Elasticsearch and MongoDB are popular document-oriented databases. While Elasticsearch is known for its advanced indexing and search capabilities, MongoDB is one of the most established NoSQL databases.
The digital world is growing at a drastic rate. As a result, there is an increase in data all around the world that needs to be managed and analyzed. Due to the large volumes of data, there has been a noticeable and rising interest in non-relational databases, also known as NoSQL databases.
Businesses operating in the present times need databases. Businesses are looking for the best database management solutions to manage large data volumes. You need a database that can continuously handle large amounts of data, scale automatically, update and retrieve data seamlessly, and a secure database. Elasticsearch and MongoDB come into the picture at this point.
Elasticsearch and MongoDB are the two commonly used NoSQL data storage platforms. When you need to handle and grow your business operations data, both document-oriented databases are simple to scale. But how do the databases differ from one another? This article will discuss Elasticsearch and MongoDB in detail. An in-depth understanding of the databases will help you decide which data storage solution works best.
An overview of ElasticsearchElasticsearch is an open-source full-text search engine built on Apache Lucene and developed in Java. Indexing and data analysis are its major capabilities. Along with Kibana and Logstash , it functions as part of the Elastic Stack for data analysis.
Elasticsearch allows you to store, search, index, and analyze huge volumes of data easily. Also, It provides real-time search and analytics of all data types. The ability to get search responses quickly is because it searches an index instead of a text.
In simple terms, with Elasticsearch, you can take data from any source and format. Then in real-time, you can search, analyze and visualize it. It improves the search's speed and accuracy.
Data in Elasticsearch is stored in schema-less JSON and use REST APIs for storing and searching. Elasticsearch is a tool that enables you to use it simultaneously without interfering with one another.
Features of Elasticsearch
- Full-text search: Elasticsearch can conduct a fast full-text search
- It is compatible with JSON and REST APIs.
- It is easy to use.
- Elasticsearch can scale large volumes of data.
- It offers real-time data visualization.
- Elasticsearch does searches on structured and unstructured data.
An overview of MongoDBMongoDB is an open-source NoSQL database that uses a document-oriented data model for large-volume data storage. MongoDB allows you to store, manage, and retrieve document-oriented data. Unlike traditional relational databases, which store data in tables and rows, MongoDB uses collections and documents to store data.
A database in MongoDB is a container of collections. Documents are stored in collections, which MongoDB uses to keep track of related data. The conventional database structure of MongoDB is shown in the figure below.
A collection in MongoDB is a fundamental component containing the same document set.
A collection doesn't need to be present to insert a document. When you add the first document or store data for a collection, MongoDB creates a collection if it doesn't exist. To put it another way, when the first document is inserted, a collection is created.
Data is stored in BSON, a binary representation of JSON documents. BSON is used as it accepts more data types. The data values accepted are various data types, documents, and arrays.
Features of MongoDB
- Flexibility: MongoDB can run on servers. Data is duplicated to protect the database system against hardware failure.
- Indexing: Fields in the document can be indexed. MongoDB uses indexing to process large volumes of data in a short time.
- Scalability: MongoDB distributes data across various servers using sharding. MongoDB has an automatic loading feature due to sharding.
- Replication: MongoDB enables data availability by ensuring multiple copies of data on the servers (redundancy). If one server is down, you can retrieve data from another server.
- Queries: It supports document-based queries.
- Document Oriented: Databases contain collections that contain sets of documents.
Key Differences between Elasticsearch vs. MongoDB
Elasticsearch and MongoDB technologies are similar in one way or another due to their design and features. But both technologies differ greatly. Elasticsearch is a search engine server, while MongoDB is a database that allows you to store, manage, and retrieve data. Let us look at their differences in some key aspects.
1. JSON Adaptability
Elasticsearch can handle JSON documents in indices. Its excellent search library enables users to manage and analyze data easily. In Elasticsearch, there is no binary conversion like in MongoDB. With Elasticsearch, it's possible to analyze data present in a document. Indexes are created after analyzing data, and values are fetched from the document.
MongoDB, on the other hand, can manage JSON documents and convert them to the binary version (BSON). BSON is optimized for speed and flexibility. The primary aim for JSON documents is that users have the flexibility to model their data based on their applications’ requirements.
2. Data Storage Architecture
Elasticsearch is developed in Java and implemented on top of Apache Lucene. It writes data to inverted indexes using Lucene segments. Elasticsearch maintains a transactional log for each index in order to avoid a low-level Lucene commit for each indexing operation. Transaction logs can also help in data recovery in the event of a crash or data corruption incident.
MongoDB data storage model is different from that of Elasticsearch. It is written in C++ and stores data in Binary JSON format (BSON) MongoDB uses a memory-mapped files to map on-disk data files to in-memory byte arrays. It manages and organizes data using a linked data structure. Documents have linked lists to each other and to any BSON-encoded data. In the event of a hard shutdown, MongoDB employs journal logs to assist with database recovery.
3. Programming Language Support
MongoDB, on the other hand, offers multiple drivers for languages. MongoDB supports C++, C, C#, Node.js, PHP, Go, Python, Java, Ruby, etc.
4. Full-text Search
Elasticsearch performs best with full-text search compared to MongoDB. It has multiple advanced features that support full-text searches like analyzers and token filters.
Full-text search is not supported by MongoDB. It is compatible with CRUD operations (create, read, update, and delete).
5. Data Recovery and Backup
Elasticsearch and MongoDB offer data backup and recovery functionalities. Elasticsearch handles data backups using a snapshot. The snapshots are incremental. This means that Elasticsearch will not duplicate any data that has previously been backed up in an index snapshot that it has already created. Snapshots are restored using the restore API. The snapshot API does not offer a query backup. Thus Elasticsearch has been reported to lose data and is not considered a good option for data backup and recovery.
MongoDB provides a variety of backup options. The majority of DevOps employ
mongodump, which is available. Both queryable backup and full database backup are offered by Mongodump. Due to its inability to manage incremental backups and inefficiency with large databases, the program has some drawbacks.
You must utilize the
MongoDB oplog to implement incremental backup on MongoDB. Additionally, you can take file system snapshots and use those to make a backup of your MongoDB deployment. The underlying data files for MongoDB are copied as a result. With MongoDB enterprise, you have access to MongoDB Atlas, MongoDB Cloud Manager, and MongoDB Ops Manager.
Mongodumps, unlike Elasticsearch snapshots, will save on local disks or any other MongoDB cloud storage.
6. Use cases
Your use case will be essential in choosing the best technology. Anytime full-text search is necessary, Elasticsearch is the preferable option. In terms of log analytics, Elasticsearch also dominates since it provides a large selection of aggregation queries and supports tools like Kibana and Logstash. The tools make log analysis easier.
Application and website search, business analytics, and data analytics are some technologies using Elasticsearch to search and index different data types.
On the other hand, MongoDB is a reliable option when the data is in NoSQL format and you need a highly scalable database for CRUD operations without full-text search capability.
Finance and e-commerce organizations frequently use MongoDB to store product information and specific details. Additionally, you can keep your brand's product catalog there. You can also use MongoDB to store and model machine-generated data. MongoDB is used by various web applications to store data. Some MongoDB use cases are content management systems (CMS), the Internet of things (IoT), and Real-time analytics.
Elasticsearch vs. MongoDB:
- Elasticsearch carries out searches for both structured and unstructured data.
- In Elasticsearch, data can be accessed using a query in any format.
- Elasticsearch is fast. It perform searches quickly as it can analyze multiple records.
- Elasticsearch copy data in multiple servers leading to high data availability.
- It finds matches for full-text searches fast, even from large data sets.
- Elasticsearch is not a good alternative for data storage compared to MongoDB.
- Although it is a strong and adaptable distributed database search engine, it can be challenging to understand.
- Flexibility to store various data types.
- High speed and performance.
- Ease of use as it offers simple query syntax that is much easier to understand.
- It uses sharding when handling large datasets
- It offers Ad-hoc query support
- Data duplication is common in MongoDB
- High memory usage.
- Limited data size.
Choosing between Elasticsearch and MongoDB
Both Elasticsearch and MongoDB are popular and established data stores. As we have explained the use-cases, you can choose between Elasticsearch and MongoDB based on your use-case. Elasticsearch is a distributed, document-oriented database best suited for search and analytics use cases. On the other hand, MongoDB is a popular choice of data store for unstructured data.
If you are looking at setting up analytics on your data, Elasticsearch can be a good choice. For example, Elasticsearch combined with Logstash and Kibana is used for Log analytics. But recently, big companies like Uber and Cloudflare have shifted their log analytics from Elastic search to ClickHouse, a columnar database much more suited to store telemetry data like logs.