The Original Query Accelerator

Before cloud-scale distributed query engines and columnar file formats, analytical databases faced a fundamental performance challenge: complex multi-dimensional queries (revenue by product category by region by month) required scanning and aggregating large fact tables, which took minutes or hours on the hardware available in the 1990s and 2000s. Business intelligence users needed sub-second query response for interactive dashboard exploration, which the raw OLTP or data warehouse tables could not provide.

OLAP (Online Analytical Processing) cubes solved this problem through pre-aggregation. A cube is a multidimensional data structure that pre-computes all possible combinations of dimensional aggregations over a fact dataset. For a cube defined on dimensions (product category, region, month) with measures (sum of revenue, count of orders), the cube pre-computes and stores the aggregated values for every combination: revenue by category, revenue by region, revenue by month, revenue by category and region, revenue by category and month, revenue by region and month, and revenue by category, region, and month simultaneously.

When a BI user queries “revenue by product category for Q1 2024 in the US-WEST region,” the OLAP server retrieves the pre-computed value from the cube in milliseconds, rather than scanning and aggregating the raw transaction records. This provided the sub-second interactive query performance that BI systems needed.

OLAP Cube Limitations

OLAP cubes delivered exceptional performance but imposed significant rigidity:

Pre-defined dimensionality: A cube’s dimensions and measures must be specified when the cube is built. Adding a new dimension or measure requires rebuilding the entire cube, which could take hours. If a business user wants to analyze a dimension that was not included in the cube definition, the query falls back to scanning the raw fact table, negating the cube’s performance benefit.

Exponential storage growth: Pre-computing all dimension combinations (the “cube” in OLAP cube refers to the multi-dimensional space of all aggregation combinations) requires storage proportional to the product of all dimension cardinalities. A cube with 100 product categories, 50 regions, and 36 months has 180,000 pre-computed cells, which is manageable. Adding a 10th dimension with 200 values multiplies the cube size by 200x.

High maintenance burden: Cube refresh (re-computing aggregations from updated fact data) is computationally expensive and must be carefully scheduled. A cube that takes 4 hours to refresh from scratch cannot be refreshed more frequently than every 4 hours, limiting data freshness.

Modern Equivalents in the Lakehouse

The modern lakehouse achieves OLAP cube performance benefits without cube rigidity through two mechanisms:

Columnar storage and vectorized execution: Modern columnar query engines (Dremio, Trino, DuckDB) can aggregate and group billions of Parquet rows in seconds using vectorized SIMD CPU instructions and multi-core parallelism. What took minutes on 2000s hardware takes seconds on modern hardware without any pre-aggregation.

Dremio Data Reflections (Aggregation Reflections): For queries that need sub-second response and exceed what columnar execution provides from raw data, Dremio’s Aggregation Reflections provide OLAP-cube-like pre-aggregation with significant advantages over traditional cubes: reflections are incrementally refreshed (only processing new Iceberg snapshot data rather than reprocessing the entire table), the query planner automatically selects the optimal reflection for each query (no need to manually route queries to the right cube), and multiple reflections with different dimensionalities can coexist, with the planner selecting the most specific match.

The combination of modern columnar execution and Dremio Data Reflections makes traditional OLAP cube architectures obsolete for most analytical use cases, providing equivalent or better query performance with dramatically less rigidity and maintenance overhead.

Learn More

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