This makes SQL a powerful option for performing advanced analytics. To conclude, SQL databases safeguard reliability of transactions whereas MongoDB ensures high availability of data. MongoDB is built on the principles of CAP Theorem which focuses on Consistency, Availability, and Partition. Unlike the ACID properties of SQL databases, CAP theorem focuses on availability of data in the case of MongoDB.

Also, you can manually configure Cassandra to meet the consistency standards you set. Hence, Cassandra and MongoDB have significant differences between their writing scalabilities. Accordingly, Cassandra was released in 2008, as one of these NoSQL databases. When making a comparison between two database systems, it is usually inferred there are shared similarities as well.

mongodb vs postgresql scalability

It is similar to Structured Query Language common in relational database management systems in that it describes data organized into tables, by columns and rows. CQL differs from relational databases in that, though ScyllaDB does support multiple tables, it does not support JOIN operations between its tables. There are many different schemas, from key-value stores, to document stores, graph databases, time series databases and wide-column stores.

Mongodb Vs Dynamodb: Comparing Nosql Databases

Thus the accuracy of data in SQL databases is higher as compared to other database types. Additionally, if you need ACID-compliant databases, NoSQL is probably not the best solution. For database transactions that ensure atomicity, consistency, isolation, and durability, it is better to use relational databases, such as MySQL or PostgreSQL. Since traditional databases weren’t able to handle a lot of unstructured data in real-time, NoSQL databases took up the challenge by scaling horizontally.

Key-value stores, or key-value databases, store data in a key-value format. In simple words, a key-value database represents a collection of key-value pairs in which a key serves as a unique identifier. RDBMS stands for Relational Database Management System and represents software that is used to manage, manipulate, query, and retrieve data stored in a relational database. MongoDB uses queries structured into JSON fragments and does not have any query language support yet.

Being a mature solution, it supports JOIN, atomic transactions with privilege and password security system. If you’ve fixed schema and structured data which is not going to change over time like Wikipedia. The cons of MongoDB include higher data size over the period of time. Due to the lack of atomic transactions, the speed is comparatively low compared to NoSQL. Also, the solution is quite infant and hence cannot replace the legacy systems directly. Thus the use of intelligent sharding keys or hashed sharding keys is critical.

mongodb vs postgresql scalability

In fact, NewSQL database solutions try to unite transactional ACID properties of SQL and the horizontal scalability of NoSQL. As we have already mentioned, relational databases can be scaled vertically, horizontal scaling is restricted because of the strict consistency requirements. Consistency enforcing mechanisms slow down the performance of the distributed database.

Deployment & Community Support

NoSQL databases were built for great performance and generally outperform SQL databases. Some NoSQL databases boast the impressive speed of data processing. After reading this article, you should have a better understanding of the difference between Cassandra and MongoDB. Ultimately, the decision between these two NoSQL databases will depend on your needs and the model your application requires. Unlike MongoDB, Cassandra has its own query language called CQL .

mongodb vs postgresql scalability

It is typically executed using a very rich set of operators, linked with each other using JSON. It natively supports boolean OR queries, but you must use a special operator ($or) to achieve it. Not only that, it offers greater efficiency and reliability which in turn can meet your data storage capacity and speed demands. Sparse data, on the other hand, refers to a database where there may be a very low percentage of fields where information is available. If you could check two or even twelve or twenty jobs over your career, it would still leave most jobs blank for your record. Isolation — Transactions occur independently of each other and the access to data is moderated.


The exact manner of supporting various NoSQL schemas is up to the various individual software developers. Implementations of NoSQL databases can be widely divergent and incompatible. For instance, even if two systems are both key-value databases, their APIs, data models, and storage methods may be highly divergent and mutually incompatible. Additionally, it supports document sizes up to 16MB—and you can extend this limit by breaking down data into multiple documents using GridFS.

A useful compromise is to allow for eventual consistency in favor of better scalability. Determining if your application data is a suitable candidate for eventual consistency is a business decision. CQL-compliant NoSQL databases have tunable consistency, which provides low latency and high write throughput best suited for analytical and time-series data. SQL databases generally support strong consistency, which makes them useful for transactional data in the banking and finance industries.

Also, schema changes require coordination across multiple separate MySQL instances which exposes the application to potential errors and downtime. Horizontal scaling includes dividing the dataset and load over multiple additional servers. Each machine handles a part of the workload with a comparatively lower cost than high-end hardware for a single machine. Though, due to the absence of joins and transactions , you need to frequently optimize your schema based on how the application will be accessing the data. MySQL has an encrypted password system to ensure the host verification for database access. In addition, MySQL 8.0 offers dual password support that allows developers to change password credentials seamlessly with no downtime.

  • By relational databases are typically meant databases that store data in a structured tabular format .
  • On the other hand, in MongoDB, there is no need to predefine any schema.
  • Sparse data, on the other hand, refers to a database where there may be a very low percentage of fields where information is available.
  • MongoDB and SQL databases are two polar opposite sides of the backend world.
  • MySQL offers multiple GUI tools that make the process of database designing, creation, and administration more convenient and time-saving than command-line tools.
  • Moreover, it is also possible to use Transport Layer Security TLS and Secure Sockets Layer SSL for encryption purposes.

Relational databases are still the popular choice for storing the data used by applications and platforms. Graph databases keep data represented as graphs with nodes and edges. Nodes keep information about data entities, while edges store information about the relationships between the nodes. Software application development is impossible without databases. In total, there are 343 different databases today—with the advances in technology, the traditional SQL databases are joined by NoSQL, NewSQL, and Cloud databases. Thus far the most popular and widely used database is MySQL with Oracle snapping at its heels.

Paradigm Of Sql Vs Nosql

Eventual consistency — Data will become consistent at some point in time, with no guarantee when. The simplicity of JSON is being embraced by application developers, who prefer JSON over the more complicated XML serialization format for transporting data. JSON also works well in agile environments because JSON allows for efficient usage of data.

These properties focus on the consistency and reliability of the transaction done in the database. The architecture of SQL databases like MySQL is governed by the principles of ACID property. As we discussed, we will compare MongoDB with MySQL which is a well-known SQL database and most of our audience will be familiar with it. But it could have been any other SQL database also like Oracle, MS SQL Server, PostgreSQL, etc for our comparison. MySQL analytics is extremely common and is thus a good reference point for looking at doing analytics on Mongo.

Boolean search is supported as a combination of terms search and phrase search. It is an easily executable feature though it has some limitations as well. Choices were quite obvious, MySQL, Oracle or MS SQL to mention a few. And the key to confronting these challenges of today’s business is the foundation of strong and flexible data infrastructure. Dense data refers to databases that require almost all fields of a database to be filled with data. Imagine a web form where most almost every element was a “required” field.

Supported Programming Languages

In strong consistency, data is sent to every replica the moment a query is made. This causes delay because responses to any new requests must wait while the replicas are updated. When the data is consistent, the waiting requests are handled in the order they arrived and the cycle repeats. Databases don’t have to be exclusively normalized or denormalized. If speed is a concern, a SQL database can be partially denormalized by removing a certain number of JOINS. If organization and redundancy is a concern, a portion of the data can be normalized in separate tables.

Mongodb Vs Dynamodb Differences

SQL and NoSQL represent two of the most common types of databases. SQL stands for Structured Query Language and is used in most modern relational MongoDB vs PostgreSQL database management systems . NoSQL means either “no SQL” or “not only SQL” (it uses both SQL and non-SQL querying methods).

However, at present, full-text indexing is not supported in clustered MySQL databases. And because of this, there isn’t much space for flexibility in the manner of storing data if you follow normalization. Development and deployment process is slowed down as well due to the fact that even a little modification in data model mandates the change in schema design. Also, this NoSQL solution is added to the benefits of auto-sharding, embedding and on-board replication which in turn provides high scalability and availability. On top of it, the schema-free implementation of MongoDB eliminates the need of defining a fixed database structure.

Therefore, for simple, read-heavy workflows, Postgres might be a worse choice than MySQL. Postgres is an object-relational database, while MySQL is a purely relational database. This means that Postgres includes features like table inheritance and function overloading, which can be important to certain applications. MySQL database can be queried with the help of Structured Query Language or SQL.

There Are Five Types Of Table Joins:

NoSQL databases like MongoDB support horizontal scaling, also known as sharding. In this case, instead of increasing the server configuration a new server is added for the purpose of scalability. This approach is usually less expensive because a cluster of low-cost commodity hardware can together meet the requirements to support high query volume in a cost-effective manner. On the other hand, in MongoDB, we cannot establish such a relationship between the unstructured data of the collections.

On the other hand, MongoDB is one of the most popular database management systems in the world with a strong community of MongoDB developers. Both platforms are perfectly capable of replication, and many cloud providers offer managed scalable versions of either database. Therefore, it’s worth it to consider the other advantages of Postgres over MySQL before you start your next project with the default database setting.

But so-called “mega-vendors” like Oracle and IBM lost two percent of the market in the past five years. And Gartner claims as much as a quarter of the SQL market consists of unpaid, open source databases like MySQL servers and PostgreSQL. For instance, DynamoDB is a managed NoSQL database service, while MongoDB is NoSQL database software. Thus, the nearest direct comparison would be with MongoDB Atlas, the managed database offered by MongoDB Inc and DynamoDB.

The highly organized data requires normalization, which reduces data redundancy and improves reliability. SQL is a highly-controlled standard, supported by the American National Standards Institute and the International Standards Organization . When there is lots of data in a database and lots of users logged in simultaneously, the query response time might decrease significantly. Primary and foreign keys in relational databases prevent data redundancy.

It was first launched in the year 2009 and has since become one of the leading databases in NoSQL space. MongoDB supports indexing to improve the performance of search operations in the database. It provides varieties of indices that hold data from a few fields. Also, the search is performed on indexes instead of the whole document, resulting in enhanced search speed and performance. NoSQL vs SQL performance comparison is based on attributes like consistency, availability and speed.