SSAS Partitioning Basics

Partitioning is a fundamental concept in SQL Server Analysis Services (SSAS) that allows you to divide large fact tables into smaller, more manageable segments. This not only improves query performance but also streamlines data management operations like loading and processing.

Why Partition? The Benefits

Before diving into the 'how', let's understand the 'why'. Partitioning offers several significant advantages:

• Performance Boost: Queries can scan only relevant partitions, drastically reducing the amount of data that needs to be processed.
• Improved Manageability: Individual partitions can be processed, backed up, or restored independently, making maintenance tasks faster and more efficient.
• Parallel Processing: SSAS can process multiple partitions in parallel, significantly reducing overall processing time for large datasets.
• Data Archiving and Purging: Older data can be moved to less performant storage or purged by dropping older partitions, saving costs and improving query speed on current data.

Understanding Partition Structures

In SSAS, a partition is associated with a specific measure group within a tabular or multidimensional cube. Each partition consists of:

• A Data Source View: The underlying table or query that provides the data for the partition.
• A Query Binding: A SQL query that defines which rows from the Data Source View belong to this specific partition.
• A Storage Mode: How the data within the partition is stored (e.g., MOLAP, ROLAP, HOLAP).

Creating Your First Partition

Let's walk through a common scenario: partitioning a large Sales fact table by year.

Step 1: Define the Partitioning Column

Identify a column that will be used for partitioning. For time-based partitioning, a date or year column is ideal. In our example, we'll use a SalesDate column.

Step 2: Design the Partitioning Scheme

For a yearly partition, you would create a separate partition for each year of data you want to store. This involves defining a query for each partition that filters data based on the year.

Step 3: Implement in SSAS

Within your SSAS project (using SQL Server Data Tools or Visual Studio with Analysis Services projects), navigate to your cube or tabular model. Right-click on the relevant measure group and select "Create Partitions...".

In the Partition Wizard, you'll typically:

1. Select Measure Group: Choose the measure group you want to partition.
2. Choose Partitioning Method: Select "Create partitions for each [Column Name]" or define them manually. For year-based partitioning, selecting "Create partitions for each value" based on a derived year column is common.
3. Define Partition Properties: For each partition, you'll specify:
   • Partition Name: A descriptive name (e.g., 'Sales_2022').
   • Data Source: The database where your fact table resides.
   • Query: A SQL query like:

   SELECT * FROM dbo.FactSales WHERE YEAR(SalesDate) = 2022

   • Storage Mode: Choose the appropriate storage mode (MOLAP is often preferred for performance).
4. Finish: Review your settings and complete the wizard.

Managing Partitions

Once partitions are created, you can manage them through:

• Processing: Process individual partitions or all partitions within a measure group.
• Aggregation Design: Aggregations can be defined per partition for further optimization.
• Backups and Restores: You can backup and restore individual partitions.

Best Practices

• Partition Granularity: Choose a granularity that balances manageability and query performance. Daily or monthly partitioning might be too granular, while yearly might be too coarse depending on data volume.
• Partitioning Key: Use a column that is frequently used in query filters (like a date column).
• Data Source Views: Ensure your Data Source Views are optimized.
• Monitoring: Regularly monitor query performance and processing times to adjust your partitioning strategy as needed.

Conclusion

SSAS partitioning is a powerful technique that is essential for building scalable and performant analytical solutions. By strategically dividing your data, you can achieve significant improvements in query speed, data loading times, and overall system manageability. Experiment with different partitioning strategies to find the optimal configuration for your specific data and business requirements.