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Online Analytical Processing, or OLAP, is a methodology design to quickly provideanswers to analytical queries that are multidimensional in nature. The term OLAP replaces the traditional database term OLTP [Online Transaction Processing].

OLAP is part of business intelligence which also includes:

  • Extract Transform Load (ETL)
  • Relational Reporting
  • Data Mining

OLAP is typically used in business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and other similar areas.


OLAP Data Models

Databases configured for OLAP use a multidimensional data model. This allows for complex analytical and ad-hoc queries with a rapid execution time.

OLAP databases utilize aspects of both navigational databases and hierarchical databases to support the speed required, making them significantly faster than relational databases.

The output of an OLAP query is typically displayed in a matrix [pivot] format. The dimensions form the row and column of the matrix; the measures, the values.


OLAP Cubes

The core concept of of any OLAP system is an OLAP cube [multidimensional cube or a hypercube].

The cube consists of numeric facts called measures which are categorized by dimensions.

  • Metadata is typically created from a star schema or snowflake schema of tables in a relational database.
  • Measures are derived from the records in the fact table.
  • Dimensions are derived from the dimension tables.

For complex queries OLAP cubes can produce an answer in around 0.1% of the time for the same query on OLTP relational data. This performance is due to the use of aggregations.


Aggregations are essentially views of a data set, built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions. The number of possible aggregations is determined by every possible combination of dimension granularities.

The combination of all possible aggregations and the base data contain the answers to every query which can be answered from the data.

Since there are potentially a large number of aggregations to be calculated, often only a predetermined number are fully calculated, whilst the rest are solved on demand.

The decision as to which aggregations [views] to calculate is known as the view selection problem.

View Selection

View selection can be constrained by:

  1. the total size of the selected set of aggregations
  2. the time to update them from changes in the base data
  3. or both.

The objective of view selection is to minimize the average time to answer OLAP queries, and also minimize the update time.

Many different approaches have been taken to view selection, including greedy algorithms, randomized search, genetic algorithms and A* search algorithms.


Derivatives of OLAP

There are several derivatives of OLAP: MOLAP, ROLAP, HOLAP. Each OLAP type has certain benefits and certain issues. Preference comes down to personal experience and confidence at to whether the goals of the database will best be achieved.


MOLAP is the 'classic' form of OLAP and is sometimes referred to as just OLAP.

MOLAP uses database structures that are generally optimal for attributes such as time period, location, product or account code. The way that each dimension will be aggregated is defined in advance by one or more hierarchies.

In MOLAP [Multidimensional OLAP] products, the cube is populated by copying snapshot of the data from the data source.


MOLAP generally delivers better performance due to specialized indexing and storage optimizations. MOLAP also needs less storage space compared to ROLAP because the specialized storage typically includes compression techniques.


Some MOLAP implementations are prone to database explosion. Database explosion is a phenomenon causing vast amounts of storage space to be used by MOLAP databases when certain common conditions are met:

  • high number of dimensions
  • pre-calculated results
  • sparse multidimensional data.

The typical mitigation technique for database explosion is not to materialize all the possible aggregation, but only the optimal subset of aggregations based on the desired performance vs. storage trade off.


ROLAP [Relational OLAP] products work directly against the data source without copying data.

ROLAP works directly with relational databases. The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. Depends on a specialized schema design.


ROLAP is generally more scalable.


Large volume pre-processing is difficult to implement efficiently so it is frequently skipped. ROLAP query performance can therefore suffer.

Since ROLAP relies more on the database to perform calculations, it has more limitations in the specialized functions it can use.


HOLAP [Hybrid OLAP] products combine the previous two approaches. There is no clear agreement across the industry as to what constitutes "Hybrid OLAP", except that a database will divide data between relational and specialized storage.

For example, for some vendors, a HOLAP database will use relational tables to hold the larger quantities of detailed data, and use specialized storage for at least some aspects of the smaller quantities of more-aggregate or less-detailed data.

HOLAP encompasses a range of solutions that attempt to mix the best of ROLAP and MOLAP. It can generally pre-process quickly, scale well, and offer good function support.

Other OLAP Types

Other forms of OLAP so so common include:

  • WOLAP - Web-based OLAP
  • DOLAP - Desktop OLAP
  • RTOLAP - Real-Time OLAP
  • SOLAP - Spatial OLAP (see Location intelligence)

OLAP Products

One of the key problems with OLAP tools is that they each use a proprietary approach to formatting data, that is not always compatable with other makes.

The main OLAP products and their 2006 market share include:

According to the influential OLAP Report site, the market shares for the top commercial OLAP products in 2006 were:

  1. Microsoft Corporation - 31.6%
  2. Hyperion Solutions Corporation - 18.9%
  3. Cognos - 12.9%
  4. Business Objects - 7.3%
  5. MicroStrategy - 7.3%
  6. SAP AG - 5.8%
  7. Cartesis SA - 3.7%
  8. Applix - 3.6%
  9. Infor - 3.5%
  10. Oracle Corporation - 2.8%