Elasticsearch vs MongoDB – What’s the Difference? (Pros Cons)
Elasticsearch vs MongoDB – What’s the Difference? (Pros Cons). Any business that handles large volumes of data needs a reliable database solution. Both MongoDB and Elasticsearch are handy options that provide robust database management. Elasticsearch has a distributed, multitenant capable search engine technology, while MongoDB has a flexible document model. Both tools are ideal options when creating an efficient NoSQL network for your company.
This article explores the features, advantages, and disadvantages of these database systems. With the right information, you make the right choice depending on your business needs.
Shall we start with Elasticsearch vs MongoDB – What’s the Difference? (Pros Cons).
What is Elasticsearch?
Elasticsearch is an open source search tool that’s part of the ELK stack that also consists of Kibana and Logstash. Distributed, RESTful search and analytics engine with numerous functionalities. In terms of database management, Elasticsearch is a document oriented database built on the Lucene library.
Elasticsearch accommodate large data sets with its distributed clustering technology. Besides, you harness its advanced analytics capabilities through Elasticsearch’s RESTful web interface, which supports JSON documents and visualization tools. Great platform for enterprise applications that require fast access.
How Elasticsearch Works
Elasticsearch provides a powerful search engine for complex queries with high scalability. Stores data in structured JSON documents, which are automatically divided into small shards and distributed across multiple machines for improved performance. When searching, Elasticsearch utilizes an inverted index to quickly look up the IDs of relevant documents according to given terms. It combines all results from different shards for the final output.
Distributed architecture ensures it scales easily. It has a cluster of interconnected nodes that share resources. Each node stores the data and provides access to the search engine. Whenever you perform a search, all nodes in the cluster receive the query. Afterward, each node searches for its own index and then sends search results to the user.
Features of Elasticsearch
Distributed Architecture
Provides a distributed architecture, allowing data storage across multiple nodes within a single cluster. The horizontal scaling method makes it possible to store and query data without negatively impacting performance. Besides, it utilizes the elastic nature of the cloud to maintain scalability.
Elasticsearch provides data sharding to increase throughput in clusters. Through sharding, it automatically distributes the data and load balancing across all the nodes. Each node holds records from multiple shard copies. This provides high levels of fault tolerance in case one or more nodes fail.
Reliable Indexing
Robust indexing capabilities, by leveraging search engine technology. It provides seamless data retrieval. Additional features, like clustering and replication, improve scalability and performance while mitigating service interruptions.
REST APIs
REST APIs allow developers to perform complex queries and build custom data integrations. They also help index large amounts of documents and analyse data in real time. By accessing all the major functions of Elasticsearch from a streamlined framework, developers quickly deploy search solutions that scale with little effort. The unified interface also minimizes code complexity while enabling fast debugging.
Powerful Full Text Search Engine
Powerful full text search engine is also used to generate quick, comprehensive searches of large databases. It provides an efficient way to query and analyse vast documents with complex structures and unstructured textual information. Leverage keyword based searches and advanced functionalities like aggregations, tokenization, and synonyms.
Security
Security rich tool with a wide variety of controls. Allows you to integrate a number of industry standard identity management systems such as LDAP, Active Directory, and Role based Access control. Also use single sign-on (SSO) solutions, such as SAML or Kerberos. Besides, it provides encryption at rest, if you want to encrypt data on a disk or in system memory. Also, uses additional encryption keys to secure sensitive configurations and log files.
Also provides an audit trail for tracking user activities. Its host based security settings enable the binding to loopback IP addresses or specifically authorized IP addresses. With these features, you always ensure your data is secure while providing access to the right users.
Integrations
Seamless integration with multiple applications, including mobile and web services. These integrations enable users to query and analyse user data simultaneously. Integrates seamlessly with Logstash and Kibana to monitor web traffic or visualize data respectively.
Alerting
Powerful alerting tool that allows you to configure notifications. Enables faster detection of potential issues to run a highly reliable database system with minimal downtime. Configure alerts to trigger whenever any sort of anomaly arises in the code, allowing for 24/7 monitoring. Basically, Elasticsearch’s alerting system is designed to enhance data recovery.
Pros of Elasticsearch
- Document oriented tool that does not use tables or schemas.
- Supports instant search and auto completion.
- Index, search, store, and analyse large volumes of data in near real time.
- Supports a wide variety of documents.
- Performs complex queries on large datasets and find best searches quickly.
Cons of Elasticsearch
- Cannot be used as a primary persistence store.
- Steep learning curve.
Up next with Elasticsearch vs MongoDB – What’s the Difference? is to introduce MongoDB.
What is MongoDB?
MongoDB is a flexible NoSQL, general purpose database management system that provides powerful data storage and retrieval features. It was designed to eliminate relational databases’ complexities while being more advanced than other NoSQL solutions. Its document-based model enables efficient manipulation of a range of data types. These include strings, numbers, arrays, embedded documents, and more.
MongoDB is famous for its scalability, allowing it to handle large datasets without sacrificing performance. This gives developers a high level of flexibility to build and deploy high performance applications. As a result, more organizations are turning to MongoDB for real-time analytics, enterprise application ecosystems, and content management systems. It’s an ideal solution that comes in handy if you are looking for easy scalability and support for real time data analytics.
How MongoDB Works
MongoDB documents are schema less i.e, one document doesn’t have to resemble another like in SQL databases. The system creates processes allowing data replication from one cluster or node to another. By leveraging Replica Sets, MongoDB ensures that once one node fails, the other nodes step up to take its place without creating downtime.
Additionally, MongoDB utilizes sharding technology which breaks up large datasets into manageable pieces. Sharding makes filtering and locating information more efficient. Organizations handle larger volumes of data while still being able to query information quickly and accurately. This is because the query process isn’t affected by the size of the dataset being managed.
Features of MongoDB
Here are the top features that make MongoDB a reliable NoSQL database:
Load Balancing
Advanced load balancing capabilities to help manage database infrastructure. By distributing millions of requests across multiple servers provides better performance, scalability, and overall system reliability. This database solution easily scales horizontally without experiencing downtime. Great solution for organizations looking for maximum uptime.
Thanks to its load balancing capabilities, MongoDB handles concurrent read and write requests. Utilizes locking protocols and world class concurrency control to ensure data consistency. When using MongoDB, you don’t have to add a third-party load balancer as it provides a consistent user experience.
Sharding
Sharding enables the splitting of large datasets across multiple distributed instances. Apply sharding to large datasets, enabling the database to distribute complex queries. This feature helps improve the scalability and performance of any MongoDB cluster.
Sharding works by dividing up data horizontally across multiple shards or nodes in the cluster. Horizontal scaling means that shards house a portion of the dataset. Basically, the shards act as separate databases. Combining multiple distributed shards forms a single, high performance database that handle the needs of popular applications without downtime.
Data Replication
A powerful data replication solution protects organizations from server failure and service interruptions. Replicates data across multiple servers to help with disaster recovery and backup. MongoDB uses replica sets to enable data replication. The primary node accepts write operations and applies them across secondary servers to replicate the data.
In the event of node or network failure, the secondary servers are elected to become the new primary node. Once the former primary node returns online, it becomes a secondary node. Furthermore, MongoDB employs its built-in algorithms to detect failures and outages and facilitate automatic failover.
Ad Hoc Queries
Supports ad hoc querying to help users make complex database queries. Basically, an ad hoc query is a short command that, when executed, may result in different results depending on the query. MongoDB optimizes how ad hoc queries are handled, and this comes in handy when there are millions of variables in question. MongoDB also supports geo queries, field queries, and regular expression searches. Utilizes indexes, multiple read preferences, and dynamic query optimization to deliver quick results while using minimal hardware resources.
Pros of MongoDB
- Supports multiple document ACID transactions.
- Integrates seamlessly with data analytic tools such as Hadoop.
- Schemaless NoSQL database that does not require you to create a schema when using it.
- Provides data replication to prevent system downtime as a result of server failure.
- Sharding feature for horizontal scalability.
- Indexes document fields with primary and secondary indices to enable easier search.
- Faster than most relational database solutions.
Cons of MongoDB
- Requires large amounts of memory for data storage.
- Limited document size of 16 MB.
- Incorrect indexing impacts performance significantly.
It is the time with Elasticsearch vs MongoDB – What’s the Difference? to perform a comparison of both tools.
Main Differences Between Elasticsearch and MongoDB
Both Elasticsearch and MongoDB are document based database systems ideal for managing NoSQL data. Highly scalable but they have key differences especially in features and use cases. Well, Elasticsearch vs MongoDB – What’s the Difference?
Data Storage Architecture
On the other hand, MongoDB is written in C++ and uses memory map files to map data files to on-memory data arrays. Therefore, it’s more suited for high write and high throughput operations.
Document Size
Elasticsearch has a default document size of 100 MB. However, you stretch the limit up to 2 GB. However, large documents often come with additional challenges. On the other hand, MongoDB’s documents have a maximum size of 16 MB. You can, however, stretch this limit using the GridFS functionality.
Licensing
Elasticsearch is an open tool provided under the Apache 2.0 license. Holds all the functionalities you need to build highly secure applications. Purchase the premium editions if you are looking for advanced security features such as IP filtering, alerting, and audit logging. On the other hand, MongoDB has two versions: free and Enterprise edition. The free edition comes with basic features such as basic monitoring tools. The Enterprise editions provide better management, an in memory database engine, and advanced monitoring.
Supported Programming Languages
Backup and Recovery
To backup data in Elasticsearch, you can use a snapshot REST API. This API provides a plugin that enables you to store backups in a repository. The snapshot repository is hosted on cloud objects storage servers such as AWS S3, Hadoop DFS, or local hardware.
In MongoDB, use the mongodump tool for small deployments. Alternatively, you take scheduled snapshots of the underlying file system. For cloud based workloads, use commercial cloud services such as MongoDB Cloud Manager and MongoDB Atlas. These tools provide fully managed backups of MongoDB.
Use Cases
Elasticsearch is mainly used for full text search. It provides advanced search features such as analyzers, token filters, and tokenizers. On the other hand, MongoDB is used to manage NoSQL data. Also, it uses text based indexes for full-text queries. MongoDB is ideal for Internet of Things (IoT), real time analytics, mobile applications, and more.
Thank you for reading Elasticsearch vs MongoDB – What’s the Difference? We shall conclude the article now.
Elasticsearch vs MongoDB - What's the Difference? Conclusion
Both Elasticsearch and MongoDB are great options if you want to eliminate the challenges of SQL based documents such as MySQL. However, these two are fundamentally different. Elasticsearch is a search engine, while MongoDB is a NoSQL database system. While you can use Elasticsearch for data storage, MongoDB is more advisable. Elasticsearch is ideal for complex search queries and data analytics. That said, you can use both tools together for maximum performance.
