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Aggregating data at query time can be slow because the system must fetch data from the datastore or an external database (DirectQuery). Aggregate providers allow pre-aggregation, so queries retrieve results from optimized structures instead of recalculating from scratch. Aggregate providers define the strategy for aggregating measures in a cube. They determine whether data is aggregated on demand or pre-aggregated during data loading. This choice directly impacts query speed, memory usage and transaction speed.

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What are the types of aggregate providers and their impact?

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Just-in-time (JIT) provider

  • Behavior: Aggregates data at query time.
  • Memory usage for storage: None.
  • Memory usage at query time: Higher than other aggregate providers
  • Query speed: Slower.
  • Use case: Small datasets or infrequent queries.
  • Impact: No pre-aggregation; queries depend on datastore scans.

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Leaf provider

  • Behavior: Pre-aggregates data during loading at leaf level.
  • Memory usage for storage: Moderate.
  • Query speed: Fast.
  • Memory usage at query time: Moderate.
  • Use case: Balanced performance for most cubes.
  • Impact: Queries retrieve pre-aggregated values using point indexes, reducing computation time.

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Bitmap provider

A bitmap aggregate provider pre-aggregates data using a leaf provider. They use a bitmap index for faster data retrieval.
  • Behavior: Pre-aggregates data and builds bitmap indexes.
  • Memory usage for storage: Higher than a leaf provider.
  • Query speed: Fastest.
  • Memory usage at query time: Lower than JIT or leaf.
  • Use case: High query concurrency and large datasets.
  • Impact: Queries use bitmap submasks for instant retrieval of aggregated values.
performance-agg-provs.png Find out more about aggregate providers by following the links below: