Elasticsearch vs MongoDB comparison
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MongoDB is an open-source NoSQL database that uses a document-based data model and provides high performance, high availability, and easy scalability. It uses collections and documents where collections are groups of documents similar to tables in a relational database. Elasticsearch is a search engine built on Lucene that provides features for data storage, analysis, and search. It has a distributed architecture and uses JSON/REST APIs. Elasticsearch supports features like distributed search, high availability, multitenancy, and horizontal scaling but lacks some search functions compared to MongoDB. MongoDB and Elasticsearch both support features like distributed architecture, high availability, and horizontal scaling but Elasticsearch has faster search speeds while MongoDB provides better support for different
Elasticsearch vs MongoDB comparison
- 2. It’s an open-source NoSQL database developed for high performance, high availability, and easy scalability. Collection and document are the two primarily used terms/concepts in MongoDB. Here, Collection is referred to a group of these documents, which is like an RDBMS table.
- 3. What is MongoDB? As a definition, MongoDB is an open-source database that uses a data model and a non-structured query language. It is one of the powerful NoSQL systems and databases around, today. MongoDB Atlas is a cloud database solution for contemporary available globally. This best-in-class automation and established fully managed MongoDB across AWS, Google Cloud, and Azure. It also ensures availability, scalability, and compliance with the security and privacy requirements. MongoDB Cloud is a unified includes a global cloud database, search, data lake, mobile, and
- 4. ElasticSearch is a fast growing technology built on Lucene. The main scope of ElasticSearch is to be a search engine. It also provides a lot of features that allow you to use it for data storage and data analysis. ElasticSearch has many innovative features like: JSON/REST-based api and natively distributed in a node/cluster. ElasticSearch can be set up on a physical or virtual server depending on RAM, CPU and disk space.
- 5. Elasticsearch vs RDBMS vs MongoDB ElasticSearch RDBMS MonogoDB Index (Indices) Database Database Shard Shard Shard Mapping/Type Table Collection Field Field Field Object (JSON Object) Record (Tuples) Record (BSON Object)
- 6. Feature of Elasticsearch •Distributed search •High availability •REST interface •Powerful query DSL •Multitenancy •Geo search •Horizontal scaling Limitations - Elasticsearch is not the perfect data store for all scenarios. It has a few limitations that need to be taken into account when choosing the right data store for your application.
- 7. Feature of MongoDB •Distributed document storage •High availability •Schemaless •Powerful queries and aggregations •Horizontal scaling •Built-in security •Great indexing capabilities •Geo search •GridFS to store any size document Limitation -MongoDB are its inability to provide full-text search at speed and its lack of some search functions, like tokenizing text.
- 8. Speed of search is better in Elasticsearch compared to MongoDB. Backup - In MongoDB, you need to use the MongoDB oplog, which is a capped collection. It is also possible to create a backup of a MongoDB deployment by taking a snapshot of the file system. This makes a copy of MongoDB’s underlying data files. Elasticsearch performs incremental backups using _snapshot REST endpoint with the help of plugins, and its backup destinations can vary from file systems to cloud storage. You can delete old snapshots easily, and the recovery of
- 9. Programming Language Support – MongoDB is more popular because it has support for more languages starting from C, C++, to Ruby Scala, Python, Go, Java, JavaScript. Therefore, all the supported, and therefore, the reach of the database has increased hand, Elasticsearch supports only Java, JavaScript, Perl, PHP, importantly .Net. DotNet is not yet supported by MongoDB. Third Party Support – Even though MongoDB and Elasticsearch started almost together, MongoDB due to its simplicity has grown That is why there are so many different 3rd party supports Cloud are the two 3rd party service support that Elasticsearch has. data with BI and SQL tools. It is going to speed by query running
- 10. Elasticsearch MongoDB A Java-based NoSQL database is called A C++-based document- Elasticsearch can handle JSON documents in documents cannot be converted to binary. It has the capacity to manage JSON documents JSON to BSON (a Binary version of JSON). To design the finest application, programmers attention. Because MongoDB is a user-friendly database, don’t need to pay as much attention to it. Full-text searches can be carried out using It enables CRUD operations without the need for Elasticsearch wins the search engine category seventh overall. In terms of document storage databases, first, and fifth overall.
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