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Implementing data snapshots

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Use case

For a dataset that contains a sequence of changes to a property over time, we want to be able to get the most recent state of said property at any given date. In this recipe, we’ll learn how to calculate snapshots of statuses at any given date for a cube with Product Id, Status, and Changed At dimensions.
We can consider the status property to be a slowly changing dimension (SCD) of type 2. Modeling data with slowly changing dimensions is an essential part of the data engineering skillset.

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Data modeling

Let’s explore the statuses cube that contains data like this: 
[
  {
    "statuses.order_id": 1,
    "statuses.status": "shipped",
    "statuses.changed_at": "2019-01-19T00:00:00.000"
  },
  {
    "statuses.order_id": 1,
    "statuses.status": "processing",
    "statuses.changed_at": "2019-03-14T00:00:00.000"
  },
  {
    "statuses.order_id": 1,
    "statuses.status": "completed",
    "statuses.changed_at": "2019-01-25T00:00:00.000"
  },
  {
    "statuses.order_id": 2,
    "statuses.status": "processing",
    "statuses.changed_at": "2019-08-21T00:00:00.000"
  },
  {
    "statuses.order_id": 2,
    "statuses.status": "completed",
    "statuses.changed_at": "2019-04-13T00:00:00.000"
  },
  {
    "statuses.order_id": 2,
    "statuses.status": "shipped",
    "statuses.changed_at": "2019-03-18T00:00:00.000"
  }
]
We can see that statuses change occasionally. How do we count orders that remained in the shipped status at a particular date? First, we need to generate a range with all dates of interest, from the earliest to the latest. Second, we need to join the dates with the statuses and leave only the most recent statuses to date. 
cubes:
  - name: status_snapshots
    extends: statuses
    sql: |
      -- Create a range from the earlist date to the latest date
      WITH range AS (
        SELECT date
        FROM GENERATE_SERIES(
          (SELECT MIN(changed_at) FROM {statuses.sql()} AS statuses),
          (SELECT MAX(changed_at) FROM {statuses.sql()} AS statuses),
          INTERVAL '1 DAY'
        ) AS date
      )

      -- Calculate snapshots for every date in the range
      SELECT range.date, statuses.*
      FROM range
      LEFT JOIN {statuses.sql()} AS statuses
        ON range.date >= statuses.changed_at
        AND statuses.changed_at = (
          SELECT MAX(changed_at)
          FROM {statuses.sql()} AS sub_statuses
          WHERE sub_statuses.order_id = statuses.order_id
        )
    dimensions:
      - name: date
        sql: date
        type: time
To generate a range of dates, here we use the GENERATE_SERIES function which is Postgres-specific. Other databases have similar functions, e.g., GENERATE_DATE_ARRAY in BigQuery.
Please note that it makes sense to make the status_snapshots cube extend the original statuses cube in order to reuse the dimension definitions. We only need to add a new dimension that indicates the date of a snapshot. We’re also referencing the definition of the statuses cube with the sql() property.

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Query

To count orders that remained in the shipped status at a particular date, we will send a query that selects a snapshot by this date and also filters by the status: 
{
  "measures": ["status_snapshots.count"],
  "filters": [
    {
      "member": "status_snapshots.date",
      "operator": "equals",
      "values": ["2019-04-01"]
    },
    {
      "member": "status_snapshots.status",
      "operator": "equals",
      "values": ["shipped"]
    }
  ]
}

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Result

If we execute a couple of such queries for distinct dates, we’ll spot the change: 
// Shipped as of April 1, 2019:
[
  {
    "status_snapshots.count": 16,
  }
];

// Shipped as of May 1, 2019:
[
  {
    "status_snapshots.count": 25,
  },
]

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Source code

Please feel free to check out the full source code or run it with the docker-compose up command. You’ll see the result, including queried data, in the console.