How to load data from the Magento to PostgreSQL

Extract your data from Magento

Magento exposes its platform through both a REST and a SOAP interface. Both can be used to pull data from it, which is also the scope of this article but also to interact with the platform. By using these interfaces, developers create rich applications and plugins for Magento. In this post, we will use the REST version of the Magento platform. As a Web API following the RESTful architecture principles, it can be accessed through HTTP. As a RESTful API, interacting with it can be achieved by using tools like CURL or Postman or by using HTTP clients for your favorite language or framework. A few suggestions:

Apache HttpClient for Java
Spray-client for Scala
Hyper for Rust
Ruby rest-client
Python http-client

Magento does not publish official SDKs but by using the SOAP interface it is possible to automatically generate clients that can act as SDKs for your favorite language or platform. For example in Java, you can create a client in Eclipse by providing the WSDL file that Magento exposes after you set up the platform.

Magento API Authentication

Magento is a self-hosted platform unless you are using the Enterprise cloud edition, so you have much more control over its access than other solutions, but if you want to access its data through the REST API that it has, then you will have to use oAuth for authentication which is supported by Magento.

Magento Rate Limiting

As a platform hosted on your own premises it doesn’t really impose any rate-limiting. In any case, as you would like to avoid stressing your e-commerce platform that is facing your customers you should make sure that your pipeline process does not overstress your Magento installation. But this is completely at your discretion.

Endpoints and Available Resources

Magento exposes the following resources:

Products. Retrieve the list of products, create, update, delete a product.
Product categories. Retrieve the list of categories assigned to a product, assign and unassign the category from a product.
Product images. Retrieve the list of websites assigned to a product, assign, unassign a website to/from a product.
Customers. Retrieve the list of customers, create, delete a customer, and update the customer information.
Customer Addresses. Retrieve the list of customer addresses, create, update, and delete the customer address.
Inventory. Retrieve the list of stock items, update required stock items.
Sales Orders. Retrieve the list of sales orders with detailed information on order addresses, items, and comments.
Order Addresses. Retrieve information on the specified order comments.
Order Items. Retrieve information on specified order items.

The API is possible to return either JSON or XML responses, this is something that you can control by providing the appropriate Accept header content type. For all the above resources we can request from the Magento platform to pull out a list of results with all the associated data, so ideally we would like to pull all the data and make sure that we keep them up to date on our analytics platform of choice for further analysis. For this post we will just see how we can pull data for one resource, the Sales Orders, the process is the same for all other resources. To pull data for the Sales Orders, we need to execute a get request to the following endpoint:

TEXT
http://magentohost/api/rest/orders

As a platform hosted by you, you need to replace the “magentohost” part of the URL with the actual URL of the host that has Magento running. The rest of the URL is the same as the above. The default response is in XML and looks like the following:

MARKDOWN
<?xml version="1.0"?>
<magento_api>
<data\_item\_1>
<customer\_id>3</customer\_id>
<base\_discount\_amount>0.0000</base\_discount\_amount>
<base\_shipping\_amount>455.0000</base\_shipping\_amount>
<base\_shipping\_tax\_amount>0.0000</base\_shipping\_tax\_amount>
<base\_subtotal>13650.0000</base\_subtotal>
<base\_tax\_amount>0.0000</base\_tax\_amount>
<base\_total\_paid></base\_total\_paid>
<base\_total\_refunded></base\_total\_refunded>
<tax\_amount>0.0000</tax\_amount>
<total\_paid></total\_paid>
<total\_refunded></total\_refunded>
<base\_shipping\_discount\_amount>0.0000</base\_shipping\_discount\_amount>
<base\_subtotal\_incl\_tax>13650.0000</base\_subtotal\_incl\_tax>
<base\_total\_due>14105.0000</base\_total\_due>
<total\_due>14105.0000</total\_due>
<base\_currency\_code>USD</base\_currency\_code>
<tax\_name></tax\_name>
<tax\_rate></tax\_rate>
<addresses>
  <data_item>
    <region>Palau</region>
    <postcode>19103</postcode>
    <lastname>Doe</lastname>
    <street>2356 Jody Road Philadelphia, PA 19103</street>
    <city>PA</city>
    <telephone>610-634-1181</telephone>
    <country\_id>US</country\_id>
    <firstname>John</firstname>
    <address\_type>billing</address\_type>
    <prefix></prefix>
    <middlename></middlename>
    <suffix></suffix>
    <company></company>
  </data_item>
  <data_item>
    <region>Palau</region>
    <postcode>19103</postcode>
    <lastname>Doe</lastname>
    <street>2356 Jody Road Philadelphia, PA 19103</street>
    <city>PA</city>
    <telephone>610-634-1181</telephone>
    <country\_id>US</country\_id>
    <firstname>John</firstname>
    <address\_type>shipping</address\_type>
    <prefix></prefix>
    <middlename></middlename>
    <suffix></suffix>
    <company></company>
  </data_item>
</addresses>
<order_items>
  <data_item>
    <sku>Sunglasses_1</sku>
    <price>150.0000</price>
    <base\_price>150.0000</base\_price>
    <base\_original\_price>150.0000</base\_original\_price>
    <tax\_percent>0.0000</tax\_percent>
    <tax\_amount>0.0000</tax\_amount>
    <base\_tax\_amount>0.0000</base\_tax\_amount>
    <base\_discount\_amount>0.0000</base\_discount\_amount>
    <base\_row\_total>13650.0000</base\_row\_total>
    <base\_price\_incl\_tax>150.0000</base\_price\_incl\_tax>
    <base\_row\_total\_incl\_tax>13650.0000</base\_row\_total\_incl\_tax>
  </data_item>
</order_items>
</data\_item\_1>
<data\_item\_2>
<customer\_id>3</customer\_id>
<base\_discount\_amount>0.0000</base\_discount\_amount>
<base\_shipping\_amount>95.0000</base\_shipping\_amount>
<base\_shipping\_tax\_amount>0.0000</base\_shipping\_tax\_amount>
<base\_subtotal>3350.0000</base\_subtotal>
<base\_tax\_amount>0.0000</base\_tax\_amount>
<base\_total\_paid>2445.0000</base\_total\_paid>
<base\_total\_refunded>1845.0000</base\_total\_refunded>
<tax\_amount>0.0000</tax\_amount>
<total\_paid>2445.0000</total\_paid>
<total\_refunded>1845.0000</total\_refunded>
<base\_shipping\_discount\_amount>0.0000</base\_shipping\_discount\_amount>
<base\_subtotal\_incl\_tax>3350.0000</base\_subtotal\_incl\_tax>
<base\_total\_due>1000.0000</base\_total\_due>
<total\_due>1000.0000</total\_due>
<base\_currency\_code>USD</base\_currency\_code>
<tax\_name></tax\_name>
<tax\_rate></tax\_rate>
<addresses>
  <data_item>
    <region>Palau</region>
    <postcode>19103</postcode>
    <lastname>Doe</lastname>
    <street>2356 Jody Road Philadelphia, PA 19103</street>
    <city>PA</city>
    <telephone>610-634-1181</telephone>
    <country\_id>US</country\_id>
    <firstname>John</firstname>
    <address\_type>billing</address\_type>
    <prefix></prefix>
    <middlename></middlename>
    <suffix></suffix>
    <company></company>
  </data_item>
  <data_item>
    <region>Palau</region>
    <postcode>19103</postcode>
    <lastname>Doe</lastname>
    <street>2356 Jody Road Philadelphia, PA 19103</street>
    <city>PA</city>
    <telephone>610-634-1181</telephone>
    <country\_id>US</country\_id>
    <firstname>John</firstname>
    <address\_type>shipping</address\_type>
    <prefix></prefix>
    <middlename></middlename>
    <suffix></suffix>
    <company></company>
  </data_item>
</addresses>
<order_items>
  <data_item>
    <sku>Sunglasses_1</sku>
    <price>150.0000</price>
    <base\_price>150.0000</base\_price>
    <base\_original\_price>150.0000</base\_original\_price>
    <tax\_percent>0.0000</tax\_percent>
    <tax\_amount>0.0000</tax\_amount>
    <base\_tax\_amount>0.0000</base\_tax\_amount>
    <base\_discount\_amount>0.0000</base\_discount\_amount>
    <base\_row\_total>1350.0000</base\_row\_total>
    <base\_price\_incl\_tax>150.0000</base\_price\_incl\_tax>
    <base\_row\_total\_incl\_tax>1350.0000</base\_row\_total\_incl\_tax>
  </data_item>
  <data_item>
    <sku>Sun_glasses</sku>
    <price>200.0000</price>
    <base\_price>200.0000</base\_price>
    <base\_original\_price>200.0000</base\_original\_price>
    <tax\_percent>0.0000</tax\_percent>
    <tax\_amount>0.0000</tax\_amount>
    <base\_tax\_amount>0.0000</base\_tax\_amount>
    <base\_discount\_amount>0.0000</base\_discount\_amount>
    <base\_row\_total>2000.0000</base\_row\_total>
    <base\_price\_incl\_tax>200.0000</base\_price\_incl\_tax>
    <base\_row\_total\_incl\_tax>2000.0000</base\_row\_total\_incl\_tax>
  </data_item>
</order_items>
</data\_item\_2>
</magento_api>

As we can see, we get back a list of items with each one representing an order, that contains all the information that we would like to use for further analysis. information like the discount that we might have applied, the taxes paid the base price of the order, etc. As we might have many order objects to retrieve, we should paginate through the results. To do that, we need to provide the “page” and “limit” parameters to our GET request.

Now that we have the results from our Magento shop, we can further process them before we are able to load them into the BI platform of our choice.

Magento Data Preparation for PostgreSQL

To populate a PostgreSQL database instance with data, first, you need to have a well-defined data model or schema that describes the data. As a relational database, PostgreSQL organizes data around tables. Each table is a collection of columns with a predefined data type as an integer or VARCHAR. PostgreSQL, like any other SQL database, supports a wide range of different data types. A typical strategy for loading data from Magento to a PostgreSQL database is to create a schema where you will map each API endpoint to a table. Each key inside the Magento API endpoint response should be mapped to a column of that table and you should ensure the right conversion to a PostgreSQL compatible data type. For example, if an endpoint from Magento returns a value as String, you should convert it into a VARCHAR with a predefined max size or TEXT data type. tables can then be created on your database using the CREATE SQL statement. Of course, you will need to ensure that as the data types from the Magento API might change, you will adapt your database tables accordingly, there’s no such thing as automatic data typecasting. After you have a complete and well-defined data model or schema for PostgreSQL, you can move forward and start loading your data into the database.

Load data from Magento to PostgreSQL

Once you have defined your schema and you have created your tables with the proper data types, you can start loading data into your database. The most straightforward way to insert data into a PostgreSQL database is by creating and executing INSERT statements. With INSERT statements, you will be adding data row-by-row directly to a table. It is the most basic and straightforward way of adding data into a table but it doesn’t scale very well with larger data sets. The preferred way for adding larger datasets into a PostgreSQL database is by using the COPY command. COPY is copying data from a file on a file system that is accessible by the PostgreSQL instance, in this way much larger datasets can be inserted into the database in less time. You should also consult the documentation of PostgreSQL on how to populate a database with data. It includes a number of very useful best practices on how to optimize the process of loading data into your PostgreSQL database. COPY requires physical access to a file system in order to load data. Nowadays, with cloud-based, fully managed databases, getting direct access to a file system is not always possible. If this is the case and you cannot use a COPY statement, then another option is to use PREPARE together with INSERT, to end up with optimized and more performant INSERT queries.

Updating your Magento data on PostgreSQL

As you will be generating more data on Magento, you will need to update your older data on PostgreSQL. This includes new records together with updates to older records that for any reason have been updated on Magento. You will need to periodically check Magento for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a PostgreSQL table is achieved by creating UPDATE statements. Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Magento does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database. In general, ensuring the quality of the data that is inserted in your database is a big and difficult issue and PostgreSQL features like TRANSACTIONS can help tremendously, although they do not solve the problem in the general case.

The best way to load data from Magento to PostgreSQL and possible alternatives

So far we just scraped the surface of what can be done with PostgreSQL and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from, and the requirements of your use case. Things can get even more complicated if you want to integrate data coming from different sources.

A possible alternative, instead of writing, hosting, and maintaining a flexible data infrastructure, is to use a product like RudderStack that can handle this kind of problem automatically for you.

RudderStack integrates with multiple sources or services like databases, CRM, email campaigns, analytics, and more.

Don't want to go through the pain of direct integration?

RudderStack's Magento integration

makes it easy to send data from Magento to PostgreSQL.