Even though Oracle and Microsoft SQL Server have dominated the database management system market for decades, a seemingly endless torrent of alternatives has recently emerged. Part of the reason is the inventiveness fueled by Open Source, which attracts highly experienced programmers who want to “scratch an itch” by making something they find personally rewarding. This article will discuss some of the top open source database software for you.
Second, new business models are being implemented in which organisations keep a community version of their product to gain awareness and popularity while also providing a commercial, add-on option.
But then what?
There are too many databases to manage. The exact number of options currently accessible, such as stack-specific object databases and less-popular university projects, is unknown, but I am convinced that it is well over a hundred. I completely agree; the thought of that is terrifying. There are too many options, too many forms to go through, and too little time to live.
Because of this, I wrote this article, which lists ten excellent database software you may utilise to supplement your own work or those of others.
While MySQL is undoubtedly the most widely used Open Source database software, it will not be included on this particular list.
MySQL is the standard starting point for database education because it is widely used, widely supported (by nearly every CMS and framework), and generally excellent. Therefore, “discovery” is not necessary with MySQL.
Keep in mind that they are not guaranteed to be suitable replacements for MySQL. That may be the case sometimes, but in other situations, there’s a different solution for a different issue. Have no worry, for I shall also speak of their uses.
Importance of Compatibility
First, I want to stress the importance of getting along before we get started. There isn’t much you can do if your project requires the use of a specific database software. Such information is useless if, for instance, you are using WordPress. Also, individuals that use JAMStack to host static websites won’t gain anything from looking for viable alternatives.
Top 12 Open Source Database Software for Your Next Project
The compatibility equation must be found by you. If, on the other hand, you are starting from scratch and are responsible for the building’s design, consider the following.
PostgreSQL may sound foreign if you’re used to the PHP ecosystem (WordPress, Magento, Drupal, etc.). The Ruby, Python, Go, etc. community all agree that this particular relational database software is the best option because it has been around since 1997.
Actually, many engineers “graduate” to PostgreSQL because of its powerful capabilities and general stability. It is a well-designed solution that will never let you down, which is hard to convey in such a short post. Multiple high-quality SQL clients provide for connectivity to PostgreSQL databases, which can be useful for administration and development. Some interesting aspects of PostgreSQL are:
- Compatibility with a wide variety of data formats including Array, Range, UUID, and Geolocation.
- Built-in adherence to XML, key-value stores, and the JSON document format (Hstore).
- Synchronous and asynchronous replication are used.
- Capable to being scripted in a variety of languages.
- Searching the entire text.
The geolocation engine (which makes working with location-based apps much easier; just try manually locating all nearby spots) and array support are two of my favorite features (many MySQL projects are undone for want of arrays, opting instead for the infamous comma-separated strings).
To what extent should PostgreSQL be used?
PostgreSQL is the best relational database management system available today. What this means is that if you’ve had bad experiences with MySQL in the past and are starting a new project, you should give PostgreSQL a shot instead. My acquaintances have stopped bothering to fix MySQL’s peculiar transactional lock problems and have moved on. By agreeing, you will not be overreacting.
PostgreSQL also excels when only a little amount of NoSQL functionality is required by a mixed data model. There is no need to look for, set up, learn, and manage a different database solution because document and key-value storage are already supported.
If your data model is not relational or you have strict architectural constraints, PostgreSQL is not the right choice for you. Think about Analytics, wherein fresh reports are constantly derived from accumulated data. This kind of system requires a lot of reading and performs poorly when forced into a predetermined structure. PostgreSQL has a document storage engine, however it starts to fall apart when working with large datasets.
That is to say, unless you are an expert, you should always use PostgreSQL.
MariaDB was created by the same person who created MySQL as a replacement for MySQL. Confused? In reality, MySQL’s creator, Monty Widenius, started a new open source project named MariaDB after Oracle bought MySQL in 2010 (through the acquisition of Sun Microsystems, which is also how Oracle came to dominate Java).
What’s the point of learning this boring information? MariaDB is an open-source database management system (DBMS) built on the same foundation as MySQL (in the open-source community, this is referred to as “forking” an existing project). For this reason, MariaDB is promoted as a “drop-in” replacement for MySQL. Incredibly easy steps separate you from MySQL and lead you to MariaDB.
Sadly, such motion is one-way only. Because of the differences between MariaDB and MySQL, switching back is not an option and would cause data corruption.
MariaDB may look like MySQL, but that comparison isn’t perfect. The gap between them has widened with the advent of databases. In light of current information, picking MariaDB should be a deliberate process on your part. Nonetheless, recent changes to MariaDB could make the switch easier.
- A lack of centralized control over MariaDB means you won’t have to worry about licensing or other changes being made on the fly.
- There are many parallel, distributed storage engines available, such as Spider for centralized transaction processing, ColumnStore for massive data warehousing, and the ColumnStore engine.
- MySQL is much quicker than Aria because of the Aria storage engine for complex queries.
- Each row in a table can have its own set of dynamic columns.
Applications of MariaDB
MariaDB is the best option if you’re looking for a MySQL substitute because the company is dedicated to innovation and has no ambitions to revert to MySQL. One excellent scenario is making use of additional MariaDB storage engines to supplement the project’s preexisting relational data format.
When MariaDB shouldn’t be used?
The only real issue is that of MySQL compatibility. But as more and more open-source initiatives, including WordPress, Joomla, Magento, etc., add MariaDB support, this is becoming less of a problem. Due to the fact that many CMSes do not yet support MariaDB, I would advise against trying to trick them into thinking otherwise.
Apparently, masochists make up the CockroachDB team. Wouldn’t the company want to live up to the brand of their product? A cockroach is a type of bug that has adapted to its environment. Cockroaches will continue to reproduce and live in the face of any and all threats, including but not limited to: bombing, flooding, perpetual darkness, spoiling food, and predators.
Apparently the CockroachDB team (made up of ex-Google personnel) was unhappy with the scalability limitations of conventional SQL databases. Originally, a single server was supposed to contain all SQL solutions (because databases weren’t very large). For a long time, there was no way to set up a cluster of SQL databases, which is why MongoDB has become so popular.
It was difficult, at best, to implement replication and clustering in MySQL, PostgreSQL, and MariaDB. The goal of CoackroachDB is to revolutionize the SQL world by bringing easy sharding, clustering, and high availability.
When to Use Cockroach Database?
If you’re a system architect, CockroachDB is the database software for you. CockroachDB is great if you are a SQL purist who has been curious in MongoDB’s scalability. You can quickly set up a cluster, perform queries against it, and then go to sleep.
Cases where you should not use CockroachDB
It’s better to have the devil you know on your side than to face an unknown. In other words, stick with your current RDBMS if it’s performing well and you’re confident in your ability to handle the growing difficulties it’s presenting. It is not in your best interest to have to learn how to use CockroachDB in the future, even though it is brand new to the brains involved. Compatibility with SQL is also crucial; if you’re performing non-standard SQL operations on your mission-critical data, CockroachDB may expose too many edge circumstances for your liking.
You need a fast, free, open-source OLAP database, right? Don’t settle for less; go with ClickHouse. It makes full use of all available technology to swiftly handle each inquiry. In most cases, the speed of a query’s processing can reach over two terabytes per second. Automatic data distribution across available copies helps keep latency to a minimum.
It supports asynchronous replication with multiple masters and may be set up across many data centers. With the nodes all being on an even playing field, it’s possible to prevent any potential weak spots. Whether a single server or the entire data center goes down, read/write throughput will never be affected.
Simply put, ClickHouse is a breeze to set up and utilise. It streamlines data processing, helps you structure your data into a system, and makes your data easily available so you can quickly produce reports. And unlike other systems, which need the use of non-standard APIs, SQL dialect allows for the expression of outcomes.
This DBMS has the flexibility to be set up as a distributed network with separate nodes and no central weak spot. In addition, it has robust security features, such as protection at the enterprise level and backup systems in case of human error.
When compared to row-oriented systems with the same CPU and I/O throughput, ClickHouse can process queries far more quickly. Faster response times are achieved by storing more data in RAM, which is made possible by its columnar data storage format.
To reduce the overall cost of ownership, standard hardware with rotational disk drives can stand in for NVMe/SSD without compromising on query latency. It minimises data transfers, improves CPU use, and enhances hard drive accessibility.
In addition, you can manage denormalised data, integrate co-located and distant data, and perform queries quickly and efficiently all thanks to its feature-rich SQL database. ClickHouse is highly adaptable to run on anything from a single server to clusters with thousands of nodes because to its horizontal and vertical scalability.
Incorporate ClickHouse with your site and app analytics, telco needs, ad network, online gaming, IoT, BI, finance, eCommerce, and more. It works with popular databases such as MySQL and PostgreSQL in addition to the popular Hadoop framework. To avoid the hassle of setting up a server, you can use Kamatera, which makes it possible to access ClickHouse with only a single mouse click.
In the last decade, connected data has become one of the most important technological advances. The world we live in is not neatly laid out in rows and columns but rather is one big web of interconnectedness.
When it comes to building data architectures like those used in social networks, SQL and document-based databases are a headache to work with. This is so because the graph, a very different thing, is the best data format for these kinds of replies. A graph database, such as Neo4j, is required for this purpose.
- Graph databases are one of a kind, and Neo4j is the only tool of their kind. This explains why it is one-of-a-kind in many ways.
- Graph analytics and transactional applications are supported.
- Capabilities for transforming tabular data at scale into graphical representations.
- The Cypher query language was developed specifically for use with graph databases.
A discussion of whether or not Neo4j should be used is pointless. If you want to connect your data in a graph structure, Neo4j is a must-have.
There has been a lot of buzz around MongoDB ever since it was released, and its popularity shows no signs of slowing down. Unlike relational databases, which store data in rows and columns, MongoDB stores data in “documents,” with relevant data kept together. To fully grasp this, picture the following set of JSON structures:
In contrast to a tabular style, here all of the user’s information, including their contact details and permissions, is stored in a single object. There is no notion of a join, and the data associated with a user object can be retrieved quickly upon retrieval. Here’s a deeper dive into what MongoDB is all about.
The following features of MongoDB have led to its adoption by a number of seasoned architects as an alternative to relational databases:
- A flexible framework that can accommodate unique and unplanned use cases.
- Simplest sharding and clustering I’ve ever seen. After setting up a cluster the first time, you may forget about it.
- There is little effort required when adding or removing nodes from a cluster.
- Transaction locks on a decentralised network. Initially lacking, this function was eventually implemented.
- As a caching solution for analytics data, it is ideal due to its high write speeds.
I’m sorry if I come off like a MongoDB salesman, but it’s hard not to exaggerate the advantages of this database software. Although some developers never get used to working with NoSQL data models, the vast majority of architects find them to be superior to traditional table-based approaches.
MongoDB’s Potential Applications
An ideal bridge between the rigid, organized world of SQL and the flexible, sometimes perplexing world of NoSQL, MongoDB is a great choice for any database. Since there is no need to worry about a schema, it works well in prototyping and in situations where scalability is critical. Using a cloud SQL service to fix database scalability issues is possible, but it will set you back a lot of money.
Finally, SQL-based approaches can fall short. If you’re trying to create a service like Canva, where users can make their own designs and modify them as much as they like, a relational database isn’t going to cut it.
When MongoDB shouldn’t be used?
For unskilled users, MongoDB’s total lack of provided schema can be a complete and utter nightmare. There is a chance of data inconsistencies, dead data, and empty fields that should not be vacant. Since MongoDB is what is known as a “dumb” data store, it is up to your application’s code to ensure data consistency.
Named so because it “rethinks” the notion and capabilities of a database in light of real-time applications, RethinkDB lives up to its billing. The software has no way of detecting when a database has been updated. When an update occurs in an application, a notification is typically transmitted to the front end over a convoluted bridge (PHP > Redis > Node > Socket.io is one such example).
However, imagine if updates to the database were sent straight to the user interface! This is something that is guaranteed by RethinkDB. If you want to make a really real-time app, you should check out Rethink DB (game, marketplace, analytics, etc.).
When talking about databases, Redis is hard to ignore. A common usage for Redis, an in-memory database, is caching. This database is so simple that you can master it in ten minutes flat. It’s just a key-value store for time-stamped strings (which can be set to infinity, of course). Redis’s efficiency and ease of use make up for its limited feature set.
Reads and writes are lightning fast since the data is stored entirely in RAM (a few hundred thousand operations per second is not out of the ordinary). Due in large part to Redis’s sophisticated pub-sub structure, this “database” is far more alluring than it already is. If your project requires caching or includes distributed parts, Redis is your best bet.
I know I’ve said we’d be done with relational databases, but SQLite is just too cute to pass up. A relational database storage engine, SQLite is a small, fast, and efficient C library. The entire database can be saved in a single.sqlite file, which can be placed wherever you like on your computer. In the end, that’s all there is to it. There is no “server” software to set up and no external service to subscribe to.
Instead of using a heavy database management system like MySQL, you may use SQLite, which is a lightweight alternative that yet delivers a significant punch.
Amazing features include:
- Support for all three transaction modes (COMMIT, ROLLBACK, and BEGIN) is provided.
- 32k rows per table column supported.
- The use of JSON.
- JOIN with up to 64 joins.
- Features include full-text searching, subqueries, etc.
- A database cannot exceed 140 terabytes in size.
- One gigabyte of storage per row maximum.
- Quicker by 35% than file I/O.
When Should You Use SQLite?
SQLite is a highly specialized database software that places a premium on efficiency. If your software is rather straightforward and you don’t want to deal with the complexity of a full-featured database, then SQLite is a good alternative for you. Smaller CMSs and proof-of-concept software can benefit greatly from it.
Cons of SQLite
Despite its many strengths, SQLite does not offer the same flexibility as standard SQL or your choice database software. Lacking are enhancements for clustering, stored procedures, and scripting. The inaccessibility of the database is compounded by the lack of a client that could be used to connect to it, run queries against it, and view its contents. As the size of the application continues to grow, its performance will inevitably degrade.
The Java community periodically drops a bombshell that proves the naysayers wrong, even if many people believe Java’s days are numbered. The case of Cassandra is illustrative. Cassandra is a columnar database, which means that its data is organized into columns rather than rows. The idea is to minimize disk access times by physically grouping all the data for a given column.
- The primary motivation behind the development of Cassandra was to meet the needs of businesses that have a zero-tolerance policy for downtime but require heavy amounts of writing. That’s what sets it apart from the competition.
- Lightning-fast handwriting. When it comes to handling massive write demands, no database compares to the speed of Cassandra. There is no increase in cluster complexity or sensitivity as more nodes are added to a cluster, therefore the cluster can grow as large as needed.
- Superior ability to withstand separation. That is, the database is designed to keep running without losing data even if multiple nodes in a Cassandra cluster fail.
Techniques for Using Cassandra
Some of the best uses for Cassandra are in the realms of logging and analytics. Each day, Apple’s Cassandra deployment handles more than 400 petabytes of data, while Netflix’s Cassandra deployment processes 1 trillion requests. Cassandra, however, has a wider range of applications than just this. Cassandra’s high availability is a key feature.
When Cassandra Should Not Be Used?
It’s important to remember that Cassandra’s columnar storage approach is not without its drawbacks. Because of the data model’s flatness, aggregations are not supported in Cassandra. In addition, it is undesirable for systems that need high read accuracy since it provides high availability at the expense of consistency (recall the CAP theorem for distributed systems).
One such development that calls for different kinds of databases is the Internet of Things (IoT). When it comes to free, open-source databases, Timescale is among the best. The time series database type used here places a premium on both the analysis and visualization of massive data sets along the time axis.
Time series databases store information that doesn’t change very often, such as temperature readings from a sensor in a greenhouse; nonetheless, new information is added constantly and can be used for analysis and reporting. Then why not just have a timestamp field in a regular database? We can attribute this to two main causes:
- General-purpose databases are not well suited for time-based data. Data storage in a generic database will be much slower for the same amount of data.
- The database needs to be able to store massive amounts of data as new data continues to pour in, and it will not be possible to delete old records or change the database’s structure in the future.
- Timescale DB is unique among databases because of its cutting-edge features.
- PostgreSQL, arguably the best open-source relational database, is used in its construction.
- Timescale will fit in without any problems if PostgreSQL is already being used in your project.
- The learning curve is lowered because the standard SQL syntax is used for queries.
- Very fast writing speeds can allow for millions of inserts per second.
- Scalability is not affected by the number of rows or the size of the data set, which can be in the billions or even in the petabytes.
Choose between a relational database and a schema-less one, depending on your specific needs. If the Internet of Things (IoT) is your specialty or you’re just seeking for a database software with similar features, give Timescale a try.
CouchDB is a low-key database management system that has a dedicated but modest user community. It was created to cope with the challenging issue of network loss and subsequent data resolution, which causes many programmers to opt for job transfers rather than tackle the issue head-on.
In practice, a CouchDB cluster is a group of servers, both big and tiny, some of which will inevitably be down at any one time. When a node joins the cluster, it immediately begins sending data back to the main hub, where it is slowly and carefully processed before being made available to all nodes.
- When compared to other relational databases, CouchDB stands apart.
- Data synchronization capabilities that work offline first.
- There are smartphone and web-optimized variants available (PouchDB, CouchDB Lite, etc.).
- Reliability that has been rigorously proven under fire.
- Direct data duplication and clustering.
Justifications for Using CouchDB
CouchDB is unparalleled in offline availability because it was built with it in mind from the start. For example, in the case of mobile applications, some of your data may persist locally on the user’s device by way of a CouchDB instance (because that is where it was generated). However, the database must be opportunistic and ready to reconcile conflicting information at a later time because it is not always possible to count on the user’s device being connected. The incredible Couch Replication Protocol is responsible for this.
When CouchDB Should Not Be Used?
The disaster that awaits anyone who try to use CouchDB for purposes other than those for which it was designed. This demands more space than is currently accessible since it must store data replicas and conflict resolution solutions indefinitely. Because of this, write times are likewise painfully slow. Due to its poor support for schema modifications, CouchDB is unfit to serve as a universal database software.
This list is meant more as a recommendation than a commandment, as I had to leave off some interesting possibilities like Riak. I think I succeeded in my piece’s intended objective, which was to offer readers a list of database software recommendations alongside a brief discussion of when and why each option should be utilized (or avoided).