Cassandra or PostgreSQL? How to Choose the Right Open-Source Database

Discover the best open-source database for your project.

May 13, 2024

Open Source Database

Anil Inamdar, VP & global head of data solutions of Instaclustr by NetApp, navigates through their distinct advantages and potential hurdles to empower the database decision-making process like never before.

The 100% open-source version of Apache Cassandra delivers extremely high availability, versatile scalability, and global data distribution. Enterprise-ready, the NoSQL database is built to support the most mission-critical applications with always-on requirements. Open-source PostgreSQLOpens a new window is the most popular relational database in the world—and with good reason. The database, now nearly 30 years old, provides a highly customizable and extendable design for storing and scaling complex data workloads, exceptional reliability, and powerful querying. Both have the chops in their fully open-source versions to capably handle huge quantities of data, and specially dedicated open-source communities support both.

But which is the strongest choice for your use case? Let’s dive into where each open-source database offers a decisive edge and the potential challenges teams should anticipate with each option.

Cassandra’s Advantages and Ideal Use Cases

Cassandra’s architecture is designed for scale. It can manage millions of concurrent users and operations per second while storing vast amounts of data, and it can increase capacity with no downtime simply by adding nodes to a cluster. Cassandra also preserves continuous availability and uptime—with no single point of failure—and the option to straddle multiple data centers easily. 

While this is a powerful database for general usage, Cassandra is especially suited to supporting applications that utilize far more writes than reads, applications that allow for an even spread of data partitions across nodes, and applications that don’t require joins, data aggregates, or frequent data updates. Cassandra shines when tasked with delivering low-latency experiences to global users by replicating data across data centers, handling large write volumes, and storing and retrieving data in real-time across multiple devices. Some ideal use cases for Cassandra include media streaming, online gaming, real-time messaging, social media data input and analysis, IoT vehicle-based telematics, order tracking, transaction logging, time-series data storage, and healthcare data storage and retrieval.

It’s also worth noting that Cassandra 5.0 is now in beta, with GA expected soon. Performance improvements and better functionality for AI/ML projects are among the highlights.

See More: Boosting Reliability: HA For SQL Server In Containerized K8s

Cassandra Challenges

Developers trained in relational database models face an all-too-common challenge when they shift to Cassandra. Making the most of what the NoSQL database offers often means unlearning SQL-based experience and modeling data around queries instead of relations or objects.

Cassandra also requires expertise to operate optimally: developers with relational database experience often start changing Cassandra’s default settings before they understand the impact of those actions, slowing cluster performance. While Cassandra is outstandingly resilient from an availability and reliability perspective, it’s by no means a set-and-forget solution. Developers who neglect to actively monitor, operate, and scale Cassandra to adjust for events (such as surges in write operations) will also suffer performance declines. 

Finally, developers often fail to understand how affordable writing is with Cassandra. They produce data models that minimize writes with expenses in mind, even though doing so is counterproductive and yields negligible savings. 

PostgreSQL Advantages and Ideal Use Cases

While Cassandra surpasses comparison when it comes to storing and writing data at a large scale, it isn’t the optimal choice for use cases that include a lot of querying. In contrast, Postgres offers a tremendously customizable and extendable option designed for handling complex queries and workloads. Developers commonly say, “Just use Postgres” because it’s a versatile choice suited to building and scaling a breadth of application types and can overcome any programming limitations through the use of extensions.

In particular, Postgres lends itself to use cases that collect and analyze data from many sources to enable fast and accurate decision-making. Enterprise data warehousing, operational data storage, online transaction processing, analytical processing, geographic information systems (GIS), and IoT equipment or asset tracking are examples where Postgres is a strong fit. Extract, Transform, Load (ETL) systems that utilize transformations to resolve data type and storage model conflicts are another apt use case. Relational Postgres also supports non-relational web and JSON querying, making it a standout flexible option for query-heavy applications. 

Postgres offers hundreds of available extensions and can install a wide variety of programming languages. Therefore, teams can enable developers with any language-specific expertise to put it to work while building Postgres applications.

PostgreSQL Challenges

Although a developer can get started with Postgres by learning just five SQL commands, fully leveraging the open-source database requires tapping into its vast extensibility features and customizing Postgres to align with the specific use case at hand. This tailored approach better unlocks Postgres’ full potential. While the database offers extensive options, this breadth can sometimes pose a challenge. As the global open-source community continuously enhances the solution’s capabilities, selecting and optimizing the ideal extensions and features demands vigilant effort and focus.

Postgres offers many ways to scale, but this flexibility can be overwhelming to the uninitiated. The database abstracts vertical scaling management from hardware, making a wide spectrum of hardware options available—for which almost zero guidelines exist. Therefore, teams must often figure out effective hardware and scaling strategies on their own.

Use the Best (Open-source) Tool for the Job

While Cassandra and Postgres have comparable similarities as open-source database options, they also have their own clear-cut roles and can function side-by-side in some applications. The right choice depends entirely on the use case. Applications that call for extremely high availability, fast horizontal scaling, distribution, or specific data modeling should certainly take advantage of Cassandra. Applications requiring complex querying, vertical scaling, high ACID consistency requirements, or deep customizations should opt for PostgreSQL.

Choosing the right tool for the job paves the way for successful applications and overcoming data layer challenges going forward. However, one thing to understand is that Cassandra and Postgres are fully capable databases in their 100% open-source versions. You do not need open core alternatives or proprietary add-ons—which, in addition to being expensive, promote lock-in and make it challenging to move data later—to be successful with these databases. 

How will you choose between Cassandra and PostgreSQL for your next project? Let us know on FacebookOpens a new window , XOpens a new window , and LinkedInOpens a new window . We’d love to hear from you!

Image Source: Shutterstock

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Anil Inamdar
Anil Inamdar

VP & Head of Data Solutions, Instaclustr

Anil Inamdar is the VP & Head of Data Solutions at Instaclustr, which provides a managed platform for open source technologies including Apache Cassandra, Apache Kafka, Redis, and PostgreSQL. Anil has 20+ years of experience in data and analytics roles. Joining Instaclustr in 2019, he works with organizations to drive successful data-centric digital transformations via the right cultural, operational, architectural, and technological roadmaps. Prior to Instaclustr, he has held data & analytics leadership roles at Dell EMC, Accenture, and Visa, among others. Anil lives and works in the Bay Area.
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