Storage Modes in SSAS (MOLAP, ROLAP and HOLAP)


There are three standard storage modes (MOLAP, ROLAP
and HOLAP) in OLAP applications which affect the performance of OLAP queries
and cube processing, storage requirements and also determine storage locations.

SSAS (2005 and 2008) supports not only these three standard
storage modes but supports proactive caching, a new feature with SSAS 2005 which
enables you to combine the best of both worlds (ROLAP and MOLAP storage) for
both frequency of data refresh and OLAP query performance.

In the part 1 of this article, I will start my
discussion with an overview of each mode and then in part 2 I will cover the
new proactive-caching feature of SSAS.

Basic storage modes

The cube data can be divided into three different types
– meta-data, detail data and aggregate data. No matter what storage is used,
the meta-data will always be stored on the  OLAP server but storage of the detail
data and aggregate data will depend on the storage mode you specify.


MOLAP (Multidimensional OLAP)

This is the default and most frequently used storage mode.
In this mode when you process the cube, the source data is pulled from the relational
store, the required aggregation is then performed and finally the data is
stored in the Analysis Services server in a compressed and optimized multidimensional

After processing, once the data from the underlying
relational database is retrieved there exists no connection to the relational
data stores. So if there is any subsequent changes in the relational data after
processing that will not reflect in the cube unless the cube is reprocessed and
hence it is called offline data-set mode.

Since both the detail and aggregate data are stored
locally on the OLAP server, the MOLAP storage mode is very efficient and
provides the fastest query performance.


the detail and aggregate data in the OLAP server in a compressed
multidimensional format; as a result the cube browsing is fastest in this mode.

maximum query performance, because all the required data (a copy of the detail
data and calculated aggregate data) are stored in the OLAP server itself and
there is no need to refer to the underlying relational database.

the calculations are pre-generated when the cube is processed and stored
locally on the OLAP server hence even the complex calculations, as a part the
query result, will be pefromed quickly.

does not need to have a permanent connection to the underlying relational
database (only at the time of processing) as it stores the detail and aggregate
data in the OLAP server so the data can be viewed even when there is connection
to the relational database.

uses compression to store the data on the OLAP server and so has less storage
requirements than relational databases for same amount of data. (Note however,
that beginning with SQL Server 2008 you can use data compression at relational
database level as well).


MOLAP  mode, you need frequent processing to pull refreshed data after last
processing resulting in drain on system resources.

just after the processing if there is any changes in the relational database it
will not be reflected on the OLAP server unless   re-processing is performed.

stores a copy of the relational data at OLAP server and so requires additional
investment for storage.

the data volume is high, the cube processing can take longer, though you can
use incremental processing to overcome this.

ROLAP (Relational OLAP)

In comparison with MOLAP, ROLAP does not pull data from
the underlying relational database source to the OLAP server but rather both
cube detail data and aggregation stay at relational database source. In order
to store the calculated aggregation the database server creates additional
database objects (indexed views). In other words, the ROLAP mode does not copy
the detail data to the OLAP server, and when a  query result cannot be obtained
from the query cache the created indexed views are accessed to provide the


to view the data in near real-time.

ROLAP does not make another copy of data as in case of MOLAP, it has less
storage requirements. This is very advantageous for large datasets which are
queried infrequently such as historical data.

ROLAP mode, the detail data is stored on the underlying relational database, so
there is no limitation on data size that ROLAP can support or limited by the
data size of relational database. In nutshell, it can even handle huge volumes
of data.


to MOLAP or HOLAP the query response is generally slower because everything is
stored on relational database and not locally on the OLAP server.

permanent connection to the underlying database must be maintained to view the
cube data.


you use ROLAP storage mode and your relational database is SQL Server, the
Analysis Services server may create indexed views for aggregation. However this
requires a few prerequisite to be available – for example, the data source
must be a table, not a view. The table name must use two part naming
convention or it must be qualified with owner/schema name etc. For a complete
list of these prerequisites you can refer to the link provided in reference



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