Convert Relational Database to MongoDB: 3 Easy Methods

Large companies and organizations have started to move most of their data into Non-Relational Databases. They are developed to store hundreds of Petabytes of data and run millions of queries per second. On the other hand, relational databases are built to store and manage relational data as efficiently as possible.

However, Relational Databases have difficulty scaling as they require a lot of memory and compute power. This is where the importance of migrating data from a Relational Database to MongoDB, one of the most popular Non-Relational Databases, emerges.

This article will cover the details of replicating data along with mapping the basic relational concepts. Let’s get started!

Migrating Data from Relational Database To MongoDB

Integrating data from a Relational Database to MongoDB can be a challenge. However, the process becomes a lot easier with MongoDB drivers and tools.

MongoDB comes with a set of native drivers for the most popular languages such as Java, JavaScript, Python, .NET, Go, etc. Some of these drivers are developed and supported by MongoDB itself, and many are community-supported drivers.

Connection can be achieved by four methods:

• Method 1: Use MongoDB Relational Migrator. Relational Migrator can suggest a schema design in MongoDB, migrate the data, and even keep it in sync, much in the same way an ETL tool can. Relational migrator also offers the following functionality:
  • Set up mapping rules
  • Generate application code
  • Convert/Validate SQL code, including Stored Procedures
• Method 2: Many Developers are comfortable in creating their own migration scripts. These scripts are used to transform source Relational Database data into a hierarchical JSON structure. This can then be imported from a Relational Database to MongoDB using the mongoimport tool.
  You can have a look at our guide on how to connect PostgreSQL to MongoDB.
• Method 3: Third-party Extract Transform Load (ETL) tools are also generally used by companies to migrate data from a Relational Database to MongoDB. A number of ETL vendors, including Informatica, Pentaho, and Talend, develop MongoDB Connectors that extract data from the source Database, transform it into the target MongoDB Schema, and then load it into MongoDB Collections. This is generally the recommended way of migrating from a Relational Database to MongoDB.
• Method 4: Another way of migrating from a Relational Database to MongoDB involves running the existing RDBMS with the new MongoDB Database in parallel, incrementally transferring production data. The records are retrieved from RDBMS and are written back to MongoDB in the required document schema by the application.

Key Differences Between an RDBMS and MongoDB

Below are a few key differences between RDBMS and MongoDB that will help you understand them better.

Aspect	RDBMS	MongoDB
Model	RDBMS is a Relational Database Management System that works on Relational Databases.	MongoDB is a Non-Relational, NoSQL Database Management System that works on a Document-based Database.
Scalability	RDBMS is vertically scalable and its performance increases with an increase in RAM.	MongoDB is both vertically and horizontally scalable. Performance increases with the addition of RAM as well as by sharding and adding servers/processors.
Schema	In RDBMS, the schema needs to be defined in advance before one starts working with the Database.	MongoDB allows schemas to be dynamically created and accessed.
Joins	RDBMS possesses a great ability to query data and execute complex joins.	MongoDB is not optimized for joins. Best practice is to embed data at write time where possible. In other words, join on write rather than on read.
Query Language	RDBMS supports SQL as a query language to query the Database.	MongoDB uses Mongo Query Language (MQL) or Aggregation Pipeline to query the Database. MongoDB Atlas and MongoDB BI connector for on-prem deployments allow using SQL to query MongoDB data.
Hierarchical Data Storage	RDBMS is not suitable for storing hierarchical data.	MongoDB provides built-in support to store hierarchical data.

Why Integrate a Relational Database to MongoRelational Databases came into existence in the 1970s, and they have been the foundation of Enterprise Data Management since then. But in today’s world, processing data, building applications, and analyzing results have become much more complex. With data incoming from a variety of sources, organizations find it difficult to manage their increasingly complex user loads with traditional Relational Databases.

This is the reason why it becomes important to migrate from a Relational Database to MongoDB. Setting an example, industry leaders such as Cisco, Verizon, etc., have migrated successfully from Relational Database to MongoDB.

MongoDB is a NoSQL solution, and it doesn’t require a Relational Database Management System (RDBMS). New world engineering applications demand the persistence of complex and dynamic forms of data to match the highly flexible and powerful languages used for software development. MongoDB, the leading NoSQL Database, addresses the above challenges and meets the demands of modern apps with a flexible and scalable solution.

Here’s what you’ll be getting after migrating from a Relational Database to MongoDB.

• The Document Data Model provides you with the best way to work with data. 
• A Distributed Systems Design allows you to intelligently put data wherever you want to.
• A unified MongoDB experience gives you the freedom to future-proof your work and eliminates vendor lock-in.

Dynamic Schema

The primary change in migrating from a Relational Database to MongoDB is the way in which the data is modeled. In MongoDB, different documents within a collection can have different schemas. For instance, one document can have 3 fields, and the other document can have 5 fields. MongoDB supports dynamic schemas, and there is no constraint on the data types of the fields.

Coming from the RDBMS background, one may find it difficult to transition from a pre-defined relational data model that stores data into tabular structures of rows and columns to a rich and dynamic document data model with embedded sub-documents and arrays.

For example, consider the following documents having different schemas within the same collection.

array (
    '_id' => new MongoId("5146bb52d8524270060001f2"),
    'address' => '123, Baker St, Dallas',
    'age' => new MongoInt32(31),
    'city' => 'Dallas',
    'dob' => '1990-03-03',
    'email' => 'richard@abc.com',
    'user_name' => 'Richard Peter',
)

array (
    '_id' => new MongoId("5146bb52d8524270060001f3"),
    'age' => new MongoInt32(25),
    'city' => 'Los Angeles',
    'gender' => 'Male',
    'occupation' => 'Doctor',
    'email' => 'mark@abc.com',
    'user_name' => 'Mark Hanks',
)

The first document contains the address and dob fields, which are not present in the second document. On the other hand, the second document contains gender and occupation fields that are not present in the first document. Modeling this dynamic schema in an RDBMS would require you to have 4 extra columns for address, dob, gender, and occupation. And, some of these columns would store null values, and hence occupy unnecessary space. MongoDB can easily design and model such complex schemas, making it highly scalable in terms of design.

Mapping Tables, Rows, and Columns

In MongoDB, Databases consist of collections that are analogous to tables in an RDBMS Database. Further, every MongoDB collection stores data in the form of documents that correspond to rows in an RDBMS Table. While an RDBMS row stores data in a set of columns, a MongoDB document has a JSON-like structure (also known as BSON in MongoDB). And similarly, the fields in MongoDB are equivalent to the columns in RDBMS.

• MongoDB Collections are equivalent to RDBMS Tables.
• MongoDB Documents are equivalent to the RDBMS Rows.
• MongoDB Fields are equivalent to the RDBMS Columns.

This is what a MongoDB Document looks like. This document is equivalent to a single row in RDBMS. The only difference is that this is in JSON format.

{

    name: “Chaitanya”,

    dob: “5/12/1995”,

    website: “beginnersbook.com”,

    hobbies: [“teaching”, “watching tv”]

}

Mapping Joins and Relationships

Coming from an RDBMS background, one must be familiar with relationships. Relationships in RDBMS are created using primary and foreign key relationships, and by ultimately querying those using joins. On the other hand, relationships in MongoDB are developed using embedded objects and linked documents.

Consider an example wherein you need to store movie information and corresponding director information in the Database. Designing this in RDBMS would require you to have 2 tables, say movie and director with primary keys movie_id and director_id respectively. To establish a relationship, the movie table contains a dir_id column which would act as a foreign key linking to the director_id field of the director table.

Now, let’s discuss how you can model such relational data in MongoDB using Linking Documents and Embedding Documents.

In MongoDB, you can use the auto-generated _id field as the primary key for identifying the documents uniquely. However, if you have your own unique key for each document, you can, and should, specify that value as the _id field as it will take advantage of the index that _id has, reducing the number of indexes as well as slightly decreasing document size.

Linking Documents

In the movie/director example above, this could be modeled by embedding the director_id in the movie collection:

{

    _id: “M12345”,

    title: “Jaws”,

    year: 1975,

    rating: “PG-13”,

    run_time: 121,

    director: “D7890”

}

Embedding Documents

Another way of implementing relationships or relational data in MongoDB is to embed the frequently accessed data for the director directly into the movie. For example:

{

    _id: “M12345”,

    title: “Jaws”,

    year: 1975,

    rating: “PG-13”,

    run_time: 121,

    director: {

      _id: “D7890”,

      name: “Steven Spielberg”,

      mpaa: True }

}

Note that in this case, we embed data that will likely be needed when we read the movie document. If more data about the director is occasionally needed, we can use the aggregation $lookup function to join to the director collection and get all of the information for that director.

Complex documents, and hierarchical data can be embedded to join entities on write rather than on read as would be done in a relational DB.

It should be noted that embedding data has some guidelines around it:

• Embed the low-cardinality side of the data if using an array. For example, a movie may have multiple directors, but the list is likely to be short. However, a director may have made many movies. In this case, store an array of directors in the movie collection rather than the other way around. 
• When embedding as an array, keep the array at 200 items or less. If the array is likely to exceed that size, consider using the subset design pattern or the outlier design pattern to accommodate these.
• Although MongoDB allows for document sizes of up to 16MB, the general rule of thumb is that documents should be far smaller (typically 200kb or less) – unless most or ALL of the data is being used by the application when read. 

Mapping Chart

To summarize, the following mapping chart will help you easily migrate from a Relational Database to MongoDB.

Limitations of the manual method or other ETL tools to convert Relational Database to MongoDB are:

• Not all ETL tools offer schema transformation in a comprehensive manner. Some require scripting or manual intervention. This can be inefficient and time-consuming. 
• It can be challenging to maintain data integrity in MongoDB without enforcing defined schemas, unlike in Relational Databases. 
• The manual method is prone to error and requires a skilled workforce with knowledge of coding. 

Use Cases of MongoDB Relational Database Conversion

• E-Commerce: MongoDB’s flexible schema allows for handling large volumes of data, such as product catalogs with different attributes. Thus, MongoDB Relational Data conversion can be helpful in the E-commerce industry for faster functionality. 
• IoT applications: As said, MongoDB can handle large volumes of structured or unstructured data efficiently. Thus, MongoDB Relational data migration enables you to use it in IoT applications.
• Gaming Industry: MongoDB’s flexible schema and global cloud database, along with services such as charts and App services, makes it go-to for game developers. 

Conclusion

This article introduced you to Relational Databases, MongoDB, and helped you differentiate between both. It then took you through various aspects of migrating from a Relational Database to MongoDB. Nowadays, many more companies want to associate themselves with MongoDB.

To get a complete overview of your business performance, it is important to consolidate data from MongoDB and other Data Sources into a Cloud Data Warehouse or a destination of your choice for further Business Analytics. 

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Share your experience of migrating from a Relational Database to MongoDB in the comments section below.

Give Hevo a try by signing up for the 14-day free trial today.

Share your experience of migrating from a Relational Database to MongoDB in the comments section below.

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