Top 11 Open Source Database for Your Next Project

Top 11 Open Source Database for Your Next Project

Data is everything. And in a broader sense, databases. Here are some great open-source choices for your next kick-ass project.

For a world dominated for so long by database suits like Oracle and SQL Server, there now seems to be an endless flood of solutions. Part of the reason is the innovation driven by open source – really talented developers who want to scratch an itch and create something to revel in.

The other part is the emergence of new business models where companies maintain a community version of their product to gain Mind Share and Traction while providing a commercial additional offering.

The result?

More databases than you can keep up with. There are no official statistics on this, but we are pretty sure we have over a hundred options available today if you combine everything from stack-specific object databases to less popular university projects.

This article will introduce ten of the best databases that you can use to improve your solutions, whether you’re creating for yourself or for others.

No MySQL

This list does not contain MySQL, although it is probably the most popular open-source database solution because MySQL is everywhere – it’s what everyone learns first, it’s supported by virtually any CMS or framework and it’s very, very good for most use cases. In other words, MySQL does not need to be “discovered”. 

Note, however, that the following options are not necessarily alternatives to MySQL. In some cases, they may be, while in others they are a completely different solution to a completely different need. Don’t worry, we’ll discuss their use.

Special Note: Compatibility

Compatibility is something you need to bear in mind. If you have a project that for some reason only supports a specific database engine, your decisions are made pretty accurately.

For example, if you are running WordPress, this article is not useful to you. Likewise, those who run static sites on JAMStack won’t gain anything if they’re too serious about alternatives.

It is up to you to find out the compatibility equation. However, if you have an empty board and the architecture is up to you, here are some nice recommendations.

01. PostgreSQL

If you come from a PHP country (WordPress, Magento, Drupal, etc.), then PostgreSQL will sound alien to you. However, this relational database solution has been around since 1997 and is the first choice in communities such as Ruby, Python, Go, and so on.

In fact, many developers eventually “graduate” to PostgreSQL because of the features offered or simply because of the stability. It’s hard to convince someone in a short article like this, but think PostgreSQL is a thoughtful product that never lets you down.

There are many good SQL clients available to connect to the PostgreSQL database for management and development.

PostgreSQL Unique features

PostgreSQL offers some fascinating features compared to other relational databases (especially MySQL), such as:

  • Integrated data types for the array, range, UUID, geolocation, etc.
  • Native support for document storage (JSON style), XML, and key-value storage (Hstore)
  • Synchronous and asynchronous replication
  • Scriptable in PL, Perl, Python, and more
  • Full

When is PostgreSQL used?

PostgreSQL is always a better choice than any other relational database engine. That is, if you start a new project and have been bitten by MySQL before, it’s a good time to think about PostgreSQL.

PostgreSQL also has a clear advantage if you partially need NoSQL features for a hybrid data model. Because document and key values are natively supported, you don’t need to search for, install, learn, and maintain another database solution.

When should PostgreSQL not be used?

PostgreSQL does not make sense if your data model is not relational and/or if you have specific architectural requirements. For example, consider Analytics, which constantly creates new reports from existing data. Such systems are read-heavy and suffer when a strict scheme is imposed on them. Sure, PostgreSQL has a document storage engine, but when dealing with large amounts of data, things fall apart. 

02. MariaDB

MariaDB was created as a replacement for MySQL by the same person who developed MySQL.

After MySQL was acquired by Oracle in 2010 (through the acquisition of Sun Microsystems, with which Oracle also controlled Java), the developer of MySQL started a new open source project called MariaDB.

MariaDB was created from the same code base as MySQL (in the open-source world, this is called “forking” an existing project). As a result, MariaDB is presented as a “drop-in” replacement for MySQL.

That is, if you use MySQL and want to migrate to MariaDB, the process is so simple that you just won’t believe it.

Unfortunately, such migration is a one-way street. It is not possible to return MariaDB to MySQL. If you try to use force, permanent damage to the database is guaranteed!

MariaDB Unique features

While MariaDB is essentially a clone of MySQL, this is not necessarily the case. Since the introduction of the database, the differences between the two have increased. At the time of writing, the acquisition of MariaDB must be a thoughtful decision on your part. Nevertheless, many new things in MariaDB can help you with this transition:

  • Really free and open: Since there is not a single business unit that controls MariaDB, you can get rid of sudden robbery licenses and other concerns.
  • Several other options of storage engines for specific requirements: example, the spider engine for distributed transactions; ColumnStore for massive data warehousing; the ColumnStore engine for parallel, distributed storage; and many, many more.
  • Speed improvements over MySQL, especially due to the Aria storage engine for complex queries.
  • Dynamic columns for different rows in a table.
  • Better replication capabilities (such as multi-source replication)
  • Multiple JSON features
  • Virtual columns

And many, many more. It’s exhausting to keep up with all MariaDB features. 

When to use MariaDB

You should use MariaDB if you really want to replace MySQL, stay on the innovation curve, and don’t plan to return to MySQL. An excellent use case is to use new storage engines in MariaDB to compliment your project’s existing relational data model.

When should MariaDB not be used?

Compatibility with MySQL is the only problem here. This means that it will be less of a problem as projects like WordPress, Joomla, Magento, etc. have started to support MariaDB. So do not use MariaDB to trick a CMS that it doesn’t support, as many database-specific tricks easily crash the system.

03. CockroachDB

The team behind CockroachDB (made up of former Google engineers) was disappointed by the limitations of traditional SQL solutions on a large scale. This is because SQL solutions were supposed to be hosted on a single computer in the past (the data wasn’t that large). For a long time, there was no way to create a cluster of databases with SQL, which is why MongoDB attracted so much attention.

Even when replication and clustering came out in MySQL, PostgreSQL, and MariaDB, it was painful at best. CoackroachDB wants to change this and provide the SQL world with effortless sharding, clustering, and high availability.

When to use CockroachDB

CockroachDB Has the system architect’s dream come true? If you swear by SQL and master the scaling capabilities of MongoDB, you’ll love CockroachDB. Now you can quickly set up a cluster, query, and sleep quietly at night.

When not to use CockroachDB

If your existing RDBMS works well for you and you think you can overcome the scaling problems that come with it, stick with it. For all geniuses, CockroachDB is a new product, and you don’t want to fight it later. Another important reason is the SQL compatibility. When you perform exotic SQL tasks and rely on critical things, CockroachDB shows too many margins for your taste.

From now on, we will consider non-SQL database solutions (or NoSQL database solutions) for highly specialized requests.

04. Neo4j

One of the most significant developments of the last decade is related data. The world around us is not divided into tables, rows, and boxes – it’s a huge mess where everything is connected to almost everything else.

Social networks are a prime example, and creating a similar data model using SQL or even document-based databases is a nightmare.

This is because the ideal data structure for these solutions is the graph, which is a completely different animal. And to do this, you need a graph database like Neo4j.

Neo4j Unique features

Graphics databases are unique in themselves, and Neo4j is pretty much the only option for working with graphics. As a result, all features are unique.

  • Support for transactional applications and chart analysis.
  • Data transformation functions for summarizing large tabular data into graphs.
  • Specialized query language (cypher) to query the chart database
  • Visualization and recognition functions

It is a contentious point to discuss when to use Neo4j and when not. If you need graph-based relationships between your data, you need Neo4j.

05. Mongodb

MongoDB was the first non-relational database to make big waves in the tech industry and continue to dominate a fair share of attention.

Unlike relational databases, MongoDB is a “document database,” which means it stores data in blocks, with related data grouped in the same block.

MongoDB Unique features

MongoDB has some serious features that have led several experienced architects to abandon the relationship country forever:

  • A flexible scheme for special/unpredictable use cases.
  • Ridiculously easy sharding and clustering. All you need to do is set up the configuration for a cluster and forget about it.
  • Adding or removing a node to a cluster is easy.
  • Distributed transaction locks. This feature was missing in earlier versions but was eventually introduced.
  • It is optimized for very fast writes and is therefore ideal for the analysis of data as a caching system.

It is hard to exaggerate the benefits of MongoDB. Sure, NoSQL data modeling is strange at first and some never understand it, but for many architects, it almost always wins through a table-based scheme.

When is MongoDB used?

MongoDB is a great crossover bridge from the structured, rigorous SQL world to the amorphous, almost confusing NoSQL world. It’s characterized by the development of prototypes because there’s simply no scheme to worry about and when you really need to scale. Yes, you can use a cloud SQL service to fix DB scaling issues, but boy, it’s expensive!

Finally, there are use cases where SQL-based solutions simply don’t work. For example, if you’re creating a product like Canva where the user can create any complex designs and edit them later, good luck with a relational database!

When should MongoDB not be used?

The complete absence of a scheme provided by MongoDB can serve as a tar pit for those who do not know what they are doing. Data mismatch, dead data, empty fields that shouldn’t be empty – all this and much more is possible. MongoDB is essentially a “stupid” data store, and if you select it, the application code must take great responsibility for maintaining data integrity.

If you are a developer, you will find that useful.

06. RethinkDB

As the name suggests, RethinkDB is rethinking the idea and capabilities of a database when it comes to real-time apps.

When a database is updated, the application cannot know. The accepted approach is for the app to trigger a notification when there is an update sent to the front end via a complex bridge (PHP -> Redis -> Node -> Socket.io is an example).

If you want to create a real-time application (game, marketplace, analysis, etc.), Rethink DB is worth a look.

07. Redis

When it comes to databases, it is almost too easy to overlook the existence of Redis. This is because Redis is an in-memory database and is primarily used in support functions such as caching.

Learning this database is a ten-minute job (literally!) and a simple key-value store that stores strings with an expiration time (which, of course, can be set to infinite). What Redis loses in functionality makes up for its usefulness and performance. Since it lives entirely in RAM, reading, and writing are incredibly fast (a few hundred thousand operations per second are not uncommon).

Redis also has a sophisticated pub sub system, which makes this “database” twice as attractive.

In other words, if you have a project that could benefit from caching or has distributed components, Redis is the first choice.

08. Sqlite

SQLite is a compact C library that provides a relational database storage engine. Everything in this database is in a single file (with the .sqlite extension) that you can place anywhere in your file system. There is no need to install server software or establish a service.

SQLite Useful functions

Although SQLite is an easy alternative to a database like MySQL, it offers a lot of power. Some of its shocking features are:

  • Full support for COMMIT, ROLLBACK, and BEGIN transactions.
  • Support for 32,000 columns per table
  • JSON support
  • 64-way JOIN support
  • Subqueries, full-text searches, etc.
  • Maximum database size of 140 terabytes!
  • Maximum line size of 1 gigabyte!
  • 35% faster than File-I/A.

When is SQLite used?

SQLite is an extremely specialized database that focuses on a no-nonsense approach. If your app is relatively simple and you don’t need the hassle of a full database, SQLite is a serious candidate. This is especially useful for small to medium-sized CMS and demo applications.

When should SQLite not be used?

Although SQLite is impressive, it does not cover all the features of standard SQL or your preferred database engine. Clustering, stored procedures, and script extensions are missing. Also, there is no client to connect, query, and search the database. Finally, performance decreases as the application size increases.

09. Cassandra

While many claims that the end of Java is near, the community drops a bomb from time to time and silences the critics. Cassandra is one such example.

Cassandra belongs to the so-called “column-shaped” database family. The memory abstraction in Cassandra is a column rather than a row. The idea here is to physically store all the data in a column together on the hard disk to minimize the search time.

Cassandra Unique features

Cassandra was developed with a view to a specific use case – the handling of write-intensive loads and the zero tolerance for downtime. These become unique selling points.

  • Extremely fast writing performance: Cassandra is arguably the fastest database on the market when it comes to processing heavy write loads.
  • Linear scalability: That is, you can continue to add as many nodes to a cluster as you want, and the complexity or brittleness of the cluster is increased by zero.
  • Mismatched partition tolerance: That is, even if multiple nodes in a Cassandra cluster fail, the database is designed to continue to function without loss of integrity.
  • Static input

When to use Cassandra

Logging and analysis are two of the best use cases for Cassandra. But that’s not all – the sweet spot is when you need to process really large amounts of data (Apple has a Cassandra deployment that processes more than 400 petabytes of data, while Netflix processes 1 trillion requests a day on Netflix), with no downtime. High availability is one of Cassandra’s trademarks.

When should Cassandra not be used?

Cassandra’s column storage scheme also has its drawbacks. The data model is quite flat, and if you need aggregations, Cassandra is not enough. Also, high availability is achieved by sacrificing consistency (think the CAP set for distributed systems), making it less appropriate for systems that require high read accuracy.

10. Timescale

New developments require new types of databases, and the Internet of Things (IoT) is one such phenomenon. One of the best open source databases for this is timescale.

The timescale is a kind of time-series database. It is different from a traditional database because this time is the primary concern and the analysis and visualization of large amounts of data is a top priority. Time series databases rarely see a change in existing data. An example is temperature values sent by a sensor in a greenhouse. New data is collected every second, which is of interest for analysis and reporting.

So why not just use a traditional database with a timestamp field? There are two main reasons for this:

  • General-purpose databases are not optimized to work with time-based data. For the same amount of data, a general-purpose database is much slower.
  • The database needs to process huge amounts of data because new data is constantly flowing in and removing data or changing the schema. later is not an option.

Timescale Unique features

Timescale DB has some exciting features that distinguish it from other databases in the same category:

  • It is based on PostgreSQL, arguably the best relational open-source database on the market. If your project is already running PostgreSQL, Timescale appears directly.
  • The query is done using the well-known SQL syntax, which shortens the learning curve.
  • Ridiculously fast write speeds – millions of insertions per second are not uncommon.
  • Billions of rows or petabytes of data – for Timescale, that’s not a big deal.
  • Real flexibility with schema – choose between relational or schemaless as needed.

If IoT is your domain or you’re looking for similar database properties, Timescale is worth a look.

11. Couchdb

CouchDB is a nice little database solution that sits quietly in a corner and has a small but dedicated following. It is designed to solve the problems of network loss and possible data resolution. This is a problem so chaotic that developers switch jobs instead of dealing with it.

Essentially, you can think of a CouchDB cluster as a distributed collection of large and small nodes, some of which are expected to be offline. Once a node goes online, it sends data back to the cluster, which is digested slowly and carefully and eventually becomes available to the entire cluster.

Couchdb Unique features

CouchDB is a unique breed when it comes to databases.

  • Offline-First Data Synchronization Features
  • Special versions for mobile and web browsers (PouchDB, CouchDB Lite, etc.)
  • Crash-proof, battle-tested reliability
  • Easy clustering with redundant data storage

When to use CouchDB

CouchDB is designed for offline tolerance and is unmatched in this respect. A typical use case is mobile apps where some of your data is on a CouchDB instance on the user’s phone (because it was generated there). The exciting thing is that you can’t rely on the user’s device being constantly connected. This means that the database must be opportunistic and ready to resolve conflicting updates later. This is achieved with the impressive couch replication protocol.

When should CouchDB not be used?

Attempting to use CouchDB outside of the intended use case will lead to a disaster. It consumes much more memory than anything else out there, simply because it needs to manage redundant copies of data and conflict resolution results. As a result, the writing speeds are also painfully slow. Finally, CouchDB is not suitable as a general schema engine because it does not work well with schema changes.

Conclusion

We had to skip many interesting candidates like Riak. So this list is more a guide than a bid. We hope that with this article we were able to achieve our goal – not only present a collection of database recommendations but also briefly discuss where and how they must be used (and avoided!).

Comments

Write a Reply or Comment

Your email address will not be published. Required fields are marked *


This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.