SQL Server Analysis Services

Partitions

Partitions provide a mechanism to divide cube data into smaller, more manageable physical storage units. This allows for improved query performance, easier data management, and incremental processing.

What are Partitions?

A partition is a division of a measure group. Each partition contains a subset of the data that belongs to the measure group. By dividing large measure groups into smaller partitions, you can:

• Improve Query Performance: Queries can sometimes be directed to specific partitions, reducing the amount of data scanned.
• Facilitate Data Management: You can update, process, or delete data within a specific partition without affecting the entire measure group.
• Enable Incremental Processing: Process only the data that has changed by creating new partitions for new data and processing them individually.
• Optimize Storage: Different storage modes can be applied to different partitions based on access patterns and data characteristics.

Types of Partitions

Partitions are typically defined based on time, geography, or any other logical division of your data.

• Aggregated Partitions: These partitions contain pre-calculated aggregations to speed up query performance.
• Detail Partitions: These partitions store the raw transaction-level data.

Creating and Managing Partitions

Partitions are created and managed within SQL Server Data Tools (SSDT) or by scripting using XMLA. The process generally involves:

1. Selecting the measure group you want to partition.
2. Defining the partition's data source and query.
3. Specifying the storage mode and processing options.

Partition Storage Modes

Analysis Services supports several storage modes for partitions, each with different performance and resource implications:

• Molap (Multidimensional OLAP): Data is stored in multidimensional structures optimized for query performance. This is the default and generally the fastest for querying.
• R tabPageL (Relational OLAP): Data is stored in a relational data source (e.g., SQL Server) and queried directly. This is useful for very large datasets or when you need to leverage existing relational infrastructure.
• Holap (Hybrid OLAP): A combination of MOLAP and R tabPageL. Aggregations are stored in MOLAP, while detail data remains in R tabPageL. This offers a balance between performance and storage efficiency.

Partition Processing

Processing a partition updates its data. You can perform full processing or incremental processing. Incremental processing is crucial for large fact tables, allowing you to process only new or changed data, significantly reducing processing time.

Incremental Processing Example (Conceptual)

To implement incremental processing for monthly sales data:

1. Create a new partition for the current month's data.
2. Configure this new partition to use R tabPageL initially, pointing to a staging table with the new data.
3. Process this new partition in MOLAP mode.
4. Once processed, you can merge this partition with existing partitions or manage it independently.

Best Practices for Partitions

• Partition by Time: Generally the most effective strategy for fact tables.
• Align Partitions with Business Cycles: Partition based on reporting periods (e.g., monthly, quarterly).
• Monitor Partition Size: Keep partitions at a manageable size to ensure efficient processing and querying.
• Choose Appropriate Storage Modes: Select MOLAP for performance-critical aggregations, R tabPageL for raw data or large datasets, and HOLAP for a hybrid approach.
• Automate Processing: Use SQL Server Agent or other scheduling tools to automate the processing of partitions, especially for incremental loads.

Understanding and effectively utilizing partitions is key to building scalable and performant Analysis Services multidimensional solutions.

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