With data requirements growing at the speed of light, the ability to not only manipulate data in real-time but also scale along with it is a must-have for most organizations around the world. SQL based Databases have played an important role in field of data storage and access. Business organisations are responsible for choosing the right SQL Database given their business requirements and needs.
In this post, we will discuss the differences between MariaDB and PostgreSQL, more importantly how these differences impact the choice of database, given an application’s nature and its constraints.
After reading this blog, it will be easier for you to decide which of these 2 databases suffice your application needs better.
Table of Contents
Parameters and Features to Consider
1. MariaDB vs PostgreSQL Parameters: Speed
If the volume of data to be handled by the database is substantial, PostgreSQL offers faster reads and writes, hence it’s the preferred database in cases where speed of data access and turnaround time matter a lot.
2. MariaDB vs PostgreSQL Parameters: Data Typing
PostgreSQL is more strictly typed, which means that if incoming data is slightly non-conformant with the destination data type, PostgreSQL will throw an error and will not allow insertion. PostgreSQL is more inclined towards strict data integrity.
Whereas MariaDB is more flexible, it autocorrects the incoming data to match the destination data type, accepts the data but also generates an alert! Hence, MariaDB is preferred in many modern applications where data input from users could be a bit erratic but the application needs to be a bit intelligent and accept slight discrepancies.
3. MariaDB vs PostgreSQL Parameters: Size
MariaDB has a much smaller footprint than Postgre, making it ideal for smaller databases that need to respond quickly, and are running on smaller machines.
4. MariaDB vs PostgreSQL Parameters: Replication Strategies
Most modern applications require physically distributed databases that should remain in sync with each other, in order to improve response times, failover, reliability, performance, etc.
Replication is used to keep distributed databases in sync, such that if one of them fails another can take its place and start serving the users. Broadly speaking there can be 2 Replication strategies for Relational Databases.
(i) Master-Slave Replication
Amongst the multiple physically distinct Database Servers, one of them assumes the responsibility of the Master, and all others become its slaves ( followers to be exact). A master is responsible for database updates and any update/change is first propagated through the Master.
After the change is committed, the Master instructs all its slaves to apply the same change and keep themselves in sync with the master. After a change is committed on a slave, the slave intimates the Master that its ready for more new updates.
- Conflict resolution is easy.
- Assume that conflicts are common, hence they implement ACID compliance more strictly.
- Non-urgent tasks, like analytics can read from a slave and keep the master free for quick transactional updates.
- Backups can be taken easily from the slaves, without impacting master performance.
- All writes have to be first made to the Master, making it a bit centralized design, writes may have to wait to be propagated.
- If the Master fails before Slaves get in sync, there can be consistency issues.
- If the Master fails there can be considerable downtime and possible loss of data.
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(ii) Master-Master Replication
Here all participating database servers act as a Master, and they equally participate in an update-anywhere model. Updates are sent to all Masters and they are expected to implement them immediately.
- If one master fails another can take its place immediately, offering the highest availability.
- Also, if one of the servers is unavailable, all others keep themselves updated.
- Masters can be located physically distant.
- Difficult to implement , eager replication systems are complex.
- Most multi-master replication systems assume that conflicts will be rare, hence they are a bit lazy and asynchronous, sometimes violating strict ACID compatibility.
Now, MariaDB offers both Master-Master and well as Master-Slave replication strategies. Hence it’s more flexible on these parameters. PostgreSQL only supports Master-Slave replication, hence it could be a difficult choice if your application demands high availability and low geographical latency.
5. MariaDB vs PostgreSQL Parameters: Advanced Features
PostgreSQL has a lot of advanced features to offer such as Materialized Views, and Partial Indexes, which helps in optimizing the Database performance.
Prior to executing a frequent query, Materialized Views allow precalculating expensive Joins and Aggregation operations and storing the results in a table in the Database.
This can increase the performance of complex queries that are fired frequently and access a large amount of data to get their results. This is useful if you have a large dataset with many huge tables, which need to be joined frequently to create aggregates.
More information regarding Materialized Views can be found here.
Another feature that speeds up queries on large and complex Databases, is Partial Indexes. Partial indexes are created on query results and not on every row of a table (unlike traditional indexes).
In most cases, queries target only a subset of the rows in a table, based on recency/high activity. If a partial index is created for query results that arise from these frequently accessed rows, this leads to much faster query execution.
More information about Partial Indexes can be found here.
6. MariaDB vs PostgreSQL Parameters: Partitioning
MariaDB supports partitioning via sharding, whereas PostgreSQL does not support partitioning of its table(s). This improves MariaDB’s query performance and availability.
Also, you can store frequently accessed recent data in a separate partition, whereas less accessed historical data can reside in some other partition, leading to increased access speeds.
7. MariaDB vs PostgreSQL Parameters: JSON and NoSQL Support
MariaDB does not support JSON directly, though there are workarounds that you can implement to get JSON working on MariaDB. In contrast, PostgreSQL has inherently good support for JSON and lends itself well for NoSQL requirements.
8. MariaDB vs PostgreSQL Parameters: Table Inheritance and Object Orientation
PostgreSQL supports table inheritance and has Object IDs (OIDs). Hence it better supports low-level referential integrity and object orientation.
The table below summarizes features and their impact when you attempt to choose a database solution based on your business requirements.
| Feature|| Use-case-Impact||Use-case-Impact|
| MariaDB|| PostgreSQL|
|Size||Suits smaller databases||Suits bigger databases |
|Data typing||Flexible data types ||Strict data integrity|
|Replication||Versatile and powerful||Only Master-Slave|
|Materialized Views, Partial Indexes||Not supported||Quicker complex queries, Faster aggregations |
|Partitioning||Faster access to partitioned data ||Not supported|
|NoSQL and JSon||Not fully supported||Suits NoSQL databases |
If you’re looking for write speed and some advanced features like materialized views and partial indexes, go for PostgreSQL. But if you need to support a small but quick database for your mobile apps or websites, where business logic rules over the dataset, MariaDB could be a good choice.
By “business logic rules over dataset” I mean business cases where there is a lot of complex computation and business logic in the code, whereas the dataset is smaller and not complex. E.g. Gaming solutions where the rules and moves of the game are implemented in the code, and the database is comparatively simpler but needs quick access.
To conclude, We hope the above discussion initiates you in identifying unique aspects of your business requirements and helps you choose an appropriate database while comparing MariaDB vs PostgreSQL.
If you are interested in comparing PostgreSQL to SQL Server you can find the guide here.
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