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MongoDB vs Elasticsearch

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There is nothing wrong to say that databases often play a significant role in the success of a very large number of businesses operating in the present time. The fact is most of the businesses in today’s world have to depend on databases and they often look for the best database management systems that can help them manage everything without investing a lot of money.

Well, you might have no idea but databases these days are not easy to handle until you are familiar with every aspect. Even some of the highly experienced programmers have different views on the best databases available for businesses. MongoDB or Elasticsearch? Reading this MongoDB vs Elasticsearch article will definitely give you a clear vision of what is best. 

MongoDB vs Elasticsearch

The two most popular databases in the present scenario are MongoDB and Elasticsearch where MongoDB is known for its user-friendly approach while Elasticsearch is gaining a lot of attention for enabling programmers to come with simply the best applications.

Actually, you can go with any of them when it comes to managing business data and information. However, it’s better to know a few things related to them before you proceed to choose any in your arena. The fact is most businesses need error-free and quick results these days without investing money in bulk.

MongoDB is a technology that is open source that can be trusted but this doesn’t mean Elasticsearch is not a wise choice. Check out the below information to know about both these technologies.

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What is MongoDB?

Mongodb is an open-source database that uses a doc oriented database written in C++ and it is a non-structured query language and in today's it is the most powerful database. in MongoDB, you can create multiple databases and each database can have multiple collections

The databases are helping businesses in several ways. Basically, they chip away at the premise of database administration methodologies that wipes out reliance between database documents and other information related to the programs or events.

Related blogCreate a database in MongoDB

What is ElasticSearch?

ElasticSearch is an advanced accumulation of projects that set up capacity structures, stacks the information that it shows up in the shape in which the projects and clients anticipate that it will be. It is also capable to hide the data from a specific or from a group of users. Thus it is a secure approach and in fact, the leading reason why it is preferred by a lot of organizations.

It is also true that several operations on information, for example, refreshing, recovery, addiction, and erasure are performed under the entire supervision of users. Thus there is no need for you to worry. Just those clients are permitted to perform such operations on information that is approved by the supervisors.

ElasticSearch can be considered to enable clients to utilize information simultaneously without being meddled with each other. This is regarded as one of the best things about ElasticSearch. To put it plainly, the greater part of the benefits that databases have over ordinary record frameworks are encouraged with the assistance of several programs. 

Businesses always need that one database should be able to cater to all their needs without showing any problem. Actually, selecting a database largely depends on the type of applications and the way data needs to be managed. In addition to this, it depends on exactly what organizations want from their database. Because ElasticSearch is capable to perform it all, it can simply be trusted.

Read these latest Interview Questions for Elastic Search that help you grab high-paying jobs!

Comparison Between the MongoDB and Elasticsearch

#1. JSON Adaptability 

MongoDB on the other side is widely regarded as one of the traditional yet powerful approaches for a document database. It is capable to handle JSON documents in its collections. It is also possible to convert them all into BSON. A BSON is nothing but just a binary version of JSON.

Its primary aim is to help users in cutting down the issues that arrive during arbitrary sized fields. This makes things quite simple for users and the overall process can be made efficient. Thus it’s a powerful approach that can be trusted.

ElasticSearch is also a powerful approach. If it is compared with MongoDB, it has an excellent search library that makes it easy for the users to manage their tasks very easily. Just like MongoDB, it is also capable to handle JSON documents into the indices.

However, it must be noted that in the case of ElasticSearch, no binary conversion is possible. Therefore MongoDB has an advantage here but in the case of Elasticsearch, it is possible to analyze the contents or data present in a document. Indexes are created after analyzing only which eliminates a lot of errors. Basically, the values of various fields are taken for effective understanding.

Related Blog: Comparison between Elasticsearch Vs Solr

#2. Comparison in terms of Data Digging

If both are compared in terms of data digging, ElasticSearch is a good option. Actually, it is capable to dig data in different methods and thus ensure reliability. It must be noted here that the overall number of Indexes is extremely important in any database.

Indexes update themselves every time when a novel record is inserted. The complexity of tasks entirely depends on the size of the Indexes. ElasticSearch faces this issue up to a great extent while MongoDB doesn’t. This is because it generally contains more indexes.

Of course, this makes you think ElasticSearch is a wise option to consider as your database. However, you need to keep this thing in your mind that most applications in the present scenario don’t need Indexes up to a great extent. Just because MongoDB has limited Indexes as compared to ElasticSearch, it is quite fast and reliable. Thus you can proceed with them in case the application size is not too large.

 MongoDB Tutorial For Beginners in 2021

#3. Data losing problem in ElasticSearch

The biggest issue with the Elasticsearch is that it has been reported loosing write operations by so many experts. This often happens while reforming the clusters. It is because of no other reason than this, ElasticSearch is not considered as a good option for the prime database.

Now, this is again confusing you to decide which one to consider. Let us make things more clear. MongoDB indexes are good but their effect on the overall Index performance is not regarded as best. Here, ElasticSearch is regarded as one of the best options.

It’s because it can be trusted for keeping the indexes simple as well as minimal. All the records can easily be kept in it and the changes to them are possible anytime. In both these technologies, there is no need for you to define the structure of a database when it comes to making any form of modification to the information.

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#4. The Data Handling capacity of both

There are a lot of programmers and experts who call MongoDB a wise choice of ElasticSearch. This is because almost every business operating currently has to handle a huge volume of data for managing the operations effectively.

MongoDB doesn’t create any issue even when there is a need to handle a huge volume of data. A MapReduce system can simply be implemented and there are certain tasks it can perform manually. This eliminates the need of users to interfere and thus a lot of time, as well as money can be saved with this approach. 

Related Page: What are Differences Between MongoDB and Amazon DynamoDB

#5. Data finding 

Advanced research is it’s another leading option. Sometimes data is too bulky that organizations face a lot of issues when they have to find a specific document or record. MongoDB is equipped with a geospatial faceted search that makes it simply the best in finding the data that is exactly required.

Actually, it is considered as one of the advanced research options that are capable to find data reliably and quickly. Also, Disk Space can easily be saved with MongoDB and this is because it is capable to store data in BSON format.

However, it doesn’t have any interface for API REST which is its drawback as compared to ElasticSearch. A strong protocol is required in case you need to have complete access to the data in case of MongoDB. Although there are several third-party software are available, but they cannot be trusted due to security regulations.

You cannot act reliably on the indexing parameters and this is because no proper or in-depth text search is possible. There are several functionalities for finding the related documents that cannot be performed with MongoDB. Instead, in several cases, you can only specify the language.

There is no possibility of ACID transactions with MongoDB. Documents cannot be related through a parallel method. Developers always have a key challenge for finding a relation between the documents and i.e. undertaking the multiple queries. This sometimes consumes a lot of time and especially when the large scale applications are to be employed.

ElasticSearch is also capable to perform an advanced search but sometimes it lacks with scalability. Its unique feature is the availability of data even if nodes are turned off due to any reason. Actually, it stores the data automatically and users don’t have to bother with this.

ElasticSearch is capable to handle queries through REST API and this is its advantage over MongoDB. Flat documents can easily be stored and without degrading the performance of the entire database. In addition to this, ElasticSearch is capable to handle data through filters.

Related PageBulk API in Elasticsearch


This gives you enough information on both MongoDB and ElasticSearch. You can consider it while choosing the primary database in your organization. In most aspects, both these approaches are powerful and simple to consider.

As far as the matter of cost is concerned, MongoDB is a budget-friendly approach that can be considered. Also, the limited Indexes make sure speedy tasks while on the other hand, you can ensure accuracy and reliability with ElasticSearch.

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Last updated: 03 Apr 2023
About Author

Prasanthi is an expert writer in MongoDB, and has written for various reputable online and print publications. At present, she is working for MindMajix, and writes content not only on MongoDB, but also on Sharepoint, Uipath, and AWS.

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