Understanding Processing Options in Analysis Services
Processing is the act of loading data into an Analysis Services cube or dimension. It involves reading data from source systems, transforming it according to dimension and measure definitions, and storing it in the Analysis Services database in an optimized format for querying. Understanding the different processing options is crucial for maintaining performance, data integrity, and efficient updates for your multidimensional models.
Key Processing Types
Analysis Services offers several processing types, each with different implications for performance and data accuracy:
- Full Process: This option processes the entire object (database, cube, dimension, partition) from scratch. It deletes all existing data and reloads it from the source. This is the most thorough but also the most time-consuming option. It's typically used for initial loads or when significant structural changes have occurred.
- Incremental Process: This option processes only the new or changed data since the last process. It's significantly faster than a full process but requires careful management of the source data and understanding of how changes propagate. This is ideal for regular, scheduled updates.
- Process Update: This option updates existing data without clearing it. It's useful for updating specific measures or dimensions where only a subset of data needs modification.
- Process Add: This option adds new data to an existing dimension or fact table without affecting existing data. It's often used for dimensions with slowly changing attributes.
- Process Clear: This option clears all data from an object without reloading it. This is useful if you want to reprocess the object entirely using a different method or if you need to reset the data.
Processing Methods for Objects
When you process an object, you can often choose a specific method:
- Process Default: Analysis Services determines the most efficient processing method based on the object's current state and configuration. This is often the recommended approach for general use.
- Process Reindex: This method rebuilds the internal indexes of the object without reprocessing the data itself. It can improve query performance after data modifications.
- Process with Verification: This method performs a full process and then verifies the data against the source to ensure consistency. This is the most resource-intensive but ensures the highest level of data integrity.
Understanding Processing Scope
You can control the scope of your processing operations:
- Database: Processing the entire Analysis Services database.
- Cube: Processing a specific cube, which includes its dimensions and measures.
- Dimension: Processing a single dimension.
- Measure Group: Processing a specific measure group within a cube.
- Partition: Processing a specific partition of a measure group. This is highly efficient for large fact tables that are divided into manageable chunks.
Important Note on Dependencies
When processing an object, Analysis Services automatically identifies and processes dependent objects in the correct order. For example, processing a cube will also process its associated dimensions and measure groups. You can override this behavior, but it requires a deep understanding of your model's dependencies.
Best Practices for Processing
- Schedule Incremental Processes: For regular data updates, schedule incremental processing to minimize downtime and resource usage.
- Use Partitions: Divide large fact tables into partitions. This allows you to process only the relevant partitions, significantly speeding up updates.
- Monitor Processing Times: Track the duration of your processing jobs to identify bottlenecks and optimize your strategy.
- Use Scripting (AMO/XMLA): Automate your processing tasks using Analysis Management Objects (AMO) or XML for Analysis (XMLA) scripts. This ensures consistency and allows for integration into larger ETL workflows.
- Test Thoroughly: Before implementing a new processing strategy in production, test it thoroughly in a development or staging environment.
Tip for Large Datasets
For extremely large datasets, consider processing dimensions and fact data separately. Often, dimensions change less frequently than fact data, allowing for more optimized and granular processing schedules.
Effective management of processing options is key to a high-performing and responsive Analysis Services solution.