Connecting MongoDB to MySQL: 2 Easy Methods

on Tutorial, Data Integration, Database, ETL, MySQL • February 21st, 2020 • Write for Hevo

MongoDB is a NoSQL database that stores objects in a JSON-like structure. It is usually classified as document-oriented storage because of the way it treats objects as documents. Schemaless databases like MongoDB offer unique versatility because of their ability to store semi-structured data.

MySQL, on the other hand, is a structured database with a hard schema. It is a usual practice to use NoSQL databases for use cases where the number of fields will evolve as the development progresses. At some point in time when the use case matures itself, organizations will notice the overhead introduced because of their NoSQL schema and would want to migrate the data to hard structured databases with comprehensive querying ability and predictable query performance. 

This article will give you a brief overview of MongoDB and MySQL. You will get to know how you can easily set up MongoDB to MySQL Integration using two methods. You will also explore the limitations of manually setting up MongoDB to MySQL Integration. In the end, you will be in the position to choose the best of both methods based on your business requirements. Read along to decide which method of connecting MongoDB to MySQL is best for you.

Table of Contents

Prerequisites

You will have a much easier time understanding the ways for setting up the MongoDB to MySQL Integration if you have gone through the following aspects:

  • An active MongoDB account.
  • An active MySQL account.
  • Working knowledge of Databases.
  • Working knowledge of Structured Query Language (SQL).

Introduction to MongoDB

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NoSQL databases offer a unique benefit when compared to their SQL counterparts. The ability to dynamically add new keys or attributes without worrying about a hard structure. This ability coupled with a comprehensive querying layer makes MongoDB one of the most widely used NoSQL databases.

MongoDB Atlas makes it possible to create MongoDB clusters in any of the available cloud platforms in a short time. Developers of MongoDB also can choose to install it on their on-premise infrastructure. It is a true cloud agnostic horizontally scalable NoSQL database. MongoDB also supports master-slave based replication making it a highly available database.

Conceptually, data in MongoDB is stored into key-value pairs called documents. A document is a collection of key and value pairs. Documents for the same purpose or end goal are grouped together to form collections. A collection is vaguely equivalent to a table in an SQL-based database. Multiple collections from a database. MongoDB supports indexes over collections. It is also capable of using the Map-Reduce paradigm for aggregation over a large amount of data. 

To know more about MongoDB, visit this link.

Introduction to MySQL

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MySQL is the most widely accepted SQL-based database which powers the day-to-day operations for some of the biggest names in the industry including Facebook, Github, etc. It has a very comprehensive querying layer that can even process programming languages like  Python. It supports a wide variety of data types for the table fields including JSON. MySQL tables support granular read and write access level controls even to the extent of individual fields. Security can also be enforced using encrypting the entries – Tables and binary logs can be encrypted. 

Conceptually, MySQL is a query layer that can run on top of a number of storage engines. InnoDB is the default storage engine. There is support for other storage engines based on memory as well as file storage. Memory-based storage engines store data in RAM, enabling MySQL to run as an in-memory database. Other than the SQL interface and parser, the querying layer also contains an optimizer and cache. There is also a connection pool that works with these components to manage the incoming connection requests. 

To know more about MySQL, visit this link.

Methods to Set up MongoDB to MySQL Integration

Method 1: Manual ETL Process to Set Up MongoDB to MySQL Integration

This method involves the use of the Mongoexport tool to convert MongoDB data into CSV files. Then, these CSV files are exported to MySQL. This method requires the working knowledge of SQL to successfully set up the connection. Moreover, coding and maintenance overhead is also associated with this method.

Method 2: Using Hevo Data to Set Up MongoDB to MySQL Integration

Hevo Data is an automated Data Pipeline platform that can move your data from MongoDB to MySQL very quickly without writing a single line of code. It is simple, hassle-free, and reliable.

Moreover, Hevo offers a fully-managed solution to set up data integration from 100+ data sources (including 30+ free data sources) and will let you directly load data to Databases such as MySQL, Data Warehouses such as Snowflake, Amazon Redshift, Google BigQuery, etc. or the destination of your choice. It will automate your data flow in minutes without writing any line of code. Its Fault-Tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data.

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Methods to Set Up MongoDB to MySQL Integration

There are many ways of loading data from MongoDB to MySQL. In this article, you will be looking into two popular ways. In the end, you will have a good understanding of each of these two methods. This will help you to make the right decision based on your use case:

Method 1: Manual ETL Process to Set Up MongoDB to MySQL Integration

MongoDB and MySQL are extremely different databases with different schema strategies. This means there are a lot of things to consider before moving your data from a Mongo collection to MySQL. The simplest of the migration will contain the few steps below.

Use the default Mongoexport tool to create a CSV from the collection.

mongoexport --host localhost --db classdb --collection student --type=csv  --out students.csv --fields first_name,middle_name,last_name, class,email

In the above command, classdb is the database name, the student is the collection name and students.csv is the target CSV file that will contain data from MongoDB. An important point here is the –field attribute. This attribute should have all the lists of fields that you plan to export from the collection. If you think about it, MongoDB follows a schema-less strategy and there is no way to ensure that all the fields are there in all the documents.

If MongoDB was being used for the purpose it was designed for, there is a big chance that not all documents in the same collection have all the attributes. Hence while doing this export, you should be sure that these fields are there in all the documents. If the fields are not in all documents, MongoDB will not throw an error, but will just populate an empty value in its place.

Create a student table in MySQL to accept the new data.

CREATE TABLE students ( id INT(6) UNSIGNED AUTO_INCREMENT PRIMARY KEY, firstname VARCHAR(30) NOT NULL, middlename VARCHAR(30) NOT NULL, lastname VARCHAR(30) NOT NULL, class VARCHAR(30) NOT NULL, email VARCHAR(30) NOT NULL, )

Load the data into the MySQL table using the below command.

LOAD DATA LOCAL INFILE 'students.csv' INTO TABLE students FIELDS TERMINATED BY ','  ENCLOSED BY '"' LINES TERMINATED BY 'n' (firstname,middlename,lastname,class,email)

You have the data from MongoDB loaded into MySQL now.

Another alternative to this process would be to exploit the document storage capability of MySQL. This allows MongoDB documents to be directly loaded as a MySQL collection rather than a MySQL table. The caveat is that you will not be able to use the true power of MySQL’s structured data storage. And in most cases, that will be the reason why you chose to move the data to MySQL in the first place. 

But the above set of steps only works for a limited set of use cases and does not reflect the true challenges in migrating a collection from MongoDB to MySQL. Let us look into them in the next section.

Limitations of Manually Setting Up MongoDB to MySQL Integration

Though the manual method of setting up MongoDB to MySQL was effective, there were numerous limitations associated with this method. Below are some of the limitations associated with manually setting up MongoDB to MySQL Integration:

  1. In real life, there are going to be hundreds of fields in a collection, and creating the first command(the first step) itself is going to be a pain trying to manually write all the fields.
  2. This approach needs you to have access to an instance with enough storage space to store the whole contents of the collection and then execute the LOAD DATA command.
  3. In MongoDB use cases, typically there will be many attributes that do not exist in all the documents. This method will populate empty values in such cases. This is not a desirable behavior and in most cases, you will need to write a custom script to handle this.
  4. This method is suitable for a first-time bulk load, but if your use case needs frequent syncing, it would be tough to handle the duplicate data rows and scheduling. 
  5. This is going to be an extremely time-consuming process if the source collection is large. If MongoDB is being used for serving a live website or mobile app, the developer will need to build additional throttling logic so that the incoming traffic is not affected.
  6. You could also end up in trouble if the fields inside your MongoDB have arrays as their values. These will need to be specifically handled using a script since the simple CSV export can lead to unexpected results here. 

A solution to all these complexities will be to use a third-party cloud-based ETL tool like Hevo.  Hevo can mask all the above concerns and provide an elegant migration process for your MongoDB collections. 

Method 2: Using Hevo Data to Set Up MongoDB to MySQL Integration

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Hevo Data, a No-code Data Pipeline, helps you directly transfer data from MongoDB and 100+ other data sources to MySQL and other Databases, Data Warehouses, BI tools, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.

Hevo Data takes care of all your Data Preprocessing needs and lets you focus on key business activities and draw a much powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. 

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The steps to load data from MongoDB to MySQL using Hevo Data are as follow:

  • Connect your MongoDB account to Hevo’s platform. Hevo has an in-built MongoDB Integration that connects to your account within minutes.
Configuring MongoDB as Source in Hevo Data
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  • Select MySQL as your destination and start moving your data.
Configuring MySQL as Destination in Hevo Data
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Hevo automatically flattens all the nested JSON data coming from MongoDB and automatically maps it to MySQL destination without any manual effort. 

Here are more reasons to try Hevo:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

Conclusion

This article gave you a comprehensive guide to MongoDB and MySQL and how you can easily set up MongoDB to MySQL Integration. It can be concluded that Hevo seamlessly integrates with MongoDB and MySQL ensuring that you see no delay in terms of setup and implementation. Hevo will ensure that the data is available in your database in real-time. Hevo’s real-time streaming architecture ensures that you have accurate, latest data in your database.

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Businesses can use automated platforms like Hevo Data to set this integration and handle the ETL process. It helps you directly transfer data from a source of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience.

Want to try Hevo?

Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. Have a look at our unbeatable pricing, which will help you choose the right plan for you.

Share your experience of loading data from MongoDB to MySQL in the comment section below.

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