MongoDB vs Elasticsearch

Difference Between MongoDB vs Elasticsearch

The world now revolves around big data analysis, machine learning, and artificial intelligence. It’s no doubt that it has made our lives much easier. The data involved in achieving this is humongous, and it is not an easy task to manage, modify, and arrange the information provided. MongoDB is a document-based database under the NoSQL category with no schema required. Data is stored in key-value pairs in BSON(Binary JSON) files, called documents. JSON format aids data transfer between client-server applications in a human-readable form. Elasticsearch is an open-source, highly scalable analytics and search engine. It is developed in Java and is a wrapper on Apache Lucene Library. It consists of an HTTP web API interface. It has no schema with JSON documents where all the data is stored. Between MongoDB vs Elasticsearch MongoDB, it is effortless to set it as it has no schema.

Head to Head Comparison Between MongoDB vs Elasticsearch (Infographics)

Below are the top comparisons between MongoDB and Elasticsearch:

Features of MongoDB and Elasticsearch

Let us study some important features of MongoDB and Elasticsearch:

• MongoDB is a database as mentioned earlier, whereas Elasticsearch is a distributed search engine. In the current scenario, we can see that Elasticsearch is being used as a general-purpose database. Although databases and search engines are interchangeable based on their usage, fundamentally they are different entities altogether.
• Choosing a database/search engine is solely based on the applications and their requirements. MongoDB is a better choice in cases where there involve many write queries to the data. When a record or a sub-record is updated, all the records’ indexes need to be re-indexed. This is much easier with MongoDB. For applications that require fast fetching of queries, elastics search is much faster. It has an inbuilt TTL feature and extensive indexes.
• Search engines, unlike databases, cannot guarantee the exact result. They rank all the results in order of how close the results are to the query given. It so happens that the closest matched results are exact. Databases work differently. There is no probability involved here. Queries are required to be written carefully as they match the exact query, and the filters are dependent on the order of execution. For example, if the data is queried satisfying 3 different filters, the whole data set is matched to the first filter, which returns a subset of matched results. The second filter is matched on the data which was matched with the first filter and so on…Hence it is essential to crafting the queries. If the earlier queries don’t match which any data, no data will be returned.
• MongoDB has various advanced indexing methods like geospatial indexing that helps in retrieving data faster. The only drawback is that it is inbuilt and doesn’t have an API interface to use the feature, unlike elastic search. Elastic search is a distributive search engine incorporated with the HTTP web interface.
• MongoDB has limited indexing; therefore, data retrieval is faster, whereas Elasticsearch is better for ensuring the retrieved data’s reliability and accuracy.

Similarities between MongoDB and Elasticsearch

Let us check some similarities between MongoDB and Elasticsearch:

• They both store data in JSON documents with no schema.
• Both support indexing data, although each has its level of complexity and ease.
• Both are used for a huge data set that requires processing.
• Both support sharding data(partitioning based on indexes for storage management).
• Both are developed by open source communities and hence have detailed documentation.
• Both support concurrency. Multiple instances of the same data can be created, and various operations can be done on the data the instances hold.

Comparison between MongoDB and Elasticsearch

Below are the top 10 comparisons between MongoDB and Elasticsearch:

MongoDB	Elasticsearch
It is a relational general-purpose database.	It is a distributive search Engine wrapper on Lucene library.
Data is retrieved by comparing documents.	Uses Vector space model, Information Retrieval etc.. to match to the most likely results.
Exact matches for query filters are returned.	Most probable matches for the given queries
Implemented in C++	Implemented in Java
No REST API interface	Has REST API Interface(Supports HTTP)
Documents are stored in BSON format (Binary JSON).	Documents are stored in JSON format.
The users should do indexing.	The Lucene Indexer does indexing.
Hosts the original data content	Queries and sometimes modifies the existing hosted data as it is just a search engine wrapper.
Compass, a GUI to visualize MongoDB data, mongo shell is also present for the command-line interface.	Kibana is a visualization tool used to visualize the logs to understand the operations.
Allows Mapreduce operations on the data to manage high scale data	It does not support Mapreduce operations on huge data but runs text search filter on huge data.
Does not have TTL feature	Has inbuilt TTL feature
Indexing is limited here due to storage constraints.	Lucene, therefore, unlimited indexing already index data.
Write operations are reliable and accurate.	Known to lose data during write operations
Since there is no inbuilt web interface, third-party applications can be used to retrieve data that is not always secure; hence, it is not entirely reliable.	Data retrievals are reliable due to the web interface.
Used mostly in applications related to search, security, metrics, maps, logging, etc.	Used mostly in applications related to the Internet of Things(IoT), Mobile, Cloud Computing, etc.
Supports almost all languages that are used currently	Supports very few languages (Java, Javascript, Perl, .Net, PHP, Python, Groovy and Ruby)
MongoDB, due to its extensive usage, had multiple third-party applications. Example: Studio 3T, DHawk, CData, ScaleGrid Etc.	Elasticsearch has only a few third-party applications. Example: Dremio Elastic cloud

Conclusion

Overall, MongoDB and elastic search seem similar in working but are very different fundamentally. Choosing either one is entirely dependent on the organizations and their applications. A detailed description of the pros and cons is given above to get a brief understanding of both the entities.

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