Neo4j vs MongoDB – What’s the Difference ? (Pros and Cons)
Neo4j vs MongoDB – What’s the Difference ? (Pros and Cons). Well, if you are planning but find it difficult to decide whether to choose a Neo4j (graph database) or a MongoDB (NoSQL database)? And you are not sure which one to choose? You happen to be in the right place.
Admittedly, both are NoSQL databases and are robust in their own way, they have some differences, making them suitable for a particular mix of resources, purposes and needs for different corporations.
For example, if your data has complex relationships with each other, Neo4j may be a better choice in this case. On the other hand, if the data is more ‘flat’ and we need fast access to a large number of documents, then MongoDB is undoubtedly is the most preferred choice.
Therefore this article narrows the differences between Neo4j vs MongoDB – What’s the Difference ? So you choose the best application for you or your company’s requirements.
What is Neo4j?
Neo4j is a popular graph database management system designed to store, manage and query large graphs with complex data relationships. A scalable, high performance, open source, ACID compliant database system that uses an attribute graph model to represent data as nodes, relationships and attributes.
Moreover, it also provides a query language called Cypher, which is used to search and manipulate the graph data. Cypher is similar to SQL, but is specifically designed for working with graph data and uses a declarative syntax to express queries as pattern matching statements.
Features of Neo4j
- Supports two kinds of Java API: Cypher API and Native Java API to develop Java applications.
- Provides REST API to be accessed by any Programming Language like Java, Spring, Scala etc.
- Uses native graph processing, which means that queries are executed using graph algorithms rather than traditional relational database algorithms. This makes it highly performant for complex queries involving many relationships.
- Fully ACID compliant, which means that it provides strong transactional consistency and durability for your data.
- Has a built in Neo4j web browser application, which is used to create and retrieve your graph data.
- Excellent index handling with Apache Licence.
- SQL like -easy query language Neo4j CQL.
- Gives Java Script the ability to access any UI MVC Framework, such as Node JS.
- Follows a data model called graph model. Contains nodes and the nodes are connected to each other. Nodes and relationships store data in key value pairs known as properties.
- Rich ecosystem of plugins and extensions that allow you to extend its functionality for specific use cases. These include integrations with popular programming languages and frameworks, as well as third party tools for data visualization and analysis.
- Designed to be highly scalable and handles large datasets with billions of nodes and edges. Built in clustering and sharding capabilities that allow you to distribute your data across multiple nodes for improved performance and availability.
Pros of Neo4j
- Complex joins are not required to retrieve connected/related data. Very easy to get neighbour node or relationship information without joins or indexes.
- Neo4j CQL query language commands are in humane readable format and very easy to learn..
- Very easy and faster to retrieve/traversal/navigation of more Connected data.
- Insert and store relationships with ease.
- UI access controlled by user credentials allows for neat access controls.
- Native support for REST API, that makes interacting with Neo4J intuitive and easy.
- Support for triggers, that’s one of the neat features of Postgres and other traditional SQL databases.
Cons of Neo4j
- It does not support Sharding.
- Native support for complex properties for nodes and relationships.
- Tough to allocate memory or other configurations.
Up next with Neo4j vs MongoDB – What’s the Difference ? we introduce MongoDB
What is MongoDB?
MongoDB is a document oriented NoSQL database that is designed to store and manage unstructured and semi structured data. First released in 2009 and has gained popularity due to its flexibility, scalability, and ease of use.
Unlike traditional relational databases, which store data in tables with a fixed schema, MongoDB stores data in JSON like documents with dynamic schemas. This makes it well suited for handling complex and rapidly changing data, such as social media posts, product catalogues, and user profiles.
Features of MongoDB
- Ranged sharding – typically used for applications that make many range requests. Data is divided into contiguous ranges based on shard key values. For example, if two documents share a key value next to each other, they are in the same segment. Also, this arrangement prevents fair distribution of the data stored on the servers.
- Indexing – In a MongoDB database, all fields in a document are indexed with primary and secondary indexes, making it easier and less time consuming to fetch or look up data from the data pool. If the data is not indexed, searching the database for each document for a given query is time consuming and inefficient.
- Aggregation -Allows you to operate on grouped data and return a single result or a calculated result. Moreover this is similar to the SQL GROUPBY clause. All in all, it provides three different aggregations: an aggregation pipeline, a map reduce function, and a single target aggregation method.
- MongoDB is known for being the aggression framework, which is particularly good for the database managers these days.
- File storage and indexing are also highlighted features of MongoDB.
- Replication of data for data backup is easy with MongoDB.
- Supports native data encryption, providing an additional layer of security for sensitive data.
Pros of MongoDB
- The MongoDB Query API allows you to query deep into documents, and even perform complex analytics pipelines with just a few lines of declarative code.
- When it comes to write performance, MongoDB offers functionalities to insert and update multiple records at once with insertMany and updateMany. These two functions offer a significant performance boost when compared to batched writes in traditional databases.
- When using the cloud based MongoDB Atlas, you choose an instance size that fits your current needs. Also adjust your cluster to automatically scale when needed. This way, you keep your costs at a minimum, while still having the flexibility to handle sudden traffic bursts. In addition to the flexible cost for dedicated clusters, you create Serverless Databases. For these databases, you only be charged for the actual usage, making it very flexible and perfect for many lower usage use cases.
- MongoDB supports ad hoc queries and has worked to optimise the way this type of query is handled. It allows for real time ad hoc query updates, for instance.
- As a personal pro from us, ThinkAutomation integrates with MongoDB and supports Mongo message stores. This means that MongoDB users easily set up and apply the benefits of automation to their datasets.
Cons of MongoDB
- If your indexing is incorrectly implemented or has any discrepancies or other errors, it negatively impacts the speed and performance of MongoDB. This means that you will need to spend time fixing and analysing the indexes you use.
- Common feedback about MongoDB is that duplicate data can prove an issue.
- Unlike a relational database, MongoDB doesn’t support joins. That is, joining your stored documents is a tricky task. You can manually code it but it is time consuming and could affect performance.
Neo4j vs MongoDB – What’s the Difference ?
Both, Neo4j and MongoDB are both popular database management systems, but they have significant differences in their underlying data models and intended use cases.
Here are the main differences between Neo4j and MongoDB:
Transactions
ACID transactions are supported to ensure data consistency and reliability. Transactions in Neo4j are based on the concept of a “transaction graph”. That is, changes to the graph made within a transaction are not visible outside the transaction until the transaction commits. If a transaction fails, you roll back to a previous state. Neo4j also supports distributed transactions across multiple servers.
MongoDB only provides eventual consistency, so data may be temporarily inconsistent during replication. Hence, MongoDB uses a “write ahead journal” for reliability, but it doesn’t support transactions the way Neo4j does. However, MongoDB supports transactions with multiple documents on the same server, which is useful in some cases.
Querying
On one hand Neo4j uses the Cypher query language, which is specifically designed for graph databases and allows for complex queries over graph structures. Cypher is a declarative query language that uses a pattern matching syntax to find nodes and relationships that match a given pattern. Cypher queries also traverse relationships and perform aggregations and filters on the results.
Uses a flexible query language based on JSON that allows for filtering, aggregation, and sorting of documents. Alternatively, MongoDB’s query language allows for complex queries using operators such as $match, $group, and $sort. Besides, MongoDB also supports indexing for faster querying.
Data model
In a graph database like Neo4j, the data model is designed to represent complex relationships between data points. This makes it particularly useful for applications that require graph processing, such as social networks, recommendation engines, and fraud detection.
In contrast, our MongoDB is a document oriented database, meaning that it stores data in document collections. A document is a JSON like data structure that contains fields and values. Each document have a unique schema, meaning that each document in a collection have different fields and data types.
Performance
The Neo4j graph database model is designed to optimize queries that involve traversing complex relationships between data points. As a result, Neo4j is very fast for queries involving complex graph processing. Moreover, Neo4j’s performance is also improved by using indexing to speed up queries.
While, MongoDB’s document oriented model is designed to optimize queries that retrieve large amounts of unstructured data. Also MongoDB is very fast for queries involving document filtering, aggregation, and sorting. Using indexing to speed up queries also improves MongoDB performance.
Use cases
Lastly, Neo4j is often used for applications that require graph processing, such as social networks, recommendation engines, and fraud detection. MongoDB is often used for applications such as content management systems, e-commerce sites, and mobile applications that require storing and retrieving large volumes of unstructured data.
Thank you for reading Neo4j vs MongoDB – What’s the Difference ? We shall conclude.
Neo4j vs MongoDB – What’s the Difference ? Conclusion
In summary, Neo4j provides charting technology that is proven in terms of performance as well as scalability. Flexible schema helps you to adapt to a growing business without hassle by offering more in minimal time. In contrast, MongoDB provides charting technology that is proven in terms of performance as well as scalability. It has a flexible schema that helps you adapt to a growing business without hassle by offering more in minimal time.
Therefore, knowing what these two applications are after this article, you will surely choose the right one for your needs.
