Designing Partitions

Partitions are fundamental to the scalability and manageability of your SQL Server Analysis Services (SSAS) multidimensional models. They allow you to divide large fact tables into smaller, more manageable segments, improving query performance and simplifying data loading and aggregation processes.

What are Partitions?

A partition is a physical division of a cube or a measure group that stores a subset of the cube's data. Each partition can be stored separately, potentially on different storage locations, and can have its own aggregation design and processing schedule. This granular control is key to optimizing performance and operational efficiency.

Why Use Partitions?

Performance: By querying smaller datasets, queries that target specific partitions can be significantly faster.
Scalability: Handle very large datasets that would otherwise overwhelm the system.
Manageability: Load, process, and back up individual partitions independently, reducing downtime and complexity.
Data Archiving: Move older, less frequently accessed data to slower, cheaper storage tiers.
Data Refresh: Process only the partitions that contain new or updated data.

Types of Partitions

SSAS supports several types of partitions:

Relational: Data is stored in relational tables in a SQL Server database. This is the most common type.
unidos (Linked): Data is stored in another SSAS database.
MOLAP (Multidimensional Online Analytical Processing): Data is stored in native SSAS multidimensional storage.
ROLAP (Relational Online Analytical Processing): Data is stored entirely in the relational source, with SSAS acting as a query engine.
HOLAP (Hybrid Online Analytical Processing): Aggregations are stored in MOLAP, while detail data is stored in ROLAP.

Designing a Partition Strategy

Effective partition design often revolves around time. Common strategies include:

Monthly Partitions: Divide data by month. This is a popular choice for many business scenarios.
Yearly Partitions: Suitable for data with a longer cycle or when querying large historical periods is common.
Rolling Partitions: Maintain a fixed number of recent partitions (e.g., the last 12 months) and archive older data.
Custom Partitioning: Based on specific business rules or data distribution patterns.

Example: Monthly Partitioning by Date

Consider a sales cube. You might create partitions for each month:

                    
-- Example SQL query for a partition filter
SELECT *
FROM FactSales
WHERE OrderDate >= '2023-01-01' AND OrderDate < '2023-02-01'

In SQL Server Data Tools (SSDT) or Visual Studio, you define these partitions by specifying a query that selects the relevant data range for each partition. This query typically references a date dimension or a date column in your fact table.

Example of partition configuration in SSDT

Screenshot illustrating the partition editor in SQL Server Data Tools.

Partition Management and Processing

Once partitions are defined, they need to be processed to load data and build aggregations. You can schedule full or incremental processing for individual partitions or the entire measure group/cube.

Incremental Processing: Updates only the data that has changed since the last processing, significantly reducing processing time.
Full Processing: Replaces all data and aggregations for a partition or measure group.

Tools like SQL Server Agent Jobs or custom scripts can automate partition management and processing tasks.

Best Practices

Start with a time-based partitioning strategy.
Align partition granularity with data load frequency and query patterns.
Monitor partition performance and adjust strategy as needed.
Utilize incremental processing where possible.
Consider aggregation design for each partition to optimize query performance.

By mastering partition design, you can unlock the full potential of your Analysis Services multidimensional models, ensuring efficient data analysis and a responsive user experience.