SQL to MongoDB Migration

Download the World Database (Zip, TGZ)

Introduction

SQL to MongoDB Migration is about migrating data from a relational database server to a MongoDB server.

Studio 3T already has a SQL Import to MongoDB feature, which allows you to import records from a single table into a single collection.

SQL to MongoDB Migration extends this by adding two new possibilities:

• You can now easily set up multiple imports, each of them coming from a main table to a target collection. What before were separate imports can now be done as independent pieces or units within a larger migration. This is simple but very useful, especially if you plan to import a full database at once.
• In addition to importing the records of a main table, you can also import data from other tables, into the same collection, as long as you define how each record of the main table is related to the records in other tables within each import unit (which, as stated before, has a main source table and a target collection).

For example, say you have a table in your relational database called post and another called comment, which are related because each post has multiple comments:

Table post

post_id	title
1	Getting Started with MongoDB
2	Users and Roles Explained

Table comment

comment_id	post_id	author	content
1	1	Alice	This is great!
2	1	Bob	This is awesome!
3	2	Carol	Best post ever!

…you can now import the table post into a posts collection in MongoDB, and each document can contain an array of comments. In short, you can embed one-to-one and one-to-many relationships into the document.

{
    "_id" : NumberLong(1),
    "title" : "Getting Started with MongoDB",
    "comments" : [
        {
            "author" : "Alice",
            "content" : "This is great!"
        },
        {
            "author" : "Bob",
            "content" : "This is awesome!"
        }
    ]
}
{
    "_id" : NumberLong(2),
    "title" : "Users and Roles Explained",
    "comments" : [
        {
            "author" : "Carol",
            "content" : "Best post ever!"
        }
    ]
}

Additionally, SQL to MongoDB Migration is integrated with the new Tasks feature, so migration tasks can be saved within Studio 3T to be run again later, or even scheduled for execution on a periodic basis.

Open SQL to MongoDB Migration

To open SQL to MongoDB Migration in Studio 3T:

1. Click on the SQL Migration button in the toolbar, or right-click into a server, database or collection in the Connection Tree and select the SQL Migration option.
2. Then select SQL → MongoDB Migration. This will open a new tab where you can configure and execute the import.

As also seen in the other import types, Studio 3T automatically detects the target connection (i.e. the connection into which the records will be imported into a collection) depending on: 1.) what you have selected in the left-hand connection tree, and 2.) whether you currently have a collection tab open.

It also detects the target database and collection wherever possible, or otherwise they are left blank. In any case, they can always be modified in the GUI later.

Connect to a SQL Server

Right after you open the SQL to MongoDB Migration tab, the first thing to do is connect to a relational database server. Currently supported servers are MySQL, PostgreSQL, Oracle Database, Microsoft SQL Server, and Sybase.

1. Click on the Set SQL Connection button.
2. Select/configure a SQL connection via the SQL Connection Manager.

Add SQL tables as import units

1. Once connected to a server, click on the Add import units (+) button:

2. Choose With source table.

3. A dialog will pop up where you can explore the tables in your SQL Server and select as many of them as you want.

4. Once you confirm, a new import unit will be created for every table.

Use custom SQL queries as import units

In addition to importing all rows from SQL tables, you can also specify a custom SQL query to control which dataset to import.

1. Click on the button to Add import units (+) button:

2. Choose With custom SQL query.

3. Write your SQL query and click Add. In the example below, we only want to import results where active is equal to true.

By using custom SQL query as an import unit, you can filter the data to only include the subset you need (instead of importing all rows from SQL tables), or extend your data (using UNION or JOIN, for example) before the migration.

4. Click on Mappings to further modify your SQL query.

This will open the Mappings tab, where you can continue to edit your query in the SQL Query panel (upper left corner).

Making changes to the SQL query

You can edit your SQL query and then refresh it, or create a new default mapping. It is important to remember the difference to avoid duplicate work.

When editing only the filter conditions of your query, it is sufficient to refresh it. In the example below, we’re still retrieving all columns and have only changed the filter condition to import the rows where active is now false instead of true.

Clicking on Refresh will update the results accordingly in the JSON output preview, on the right.

However, after making any changes related to columns, you must create a new default mapping for your updated query by clicking on the icon below.

Creating a new default mapping will update the schema – and the results – in the JSON output preview. Note below that only first_name , last_name and active appear.

Refreshing the query in this case will result in an error in the JSON output preview. It wouldn’t be able to map the new results based on the old schema from the previous SQL query.

Execute the import

At this point, you can go ahead and execute the import job. You can, of course, always tweak the configuration of each unit before executing the import, as we explain further down.

The import units will be executed in parallel – within a reasonable limit of simultaneous processes – for faster completion.

At the end of the import, a new collection will be created for each table if it didn’t exist before.

Configure import units

Directly from the list of import units, you can:

• Change the target database and collection of an import unit
• Change the insertion mode
• Include/exclude a unit from the job
• Execute only one (or a few) of the units
• Delete and add new units… and so on

Additionally, you can customize how the data from the main table – and possibly from other tables – should be transformed into documents for each import unit. That’s the biggest part of the configuration, which has its own dedicated tab.

The import unit configuration tab

To access the import unit configuration tab, right click on a import unit and select Edit, or double-click on the first column, or select the import units and then click the pencil button:

The import unit configuration tab has three sections:

• The schema view (upper left corner) shows the specification of the documents you want to import. That is, their fields (which can include nested objects and arrays), their types, and which dataset and column is the source for each leaf field. In the case of arrays, you can also see which one-to-many relationship they represent.
• In the datasets view (lower-left corner), you can see which datasets are part of any given import unit and the relationships between them. By default, each import unit has only one dataset (the root table), but you can add more, as we’ll explain later.
• The JSON preview (right side of the screen) shows a sample of the documents that will be imported, in their final state.

Customize fields

You can customize all the fields in your schema, for example change their name, the SQL columns they come from, and their target mongo type.

There are two ways you can edit a field:

• Right-click – Right-click on the field and choose the Edit/Rename option
• Button – Select the field and click on the “pencil” icon

You can also rename a field directly by double-clicking on its name.

Manipulate the schema

You can manipulate the “shape” of your target schema as you wish.

Using JSON Preview, let’s look at the example below, which is the first document produced post-import with its initial schema. It has nine fields: customer_id, store_id, first_name, last_name, email, address_id, active, create_date,  and last_update.

Now let’s run through the changes we can make to the schema.

Remove fields

We can remove fields from the schema (e.g. delete store_id, active, create_date, and last_update):

Rename fields

We can rename the fields (e.g. change first_name to first, last_name to last):

Reorder fields

We can reorder the fields, which can be done via the arrow buttons in the toolbar, or via drag and drop:

Create new field with object values

We can also create a new field whose value is an object.

To do this:

1. Right-click on the parent object (in this case, the root node).
2. Select Add object field. Alternatively, select the parent object and click on the Add object button.

Move fields from one object to another

We can move fields from one object to another via drag and drop.

In this case we’ll move the first and last fields out of the root object and into the new object that we just created as the value of the name field.

Set up array fields

We can also set up array fields, but this requires defining a one-to-many relationship with another dataset, which will be discussed later.

Relationships between datasets

The migration feature makes it possible to import multiple datasets into a single collection.

Importantly, there must be – and there can only be – one root dataset. The root dataset is the one that forms the basis of the final collection. Each record in the table of the root dataset will result in one document in the output. Every other (non-root) dataset must depend on a previously defined dataset via a one-to-one or one-to-many relationship.

One-to-one relationships

The meaning of “one-to-one” within this feature differs slightly from what the term usually means.

If we have a dataset, A, and then define a one-to-one relationship with another dataset, B, this means that for each row in A, there is (optionally) one corresponding row in dataset B.

For example:

• Employee – Social Security Number. Because an employee may still not have the SSN entered at some point, this is strictly a one-to-zero-or-one (1 – 0..1) in the modeling world. However, as far as this feature is concerned, it’s just a one-to-one. SQL to MongoDB Migration tolerates the absence of a record on the right side.
• City – Country. On the SQL side this is clearly a many-to-one relationship, because there can be many cities in any particular country. But for the purposes of this feature, if we define the city dataset first, we can then define a one-to-one relationship to country, because for one city there is one country.

Set up one-to-one relationships

Let’s take a look at an example.

Say we’ve set up the root dataset of our import to be city and we left the schema as-is by default:

We then want to add a one-to-one relationship to country (which, as we said before, is really a many-to-one, but it’s handled the same as a one-to-one in this feature). We can then include the details of the country in the city document itself.

We will right-click on the root node and select Add one-to-one relationship, or alternatively, select the root node and click on the Add one-to-one button: 

A dialog pops up:

In the top part of the dialog, we will specify which table we’re adding (the “child table”), and how it’s related to the preexisting table (the “parent table”).

Note that “parent table” and “child table” refer to the fact that the child table depends on the parent table in our import configuration.

In this case, the parent table is “city” and the child table is “country” because we’re defining the country as dependent on the city.

There are two ways of specifying how two tables are related:

1. We can choose an existing foreign key, which Studio 3T would have already detected automatically, or
2. We can manually specify the parent table, child table, and the column pairs that make up the join conditions.

In this case, there is a foreign key that links city with country, so we’ll use just that (option 1 above).

Next, we can choose if (and how) we want new fields to be created automatically for the columns in this new table.

Note that these new fields are not part of the relationship per se; the one-to-one relationship can exist independently of whether any fields reference the new table and where these fields are located. This is simply a convenience feature, so that we don’t have to add all the fields that we need one by one afterwards.

The options are:

Add fields in a nested object

This will create a new field whose value is an object, and that object will have a field for each of the columns in the new dataset.

Add fields on the same level

This will add the new fields in the top-most level possible. In this example it’s the root level, but it could also be inside an array if the parent dataset is the “many” side of a one-to-many relationship.

Skip adding fields

The option “Do not add any fields yet” will simply add the new dataset without changing the schema. That means that the new dataset is not used at this point, but we can reference it later by manually adding fields into the schema, or adding another dataset that references the one we just added.

Reusing and removing datasets

Your datasets, both used and unused will be listed in the Datasets View pane. Unused datasets will be greyed out. When adding one-to-one relationships, you also have the option to reuse a dataset. This makes it easier to add back all fields in a previously-used dataset. Once you click on the *Add one-to-one button* the option to “Reuse Dataset” will automatically appear as a tab. Select the unused dataset you’d like to add from the list to reuse it.

You can also clear any unused datasets. Right click anywhere in Datasets View and select “Clean up unused datasets” to remove unused datasets. Once removed, you will not be able to reuse the dataset.

One-to-many relationships

We can add new datasets that are related to existing datasets via a one-to-many relationship. This allows us to embed a collection of entities that are related to the main entity as an array in the same document. Let’s look at an example.

Add a one-to-many relationship

Assume that we want to import the table sakila.customer into a customers collection. We also want to include, in each resulting document, a list of all of the rentals by this customer.

First we set customer as our root dataset (we’ve omitted some fields for clarity):

Then we add an array.

We do so by right-clicking on the root node and choosing Add array field or selecting the root node and clicking the Add array button:

We will then be asked to define the one-to-many relationship that is represented by this array.

As seen in the screenshot above, we chose to define our relationship using the existing foreign key fk_rental_customer, and we chose rentals as the name for the field that will hold our new array.

Note that the Array content option is set to Object with a field per column. This means that in the final document, there will be an array of objects for each rental, and each array will contain a list of fields that map directly to the columns in the sakila.rental table. We’ll have a look at the other options later.

After clicking OK, we see the following schema along with the JSON preview, which show the new array:

We can also see the newly added dataset in the Datasets view:

On context

Adding a one-to-many relationship adds a new “context” containing (initially) just the dataset that was added.

From this new context, you can reference all columns of the new dataset as well as columns of the parent dataset.

You can, for example, include the customer name within each rental object:

You cannot, however, add a field in the top-level object that references the rental dataset, because that dataset is not accessible (not in context) from the top-level object.

That wouldn’t make sense: because the top-level fields are there only once per customer, but there are many rentals per customer, how could you add the rental_date as a top-level field? There’s no way to know which of the rentals are we referring to.

Array of final values

In the examples above, the array always contained objects. However, we can also define an array which contains final values (i.e. values which are not objects nor arrays). The content of the array will simply be a reference to a column of a dataset.

For example: we want to import the country dataset into a countries collection, and for each country we want a list with all the cities that the country contains.

But we don’t want an object with multiple fields per city; we just want the name. So the array should be an array of names of cities.

In order to do that, we add an array that is linked to the city dataset, related to country via a one-to-many relationship.

In the array definition dialog, we’ll set the content as Final value.

Next we’ll be asked to specify which column we want to reference. In the example we reference the column city in the dataset city. That column contains the name of the city.

The result is as we wanted: a cities array which contains only the names of the cities

Import many-to-many relationships

When migrating data form a relational server to MongoDB, in many cases you can just directly map each table to a collection, and be done with it.

When there is a one-to-many or many-to-one relationship in the relational database, you will end up with a collection that has a field which references another collection, just like foreign keys work in the SQL world, except that of course, in MongoDB these “foreign keys” are implicit and unenforced.

For example, you can import customers and rentals such that the rentals collection has a field customer_id. The value of that field corresponds to the field customer._id for the relevant customer.

Alternatively, you can embed the list of rentals in each customer’s document, as seen in the example for one-to-many relationships. The decision of which approach to follow (implicit reference vs nested list) can depend on various factors, such as the access patterns of the application and on how many rentals does each customer have. This topic is excellently covered in MongoDB’s posts 6 Rules of Thumb for MongoDB Schema Design: Part 1, 2 and 3.

Many-to-many relationships, however, are a bit different. In relational databases, those are usually modeled by means of an associative table (or join table, among other names).

The Sakila database provides a classical example of this: there are films, and there are actors. Because every film can have several actors, and every actor may have starred in several films, we cannot have the films directly reference the actors, or the actors directly reference the films. Instead, a film_actor join table is used, which contains a record for every instance of a particular actor having starred in a particular film. It looks like this:

The actor table

actor_id	first_name	last_name	…
1	PENELOPE	GUINESS	…
2	NICK	WAHLBERG	…
3	ED	CHASE	…
…	…	…	…

The film table

film_id	title	…
1	ACADEMY DINOSAUR	…
2	ACE GOLDFINGER	…
3	ADAPTATION HOLES	…
…	…	…

The film_actor table

actor_id	film_id
1	1
1	23
1	25
…	…
2	3
2	31
…	…

If we use Studio 3T’s SQL to MongoDB Migration to import these three tables directly into MongoDB, with no further configuration, we’ll end up with a film_actor collection. Not the end of the world, but certainly not the MongoDB way of doing things. A more common approach would be to store an array of actor_ids within each film. This way, given a film, we can find all of its actors without the extra step of consulting the intermediate collection. The complementary query, knowing all the films a particular actor has starred in, is also possible with the help of an index which covers the array.

Now let’s look at how we can set up the migration to achieve that result. This is really just a particular case of the Array of final values example above. We’ll import the film and actor tables to separate collections. We’ll tweak the configuration for the films collection so as to include a cast field, which will be simply an array containing the IDs of the relevant actors, taken from the film_actor table. Note that while the film_actor table is used as part of importing the films collection, it doesn’t get imported into its own collection.

As an aside, note how we renamed the actor.actor_id column to actor._id. If we don’t do that, each document will be assigned a new ObjectId as its _id. This is fine, but then we’d need to remember to create an index on the actor_id field to allow for efficient querying. Renaming actor_id to _id prevents the unnecessary creation of a new ID and keeps the result more consistent with how it was in the relational database.

Automate MongoDB to SQL Migrations

Interested in automating SQL to MongoDB migrations? Help us shape the future of job automation for MongoDB.

In the meantime, you can already run and schedule SQL to MongoDB migrations on your local machine using Studio 3T’s Task Scheduler tool.