On the Efﬁcient Construction of Query Optimizers for Distributed
Heterogeneous Information Systems
A Generic Framework
Tianxiao Liu, Dominique Laurent and Tuyˆet Trˆam Dang Ngoc
ETIS, CNRS, ENSEA - Cergy-Pontoise University, 95000 Cergy-Pontoise, France
Distributed Heterogeneous Information Systems, Query Optimization, Search Strategy, Cost Model.
It is now common practice to address queries to Distributed Heterogeneous Information Systems (DHIS).
In such a setting, the issue of query optimization becomes crucial, and more complex than in centralized
homogeneous approaches. Indeed, the optimization processing must be as ﬂexible as possible so as to apply to
different database models, and integrate different cost models. In this paper, we present a generic framework
for query optimization in the context of DHISs, with the goal of facilitating the implementation of efﬁcient
query optimizers. To this end, we identify all necessary components for building such a query optimizer and
we deﬁne the basic functions that have to be implemented. Moreover, we report on experiments showing that
our approach allows for an efcient query optimization in the context of DHISs.
It is well known that one of the main features of re-
lational database systems is to allow for query opti-
mization. When optimizing a query Q, Q is ﬁrst trans-
formed into an initial execution plan, which is then
transformed into other equivalent plans using trans-
formation rules. These candidate plans form a search
space that is explored by the query optimizer module
in order to ﬁnd an optimized execution plan (i.e., an
execution plan having a lower execution cost). As the
size of the search space is generally huge, a search
strategy is used to efﬁciently ﬁnd such an optimized
execution plan. Query optimization processing in a
given database is based on the following information:
Meta-data Model: database schema, data loca-
tion, data accessibility, etc.
Data and Query Model: relational, object ori-
ented, semi-structured, services, etc.
Optimization Goals: minimize runtime, mini-
mize money cost, minimize the access to net-
works, etc.
A Search Strategy: exhaustive, incremental, ge-
netic, dynamic, etc.
When queries are addressed to a single database for
which the information above is known in advance,
query optimization allows for efﬁciently minimizing
the computation cost. However, it is well known that
changing a piece of the information mentioned above
requires signiﬁcant efforts in source code writing.
On the other hand it is now common practice to
address queries to Distributed Heterogeneous Infor-
mation Systems (DHIS). In this setting, the evalua-
tion of a given query requires accessing different het-
erogeneous data sources, and so, query optimization
becomes more complex. Indeed, in a DHIS, the opti-
mization processing must be as ﬂexible as possible so
as to (1) consider databases located in different sites,
(2) apply to different data models, and (3) integrate
different cost models. The contribution of this paper
is to propose a generic framework for integrating vari-
ous optimization techniques in order to build efﬁcient
optimizers in the context of DHISs. In this frame-
work, we consider the following components:
Plug-in modules dealing with meta-data, data
models, queries, search strategies and transforma-
tion rules, respectively.
Basic functions for an easy and efﬁcient imple-
mentation of search strategies.
We have implemented our generic framework, and the
experiments reported in this paper show that our ap-
proach offers the necessary ﬂexibility when designing
efﬁcient query optimizers for DHISs. More precisely,
we show that, by using our approach:
1. the number of code lines for implementing a new
Liu T., Laurent D. and Trâm Dang Ngoc T..
On the Efﬁcient Construction of Query Optimizers for Distributed Heterogeneous Information Systems - A Generic Framework.
DOI: 10.5220/0003987801870190
In Proceedings of the 14th International Conference on Enterprise Information Systems (ICEIS-2012), pages 187-190
ISBN: 978-989-8565-10-5
2012 SCITEPRESS (Science and Technology Publications, Lda.)
strategy or designing a new optimizer is drasti-
cally reduced, as compared with an implementa-
tion from scratch;
2. the generated optimized execution plan reduces
the processing time by 28 times.
The paper is organized as follows: In Section 2 we
describe our generic framework, in Section 3, we re-
port on experiments, in Section 4, we overview re-
lated work, and in Section 5, we conclude the paper
and offer research directions for future research.
Figure 1 shows the main components of our generic
framework, namely:
1. Source Description, such as data schema, source
location, cost model, etc.
2. Transformation Rules, used to transform an ex-
ecution plan into another equivalent execution
3. A Collection of Search Strategies, which are meta-
heuristic algorithms used for optimization.
4. A Toolbox of Five Basic Functions, implemented
only once and reused to combine optimization
processes for different search strategies.
Search of
Search space
Optimal Plan
Figure 1: The main components of our framework.
2.1 Generating Execution Plans
We implemented a description module called GSD
(standing for Generic Source Description and for-
merly called TGV in (Dang-Ngoc and Travers, 2007))
for annotating any kind of information about data
sources. However, any module collecting data source
information can be used, as long as it implements the
following function, taking an execution plan as input
and returning the annotated execution plan:
annotate(exec plan) annotated exec plan
Given an annotated execution plan, the optimizer
should be able to calculate the cost of this execution
plan. In our generic framework, this step is achieved
through a call to the following function:
plan) cost value
Clearly, the implementation of this function depends
on the characteristics of the data source where the ex-
ecution plan is to be run. This information is provided
by the annotation returned by the annotate function.
On the other hand, in order to generate a new ex-
ecution plan from a given one, transformation rules
are applied. Usually, the following kinds of transfor-
mation rules are considered: (1) Logical rules, that
reﬂect basic properties of the underlying algebra; (2)
Physical rules that specify the best way a given opera-
tion can be computed; (3) User deﬁned rules, such as
speciﬁc commutation rules.
In our generic framework, such a rule manager is
implemented through the following two functions:
plan) set of rules
applyRule(rule, exec
plan) exec plan
A call to the function extractRules for a given exe-
cution plan generates all rules that can be applied for
transforming this execution plan into another one. On
the other hand, a call to the function applyRule, for
a given rule and a given execution plan, applies the
given rule to generate a new execution plan.
We emphasize that these four functions do not de-
pend on the optimization strategy. Therefore, they are
implemented only once for a ﬁxed DHIS, and chang-
ing the optimization strategy does not require any ad-
ditional implementation work in this respect.
2.2 Search Strategy
We recall that, in order to avoid searching the whole
set of execution plans of a given query, search strate-
gies are used. These strategies are based on well
known algorithms such as Dynamic Programming
(Selinger et al., 1979), Simulated Annealing (Kirk-
patrick et al., 1987), or Genetic Algorithm (Goldberg,
1989). Of course each strategy has its own character-
istics, and thus, changing from one search strategy to
another requires some implementation work.
However, in our generic framework, only the fol-
lowing three functions have to be reconsidered when
changing the strategy:
plan, integer) set of rules
updateRuleWeight(rule) value
plan) exec plan
The function chooseRules returns a set of rules
(whose cardinality is the value of the second param-
eter) chosen from the set of rules returned by a call
to extractRules. These rules are applied to the cur-
rent execution plan, to generate new execution plans
whose costs are computed using the function calcu-
lateCost. Finally, the function getOptimizedPlan al-
lows to compute the execution plan with lowest cost,
using a given search strategy.
We now present experimental results on the devel-
opment of our generic framework. The consid-
ered DHIS contains 12 distributed heterogeneous data
sources (relational, xml, object-relational and Web
service), and we have chosen typical queries with
inter-site binary operators to be optimized.
Search strategies are compared in the following
two respects: We ﬁrst compare their quality with re-
spect to the absolute optimal plan obtained by search-
ing the whole search space, and second, we compare
the runtimes needed by each search strategy for the
computation of its output optimized execution plan.
Figure 2 shows the comparison of various search
strategies: Simulating Annealing, Genetic Algorithm,
Incremental (Nahar et al., 1986), Dynamic Program-
ming (Selinger et al., 1979), Random (Swami, 1989)
and Ant Colony (A. Colorni, 1991).
Figure 2: Quality of optimization strategies.
For queries with 10, 15 and 20 inter-site operators,
we compare the quality of execution plans found by
each strategy with the absolute optimal. We can see
that for most strategies, the optimized execution plan
is very close to the absolute optimal execution plan.
Figure 3 shows that the time spent for ﬁnding the
optimized plan increases almost linearly with respect
to the complexity of the query being optimized. It can
also be seen that the average time for computing the
optimized execution plan is in the order of 2 seconds,
whereas we found that computing the absolute opti-
mal execution plan takes about 2 minutes in average.
This clearly shows that any search strategy is much
more efﬁcient than the exhaustive strategy.
Figure 3: Runtime for the computation of the optimal plan.
We also note from Figure 3 that the Incremen-
tal strategy is the most efﬁcient in terms of runtime,
whereas the other strategies show similar and accept-
able performance. However, Figure 2 shows that,
among all other strategies, the efﬁciency of the Incre-
mental strategy is obtained at the cost of computing
the worst execution plan, in terms of quality. More
generally, our experiments have shown that the run-
time of the optimized execution plan is reduced by
up to 28 times, as compared with the runtime of the
non optimized initial execution plan. We refer to (Liu,
2011) for more details in this respect.
Regarding now the efforts in coding search strate-
gies, we recall that applying a new search strategy
only requires to modify the implementation of two the
functions getOptimizedPlan and chooseRules. Our
experiments show that the Exhaustive search strat-
egy can be implemented with about 260 lines of Java
code. Knowing that the total number of code lines
of the whole implementation of the query optimizer
is 5000, implementing the Exhaustive search strategy
represents only 5.2% of these 5000 lines.
Assuming that the query optimizer has already
been implemented using the Exhaustive strategy,
changing from the Exhaustive search strategy to the
Incremental search strategy requires to replace the
260 code lines of the Exhaustive strategy with the 280
code lines for implementing the Incremental strat-
egy. This represents only 5.2% of the 5000 code lines
of the whole implementation. This example clearly
shows that our generic framework is ﬂexible enough
to allow query optimization in various environments.
Although search strategies are the core component of
query optimization, most mediation systems use ex-
haustive search strategies on a portion of the search
space (System R* (Daniels et al., 1982), DIOM (Liu
and Pu, 1997), DISCO (Naacke et al., 1998), Garlic
(Roth et al., 1999)) or dynamic programming (Star-
Burst (Haas et al., 1989), Garlic (Roth et al., 1999)).
In (Josifovski and Risch, 2002), the authors propose
an approach using the AMOSII mediator database
system, in which a given query is transformed into an
executable object algebraic execution plan. The opti-
mization process is based on built-in algebraic opera-
tors and a built-in cost model for local data.
Moreover, in (Josifovski and Risch, 2002), Dy-
namic Programming, Simulated Annealing, or Ran-
dom can be used as search strategies and so, this ap-
proach is the only system that offers the choice be-
tween different search strategies, as we do. How-
ever, the strategies proposed in (Josifovski and Risch,
2002) are hard-coded, which offers less ﬂexibility
than in our approach.
We ﬁnally mention that, in (Stonebraker 2008),
the author stresses that database systems are now
more and more specialized (e.g. OLPT vs. OLAP
systems), and thus, that there is no hope in design-
ing efﬁcient common optimization techniques for all
these systems. We therefore conclude that our ap-
proach of providing a generic framework for the inte-
gration of different optimization techniques can con-
tribute in the design of efﬁcient and ﬂexible query op-
timizers in DHISs.
In this paper, we have proposed a generic framework
for query optimization in the context of DHISs. Our
framework is composed of a set of basic functions,
whose implementation takes into account all aspects
of query optimization such as transformation rule,
cost estimation, construction and annotation execu-
tion plans, and search strategy. The experimental re-
sults reported in this paper show the high ﬂexibility of
our framework used to create or upgrade easily opti-
mizers with high performance. Moreover, our exper-
iments also show that using our generic framework
allows for signiﬁcant gains of runtime.
Regarding future work, we plan to investigate the
following issues: (i) implementing a cache system in
order to optimize the cost computation of a given exe-
cution plan, (ii) consider cost models in the context of
cloud computing, and (iii) incorporate multi-criteria
optimization techniques in our framework.
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