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Ecommerce Manifest Review

Let's do a quick review of what we have done so far:

  • We started off by learning about the Ecommerce schema, its tables, and the relationships amongst them.

    [ref: The Ecommerce schema ↗]

  • Then, we learnt that in order to generate data for any schema with Dore we need to create a Manifest for the schema which defines requirements of the target data.

    [ref: Dore Manifest ↗]

  • We then proceeded with creating the Manifest and providing the Manifest with an ID.

    [ref: Creating the Manifest ↗]

  • Next, we learnt about Datastores and we defined the ecommerce Datastore, which defines details for the Dore_Ecommerce MySQL database in the Manifest.

    [ref: Datastore ↗]

  • Then, we learnt about Models and Attributes. We defined the customer and order Models and their Attributes.

    [ref: Models and Attributes ↗]

  • We then learnt about Attribute Value Definitions and defined how Dore should generate values for each of the attributes of the customer and order models.

    [ref: Models and Attributes ↗]

  • Finally, we learn about Protocol specific properties for Models and Attributes and defined protocol specific properties for each of the models and their attributes in our Manifest.

    [ref: Model and Attribute Properties ↗]

The following snippet contains the complete ecommerce manifest:

dore-ecommerce-manifest.json
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{
  "id": "ecommerce-example",
  "datastores": {
    "ecommerce": {
      "protocol": "mysql",
      "properties": {
        "database": "Dore_Ecommerce",
        "host": "127.0.0.1",
        "port": "3306",
        "user": "root",
        "password": "password"
      }
    }
  },
  "models": {
    "customer": {
      "datastore": "ecommerce",
      "records": 10,
      "properties": {
        "tableName": "Customer"
      },
      "attributes": {
        "customerId": {
          "properties": {
            "columnName": "customer_id",
            "columnType": "CHAR(50)"
          },
          "value": {
            "faker": {
              "uuid4": {}
            }
          }
        },
        "customerName": {
          "properties": {
            "columnName": "customer_name",
            "columnType": "CHAR(50)"
          },
          "value": {
            "faker": {
              "name": {}
            }
          }
        }
      }
    },
    "order": {
      "datastore": "ecommerce",
      "records": 100,
      "properties": {
        "tableName": "Order"
      },
      "attributes": {
        "orderId": {
          "properties": {
            "columnName": "order_id",
            "columnType": "CHAR(50)"
          },
          "value": {
            "faker": {
              "uuid4": {}
            }
          }
        },
        "customerId": {
          "properties": {
            "columnName": "customer_id",
            "columnType": "CHAR(50)"
          },
          "value": {
            "ref": "customer.customerId"
          }
        },
        "orderDate": {
          "properties": {
            "columnName": "order_date",
            "columnType": "DATE"
          },
          "value": {
            "faker": {
              "date_between": {
                "start_date": "-1y"
              }
            }
          }
        }
      }
    }
  }
}

Next, we're going to try invoking Dore with the above manifest and verify that we're able to generate data as per the schema requirements.


➡ Next: Invoke Dore