Elasticsearch and Mongodb

Elasticsearch and MongoDB are two products with significant differences between document-based storage technologies. Although Elasticsearch is often classified as a NoSQL database in many sources, I personally do not fully agree with this classification. Elasticsearch is a search and analytics product developed in Java that utilizes the Apache Lucene infrastructure. Therefore, positioning Elasticsearch as a database can lead to performance and cost disadvantages.

On the other hand, MongoDB is a document-based database that supports high availability and scalability principles and is developed in C++. When it comes to storing documents, Elasticsearch indexes them in JSON format, whereas MongoDB stores JSON documents in a binary format called BSON for more efficient read and write operations. Both MongoDB and Elasticsearch allow nested fields to be stored within a document. Elasticsearch utilizes inverted indexes to store documents, enabling efficient search performance. MongoDB, on the other hand, employs an indexing structure for its database.

Elasticsearch is particularly well-suited for scenarios that require robust full-text search and analysis capabilities. For instance, by indexing the “orders” table from an RDBMS, Elasticsearch can enable users to perform quick searches on desired fields. However, directly storing the “orders” table in Elasticsearch for functional purposes is not a suitable approach in terms of its operational principles.

Both Elasticsearch and MongoDB operate in a cluster structure, offering scalability and high availability features. In Elasticsearch, the cluster structure consists of a master node, and each index can have different replication counts. In MongoDB, the cluster includes primary and secondary nodes. With the replica set feature, the number of data copies can be determined, and the sharded cluster feature ensures data distribution among servers within the cluster. Elasticsearch inherently supports this feature, as when you create an index and specify the shard count, the index data is distributed across the nodes in the cluster. However, for MongoDB to utilize the sharded feature, the cluster needs to be set up accordingly.

Elasticsearch and MongoDB both have open-source and community editions available, which means both products can be used for free. However, these versions lack many security-focused features. To utilize enhanced features, a license fee is required. Additionally, both products offer cloud services. Elastic Cloud and MongoDB Atlas provide cloud services to their users.

In Elasticsearch, backups are performed using the snapshot method, and the backups are stored in a snapshot repository. The snapshot repository directory can be NFS, Hadoop, or S3. For smaller databases in MongoDB, the mongodump method can be easily used for backups. However, for larger databases, the backup method of Ops Manager is utilized.

When we examine usage trends, we see that MongoDB naturally has a greater presence because it is more prevalent in terms of data storage and fulfilling search requirements.

Here are some sample use cases for Elasticsearch and MongoDB:

Use cases for Elasticsearch

It is ideal for applications developed in areas such as e-commerce websites, log analysis, analyzing system metrics, anomaly detection, data visualization, and searching time-series data.

Use cases for MongoDB

It is ideal for cloud-based applications, applications requiring fast read and write operations for large datasets, querying document-based data using CRUD operations, and applications with a need to store unstructured data.

Although both products can store data at their core, as seen from their development and usage purposes, they differ from each other and even complement each other. You can access technical articles about Elasticsearch and MongoDB under the NoSQL category. And Follow our YouTube Channel from here.

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