11.query optimization to improve performance of the code execution

Computer Engineering and Intelligent Systems www.iiste.org
ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)
Vol 3, No.1, 2012

Query Optimization to Improve Performance of the Code
Execution
Swati Tawalare * S.S Dhande
Dept of CSE, SIPNA’s College of Engineering & Technology, Amravati, INDIA
* E-mail of the corresponding author : swatitawalare18@rediffmail.com

Abstract
Object-Oriented Programming (OOP) is one of the most successful techniques for abstraction. Bundling
together objects into collections of objects, and then operating on these collections, is a fundamental part of
main stream object-oriented programming languages. Object querying is an abstraction of operations over
collections, whereas manual implementations are performed at low level which forces the developers to
specify how a task must be done. Some object-oriented languages allow the programmers to express
queries explicitly in the code, which are optimized using the query optimization techniques from the
database domain. In this regard, we have developed a technique that performs query optimization at
compile-time to reduce the burden of optimization at run-time to improve the performance of the code
execution.
Keywords- Querying; joins; compile time; run-time; histograms; query optimization
Introduction
Query processing is the sequence of actions that takes as input a query formulated in the user language and
delivers as result the data asked for. Query processing involves query transformation and query execution.
Query transformation is the mapping of queries and query results back and forth through the different levels
of the DBMS. Query execution is the actual data retrieval according to some access plan, i.e. a sequence of
operations in the physical access language. An important task in query processing is query optimization.
Usually, user languages are high-level, declarative languages allowing to state what data should be
retrieved, not how to retrieve them. For each user query, many different execution plans exist, each having
its own associated costs. The task of query optimization ideally is to find the best execution plan, i.e. the
execution plan that costs the least, according to some performance measure. Usually, one has to accept just
feasible execution plans, because the number of semantically equivalent plans is to large to allow for
enumerative search A query is an expression that describes information that one wants to search for in a
database.
For example, query optimizers select the most efficient access plan for a query based on the estimated costs
of competing plans. These costs are in turn based on estimates of intermediate result sizes. Sophisticated
user interfaces also use estimates of result sizes as feedback to users before a query is actually executed.
Such feedback helps to detect errors in queries or misconceptions about the database. Query result sizes are
usually estimated using a variety of statistics that are maintained for relations in the database. These
statistics merely approximate the distribution of data values in attributes of the relations. Consequently,
they represent an inaccurate picture of the actual contents of the database. The resulting size-estimation
errors may undermine the validity of the optimizer’s decisions or render the user interface application
unreliable. Earlier work has shown that errors in query result size estimates may increase exponentially
with the number of joins. This result, in conjunction with the increasing complexity of queries,
demonstrates the critical importance of accurate estimation .Several techniques have been proposed in the
literature to estimate query result sizes, including histograms, sampling, and parametric techniques. Of
these, histograms approximate the frequency distribution of an attribute by grouping attribute values into

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“buckets” (subsets) and approximating true attribute values and their frequencies in the data based on
summary statistics maintained in each bucket. The main advantages of histograms over other techniques are
that they incur almost no run-time overhead, they do not require the data to fit a probability distribution or a
polynomial and, for most real-world databases, there exist histograms that produce low-error estimates
while occupying reasonably small space Although histograms are used in many systems, the histograms
proposed in earlier works are not always effective or practical. For example, equidepth histograms work
well for range queries only when the data distribution has low skew, while serial histograms have only been
proven optimal for equality joins and selections when a list of all the attribute values in each bucket is
maintained. Implementing operations over these collections with conventional techniques severely lacks in
abstraction. Step-by-step instructions must be provided as to how to iterate over the collection, select
elements, and operate on the elements. Manually specifying the implementation of these operations fixes
how they are to be evaluated, rendering it impossible to accommodate changes in the state of the program
that could make another approach superior. This is especially problematic when combining two collections
of objects together, frequently an expensive operation. Object querying is a way to select an object, or set of
objects, from a collection or set of collections.
JQL is an addition to Java that provides the capability for querying collections of objects. These queries can
be applied on objects in collections in the program or can be used for checking expressions on all instances
of specific types at run-time. Queries allow the query engine to take up the task of implementation details
by providing abstractions to handle sets of objects thus making the code smaller and permitting the query
evaluator to choose the optimization approaches dynamically even though the situation changes at run-time.
The Java code and the JQL query will give the same set of results but the JQL code is elegant, brief, and
abstracts away the accurate method of finding the matches. Java Query Language (JQL) by generating the
dynamic join ordering strategies. The Java Query Language provides straightforward object querying for
Java. JQL designed as an extension to Java, and implemented a prototype JQL system, in order to
investigate the performance and usability characteristics of object querying. Queries can be evaluated over
one collection of objects, or over many collections, allowing the inspection of relationships between objects
in these collections. The Java Query Language (JQL) provides first-class object querying for Java.
For example A difficulty with this decomposition is representing students who are also teachers. One
solution is to have separate Student and Teacher objects, which are related by name.
The following code can then be used to identify students who are teachers:
List<Tuple2<Faculty, Student>>
matches = new Array List<..>();
for(Faculty f : all Faculty) { for(Student s : all Students) {
if(s.name.equals(f.name)) {
matches. add(new Tuple2<Faculty, Student>(f,s));
}}}
In database terms, this code is performing a join on the name field for the allFaculty and allStudent
collections. The code is cumbersome and can be Replaced with the following object query, which is more
succinct and, potentially, more efficient:
List<Tuple2<Faculty, Student>> matches;
Matches = selectAll (Faculty f=allFaculty, Student s=allStudents: f.name.equals (s.name));
This gives exactly the same set of results as the loop code. The selectAll primitive returns a list of tuples
containing all possible instantiations of the domain variables (i.e. those declared before the colon) where
the query expression holds (i.e. that after the colon). The domain variables determine the set of objects
which the query ranges over: they can be initialized from a collection (as above); or, left uninitialized to
range over the entire extent set (i.e. the set of all instantiated objects) of their type. Queries can define as

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many domain variables as necessary and can make use of the usual array of expression constructs found in
Java. One difference from normal Java expressions is that Boolean operators, such as && and ||, do not
imply an order of execution for their operands. This allows flexibility in the order they are evaluated,
potentially leading to greater efficiency. As well as its simplicity, there are other advantages to using this
query in place of the loop code. The query evaluator can apply well-known optimizations which the
programmer might have missed. By leaving the decision of which optimization to apply until runtime, it
can make a more informed decision based upon dynamic properties of the data itself (such as the relative
size of input sets) something that is, at best, difficult for a programmer to do. A good example, which
applies in this case, is the so-called hash-join The idea is to avoid enumerating all of all Faculty × all
Students when there are few matches. A hash-map is constructed from the largest of the two collections
which maps the value being joined upon (in this case name) back to its objects. This still requires O(sf)
time in the worst-case, where s = |all Students| and f = |all Faculty|, but in practice is likely to be linear in
the number of matches (contrasting with a nested loop which always takes O(sf) time).
Implementation
We have prototyped a system, called the Java Query Language (JQL), which permits queries over object
extents and collections in Java. The implementation consists of three main components: a compiler, a query
evaluator and a runtime system for tracking all active objects in the program. The latter enables the query
evaluator to range over the extent sets of all classes. Our purpose in doing this is twofold: firstly, to assess
the performance impact of such a system; secondly, to provide a platform for experimenting with the idea
of using queries as first-class language constructs.
JQL Query Evaluator
The core component of the JQL system is the query evaluator. This is responsible for applying whatever
optimizations it can to evaluate queries efficiently. The evaluator is called at runtime with a tree
representation of the query (called the query tree). The tree itself is either constructed by the JQL Compiler
(for static queries) or by the user (for dynamic queries).
Evaluation Pipeline.
The JQL evaluator evaluates a query by pushing tuples through a staged pipeline. Each stage, known as a
join in the language of databases, corresponds to a condition in the query. Only tuples matching a join’s
condition are allowed to pass through to the next. Those tuples which make it through to the end are added
to the result set. Each join accepts two lists of tuples, L(eft) and R(ight), and combines them together
producing a single list. We enforce the restriction that, for each intermediate join, either both inputs come
from the previous stage or one comes directly from an input collection and the other comes from the
previous stage. This is known as a linear processing tree and it simplifies the query evaluator, although it
can lead to inefficiency in some cases. The way in which a join combines tuples depends upon its type and
the operation (e.g. ==, < etc) it represents. JQL currently supports two join types:
nested-loop join and hash join. A nested-loop join is a two-level nested loop which iterates each of L × R
and checks for a match. A hash join builds a temporary hash table which it uses to check for matches. This
provides the best performance, but can be used only when the join operator is == or equals(). Future
implementations may take advantage of B-Trees, for scanning sorted ranges of a collection.
JQL Implementation
Our implementation of JQL has two main components. The first is a frontend, which compiles JQL
expressions into Java code. The second component is the query evaluation back-end, which is responsible
for evaluating the query and choosing the most optimal evaluation strategy it can.
JQL Compiler
The JQL compiler is relatively straightforward — it compiles JQL expressions into equivalent Java code,
which can then be compiled by a normal Java compiler. This Java code binds domain variables to

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Vol 3, No.1, 2012

collections, and builds a tree representation of the query expression, which the evaluator uses at runtime.
The compiler ‘inlines’ queries which only use one domain variable; they are compiled into normal loops,
but with hooks to allow for query caching. Inlined queries cannot have their stages dynamically ordered,
but dynamic ordering is of almost no importance to single-variable queries (whilst different orderings may
‘short-circuit’ evaluation for individual objects more quickly, the gain is minute compared to the
performance gain of inlining the query). Using the compiler also allows for checking of the
well-formedness of the query — for example, that query expressions only use domain variables that are
declared for that query.
Query Evaluator
The core of query evaluation is carried out through the query’s ‘Query Pipeline. A query pipeline consists
of a series of stages, each corresponding to a part of the query expression. Each stage can take either one or
two input sets of tuples, and for all stages except the very first, one of these input sets is the result set of the
previous stage in the pipeline. This structure is known, in the database community, as a ‘left-branching tree’,
and we impose it to simplify the problem of ordering stages. In each stage, if there is more than one input
set, the input tuples are combined (we describe the join techniques used for this combination shortly), and
the stage’s expression is evaluated using the values in the combined tuple in the place of their
corresponding domain variables. If the expression evaluates to true, the combined tuple is added to the
stage’s result set. The set of tuples which pass the final stage are returned to the program as the results of
the query
Literature Review & Related Work
The Program Query Language (PQL) (S. Chiba 1995) is a similar system which allows the programmer to
express queries capturing erroneous behavior over the program trace . A key difference from other systems
is that static analysis was used in an effort to answer some queries without needing to run the program. As a
fallback, queries which could not be resolved statically are compiled into the program’s byte code and
checked at runtime. (C. Hobatr & B. A. Malloy 2001)Hobatr and Malloy present a query-based debugger
for C++ that uses the Open++ Meta-Object Protocol and the Object Constraint Language(OCL) (Ihab F.
Ilyas et al. 2003) This system consists of a frontend for compiling OCL queries to C++, and a backend that
uses Open C++ to generate the instrumentation code necessary for evaluating the queries.JQL is the recent
development of Microsoft’s Language Integrated Query (LINQ) project LINQ aims to add first class
querying support to .NET languages (in particular Visual Basic .NET and C# )
(Y.E. Ioannidis,R. Ng, K. Shim & T.K. Selis 1992) LINQ operates by translating queries into additional
methods on collections of objects, which then perform filtering and mapping on the collection. LINQ’s
scope is wider than JQL’s, providing integrated querying for object collections, XML structures and SQL
databases. It is unclear at present what optimizations LINQ provides for object querying, or if it provides
incrementalized caching Finally, there are a number of APIs available for Java which provide access to
SQL databases. These are quite different from the approach we have presented in this work, as they do not
support querying over collections and/or object extents. Furthermore, they do not perform any query
optimizations, instead relying on the database back end to do this.(Darren Willis & David J. Pearce, James
Noble 2008),Our main contribution is the design and implementation of JQL, a prototype object querying
system that allows developers to easily write object queries in a Java-like language. This prototype provides
automatic optimization of operations that join multiple collections together.
Analysis of Problem
In our research, we prefer static query optimization at compile-time over dynamic query optimization
because it reduces the query run-time. In this method they are using selectivity estimate based on sampling
some number of tuples, but that does not lead to efficient ordering of joins and predicates in a query.
Therefore, we propose using the estimates of selectivities of joins and the predicates from histograms to
provide us an efficient ordering of joins and predicates in a query. Once we collect this information, we can
form the query plan by having the order of joins and predicates in a query. After we get the query plan at

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compile-time, we execute that plan at run-time to reduce the execution time. Experimental results indicate
that our approach reduces run-time execution less than the existing JQL code’s run-time due to our
approach of optimizing the query and handling data updates using histograms.
Given a query Q,
1. we use the histogram H to get the estimate of the selectivity of the query predicates and the selectivities of
the joins
2. We have the join order and predicate order in a query which will be used to construct a query plan.

A.The first execution of Query Q uses the histogram H1 to estimate the selectivity. Then the result of
the query is computed. But for the subsequent execution of the same query Q after a time T, the same
histogram H can be left invalid. This situation arises because there is a possibility that the underlying data
has been updated between the first and the second executions of the same query.
B. Firstly, we check if the query is present in the log then the time period difference between consecutive
executions of the same query Q from the query log is computed and if that value is greater than a pre
specified time interval then we directly recomputed the histogram because we have assumed the data may
be modified within a pre specified time interval.
C. we first compute the error through error estimate function and then based on the error estimate we
decide whether to recomputed the histogram or not . If the query is not present in the log, then we execute
the query based upon the initial histogram that reduces the overhead cost of incremental maintenance of
histogram the experimental results of how our approach various types of queries the comparison of
run-times of our approach and the JQL approach for all the four benchmark queries. The difference in
run-times has occurred because in our approach, we have estimated selectivities using histograms and these
histograms are incrementally maintained at compile time which provide the optimal join order strategy
most of the times faster than the exhaustive join order strategy used by JQL. we can see that as the number
of increase in a query, JQL’s approach becomes more expensive and our approach performs much better
than the exhaustive join ordering strategy of JQL.
Proposed Work
We can form the query plan by having the order of joins and predicates in a query. After we get the query
plan at compile time, we execute that plan at run-time to reduce the execution time. Experimental results
indicate that our approach reduces run-time execution less than the existing JQL code’s run-time due to our
approach of optimizing the query and handling data updates using histograms. We intend to have the query
plans generated at compile time Query plans are a step by step ordered procedure describing the order in
which the query predicates need to be executed. Thus, at run- time, the time required for plan construction
is omitted. So we need to have the code working in static mode, i.e., without knowing the inputs at
compile-time, we need to be able to derive some information about inputs like sizes of relations by
estimating them to generate the query plan. Given a join query ,its selectivity needs to be estimated to
design plans. A histogram is one of the basic quality tools. It is used to graphically summarize and
display the distribution and variation of a process data set. A frequency distribution shows how often each
different value in a set of data occurs. The main purpose of a histogram is to clarify the presentation of data.
You can present the same information in a table; however, the graphic presentation format usually makes it
easier to see relationships. It is a useful tool for breaking out process data into regions or bins for
determining frequencies of certain events or categories of data. The approach followed for maintenance in
nearly all commercial systems is to recomputed histograms periodically (e.g., every night), regardless of the
number of updates performed on the database. This approach has two disadvantages. First, any significant
updates to the data since the last recomputation could result in poor estimations by the optimizer. Second,
because the histograms are recomputed from scratch by discarding the old histograms, the recomputation
phase for the entire database can be computationally very intensive and may have to be performed when the

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system is lightly loaded
4.1. The Split & Merge Algorithm
The split and merge algorithm helps reduce the cost of building and maintaining histograms for large tables.
The algorithm is as follows: When a bucket count reaches the threshold, T, we split the bucket into two
halves instead of recomputing the entire histogram from the data. To maintain the number of buckets (β)
which is fixed, we merge two adjacent buckets whose total count is least and does not exceed threshold T,
if such a pair of buckets can be found. Only when a merge is not possible, we recomputed the histogram
from data. The operation of merging two adjacent buckets merely involves adding the counts of the two
buckets and disposing of the boundary between them. To split a bucket, an approximate median value in
the bucket is selected to serve as the bucket boundary between the two new buckets using the backing
sample. As new tuples are added, we increment the counts of appropriate buckets. When a count exceeds
the threshold T, the entire histogram is recomputed or, using split merge, we split and merge the buckets.
The algorithm for splitting the buckets starts with iterating through a list of buckets, and splitting the
buckets which exceed the threshold and finally returning the new set of buckets.After splitting is done, we
try to merge any two buckets that add up to the least value and whose count is less than a certain threshold.
Then we merge those two buckets. If we fail to find any pair of buckets to merge then we recomputed the
histogram from data. Finally, we return the set of buckets at the end of the algorithm. Thus, the problem of
incrementally maintaining the histograms has been resolved. Having estimated the selectivity of a join and
predicates, we get the join and the predicate ordering at compile-time
4.2 Incremental Maintenance of Histograms
we propose an incremental technique, which maintains approximate histograms within specified errors
bounds at all times with high probability and never accesses the underlying relations for this purpose. There
are two components to our incremental approach: (i) maintaining a backing sample, and (ii) a framework
for maintaining an approximate histogram that performs a few program instructions in response to each
update to the database, and detects when the histogram is in need of an adjustment of one or more of its
bucket boundaries. Such adjustments make use of the backing sample. There is a fundamental distinction
between the backing sample and the histogram it supports: the histogram is accessed more frequently than
the sample and uses less memory, and hence it can be stored in main memory while the sample is likely
stored on disk. A backing sample is a uniform random sample of the tuples in a relation that is kept up to-
date in the presence of updates to the relation. For each tuple, the sample contains the unique row id and
one or more attribute values. We argue that maintaining a backing sample is useful for histogram
computation, selectivity estimation, etc. In most sampling-based estimation techniques, whenever a sample
of size _ is needed, either the entire relation is scanned to extract the sample, or several random disk blocks
are read. In the latter case, the tuples in a disk block may be highly correlated, and hence to obtain a truly
random sample, _ disk blocks must be read. In contrast, a backing sample can be stored in consecutive disk
blocks, and can therefore be scanned by reading sequential disk blocks. Moreover, for each tuple in the
sample, only the unique row id and the attribute(s) of interest are retained. Thus the entire sample can be
stored in only a small number of disk blocks, for even faster retrieval. Finally, an indexing structure for the
sample can be created, maintained and stored; the index enables quick access to sample values within any
desired range.
4.3 Estimating Selectivity Using Histogram
The selectivity of a predicate in a query is a decisive aspect for a query plan generation. The ordering of
predicates can considerably affect the time needed to process a join query. To have the query plan ready at
compile-time, we need to have the selectivities of all the query predicates. To calculate these selectivities,
we use histograms. The histograms are built using the number of times an object is called. For this, we
partition the domain of the predicate into intervals called windows. With the help of past queries, the
selectivity of a predicate is derived with respect to its window. That is, if a table T has 100,000 rows and a
query contains a selection predicate of the formT.a=10 and a histogram shows that the selectivity of T.a=10

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is 10% then the cardinality estimate for the fraction of rows of T that must be considered by the query is 10%
x 100,000= 10,000. This histogram approach would help us in Estimating the selectivity of a join and hence
decide on the order in which the joins have to be executed. So, we get the join ordering and the predicate
ordering in the query expression at compile-time itself. Thus, from this available information, we can
construct a query plan.
4.4 Building the Histogram
From the data distribution, we build the histogram that contains the frequency of values assigned to
different buckets. If the data is numerical, we can easily assign some ranges and assign the values to
buckets accordingly. If the data is categorical then we have to partition the data into ranges with respect to
the letter they start with and assign the appropriate values to buckets. Next, we perform some sample query
executions.
4.5 Method Outline for Error Estimation
For each attribute in the database table, we compute the error estimate by using standard deviation between
updated data values and old data values in the histogram buckets. Then, for every table, we have error
estimates for all the attributes. Then, we take a weighted average of all the attributes error estimates. The
underlying data could be mutable. For such mutable data, we need a technique by which we can restructure
the histograms accordingly. Thus, in between multiple query executions if the database is updated, then we
compute the estimation error of the histogram by using the equations (1).
Conclusion
In this paper we have presented the query optimization strategies from database domain can be used in
improving the run time executions in programming language .We proposed a technique for query
optimization at compile-time by reducing the burden of optimization at run-time. We proposed using
histograms to get the estimates of selectivity of joins and predicates in a query and then based on those
estimates, to order query joins and predicates in a query. From the join and predicate order, we have obtained
the query plan at compile-time and then we executed the query plan at run-time from this we can improve the
performance of the code execution.
References
C. Hobatr and B. A. Malloy(2001), “Using OCL-queries for debugging C++” ,In Proceedings of the IEEE
International Conference on Software Engineering (ICSE), pages 839–840.IEEE Computer Society Press,
2001.

Darren Willis ,David J. Pearce & James Noble(2008), “Caching and Incrementalisation in the Java Query
Language”, Proceedings of the 2008 ACM SIGPLAN conference on Object-oriented programming systems
languages and applications, pp. 1-18, 2008.

Ihab F. Ilyas et al. (2003), “Estimating Compilation Time of a Query Optimizer”, Proceedings of the 2003
ACM SIGMOD international conference on Management of data, pp. 373 –384, 2003.

S. Chiba(1995), “A meta object protocol for C++”,In Proceedings of the ACM conference on
Object-Oriente Programming, Systems, Languages and Applications (OOPSLA), pages 285–299. ACM
Press, 1995.

Y.E. Ioannidis,R. Ng, K. Shim & T.K. Selis(1992), “Parametric Query Optimization”, In Proceedings of
the Eighteenth International Conference on Very Large Databases (VLDB), pp. 103-114, 1992.

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Venkata Krishna et al.(2010), “Exploring Query Optimization in Programming Codes by Reducing
Run-Time Execution” ,Department of Computer Science, Missouri University of Science and Technology,
Rolla, MO 07303157/10©2010IEEEDOI10.1109/COMPSAC.2010.48, NY, USA, 2006), ACM Press, pp.
706–706.

Fig. 1.Optimization process

Fig 2. split and merge algorithm

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-----------(1)
where compute the estimation error of the histogram by using the equations..
µa is the estimation error for every attribute
• β is the number of buckets ,
• N is the number of tuples in R
• S is the number of selected tuples
• Fi is the frequency of bucket i as in the histogram
• qf= S/N is the query frequency
• Bi is the observed frequency
• Ti is the error estimate for each individual table
• Wi are the weights with respect to every attribute depending on the rate of change

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