Partitions in SQL Server Analysis Services Multidimensional Modeling

Partitions are a fundamental concept in SQL Server Analysis Services (SSAS) multidimensional models. They allow you to divide large amounts of data within a cube into smaller, more manageable logical units. This partitioning strategy is crucial for improving query performance, managing data storage, and enabling incremental processing of cube data.

Why Use Partitions?

  • Performance: By dividing data, SSAS can optimize queries by scanning only the relevant partitions, significantly reducing query response times.
  • Manageability: Smaller partitions are easier to process, back up, and restore.
  • Incremental Processing: You can process or reprocess individual partitions without affecting the entire cube, which is invaluable for large datasets where full processing can be time-consuming.
  • Data Aggregation: Partitions can be defined to store different levels of aggregation, further enhancing performance for frequently accessed summary data.
  • Data Archiving: Older data can be moved to slower, cheaper storage by assigning its partition to a different storage location.

Types of Partitions

In SSAS multidimensional modeling, you primarily work with two main types of partitions:

1. Standard (or MOLAP) Partitions

Standard partitions are the default and most common type. Data is stored in a multidimensional array format optimized for analysis. This offers the best query performance but can consume more storage space compared to other types.

2. Relational (or ROLAP) Partitions

Relational partitions store data directly in relational database tables. Queries are translated into SQL queries executed against the underlying data source. This can save storage space and allow for direct access to the source data, but query performance might be lower than MOLAP partitions.

3. Hybrid (or HOLAP) Partitions

Hybrid partitions combine aspects of both MOLAP and ROLAP. Aggregated data is stored in MOLAP format for fast querying, while detailed data remains in the relational source. This provides a balance between performance and storage efficiency.

Creating and Managing Partitions

Partitions are typically managed within SQL Server Data Tools (SSDT) or SQL Server Management Studio (SSMS) when developing or managing an Analysis Services project.

Steps to Create a Partition (Conceptual):

  1. Select the Measure Group: In your SSAS project, navigate to the cube designer and select the measure group for which you want to create partitions.
  2. Access the Partitions Tab: Open the Partitions tab for the selected measure group.
  3. Create New Partition: Click the "New Partition" button.
  4. Define Partition Properties:
    • Name: Give your partition a descriptive name (e.g., "Sales_2023", "Europe_Q1").
    • Data Source View: Select the Data Source View that contains the tables for your measure group.
    • Storage Mode: Choose between MOLAP, ROLAP, or HOLAP.
    • Query Mode: (For ROLAP/HOLAP) Define how queries are processed.
    • Source Table/View: Specify the underlying table or view in your relational database that holds the data for this partition.
    • Partitioning Scheme: This is where you define the criteria for which data belongs to this partition. You can partition by a specific date range, region, product category, etc.
    • Aggregation Design: Configure aggregations to be stored within this partition.
  5. Process Partition: After creation, you will need to process the partition to load data into it.

Example Partitioning Scheme (Date-Based):

A common strategy is to partition by year or month. For example, you might create partitions for each year's sales data.

You can define the partition source based on a filter expression in the Data Source View, often using date columns:


<!-- Example of a filter in a Data Source View to isolate data for a partition -->
<Table ID="FactSales" Name="dbo.FactSales">
    <Columns>
        <Column ID="SalesKey" Name="SalesKey" />
        <Column ID="OrderDateKey" Name="OrderDateKey" />
        <Column ID="Amount" Name="Amount" />
        <!-- ... other columns ... -->
    </Columns>
    <Where Condition="[OrderDateKey] BETWEEN 20230101 AND 20231231" />
</Table>

In the partition definition within SSAS, you would then specify the "FactSales" table and select a filter that corresponds to the desired date range for that specific partition.

Tip: When using ROLAP or HOLAP partitions, ensure that the underlying relational tables are well-indexed and the database server is properly tuned for optimal query performance.

Best Practices for Partitioning

  • Partition by Time: Time-based partitioning (e.g., by year, quarter, or month) is the most common and effective strategy for managing historical data and enabling incremental processing.
  • Consider Data Volume: If a single partition becomes too large (millions of rows), consider splitting it further.
  • Match Processing Needs: Design partitions so that your processing tasks align with how data changes. If only the last month's data is updated frequently, dedicate partitions to it.
  • Monitor Performance: Regularly monitor query performance and partition processing times to identify bottlenecks and adjust your partitioning strategy.
  • Balance MOLAP and ROLAP: Use MOLAP for frequently accessed data requiring high performance, ROLAP for less frequently accessed or very large datasets where storage is a concern, and HOLAP for a hybrid approach.

Further Reading