What is Molap and Rolap? What is Diff between Them?
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Answer Posted / edar
The idea of adopting the relational technology to store
data to a data warehouse has a solid foundation if you
consider the huge amount of literature written about the
relational model, the broadly available corporate
experience with relational database usage and management,
and the top performance and flexibility standards of
relational DBMSs (RDBMSs). The expressive power of the
relational model, however, does not include the concepts of
dimension, measure, and hierarchy, so you must create
specific types of schemata so that you can represent the
multidimensional model in terms of basic relational
elements such as attributes, relations, and integrity
constraints. This task is mainly performed by the well-
known star schema. See Chapter 8 for more details on star
schemata and star schema variants.
The main problem with ROLAP implementations results from
the performance hit caused by costly join operations
between large tables. To reduce the number of joins, one of
the key concepts of ROLAP is denormalization—a conscious
breach in the third normal form oriented to performance
maximization. To minimize execution costs, the other key
word is redundancy, which is the result of the
materialization of some derived tables (views) that store
aggregate data used for typical OLAP queries.
From an architectural viewpoint, adopting ROLAP requires
specialized middleware, also called a multidimensional
engine, between relational back-end servers and front-end
components. The middleware receives OLAP queries formulated
by users in a front-end tool and turns them into SQL
instructions for a relational back-end application with the
support of meta-data. The so-called aggregate navigator is
a particularly important component in this phase. In case
of aggregate views, this component selects a view from
among all the alternatives to solve a specific query at the
minimum access cost.
In commercial products, different front-end modules, such
as OLAP, reports, and dashboards, are generally strictly
connected to a multidimensional engine. Multidimensional
engines are the main components and can be connected to any
relational server. Open source solutions have been recently
released. Their multidimensional engines (Mondrian, 2009)
are disconnected from front-end modules (JPivot, 2009). For
this reason, they can be more flexible than commercial
solutions when you have to create the architecture (Thomsen
and Pedersen, 2005). A few commercial RDBMSs natively
support features typical for multidimensional engines to
maximize query optimization and increase meta-data
reusability. For example, since its 8i version was made
available, Oracle's RDBMS gives users the opportunity to
define hierarchies and materialized views. Moreover, it
offers a navigator that can use meta-data and rewrite
queries without any need for a multidimensional engine to
be involved.
Different from a ROLAP system, a MOLAP system is based on
an ad hoc logical model that can be used to represent
multidimensional data and operations directly. The
underlying multidimensional database physically stores data
as arrays and the access to it is positional (Gaede and
Günther, 1998). Grid-files (Nievergelt et al., 1984; Whang
and Krishnamurthy, 1991), R*-trees (Beckmann et al., 1990)
and UB-trees (Markl et al., 2001) are among the techniques
used for this purpose.
The greatest advantage of MOLAP systems in comparison with
ROLAP is that multidimensional operations can be performed
in an easy, natural way with MOLAP without any need for
complex join operations. For this reason, MOLAP system
performance is excellent. However, MOLAP system
implementations have very little in common, because no
multidimensional logical model standard has yet been set.
Generally, they simply share the usage of optimization
techniques specifically designed for sparsity management.
The lack of a common standard is a problem being
progressively solved. This means that MOLAP tools are
becoming more and more successful after their limited
implementation for many years. This success is also proven
by the investments in this technology by major vendors,
such as Microsoft (Analysis Services) and Oracle (Hyperion).
The intermediate architecture type, HOLAP, aims at mixing
the advantages of both basic solutions. It takes advantage
of the standardization level and the ability to manage
large amounts of data from ROLAP implementations, and the
query speed typical of MOLAP systems. HOLAP implies that
the largest amount of data should be stored in an RDBMS to
avoid the problems caused by sparsity, and that a
multidimensional system stores only the information users
most frequently need to access. If that information is not
enough to solve queries, the system will transparently
access the part of the data managed by the relational
system. Over the last few years, important market actors
such as MicroStrategy have adopted HOLAP solutions to
improve their platform performance, joining other vendors
already using this solution, such as Business Objects.
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