9.4: Add a 'Type' attribute to every item

You’ll use different patterns for the primary key of each entity to distinguish between entity types. But it can be difficult to easily distinguish between this with a glance or when doing a filter expression.

One tip I suggest is to include a Type attribute on every item I write to the table. This attribute will be a simple string declaring the type of entity: User, Order, SensorReading, etc.

        {
            "PK": { "S": "USER#antonychiramel" },
            "SK": { "S": "USER#antonychiramel" },
            "GSI1PK": { "S": "ORG#facebook" },
            "GSI1SK": { "S": "USER#antonychiramel" },
            "Type": { "S": "User" },
            "Username": { "S": "antonychiramel" },
            "FirstName": { "S": "Antony" },
            "LastName": { "S": "Chiramel" },
            "OrganizationName": { "S": "Facebook" },
            ...
        }

In the above dataset we have added a "Type" attribute right below "GSI1SK"

I use this attribute for a few things. First, as mentioned, it makes it easier to orient myself if I am exploring my table in the AWS console. But it’s more important for a few other situations.

In the migration strategies discussed in Chapter 15, we discuss that you may need to modify existing items to decorate them with new indexing attributes. To do that, you do a background ETL operation that scans your table, finds the items you need to modify, and adds the attributes.

When doing this, you usually only need to update certain entity types. I like to use a filter expression on the Type attribute when scanning my table to ensure I’m only getting the items that I need. It simplifies the logic in my ETL script.

A final reason to add this Type attribute is for analytics reasons when you export your data to an external system. DynamoDB is not great at performing ad-hoc OLAP-style queries, so you will probably import your data to Amazon Redshift or export it to Amazon S3 to query with Amazon Athena. But all your entities will be in a single table, which is not how a relational database expects to use your data. After your initial export of data, you’ll want to "re- normalize" it by moving your different entity types into their own tables. Having a Type attribute makes it easier to write the transformation query and find the right items to move around.