Partitions in Tabular Models

This document explains how to use partitions to divide tables into smaller, manageable pieces for improved performance and manageability in SQL Server Analysis Services (SSAS) tabular models.

What are Partitions?

In a tabular model, partitions are used to divide a table into smaller logical and physical parts. This division is primarily for data management and performance optimization. Each partition stores a subset of a table's data. By managing data in smaller chunks, you can:

Improve Query Performance: Queries can be directed to only the partitions containing relevant data, significantly reducing the amount of data scanned.
Streamline Data Refresh: You can refresh individual partitions independently, allowing for incremental updates and faster data loading.
Simplify Data Management: It becomes easier to manage historical data, archive old data, or reload specific data segments.

Creating and Managing Partitions

Partitions are managed using tools like SQL Server Management Studio (SSMS) or Visual Studio with the Analysis Services projects extension.

Using SQL Server Management Studio (SSMS)

Connect to your Analysis Services instance in SSMS.
Navigate to the tabular database containing your model.
Right-click on the table for which you want to create partitions and select Partitions.
In the Partitions Manager dialog, you can:
- Create New Partition: Define the source query for the new partition.
- Edit Partition: Modify the source query, name, or properties of an existing partition.
- Delete Partition: Remove a partition.
- Duplicate Partition: Create a new partition based on an existing one.
You can define partitions based on date ranges, specific criteria, or data segments.

Using Visual Studio

When developing a tabular model in Visual Studio:

Open your tabular model project.
In the Model Explorer, right-click on the table.
Select Partitions.
The Partitions Manager will open, similar to the SSMS experience, allowing you to create, edit, and manage partitions for your tables.

Partitioning Strategies

Effective partitioning requires a well-thought-out strategy. Common strategies include:

Date-Based Partitioning: Divide data by time periods (e.g., monthly, yearly). This is very common for transactional data.
Note: When partitioning by date, ensure your source queries are robust to handle date ranges correctly.
Range-Based Partitioning: Divide data based on numerical ranges or categories.
Full Load vs. Incremental Load: You can have a "full load" partition for recent data that is frequently updated and "historical" partitions that are less frequently updated or read-only.

Best Practices for Partitions

Keep Partitions Manageable: Avoid creating an excessive number of very small partitions, as this can introduce overhead. Conversely, partitions that are too large may not offer significant performance benefits.
Align with Business Requirements: Design partitions based on how data is accessed and managed for business reporting.
Automate Data Refresh: Use scripting (e.g., AMO, TOM) or tabular deployment tools to automate the creation and refresh of partitions, especially for time-series data.
Monitor Performance: Regularly monitor query performance and data refresh times to ensure your partitioning strategy is effective.
Consider Partition Granularity: The ideal number of partitions depends on the size of your data, query patterns, and data refresh needs.

Tip: For tabular models in Azure Analysis Services or SQL Server Analysis Services 2017 and later, partitions can significantly boost performance for large datasets.

Example: Date-Based Partitioning Query

Here's a conceptual example of how you might define a partition for sales data from 2023:

                
SELECT *
FROM Sales
WHERE OrderDate >= '2023-01-01' AND OrderDate < '2024-01-01'

You would then create separate partitions for other years or time periods using similar queries.