Database

The database is used to solve the problem related to any organization or project. It can be stored in an excel spreadsheet or text. The database is used by large companies to save and organize the data in synchronized form. Data can be anything related to the organization like numbers, texts, images, amounts, audio, video, documents, etc. with the help of the data processing and management system you can create a database, update it, manage it, and retrieve any information from the database.

The uses of the database in various sectors:

• Helps to avoid data duplication and data redundancy.
• Data is validated on the basis of business rules.
• Increase inconsistency and reduction in updating errors.
• Facilitating the development of new applications program
• Controlling the security as only specific users can update, add, or delete any data.
• Reduction in cost of data entry, retrieval, and storage.
• The format of the data can be easily changed.
• Validation of the data according to the business rule
• Modification of data becomes easy if the database is growing faster.

MongoDB

MongoDB is a NoSQL database, which is document-oriented. It is used for storing a high volume of data. MongoDB does not use the traditional relational dates of tables and rows, instead, they use documents and collections. Documents consist of various key-pairs, that are the primary unit of data. Collections have a set of documents and functions which are equivalent to rational database tables.

MongoDB is built on a scale-out architecture that is distributed and has become a comprehensive cloud-based platform. It helps to manage and deliver data to various applications. It also handles operational, transactional, and analytical workloads. If you are concerned about developer productivity, supporting DevOps, time to the market, and agile methodologies then, MongoDB is the best service for your concerns.

Main Benefits of MongoDB

• Uses BSON format

One of the key features of MongoDB is the use of the BSON format. BSON stands for binary JSON that is a binary-encoded serialization used for storing documents in collections. It helps to enable MongoDB to map the document properties and internally index them and even nest the documents.

• Aggregation framework

Mongo DB provides an aggregation framework that helps in the batch processing of data. You can also use MapReduce which is an associated implementation that helps in processing and generating big data sets in a distributed algorithm in the cluster.

• Schema-less database

This feature helps in collecting databases and hold them in various types of documents. Thus, with the help of this feature, a single collection in MongoDB can hold multiple documents. These documents may consist of different content, size, and numbers of fields. This helps to provide greater flexibility to databases in MongoDB.

• It is document-oriented.

All the data is stored in documents instead of tables. in this document instead of rows and columns data stored in fields that have key-value pairs. This makes the data flexible in comparison to other RDBMS. Each document contains a unique object ID.

• Indexing

To make databases easier, documents are indexed with secondary and primary indices. This leads to saving time while searching any data from the pool of data. If the data is not indexed, it takes a lot of time for the database search to check each document and verify the query which takes a lot of time and is not an efficient method.

• Scalability

MongoDB provides horizontal scalability with the help of sharding. Sharding is the process of distributing data on multiple servers. With the help of a shard key, large amounts of data are partitioned into data chunks and are evenly distributed across shards residing across the various physical servers. It also helps to add new machines to a running database.

• Replica

MongoDB helps to provide redundancy and high availability through replication. It creates various copies of data and sends them to different servers. The reason being if one failed then the data can be retrieved from another server.

• High-performance

As compared to other databases, MongoDB has a high data persistence. This is due to the features like a replica, indexing, scalability, etc.

How Does MongoDB Application Work?

Now we know that working with MongoDB is easier than any other relational database. It does not contain any table and all the data is stored in JSON format. a unique key is defined with the value associated with it. The key-value pairs are stored in a document which in turn are stored in a collection. The collection can have any number of documents with their number of key-value pairs.

The data of the MongoDB database is stored in BSON. It is nothing but an extension of JSON which helps to support more data types than JSON. We can store anything like binary data, object, JavaScript code, double, Boolean, string, integer, and many more. means of for the group inside the collection which is equivalent to a table in a relational SQL database. There is no predefined structure of a collection.

Disadvantages of MongoDB

• Transaction

MongoDB trench the exchange bolster for accomplishing high scalability and performance. This makes the database simple to scale which uses various shoddy equipment for adjusting heap. It restrains the zones where MongoDB can be utilized to reshape the information model.

• Join operation.

As the document contains all the information required in the document, there is no need to join the information. This helps to keep in mind the end goal of having a capacity to effortlessly scale horizontally. But you can play out your join through a number of queries.

• Limited RAM

MongoDB helps to utilize the memory-mapped record. It also lets the operating system handle the storage. The time frames of the database are restricted by virtual memory. There is a restricted RAM of 4GB, and the OS uses approximately 2GB of space. On the 32bit machine, when you are left with just 2 GB of space, it makes it difficult for you to genuinely do anything

PostgreSQL

PostgreSQL is a relational database management system, which is free and has an open-source. It was first launched in Berkeley by the scientists of the University of California. It was named Ingres but over time with further improvements, it expanded its functionality. It was then named the Postgre95 and finally PostgreSQL.

In the current years, a lot of companies have started using the PostgreSQL project for development purposes. The PostgreSQL system is constantly improving and has adapted subsequent platforms.

As you know, PostgreSQL is the world’s most advanced and open-source database. It offers a variety of built-in PostgreSQL data types, which includes HSTORE, JASON, XML, Geo-spatial, etc. It also helps in flexible indexing which helps to feature First composite indexes, full-Text Search, etc. PostgreSQL runs on all major operating systems that include UNIX, Windows, and Linux. It offers various programming languages like Python, Java, Ruby, C, C++, PHP, Lisp, .NET, etc.

PostgreSQL also provides enterprises with database solutions that are used by a lot of enterprises across a lot of industries including government, information technology, financial services, and media and communication.

Strengths of PostgreSQL

• Open-source DBMS

PostgreSQL provides functions and enterprise-class performance among with no end of the developmental possibilities. The users can directly participate in the community. They also share and post bugs and inconveniences.

• Diversified community

There are wide communities in PostgreSQL. Users can easily develop modules and propose. With all different kinds of people organized from the global community by collecting opinions of developmental possibilities. Within the communities, collective intelligence facilities the transmission of indigenous knowledge.

• Functions

SQL functions are also called “Store Procedure” which can be used for a server environment. It also supports different languages like PL/SQL in Oracle, PL/Python, C, C++, etc.

• Transaction and Acid

PostgreSQL supports ACID i.e., Atomicity, consistency, durability, and isolation.

• Diversified indexing techniques

PostgreSQL provides B+ tree index techniques, GIN, First, etc.

• Flexible Full-Text search

When searching for strings, Full-Text search is available with the execution of string search and vector operation.

• Diversified replication

PostgreSQL supports a different variety of replication approaches like Slony-I, Streaming Replication, and cascading.

• Diversified extension functions

PostgreSQL supports various kinds of techniques for storing geographical data like DBLink, Key-Value store, and PostGIS.

Disadvantage of PostgreSQL

• PostgreSQL helps to create separate services for each client which helps in the utilization of a lot of memory.
• It has performance issues.
• Lack of skilled professionals.
• PostgreSQL is not worthy of speedy performance. It lacks in comparison to other tools.
• The replica is more difficult and complex.
• Installation is not easy to understand for any beginner.

Making the final call: PostgreSQL vs MongoDB

It is easier to walk away from SQL which means moving ahead of the large ecosystem of technology, which uses SQL. A lot of data management and BI tools rely on SQL. These are programmatically generated and are complex SQL statements, which help to get the right section of data from the database. In such cases, PostgreSQL does well as it is an enterprise-grade and robust implementation which is understood by a lot of developers.

Even with flat, tabular models, it is not going to change and there is no need for scaling-out relational database and SQL. PostgreSQL has a certain downside to it while comparing with MongoDB. It relies on relational data models. These are unfriendly to the data structure developers that work within the code. They also define in advance the slowing progress as and when change is required.

MongoDB supports an iterative and rapid cycle of development because of the document database which turns the data into code. It is under the control of the developers. The speed gets disrupted but tabular, and rigid data models that use relational databases. These are usually reshaped through an intermediate process but the database management administrators, slowing the entire process of development. These bottlenecks hamper innovation.

While battling for scalability, PostgreSQL users use a scale-up strategy to fight. At some point, you might hit a wall or have to divert the resources to find other ways for high-performance use cases. You can find other ways through denormalizing data, caching, or other strategies.

In MongoDB techniques, scalability is built-in through sharding. This enables the project to have a horizontal scale-out approach. After proper sharding, you can always add more instances. MongoDB Atlas has a broad multi-cloud, that is a globally aware platform that is fully managed by you.

PostgreSQL helps to support replication, while MongoDB helps to support advanced features like automatic failover. PostgreSQL needs a third-party product for developing databases independently. This makes the PostgreSQL approach more complex and works slower. While MongoDB comes with in-built self-healing capabilities.

Coding difference between MongoDB & PostgreSQL

Selecting records from the customer tables in PostgreSQL:SELECT * FROM customer;MongoDB:db.customer.find()Inserting records into the customer tablePostgreSQL:INSERT INTO customer(cust_id, branch, status) VALUES ('appl01', 'main', 'A');MongoDB:db.customer.insert({cust_id: 'appl01',branch: 'main',status: 'A'})Updating records in the customer tablePostgreSQL:UPDATE customer SET branch="main" WHERE custage > 2;MongoDB:db.customer.update({custage: { $gt: 2 }},{$set: { branch: 'main' }},{multi: true})

Conclusion

We now know that MongoDB and PostgreSQL both are reputable, robust database which has their disadvantages and advantages. Both of the databases are good according to their field. The most important thing is what structure will your data have, how your data is going to be used, and how will your application scale. As you get the answers to these questions your choice will become clear.

Nature and target use cases of your data are vital in selecting the database. You can use a relational data model if you have a large ecosystem of SQL skills and tools. MongoDB succeeded in enterprises as it opens the door to new levels of developer productivity. While roadblocks are often introduced through statistic relational tables. If you have data that needs to be delivered at a scale and will benefit the developer to control the schema you must prefer a database like MongoDB.