In traditional relational databases, slowly changing dimensions (SCDs) are used to track and manage changes in data over time. However, when working with document databases like MongoDB or CouchDB, implementing SCDs can be a bit different. In this blog post, we will explore how to handle SCDs in document databases.

What are Slowly Changing Dimensions?

Slowly changing dimensions refer to the data attributes that change slowly over time. For example, consider the customer information in an e-commerce system. The customer’s address may change occasionally, but their name or email address is less likely to change frequently. These changes need to be tracked and managed in a systematic way.

Strategies for Implementing SCDs in Document Databases

Strategy 1: Embedded Documents

One approach to implementing SCDs in document databases is to use embedded documents. In this strategy, each document represents a version of the entity, and the changes are stored within the same document.

Here’s an example in MongoDB:

{
  _id: "customer123",
  name: "John Doe",
  email: "johndoe@example.com",
  address: {
    street: "123 Main Street",
    city: "New York",
    state: "NY",
    country: "USA"
  },
  history: [
    {
      validFrom: ISODate("2021-01-01"),
      validTo: ISODate("2021-06-30"),
      address: {
        street: "456 Elm Street",
        city: "New York",
        state: "NY",
        country: "USA"
      }
    },
    {
      validFrom: ISODate("2021-07-01"),
      validTo: null,
      address: {
        street: "123 Main Street",
        city: "New York",
        state: "NY",
        country: "USA"
      }
    }
  ]
}

In this example, the customer’s address history is stored within the history array, allowing you to query and retrieve the correct address based on the validity dates.

Strategy 2: Separate Collection

Another strategy is to use a separate collection to store the historical changes. Each document in the historical collection contains the full state of the entity at a specific point in time.

For example, in MongoDB, you can create a separate collection called customer_history:

{
  _id: ObjectId("6171d3730f156c12a8e8aa2e"),
  customerId: "customer123",
  validFrom: ISODate("2021-01-01"),
  validTo: ISODate("2021-06-30"),
  name: "John Doe",
  email: "johndoe@example.com",
  address: {
    street: "456 Elm Street",
    city: "New York",
    state: "NY",
    country: "USA"
  }
},
{
  _id: ObjectId("6171d3730f156c12a8e8aa2f"),
  customerId: "customer123",
  validFrom: ISODate("2021-07-01"),
  validTo: null,
  name: "John Doe",
  email: "johndoe@example.com",
  address: {
    street: "123 Main Street",
    city: "New York",
    state: "NY",
    country: "USA"
  }
}

In this approach, you would query the customer_history collection based on the validity dates and the customer ID to retrieve the appropriate state of the entity.

Conclusion

Implementing slowly changing dimensions in document databases requires different strategies compared to relational databases. By leveraging embedded documents or separate collections, you can effectively track and manage changes in your document-based data.

Using these strategies, you can maintain a historical record of changes, allowing you to analyze data trends and retrieve accurate information based on specific time frames.