Any organization dealing with large volumes of data requires a robust database solution to function smoothly. MongoDB is a well-established platform and is used by top companies like Toyota, Cisco, Bosch, and Forbes. Another popular option gaining recognition is Elasticsearch, with BMW, StackOverflow, and Apna as its customers.
While both platforms are versatile and support document-based storage, they cater to different use cases. This article will provide an in-detail comparison between Elasticsearch vs MongoDB to help you better understand which platform is the right choice for your data workloads in the long run.
An Overview of Elasticsearch
Elasticsearch is an open-source, distributed platform that you can use to search, index, store, and analyze data in near real-time. This vector database was first released in 2010 and is built on Apache Lucene. Elasticsearch is document-oriented and stores data in JSON format. Its integration with Kibana and Logstash allows you to perform log data analytics and visualization with ease. You can also extend Elasticsearch’s functionalities with the HTTP web interface.
How Elasticsearch works
When you add data to Elasticsearch, it stores it in JSON documents, the basic units of information that can be indexed. A collection of such documents with similar characteristics forms an index. It is the highest level of entity you can query against.
Elasticsearch uses an inverted indexing data structure to store the mapping of all the unique content to its location within the document or set of documents. When you run a search query, Elasticsearch returns the document with the maximum number of matching terms, enabling you to perform efficient full-text searches.
Key Features of Elasticsearch
Here are some distinct features of Elasticsearch:
- Clustering for High Availability: The clustering feature enables you to distribute data and provide federated indexing and search capabilities across multiple nodes, improving performance and data availability.
- Vector Search: Elasticsearch’s _knn_search API endpoint uses approximate nearest neighbor (ANN) based on the HNSW algorithm to provide faster, more scalable vector similarity search on large datasets. It achieves this by sacrificing a little bit of data accuracy for better performance.
- Dynamic Mapping: With this feature, Elasticsearch automatically creates new fields based on the document it indexes. This flexibility makes it easy to work with evolving data structures. You can also customize the mapping rules based on your requirements.
- Data Snapshot and Restore: A snapshot is a backup of your Elasticsearch cluster. You can create backups of specific indices or the entire cluster and store them in a repository on a shared file system. Plugins supporting remote repositories are also available.
- Audit Logging: Auditing and logging provides visibility into security-related events. This helps you track and monitor suspicious activities, authentication errors, or refused connections and provides evidence if an online attack occurs.
An Overview of MongoDB
MongoDB is an open-source NoSQL relational database management system (RDBMS) written in C++ programming language. Like Elasticsearch, it is document-oriented and has a distributed architecture. However, it allows you to store data in BSON (Binary JSON) format. Additionally, MongoDB offers Atlas, a Database-as-a-Service (DBaaS), to help simplify the deployment and management of highly scalable databases.
How MongoDB works
The backend of the application layer contains a server that performs server-side logic and contains the drivers or Mongo shell. You can interact with MongoDB by using these drivers or Mongo shell and send queries to the MongoDB server in the Data layer.
The MongoDB server doesn’t directly read or write data to files or memory; instead, it passes the queries to the storage engine. The storage engine handles the reading or writing data operations.
Key Features of MongoDB
Here are some of the key features of MongoDB for you to explore:
- Ad-Hoc Queries: With Ad-Hoc queries, you can search and retrieve data dynamically using field queries, range queries, or regular expressions. It provides the flexibility to access the most relevant data in real-time without needing a fixed schema.
- Sharding: It enables you to practice data partitioning and split large datasets into manageable chunks called shards. With sharding, you can achieve horizontal scalability and handle increasing data operations with zero downtime.
- Replication: You can leverage replication to distribute data across multiple servers, ensuring high availability and stability. MongoDB uses these replica sets for backup and recovery in case of hardware failures, service interruptions, or system crashes.
- Load Balancing: MongoDB helps you distribute client requests across multiple servers to balance workloads. This mechanism prevents server bottlenecks while efficiently handling requests during heavy traffic.
- Indexing: Appropriate indexing allows you to organize your data for faster lookups, locate specific documents without scanning entire collections, and optimize performance for large datasets.
Comparative Analysis: MongoDB vs Elasticsearch
Selecting the right database solution is a crucial aspect of your data strategy. This section highlights several important factors you should consider when deciding between Elasticsearch database vs MongoDB.
MongoDB vs Elasticsearch: Querying and Search Capabilities
In Elasticsearch, you can define queries using a JSON-based Query Domain Specific Language (DSL). It supports the execution of joining queries, compound queries, and percolate queries. You can also utilize its advanced search features like full-text search, fuzzy matching, faceted search, and geospatial search.
MongoDB also supports complex query execution, such as ad hoc queries, field queries, and geo queries. However, it is not as robust as Elasticserch when it comes to search capabilities.
Elasticsearch vs MongoDB: Performance and Scalability
MongoDB scales horizontally through sharding and data replication. This allows you to achieve distributed data storage across multiple servers and handle high-volume data effectively. MongoDB’s flexibility to manage diverse types of data ensures high performance in operational workloads.
Elasticsearch also supports horizontal scaling but is optimized for concurrent searches and analytics. It offers high performance when dealing with unstructured data and heavy analytics. Between Elasticsearch vs MongoDB, the speed of executing data operations depends on your specific use case.
MongoDB vs Elasticsearch: Relational Data Handling
Elasticsearch enables you to manage relational data through nested objects and parent-child relationships. However, using these methods is not feasible as they result in memory overhead and excessive utilization of computational resources.
On the other hand, MongoDB supports relational data handling through embedded documents and references. These methods allow you to model one-to-one and one-to-many relationships.
Elasticsearch vs MongoDB: Backup and Recovery
Elasticsearch offers an incremental snapshot REST API and plugins for storing backups in snapshot repositories. You can host these repositories locally or through cloud data storage solutions such as Microsoft Azure, Google Cloud Storage, and AWS S3.
With MongoDB, you can use the mongodump tool for binary export of your database and MongoDB Cloud Manager, a hosted backup service for point-in-time recovery. MongoDB provides more flexible backup and recovery facilities than Elasticsearch.
MongoDB vs Elasticsearch: Cost Considerations
MongoDB and Elasticsearch both offer a licensing model with open-source (free to use) and commercial versions. The pricing model for MongoDB’s Atlas edition includes a pay-as-you-go Dedicated plan starting at $0.08 /hour and a pay-per-operation Serverless plan starting at $0.10/1M reads.
On the contrary, Elasticsearch provides four plans: Standard, Gold, Platinum, and Enterprise, with the cheapest plan starting at $95/month. You can consult with their respective teams and get clarity on the expenses based on your specific use case.
Elasticsearch Database vs MongoDB: Comparison Table
Here is a table briefly outlining the differences between MongoDB vs Elasticsearch:
Elasticsearch vs MongoDB: When to Use Which?
Below are the use cases where you can leverage Elasticsearch:
- Enterprise Search: Elatsicsearch offers features such as token filters, analyzers, and tokenizers to help you perform search operations, including product searches and people searches within company intranets.
- Security Analytics: You can use the Elasticsearch, Logstash, and Kibana (ELK) stack to better understand your system's security through real-time analysis of access logs and related logs. This allows you to identify bottlenecks and troubleshoot them quickly.
- Infrastructure Metrics Monitoring: Elasticsearch provides deeper insights into your infrastructure’s performance. By collecting and analyzing metrics and parameters that vary by use case, you can optimize resource utilization and ensure the overall health of your systems.
These are various use cases where MongoDB can be advantageous:
- IoT Data Storage and Analysis: MongoDB offers scalability that enables you to store large volumes of high-frequency sensor data from IoT devices. It also helps you implement real-time data processing using Atlas Stream Processing.
- Hybrid and Multi-Cloud Deployments: You can deploy MongoDB on a desktop, large computer clusters at data centers, and cloud environments by installing the software or using Atlas as Database-as-a-Service.
- Content Management Systems: MongoDB's schemaless design makes it ideal for storing and managing NoSQL database content such as text, images, audio files, and videos. It allows you to perform CRUD operations on such data with ease.
Streamline Your MongoDB & Elasticsearch Data Pipelines with Airbyte
Whether you choose to work with MongoDB or Elasticsearch, having secure data pipelines for effective data movement is necessary to achieve operational efficiency. Airbyte, with its library of over 400 pre-built connectors, can help you with this. It is an AI-enabled replication tool that allows you to load data from Elasticsearch to MongoDB or any other source-destination combination in minutes.
Airbyte is a versatile tool for data professionals of all skill levels. Its user-intuitive interface and the flexibility to create custom connectors using the low-code Connector Development Kit encourage even your non-tech staff to explore data actively. Conversely, Airbyte’s Python library, PyAirbyte, is a developer-friendly feature.
Using PyAirbyte, your tech teams can leverage Airbyte connectors to extract data from multiple sources and load it into SQL caches like DuckDB, Snowflake, and BigQuery. This SQL-cached data is compatible with popular AI frameworks like LangChain and LlamaIndex, enabling you to build LLM-powered applications.
Below are some more features of Airbyte that can streamline your data integration efforts:
- Terraform Provider: You can automate the process of setting up your data pipelines using Terraform provider. It is an Infrastructure-as-Code (IaC) solution that helps you manage Airbyte resources such as connections, sources, and destinations.
- Schema Change Management: You can configure Airbyte’s settings to indicate how the platform should handle scheme changes occurring at the source and propagate them to the destination. This ensures accurate and efficient data synchronizations.
- Data Transformation: Airbyte provides automatic chunking and indexing options that allow you to transform raw data and store it in eight different vector databases. Additionally, you can integrate Airbyte with dbt Cloud to clean and enrich data by implementing dbt transformations.
To learn more about or familiarize yourself with the platform, you can refer to the documentation or try Airbyte for free.
Wrapping It Up
This article provides a simplistic explanation of the key differences between Elasticsearch vs MongoDB. To sum it up, Elasticsearch is a great choice when you want to perform full-text searches and complex queries on high-volume unstructured data.
Conversely, MongoDB is an ideal data storage solution with enhanced flexibility and scalability for diverse data structures. Depending on your organization's infrastructure requirements, you can choose what works best for you in the long run.