Fast Changing Dimension

Ch.02: DWH | DWH Components | Data Modeling | Dimension Types

Lesson Notes

Fast Changing Dimension Lesson Notes:

1. Presentation Mode
2. Static Mode

Video

Fast Changing Dimension (Mini Dimension)

• When we have a dimension with one or more of its attributes changing very fast.

• It causes a performance issue if we tried to handle this case similar SCD Type 2 because of the rapidly changing in this dimension and the table will includes a lot of rows for this dimension

• We solve this case by separation the attributes into one or more dimensions. This technique also called mini-dimensions.

Fast Changing Dimension Handling

• How to handle FCD?

  1. Identify the fast changing columns in dimension.
  2. Split the fast changing columns to a separate junk dimension.
  3. Map the junk dimension with the main dimension using mini-dimension

• In the following example, we have Weight & B_pressure columns are FCD.

• Split FCD columns from the other column.

  • Static columns

  Patient_id	Name	Gender	BirthDate
  123	Anna	F	1968-01-12

  • FCD

  Patient_Key	Weight	B_Pressure
  1	50	110.0
  2	55	130.0
  3	59	115.0
  4	65	120.0

  • Patient Mini Dimension*

  Patient_id	Patient_Key	Start_Date	End_Date
  123	1	2019-01-01	2019-01-07
  123	2	2019-01-07	2019-01-14
  123	3	2019-01-14	2019-01-21
  123	4	2019-01-21	null

Fast Changing Dimension Design Discussion

• Fast changing dimension design came from the need to handle the performance issue when we have a fact table that needs to join with a dimension that contains static and fast changing columns together.

• The main reason to introduce this design was to help to enhance the performance for retrieving the information between the Fact and the dimension with minimum data redundancy.

• If we check the first table in our example, we could think about the frequency of the change and the application which we need to handle. In this case, we have three scenarios as following:

  • First use case, assume we have a fact named clm_svc_dtl (claim service details) table that needs to get some of the static information from the patient dimension for example: patient gender, age, or just the reference for the patient_id. In this case, we will join directly with the patient dim on Patient_id.
    This case will be very fast as it will be one to one join with one record for each patient.

    • clm_svc_dtl

    clm_id	clm_svc_dt	patient_id	clm_type	clm_amount
    123	2020-01-01	123	1	110.0
    245	2020-01-01	123	1	130.0
    367	2020-01-01	123	1	115.0

  • Second use case, assume we have a fact table membr_vst_dtl (member visit detail) that has the transactional data for the patient, and it needs to get the latest Weight and Blood Pressure. In this case, the fact will include the junk dimension surrogate key and join with the junk dimension to get the values.

    • membr_vst_dtl

    clm_id	vst_dt	Patient_Key
    123	2019-01-01	1
    245	2019-01-07	2
    367	2019-01-14	3
    367	2019-01-21	4

  • Third use case, assume we have a fact table membr_rsk_score (member risk_score) that calculates risk score based on Weight and Blood Pressure. This risk score needs to check historical data every day and compare the results with the previous for simple example: assume for each day we get the log of (the difference between current blood pressure minus the previous one plus the same for the Weight). In this case, we will join fact with junk with the mini dimension. Note: This case is not the normal case to check the historical data every day for all facts. So, the requirements to access the start and end date for this fact is not frequent request case.

    • membr_rsk_score

    rsk_score	calc_dt	Patient_Key
    0.5	2019-01-01	1
    0.6	2019-01-07	2
    0.3	2019-01-14	3
    0.67	2019-01-21	4

• Let’s analyze the implementation strategies which can handle the above three use cases.

  • Keep the patient dimension as is and join with fact directly. The drawback of this design is that the size of the patient dimension will be huge, with the redundancy of data and performance issues when joining between Fact and dimension.

  • Split the patient dimension into two-dimension static and FCD. This solution has an example below, and it will work, but it has some problems. So, Let’s analyze why we can’t remove the junk table and use only the mini-dimension table?

    • The new dimension which contains the fast changing columns will have a bigger size (for more detail about how to choose the number of junk dimensions per table, please refer to the junk dimension lecture here). Now we will have a performance issue when joining between the new dimension and fact table.
    • The new dimension will include some columns which not required for the daily query results (start_data and end_date). These columns will cause to make the new table to be the bigger size and affect the performance.
    • One more issue here if we have a bigger number of the fast changing column the combination (as it is the cartesian product of the junk columns values) will make the table records to be bigger.

      Patient_id	Patient_Key	Weight	B_Pressure	Start_Date	End_Date
      123	1	50	110.0	2019-01-01	2019-01-07
      123	2	55	130.0	2019-01-07	2019-01-14
      123	3	59	115.0	2019-01-14	2019-01-21
      123	4	65	120.0	2019-01-21	null

• The recommended solution as following

  • Split the patient dimension into three tables:

    • Patient Dimension.
    • Junk Dimension(s).
    • Mini Dimension(s).

  • This solution has some perspectives which we need to clarify it.

    • In case we have multi fast changing columns with lots of combinations, we could split it into one or more junk and handle this with one or mini dimension tables.

    • When we need to get the junk dimension information, we join with a smaller table, which will increase the performance as it is fewer table columns and size.

    • In case we need to get the historical data with the date columns, we can still get this information by joining between the fact, junk, and mini-dimension.

    • Patient Dimension (static columns)

    Patient_id	Name	Gender	BirthDate
    123	Anna	F	1968-01-12

    • Junk Dimension

    Patient_Key	Weight	B_Pressure
    1	50	110.0
    2	55	130.0
    3	59	115.0
    4	65	120.0

    • Mini Dimension

    Patient_id	Patient_Key	Start_Date	End_Date
    123	1	2019-01-01	2019-01-07
    123	2	2019-01-07	2019-01-14
    123	3	2019-01-14	2019-01-21
    123	4	2019-01-21	null

Further Reading

Dimensional Modeling: In a Business Intelligence Environment. The book is free, and you can download it from this link. You can read the following:

• Chapter 5.4.7 Slowly Changing Dimensions page 162.
• Chapter 6.3.6 Fast Changing dimensions page 269.

Previous Chapters

Overview | Ch.01: Intro