for user authentication.

PostgreSQL

PostgreSQL supports user and group management and granting SQL privileges to roles. It supports IP-based client filtering and authentication using PAM and Kerberos.

Why it matters

In terms of security, both databases have comparable options. 

Commercial support

MySQL

MySQL comes in many flavors, including community, standard and enterprise editions. You can get a support contract with Oracle.

PostgreSQL

No official commercial release includes support. Some professional services and consulting companies have their own release of PostgreSQL, and offer support around it. 

Why it matters

No matter which database you decide to go with, sooner or later you will definitely run into issues that you need help with. With commercial support available, developers can get peace of mind when it comes to deploying mission-critical applications. Learning about these options early on during your decision-making process can prevent major headaches later on.

PostgreSQL vs. MySQL: Which Is Better for Your Use Case?

When it comes to choosing between PostgreSQL and MySQL, the choice is not easy. Your cloud platform provider might offer additional benefits to running one database over the other, the application framework you have picked might be better suited for one of the databases, or your fellow developers may have opinions. In the end, it really depends on what application requirements you want to meet.

MySQL is famous for its ease of use and speed, while some developers prefer PostgreSQL for its many advanced features. A key difference is that PostgreSQL is an object-relational database, while MySQL is a purely relational database. This means PostgreSQL enables easy modeling of complex application object structures. However, this comes at a cost because it requires additional computational resources for processing the objects.

Another factor to consider is the availability of community support and third-party documentation. MySQL has seen widespread adoption and has become a default tool for building applications, while PostgreSQL is still catching up to MySQL’s popularity. Ultimately, fewer developers are working with PostgreSQL, and therefore support can be more challenging, especially since it requires a bit more technical expertise to set up.

One simple rule of thumb is that if your enterprise application needs complex querying capabilities, including joins, subqueries, filtered results and group-bys, then PostgreSQL might be a good option. If you’re new to the world of databases and don’t need extreme scale for your use cases, then you might want to stick with MySQL.

PostgreSQL vs. MySQL: Extract Data for BI

As your application needs grow, you will eventually need to deploy both operational and analytical database systems to meet your growing needs. To get a complete 360-degree view of your business, you’ll need to continuously keep your operational and analytical systems in sync — extracting transactional data from your MySQL or PostgreSQL database and feeding it into your data warehouse, where it can be used to power your data analytics and visualized by business intelligence (BI) tools like Tableau or Looker. This is where Fivetran can help — our automated, maintenance-free pipelines allow you to extract data from applications, files, events and databases, and securely load it into your data warehouse.

Current data strategies have a common overarching mission — move data stored in operational databases like MySQL and PostgreSQL from its moment of creation and make it actionable at the moment of need by the decision-makers using analytical systems. This might sound pretty simple, but extracting, synchronizing and integrating data across an enterprise is a complex problem that requires serious effort to get right. Join us for a demo of Fivetran and learn how to bring your different data sets into one warehouse, speeding up your ability to unlock actionable insights and increase top-line revenue.

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