Difference Between MongoDB, MySQL, and PostgreSQL

MongoDB is a fantastic fit throughout development and production — particularly if you need to scale. MongoDB Atlas has been expanded via MongoDB Realm to make development of apps easier, through Lucene-powered Atlas Search. It has features supporting data lakes that have been built on cloud object storage.

This provides redundancy and protection against any downtime that might occur in the event of a scheduled break for maintenance or a system failure, thus increasing the fault tolerance of the database. Replication is the process of creating a copy of the same dataset on more than one server. It enables database administrators to provide high data redundancy and high availability of data.

Firebase databases

Moreover, both PostgreSQL and MongoDB support several extensions and plugins like Adminer for database management. In the next section, we’ll elucidate the differences between MongoDB and PostgreSQL to help you make that decision easily. Our information is based on key factors like architecture, ACID compliance, extensibility, replication, security, and support to name a few. MongoDB is wielded by thousands of organizations worldwide for data storage needs or as their applications’ database service.

The developers are available for this technology more in number rather than for MongoDB. MongoDB is also getting popular as it getting used with new technologies like ReactJS etc. PostgreSQL uses FDW to retrieve the data from other systems as it can change into any form of a data source.

Extensibility support

The majority of changes in schema require a migration procedure capable of taking the database offline or reducing the performance of an application while it’s not running. So much of the conversation in https://globalcloudteam.com/ the world of computer science covers isolation levels in database transactions. PostgreSQL defaults to the read committed isolation level, enabling users to adjust it to the serializable isolation level.

When it comes to collaboration, PostgreSQL includes user-level privileges, role inheritance, and table-level privileges. You can manage users and grant them read and write privileges. In case databases need to be upgraded, PostgreSQL doubles their storage capacity.

MongoDB vs PostgreSQL: Which Should You Choose?

Hive isn’t strictly necessary, but with it, you can create tables using the result from joins between Mongo and Postgres . There is also a paid version in the marketplace, which is heavily optimised, and has a 30 day trial. The less often queried document data is strongly separated from the far more often queried metadata.

It is document-oriented, and uses JSON-like documents with optional schemas. Under his leadership, EDB is recognized as a leading open source Postgres database company with over 3,500 customers worldwide. Ed’s passion for technical product excellence and world-class support and services has been the catalyst for delivering the industry-leading Postgres products now in use at many of the Fortune 500. Ed earned his MBA from Harvard Business School and has previously served as a Captain in the US Army. Amir brings proven success in leading and scaling businesses by orders of magnitude in both start-up and public company settings as well as multiple exits and M&As.

However, as data is stored in key-value pairs in one record, it lacks the security boasted by PostgreSQL; MongoDB’s main focus remains on speed. PostgreSQL stores the information about the columns, and tables, along with information regarding the data types, functions, and access methods present. By storing data in fields such as nested subdocuments and arrays, related information in JSON documents can be stored together for quick query access through the MongoDB query language. The real question isn’t MongoDB vs PostgreSQL, but rather the best document database vs the best relational database. Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process.

You’ll probably be able to find assistance to make your general SQL project work properly, and for your specific PostgreSQL project too. Various deployment options for PostgreSQL are also available. These use a standard SQL interface to link to other databases or streams. They have also highlighted that, at present, there are no relational databases that fully conform to that standard. While document databases are able to do JOINs, they’re performed in a different way from multi-page SQL statements that are often needed and generated automatically by BI tools.

How to choose a database management system

It is frequently referred to as the most widely used database. MySQL is the most used data storage solution, according to a recent developer survey. It all depends on your business model and your business needs. When speaking of analytic tools without multiple data layers, it may be reasonable to opt for NoSQL databases like MongoDB.

Below are a few examples of SQL statements and how they map to MongoDB. A more comprehensive list of statements can be found in the MongoDB documentation. The strength of SQL is its powerful and widely known query language, with a large ecosystem of tools. The right answer for your needs is based of course on what you are trying to do. Our goal in this article is to help to explain the personality and characteristics of each of these databases so you can better understand whether it meets your needs. Both MongoDB and PostgreSQL are really great tools for certain use cases.

Performance

The challenge of using a relational database is the need to define its structure in advance. Changing structure after loading data is often very difficult, requiring multiple teams across development, DBA, and Ops to tightly coordinate changes. In a relational database, the data in question would be modeled across separate parent-child tables in a tabular schema.

• He is also a major developer and code committer of the PostgreSQL project and has contributed major features in each of the last 8 versions of PostgreSQL.
• This is particularly valuable with the ongoing deployment of new application functionality.
• I have some experience with PostgreSQL and ClickHouse but never got the chance to play with TimescaleDB.
• With PostgreSQL 9.3, JSON support capability was improved with additional constructor and extractor methods.

It can be difficult to adjust the structure of the database once it’s loaded. It needs several teams in development, ops, and the database administrator to coordinate the changes made in the structure carefully. Unlike MongoDB, PostgreSQL depends on a scale-up strategy for data volumes and scaling writes. It’s performed by adding more hardware resources like disks, CPUs, and memory to an existing database node.

Performance, Security, and Reliability

As with Linux, PostgreSQL is a great example of an open-source project that has been managed well. It’s one of the most widely adopted relational databases, and it emerged from the POSTGRES project that began in 1986 at the University of Berkeley. It all comes down to the type of database you’re looking for based on your unique requirements — a document database or a relational database.

Additionally, developers can always expect free and prompt community assistance. Not only does installation require a lot of disk space, but you’ll also have to consider constant hardware updates if you deploy it on-premises. Starting with Oracle 12c release, when the software entered the hybrid cloud era, new cloud computing technologies have appeared on a regular basis. With every new release, Oracle tries to keep up with the innovation pace while focusing on information security including active data guard, partitioning, improved backup, and recovery.

It isn’t extremely new but it only is a key/value store, and is incapable of nested data structures, unlike json and xml. Either of these field types would do the trick for storing schemaless data within the context of a relational DB. The only reason I don’t jump to this solution immediately is it is relatively new (introduced in version 8.4 so not that new), I have zero previous exposure to it and I am suspicious.

PostgreSQL, on the other hand, uses the GROUP_BY to process and run queries. MongoDB has the potential for being ACID Compliant whereas PostgreSQL has it built-in. The ACID properties are the fundamental properties of databases so that transactions can be tracked properly. It has a robust access control system that has additional features like row and column level security and multi-factor authentication with certificates.

PostgreSQL uses joins to combine data from multiple tables into a single table. As long as you have 2 tables, you can use joins to combine them in PostgreSQL. Similar to traditional SQL, there are 4 types of joins in PostgreSQL- Inner, Left, Right, and Full Join. If you want all the data from both tables into a single table, you can use a Full Join. MongoDB is a schema-free NoSQL database that supports a distributed architecture.

PostgreSQL: A Modern SQL Database

Given currently existing differences between MariaDB 10.4 and MySQL 8.0, further deviations are yet to come. Additionally, MySQL engineers introduce some native features to the code that are only available to commercial MySQL users. This can create compatibility issues or migration problems from MariaDB back to MySQL. With a basic postgresql has many modern features including set of tools for individual use, MySQL community edition is a good option to begin with. Of course, there are other, prepaid options for Enterprise or Cluster purposes with richer functionality. Nevertheless, if your company is too small to pay for one of them, the free-to-download model is the most suitable for a fresh start.

It’s very helpful when creating or updating a customer’s profile in terms of the workload that real-time engagement usually demands. One of Elasticsearch’s peculiarities is its robust distributed architecture. Its key structure options, such as clustering, indexing, sharding, and many more, provide extensive horizontal scaling, which allows for accommodating terabytes of records with further automation. The architecture’s abstraction levels streamline system management on both individual and aggregate levels.