- Switched to CRPIT style. - Incorporated base changes from discussion paper version. - Added note about generality of common tuning techniques. - Deleted obsolete commented out crud. - Fixed up hashing description. - Added note about reclustering. - Cleaned up "logical" vs. "conceptual" terminology (I hope). - Added bitmap indexes and updated index symbols. - Added note about alternative (discarded) replication symbol. - Added discussion paper reference. - Revamped future work section.

nigel.stanger / Publications

Browse code - Switched to CRPIT style. - Incorporated base changes from discussion paper version. - Added note about generality of common tuning techniques. - Deleted obsolete commented out crud. - Fixed up hashing description. - Added note about reclustering. - Cleaned up "logical" vs. "conceptual" terminology (I hope). - Added bitmap indexes and updated index symbols. - Added note about alternative (discarded) replication symbol. - Added discussion paper reference. - Revamped future work section. - Added acknowledgements section. Pillay_2005
1 parent 3779941 commit b0fd15542ed8b1a3168c571745b43f1ea48c2aba nstanger authored on 23 Aug 2005

Browse code

- Switched to CRPIT style.

- Incorporated base changes from discussion paper version.
- Added note about generality of common tuning techniques.
- Deleted obsolete commented out crud.
- Fixed up hashing description.
- Added note about reclustering.
- Cleaned up "logical" vs. "conceptual" terminology (I hope).
- Added bitmap indexes and updated index symbols.
- Added note about alternative (discarded) replication symbol.
- Added discussion paper reference.
- Revamped future work section.
- Added acknowledgements section.

Pillay_2005

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\documentclass{CRPITStyle} \usepackage{harvard} \usepackage[hang]{subfigure} \usepackage{graphicx} \pagestyle{empty} \thispagestyle{empty} \title{A Graphical Notation for Physical Database Modelling} \author{Antonia Pillay \and Nigel Stanger} \affiliation{Department of Information Science, \\ University of Otago, \\ PO Box 56, Dunedin, New Zealand \\ Email:~\texttt{nstanger@infoscience.otago.ac.nz}} \hyphenation{da-ta-base da-ta-ba-ses co-pies} \begin{document} \maketitle \begin{abstract} For database systems, physical level design is often as important as conceptual level design, as it is the physical level that ultimately determines the performance of a database system. It is therefore somewhat surprising that there have been relatively few prior attempts to devise an abstract graphical notation for physical level modelling. Such a notation would provide database designers and administrators with a means to consistently model the physical structure of new and existing databases, thus enabling them to make more proactive and informed tuning decisions, compared to the more typical situation where database monitoring tools are used to identify and remedy performance problems in a reactive and ad hoc manner. In this paper we propose an abstract graphical notation for physical level database modelling, and compare it with earlier work in this area. \end{abstract} \section{Introduction} As with most information systems, the design and implementation of a database goes through several phases, including conceptual, logical and physical modelling \cite{BeDa-P-2003}. These three phases are of particular interest, as they embody the progression from higher to lower levels of abstraction \cite{Tsic-D-1978}. Conceptual models are typically highly abstract, using techniques such as entity-relationship modelling. Logical models represent the database structure in a form that is closer to the physical representation, yet still sufficiently abstract to isolate applications from the physical representation \cite{Codd-EF-1970}, and are expressed using formalisms such as the relational model. A logical model for a database can be derived by transforming the corresponding conceptual model. Physical models represent the database structure in terms of the internal physical storage implementation of a specific database management system (DBMS) such as Oracle or DB2. A physical model for a database can be derived by transforming the corresponding logical model \cite{Bato-DS-1985,Conn-TM-2002}. Because of their low level of abstraction, physical level database models have tended to not be expressed using graphical notations, unlike models at higher levels of abstraction. Physical level modelling, however, is equally as important as, if not \emph{more} important than the higher levels, because it is the physical level that determines the performance of a database \cite{BeDa-P-2003}. It is therefore somewhat surprising that there have been relatively few attempts to devise a graphical physical modelling notation, because such a notation can provide several advantages \cite{BeDa-P-1992-PDD,Conn-TM-2002,Tuft-ER-1997,Will-J-1992}: \begin{itemize} \item it can reduce complexity and thus improve understandability; \item it can provide a more complete and integrated display of performance tuning techniques in a database; \item database developers can be more confident about the design decisions that they make for the performance of the database; \item potential database performance problem areas are more easily visualised using a graphical notation; and \item a specific methodology is developed and used, thus enabling developers to resolve physical performance issues more systematically. \end{itemize} Many of these benefits are embodied in modern database performance monitoring tools, which provide higher-level visualisations of a database's internals in order to easily identify and highlight performance problems. Such tools, however, are primarily \emph{monitoring} tools rather than \emph{design} tools. They may therefore unintentionally encourage database administrators (DBAs) into a \emph{reactive} mode of continually ``tweaking'' the database to resolve performance issues, rather than a \emph{proactive} mode of anticipating and designing for expected usage. It may also be difficult for a DBA using such tools to gain a clear and comprehensive overview of all the tuning techniques that are in use within a particular database \cite{Core-MJ-1997-OracleDW}. It could be argued that physical level structures vary so much from one DBMS to another that it is futile to even consider a generic physical modelling notation. However, examination of the major DBMS products in use today reveals that they share many of the same physical tuning techniques. The specific implementation of (for example) clustering may vary in detail from platform to platform, but the feature is in general largely similar across all implementations. We provide a brief overview of commonly used physical tuning techniques in Section~\ref{sec-techniques}. In this paper we propose a graphical notation for physical database modelling. In Section~\ref{sec-previous} we discuss two earlier approaches upon which our work is partially based. Section~\ref{sec-notation} introduces our notation, and Section~\ref{sec-future} discusses possible future work. The paper concludes in Section~\ref{sec-conclusion}. \section{Common Physical Tuning Techniques} \label{sec-techniques} Database management is generally an I/O bound task, so the main performance bottleneck in most databases will be the performance of tertiary storage devices such as disk drives. Retrieving data from a hard disk is theoretically about six orders of magnitude slower than retrieving data from RAM\footnote{On the order of milliseconds (\(10^{-3}\)) for disk versus nanoseconds (\(10^{-9}\)) for RAM.}. The aim of any physical tuning strategy must therefore be to minimise the impact of slow tertiary storage, either by directly reducing the number of physical disk accesses required, or by parallelising access to disk in order to reduce contention. These considerations have led to the development of five general physical tuning techniques, which are implemented with slight variations by most modern mainstream DBMS products: \begin{description} \item[Indexes] reduce the number of physical disk accesses required to retrieve a specific record \cite{Roti-S-1996}, most typically by building a B+-tree \cite{Knut-DE-1997-Art} based on some key value within the data. Without any indexes, a DBMS often has little choice but to perform a sequential scan in order to locate a specific record. If we assume one disk access per database block, a sequential scan has average and worst case performance of \(K/2b\) and \((K - 1)/b\) disk accesses required, respectively, where \(K\) is the number of records and \(b\) is the number of records per database block. By comparison, a B+-tree has a worst case performance of \(\log_{n/2}(K)\) \cite{Silb-A-2002-4E}, where \(n\) is the number of key values per index node. \item[Hashing] is a method of quickly locating specific records by passing a key value to a \emph{hash function}. This function ideally returns a unique physical location of a hash bucket containing the requested physical record. Hashing schemes typically require only one or two physical disk accesses to retrieve a specific record, and perform best for exact key matches on very large tables. Hashing generally performs poorly for queries that require the retrieval of many records. \item[Clustering] minimises disk access by ensuring that related records (such as an order header and its associated order lines) are physically adjacent on disk. This usually means that related records will be stored in the same database block, and can thus be retrieved with a single disk access. Clustering can, however, be expensive to maintain in a high update environment, because of the frequent ``reclustering'' required. \item[Partitioning] provides parallel access paths to data by physically splitting a table into disjoint parts (either vertically by columns or horizontally by rows) and placing them on separate disks. This is particularly advantageous when multiple users wish to access different subsets of a set of records, because it provides a separate physical access path to each of the partitions. Partitioning can also reduce the number of disk accesses required, because there are fewer records to scan in each partition than if the table were not partitioned. \item[Replication] provides parallel access paths to data by making multiple copies of the same records and placing them on separate disks. This is particularly advantageous when multiple users need to access the same sets of records, but is more complex to manage due to the need to keep replicas synchronised. \end{description} These techniques are normally applied to different parts of a database to achieve different effects. In order to choose an appropriate physical tuning technique, the DBA must consider various factors that may benefit only some users of the database, or may improve the performance of the database as a whole. While most of the techniques can be combined to varying degrees, simply applying all techniques is usually not optimal, because each technique excels under different conditions. That is, what are optimal conditions for one technique may be the exact opposite for another, so the DBA needs to be able to model all available information in order to develop an appropriate physical design. \section{Prior Physical Modelling Techniques} \label{sec-previous} To achieve an effective physical design requires a large amount of information, particularly with regard to the predicted or actual volume and usage of data within the database \cite{BeDa-P-2003}. Incorporating this information into a graphical model can provide a more concise and clearer overview of the physical aspects of a database system. In this section we briefly discuss two previous efforts at modelling such information in a graphical manner. \subsection{\emph{Agile Modeling} (Ambler)} Ambler proposed a physical modelling notation based on the Unified Modelling Language (UML), as part of a broader effort to produce a ``traditional'' style data modelling profile for the UML \cite{Ambl-SW-2003-ADT,Ambl-SW-2004-ObjPrimer3}. Ambler and others have argued the need for such a profile for some time \cite{Ambl-SW-1998-BOA,Naib-EJ-2001-UMLDD}. Ambler's notation focuses on the physical modelling of relational databases. The notation uses class boxes without stereotypes to represent physical tables, while indexes are represented by class boxes with the stereotype \verb\|<<index>>\|, as shown in Figure~\ref{fig-Ambler}. There appear to be no stereotypes for other physical tuning techniques such as partitioning, although these could be easily incorporated. \begin{figure}[htb] \fbox{\parbox[b]{.99\linewidth}{% \vskip 0.5cm% \centerline{\includegraphics[scale=0.9]{Ambler}}% \vskip 0.5cm% }} \caption{Ambler's physical modelling notation (adapted from \protect\cite{Ambl-SW-2003-ADT})} \label{fig-Ambler} \end{figure} Ambler's approach suffers from two serious disadvantages. First, the notation is very limited in the types of symbol used. All physical level constructs are represented by identical class boxes, which in a complex diagram could make distinguishing them difficult. This limitation probably arises from the constraints on developing a new notation within the existing UML framework. Second, his approach appears to consistently confuse the conceptual and physical levels of abstraction: the same notations are used to represent not only physical but also conceptual elements \cite{Ambl-SW-2003-ADT}. This confusion is illustrated by the inclusion of a view (a non-physical construct) in Figure~\ref{fig-Ambler}. In summary, while Ambler's notation graphically models the physical level of a database, the similarity of the graphical symbols and the evident confusion between the physical and logical levels diminish its usefulness. \subsection{\emph{Physical Design Using an Entity Model} (Beynon-Davies)} Beynon-Davies proposed a method for analysing and modelling the physical usage patterns of a database \cite{BeDa-P-1992-PDD}. In his method, various aspects of the physical performance of a database are measured, such as the size and expected growth rates of tables (volume analysis), the volatility of tables, and the frequency of transactions (usage analysis). The data obtained from these analyses are then used to annotate a logical level entity-relationship diagram (ERD) of the database, producing what is known as a \emph{composite usage map} (see Figure~\ref{fig-Beynon-Davies} for an example). Beynon-Davies' method provides a very good mechanism for representing the usage statistics of a database in a coherent manner, but is rather complex and time-consuming to undertake without some form of automation. Our experience with teaching this method at undergraduate level shows that even with a relatively small database, the designer can quickly become overwhelmed by the sheer volume of usage data involved. In addition, Beynon-Davies' method does not produce any conclusions as to which physical tuning methods should be implemented---rather it summarises the information required to facilitate these decisions. Beynon-Davies' method is thus more a notation for summarising the physical usage patterns of a database, rather than a notation for physical modelling per se. \section{A New Physical Notation} \label{sec-notation} Both of the notations discussed in the previous section are limited in their ability to graphically model the physical level of a database. Ambler's notation lacks clarity and is thus potentially confusing, while Beynon-Davies' notation only summarises the physical usage patterns of a database rather than providing an actual physical level model. We have therefore adopted aspects from both approaches to devise a graphical notation that enables database designers to graphically model the common physical database tuning techniques discussed in Section~\ref{sec-techniques}. The symbols that we have adopted for this notation are shown in Figure~\ref{fig-notation}. Some of these symbols are adapted from other notations, while some we have created ourselves. The symbols have been chosen to be intuitive and simple to draw, so as to produce diagrams that are as clear and uncluttered as possible. Physical models may be constructed using this notation either with or without a prior Beynon-Davies style analysis. \begin{figure}[htb] \fbox{\parbox[b]{.99\linewidth}{% \vskip 0.5cm% \mbox{}% \hfill% \subfigure[Physical\newline{}table]{\label{fig-notation-table}\includegraphics[scale=0.9]{notation-table}}% \hfill% \subfigure[Indexes]{\label{fig-notation-index}\includegraphics[scale=0.9]{notation-index}}% \hfill% \subfigure[Hashing]{\label{fig-notation-hash}\includegraphics[scale=0.9]{notation-hash}}% \hfill% \subfigure[Replication]{\label{fig-notation-replica}\includegraphics[scale=0.9]{notation-replica}} \hfill% \mbox{} \\% \mbox{}% \hfill% \subfigure[Clustering]{\label{fig-notation-cluster}\includegraphics[scale=0.9]{notation-cluster}}% \hfill% \subfigure[Horizontal\newline{}partitioning]{\label{fig-notation-partition-h}\includegraphics[scale=0.9]{notation-partition-h}}% \hfill% \subfigure[Vertical partitioning]{\label{fig-notation-partition-v}\includegraphics[scale=0.9]{notation-partition-v}}% \hfill% \mbox{}% \vskip 0.5cm% }} \caption{Symbols representing physical tuning techniques} \label{fig-notation} \end{figure} A physical table is represented by a simple box, as shown in Figure~\ref{fig-notation-table}. This is similar to most entity-relationship modelling notations. The fields of the physical table may be included, with or without physical data types, as appropriate. It could be argued that a different symbol should be used to avoid confusion between, for example, physical tables and conceptual entities. These constructs belong to different levels of abstraction, however, so they should not both appear on the same diagram. Consequently there is no real potential for confusion. B-tree indexes are indicated by the symbol ``\(\bigtriangleup\)'' (representing a tree-like structure) within a table, as shown in Figure~\ref{fig-notation-index}. The index key is listed next to this symbol. Composite keys are indicated by a grouping symbol, and a unique index is indicated by a subscript ``1'', that is, \(\bigtriangleup_{1}\). Bitmap indexes and hashing are represented in a similar way, using the symbols ``\(\smash{\vdots\vdots\vdots}\)''\rule{0pt}{1.02\baselineskip} and ``\#'', respectively (see Figure~\ref{fig-notation-index} and~\ref{fig-notation-hash}). These symbols could easily be extended to cater for other types of index, such as R-trees. Clustering is represented by nesting one table inside another, as shown in Figure~\ref{fig-notation-cluster} (adapted from \cite{BeDa-P-1992-PDD}). The cluster key is indicated by an asterisk () attached to the appropriate field(s). Tables may be nested to as many levels as required in order to represent more complex clustering schemes. This notation is intuitive, and clearly indicates the field(s) on which the records are clustered. (Note that the physical relationship connecting the clustered tables has been omitted in Figure~\ref{fig-notation-cluster}.) Partitioning is represented by splitting a table into either vertical or horizontal partitions according to the style of partitioning, as shown in Figure~\ref{fig-notation-partition-h} and \ref{fig-notation-partition-v} (adapted from \cite{Silb-A-2002-4E}). Once again, the notation is intuitive, and allows the partition definitions to be easily indicated. Replication is indicated by placing a diagonal bar across the lower-right corner of the table to be replicated, along with the total number of replicas, as shown in Figure~\ref{fig-notation-replica}. This is adapted from a similar notation used in data flow diagrams \cite{Gane-C-1979}. This notation could also be used to indicate replication of individual table partitions, for DBMSs that permit this combination. We considered an alternative symbol that looked like a ``stack'' of tables, on the basis that it might be useful to differentiate between different replicas. All the replicas are identical, however, which diminishes the usefulness of such a symbol, and the symbol was also more complex to draw. Consider the entity-relationship diagram shown in Figure~\ref{fig-ERD}, which uses Martin notation \cite{Mart-J-1990-IE2} to depict a database for a consumer electronics manufacturer (this was a case study used in an undergraduate advanced database course). A corresponding Beynon-Davies' composite usage map based on fourteen significant transactions \cite{Pill-A-2005-DP} is shown in Figure~\ref{fig-Beynon-Davies} (some details have been omitted for clarity). The arrows represent physical access paths, while the number attached to each access path indicates the number of disk accesses per hour along that path. The diagram clearly highlights some potential performance problem areas in the database, for example: \begin{itemize} \item There are many disk accesses per hour along the access paths between the \textsf{Sale\_head}/\textsf{Sale\_line} and the \textsf{Order\_head}/\textsf{Order\_line} tables. Since each pair of tables will normally be accessed together, both pairs could perhaps be candidates for clustering (depending on the mix of update versus read operations). \item There appear to be multiple transactions accessing the \textsf{Staff} table along different access paths. This could imply a need for partitioning. \item There is an extremely high access rate on the \textsf{Customer} table. Further investigation, however, reveals that this rate only occurs for a short period once per month, and that the transaction in question only requires read access. Replication of the \textsf{Customer} table could therefore be a suitable solution to ensure that this short, intense and intermittent transaction does not interfere with normal day-to-day transaction processing. \end{itemize} \begin{figure}[htbp] \fbox{\parbox[b]{.99\linewidth}{% \vskip 0.5cm% \centerline{\includegraphics[scale=0.9]{ERD}}% \vskip 0.5cm% }} \caption{Logical level ERD of the example database} \label{fig-ERD} \end{figure} \begin{figure}[htbp] \fbox{\parbox[b]{.99\linewidth}{% \vskip 0.5cm% \centerline{\includegraphics[scale=0.9]{Beynon-Davies}}% \vskip 0.5cm% }} \caption{Beynon-Davies composite usage map for the example database} \label{fig-Beynon-Davies} \end{figure} The suggestions above can be represented as a physical model using our modelling notation, as shown in Figure~\ref{fig-physical-model} (some details have been omitted to save space). Note that we have placed indexes on all primary keys as a matter of course, as almost all DBMS products do this automatically. \begin{figure}[htb] \fbox{\parbox[b]{.99\linewidth}{% \vskip 0.5cm% \centerline{\includegraphics[scale=0.875]{Physical-Model}}% \vskip 0.5cm% }} \caption{Physical level model of the example database using the new notation} \label{fig-physical-model} \end{figure} \section{Future Work} \label{sec-future} The current notation, while it covers the most common aspects of physical modelling, is not complete and could be extended in various ways, for example: \begin{itemize} \item The current notation only caters for B-tree indexes, bitmap indexes and hashing. An obvious extension is to define symbols for other types of index, such as R-trees. We also plan to investigate symbols for index variations such as reverse-key and integrated indexes. \item Many DBMS products support partitioning of indexes in addition to tables. Our notation does not cater for this, but could possibly be modified so that indexes are modelled separately from their associated physical tables (similar to Ambler's notation). We would need to evaluate whether the benefits gained from such an extension would outweigh the additional complexity introduced. \item There is currently no way to specify physical placement information, for example, which devices different partitions should be placed on. \item The clustering notation effectively assumes that there are at least two tables being clustered, but most DBMS products support single-table clusters, which in effect physically sort the contents of the table on the value of the cluster key. \end{itemize} We are currently evaluating the efficacy of the notation with undergraduate students in an advanced database course. We will then compare this with using Beynon-Davies' method alone. \section{Conclusion} \label{sec-conclusion} In this paper we have described a graphical notation for physical database modelling, which enables database administrators (DBAs) to model the physical structure of new and existing databases in a more abstract manner. This will enable them to make more proactive and informed tuning decisions, compared to existing database monitoring tools, which tend to encourage a more reactive approach to database tuning. The notation uses simple and intuitive symbols to represent major physical database structures, and can easily represent complex physical schemas. The notation is to be evaluated with undergraduate students in an advanced database course; the results of this evaluation will be compared with other physical modelling methods. \section{Acknowledgements} The authors would like to thank the students of \emph{INFO 321: Database Systems} 2004 and 2005 for participating in the development and evaluation of this new notation. \bibliographystyle{agsm} \bibliography{Physical_modelling} \end{document} \documentclass{llncs} \usepackage{subfigure} \usepackage{graphicx} \title{A Graphical Notation for \\ Physical Database Modelling} \author{Antonia Pillay\inst{1} \and Nigel Stanger\inst{2}} \institute{???? Malaysia \and Department of Information Science, University of Otago, Dunedin, New Zealand \email{nstanger@infoscience.otago.ac.nz}} \begin{document} \maketitle \begin{abstract} In this paper we describe a graphical notation for physical database modelling. This notation provides database administrators (DBAs) with a means to model the physical structure of new and existing databases, thus enabling them to make more proactive and informed tuning decisions, compared to existing database monitoring tools. \end{abstract} \section{Introduction} As with most information systems, the design and implementation of a database goes through several phases, including conceptual, logical and physical modelling \cite{BeDa-P-2003}. These three phases are of particular interest, as they embody the progression from higher to lower levels of abstraction \cite{Tsic-D-1978}. Conceptual models are typically highly abstract, using techniques such as entity-relationship modelling. Logical models represent the database structure in a form that is closer to the physical representation, yet still sufficiently abstract to isolate applications from the physical representation \cite{Codd-EF-1970}, and are expressed using formalisms such as the relational model. A logical model for a database can be derived by transforming the corresponding conceptual model. Physical models represent the database structure in terms of the physical storage implementation of a specific database management system (DBMS) such as Oracle or DB2. A physical model for a database can be derived by transforming the corresponding logical model \cite{Bato-DS-1985,Conn-TM-2002}. Because of their low abstraction level, physical level database models have tended to not be expressed using graphical notations, unlike models at higher levels of abstraction. Physical level modelling, however, is equally as important as, if not \emph{more} important than the higher levels, because it is the physical level that determines the performance of a database \cite{BeDa-P-2003}. It is somewhat surprising that there have been relatively few attempts to devise a graphical physical modelling notation, because such a notation can provide several advantages \cite{Conn-TM-2002,BeDa-P-1992-PDD,Will-J-1992}: \begin{itemize} \item it can reduce complexity and thus improve understandability \cite{Tuft-ER-1997}; \item it can provide a more complete and integrated display of performance tuning techniques in a database; \item database developers can be more confident about the design decisions that they make for the performance of the database; \item database performance problems are more easily visualised using a graphical notation; and \item a specific methodology is developed and used, thus enabling developers to resolve physical performance issues more systematically. \end{itemize} These benefits are embodied in modern database performance monitoring tools, which provide higher-level visualisations of a database's internals in order to easily identify and highlight performance problems. Such tools, however, are primarily \emph{monitoring} tools rather than \emph{design} tools. They may therefore unintentionally encourage DBAs into a \emph{reactive} mode of continually ``tweaking'' the database to resolve performance issues, rather than a \emph{proactive} mode of anticipating and designing for expected usage. It may also be difficult for a DBA using such tools to gain a clear and comprehensive overview of all the tuning techniques that are in use within a particular database \cite{Core-MJ-1997-OracleDW}. In this paper we propose a graphical notation for physical database modelling. In Sect.~\ref{sec-techniques}, we provide a brief overview of commonly used physical tuning techniques. We then discuss in Sect.~\ref{sec-previous} two earlier approaches upon which our work is partially based. Sect.~\ref{sec-notation} introduces our proposed notation, and Sect.~\ref{sec-future} discusses possible future work. The paper concludes in Sect.~\ref{sec-conclusion}. \section{Physical Tuning Techniques} \label{sec-techniques} Database management is generally an I/O bound task, so the main performance bottleneck in most databases will be the speed of offline storage such as disk drives. Retrieving data from a hard disk is theoretically about six orders of magnitude slower than retrieving data from RAM\footnote{On the order of milliseconds (\(10^{-3}\)) for disk versus nanoseconds (\(10^{-9}\)) for RAM.}. The aim of any physical tuning strategy must therefore be to minimise the impact of slow storage, either by directly reducing the number of physical disk accesses required, or by parallelising access to disk in order to reduce contention. These considerations have led to the development of five general physical tuning techniques, which are implemented to various degrees by most modern mainstream DBMS products: \begin{description} \item[Indexes] reduce the number of physical disk accesses required to retrieve a specific record, most typically by building a B+-tree \cite{Knut-DE-1997-Art} based on some key value. Without any indexes, a DBMS often has little choice but to perform a sequential scan in order to locate a specific record. % A common database operation is to request a specific data record % identified by a some key value. When such a request occurs, the DBMS % must locate this record on disk (assuming that it is not already cached % elsewhere). If we assume that the records are randomly ordered with % respect to the chosen key value, then the DBMS has no option but to % perform a sequential scan of all the records, which will require on % average\(n/2b\) disk accesses, where \(n\) is the number of records and % \(b\) is the number of records per database block (the worst case is % \((n - 1)(b)\)). Sorting the records on the key value will obviously % help for searches based on that key value, but will not help for % searches based on a different key value. % % One solution is to build a separate index structure that associates key % values with their corresponding physical record. Because indexes are % stored separately, we can provide multiple indexes on different key % values for the same set of records. In addition, by choosing an % appropriate data structure for the index, we can improve the efficiency % of searching and thus reduce the number of physical disk accesses % required \cite{Roti-S-1996}. Most modern DBMSs use some variant of the % \emph{B+-tree} structure \cite{Knut-DE-1997-Art}. This structure has % average and worst case performance of (formula) and (formula), % respectively. % % Indexes are a ``pure'' disk access minimisation technique, that is, they % reduce the number of physical disk accesses required, but do not provide % any form of paralellism. B-tree indexes perform well for a wide range of % typical database queries (ref), but can suffer in high update % environments due to the need to also update the index. \item[Hashing] is a method of quickly locating specific records by passing a key value to a \emph{hash function}. This function returns the physical location of a hash bucket, which contains a pointer to the associated physical record (check). Hashing schemes typically require only a single disk access to retrieve a specific record. % Hashing is another pure disk access minimisation technique that performs % well for exact key queries on very large tables. Rather than build a % separate index structure, a key value is passed to a \emph{hash % function}. This function returns the physical location of a hash bucket, % which contains a pointer to the associated physical record (check). % Hashing schemes typically require only a single disk access to retrieve % a specific record, but perform poorly for queries that require the % retrieval of multiple records. \item[Clustering] minimises disk access by ensuring that related records (such as an order header and its associated order lines) are physically adjacent on disk. This usually means that related records will be stored in the same database block, and can thus be retrieved with a single disk access. % Clustering is a disk access minimisation technique that can be applied % when two different record types are logically related, and thus commonly % retrieved together; for example, an order header and its order lines. % Without any further optimisation, such an operation could at worst % require one disk access for the order header and one disk access for % each of its order lines. % % To improve this situation, we \emph{cluster} the two record types, so % that related physical records are physically adjacent to each other % \cite{Chan-N-2003-clustering}. This usually means that all the records % will be stored in the same database block, and can thus all be retrieved % with a single disk access. Clustering can, however, be expensive to % maintain in a high update environment. \item[Partitioning] provides parallel access paths to data by physically splitting a table into disjoint parts and placing them on separate disks. This is particularly advantageous when multiple users wish to access different subsets of a set of records, because it provides a separate physical access path to each of the partitions. % Partitioning can also reduce the number of disk accesses % required, because there are fewer records to scan in each partition % than if the table were not partitioned. % In a typical untuned database, all data will be stored on the same disk. % If the database is heavily accessed, then contention for the single I/O % channel to this disk becomes a major bottleneck. Partitioning reduces % this contention by splitting the database into disjoint partitions, each % of which is assigned to a different disk. This approach is particularly % advantageous in situations where multiple users wish to access different % subsets of a set of records, because it provides a disjoint physical % access paths to each of the partitions. This reduces I/O channel % contention and thus improves performance. Partitioning is thus primarily % an access parallelism technique. % % Partitioning can also provide some disk access minimisation benefits, % because the amount of data to be searched in each partition is smaller % than if the database were not partitioned. \item[Replication] provides parallel access paths to data by making multiple copies of the same records and placing them on separate disks. This is particularly advantageous when multiple users need to access the same sets of records, but is more complex to manage due to the need to keep replicas synchronised. % Replication is an access parallelism technique in which multiple copies % of the same records are placed on different disks, thus providing % multiple physical access paths to the same data. \end{description} These techniques are normally applied to different parts of a database to achieve different effects. In order to choose an appropriate physical tuning technique, the DBA must consider various factors that may benefit only some users of the database, or may improve the performance of the database as a whole. While most of the techniques can be combined to varying degrees, simply applying all techniques is usually not optimal, because each technique excells under different conditions. That is, what are optimal conditions for one technique may be the exact opposite for another, so the DBA needs to be able to model all the available information in order to develop an appropriate physical design. \section{Prior Physical Modelling Techniques} \label{sec-previous} To achieve an effective physical design requires a large amount of information, particularly with regard to the predicted or actual volume and usage of data within the database \cite{BeDa-P-2003}. Incorporating this information into a graphical model can provide a more concise and clearer overview of the physical aspects of a database system. In this section we briefly discuss two previous efforts at modelling such information in a graphical manner. \subsection{Agile Modeling (Ambler)} Ambler proposed a physical modelling notation based on the Unified Modeling Language (UML), as part of a larger effort to produce a ``traditional'' style data modelling profile for the UML \cite{Ambl-SW-2003-ADT,Ambl-SW-2004-ObjPrimer3}. Ambler and others have argued the need for such a profile for some time \cite{Ambl-SW-1998-BOA,Naib-EJ-2001-UMLDD}. Ambler's notation focuses on the physical modelling of relational databases. The notation uses class boxes without stereotypes to represent physical tables, while indexes are represented by class boxes with the stereotype \verb\|<<index>>\|, as illustrated in Fig.~\ref{fig-Ambler}. There appear to be no stereotypes for other physical tuning techniques such as partitioning, although these could be easily incorporated. \begin{figure} \includegraphics[width=\columnwidth,keepaspectratio]{Ambler} \caption{Ambler's physical modelling notation (adapted from \cite{Ambl-SW-2003-ADT})} \label{fig-Ambler} \end{figure} Ambler's approach suffers from two serious disadvantages. First, the notation is very limited in the types of symbol used. All physical level constructs are represented by class boxes, which in a complex diagram could make distinguishing them difficult. This limitation probably arises from the constraints on developing a new notation within the existing UML framework. Second, his approach appears to consistently confuse the logical and physical levels of abstraction: the same notations are used to represent not only physical but also logical and conceptual elements \cite{Ambl-SW-2003-ADT}. This confusion is illustrated by the inclusion of a view (a non-physical construct) in Fig.~\ref{fig-Ambler}. In summary, while Ambler's notation does graphically model the physical level of a database, the similarity of the graphical symbols and the evident confusion between the physical and logical levels diminish its usefulness. % . He states that since Unified % Modelling Language (UML) does not cover data (i.e., ER) modelling yet, % he presents the solution in this paper. Ambler has argued for some time % for the presence of data modelling in UML \cite{Ambl-SW-1998-BOA}, and % has suggested various ways that it should be done. It is enlightening to % know that Ambler is not alone in his quest for adopting an industry % standard in data modelling. Other methodologies like % \cite{Naib-EJ-2001-UMLDD} have recognized the need as well. This model % type is built on the practice of UML 2.0 of separating core methodology. % Ambler admits that the methodology that he has presented is not perfect % and it focuses on the physical modelling of a relational database. In % this model he also tries to stress style issues that according to him % are not appropriate for a proper UML profile \cite{Ambl-SW-2003-ADT}. % This model suggests that a class box without a stereotype in a physical % database design is a table. It also represents views that have % dependencies on the table structures. Tables, entities and views are all % modelled using class boxes. The class boxes that appear on the logical % and conceptual model are entities so the stereotype is optional. % Similarly, it is assumed that any class box without a stereotype on a % physical data model is a table. Ambler's methodology seems to be limited % in terms of annotations. All representations whether tables or indices % are modelled using class boxes. This representation can sometimes be % confusing and exact identification of tables and indices in the system % has to be known by the database designer. In this model, relationships % are modelled using associations that are derived from the logical data % model. He further discusses his methodology for modelling attributes and % columns, keys, constraints and triggers, stored procedures and sections. % Ambler states that requirements for something should be identified % before it is built. He states the requirements of each model, e.g., % requirements that are needed to model entities and tables, requirements % that are needed to model relationships, etc. % % An example of Ambler's notation is shown in Fig.~\ref{fig-Ambler}. % Even though a lot of detail is covered in his suggestion, Ambler's % methodology somehow seems relatively weak. It is much too general in % annotations and has no specific description. The methodology seems to % consistently confuse the logical and physical design. For example: both % are modelled using class boxes but are later explained as representing % different objects. However, this model will definitely be useful as a % stepping-stone to work towards an official UML data modelling profile. \subsection{Physical Design Using an Entity Model (Beynon-Davies)} Beynon-Davies proposed a method for analysing and modelling the physical usage patterns of a database \cite{BeDa-P-1992-PDD}. In his method, various aspects of the physical performance of a database are measured, such as the size and expected growth rates of tables (volume analysis), the volatility of tables, and the frequency of transactions (usage analysis). The data obtained from these analyses are then used to annotate a logical level entity-relationship diagram (ERD) of the database, producing what is known as a \emph{composite usage map} (see Fig.~\ref{fig-Beynon-Davies} on page~\ref{fig-Beynon-Davies} for an example). Beynon-Davies' method provides a very good mechanism for representing the usage statistics of database in a coherent manner, but is somewhat complex to execute without some form of automation. Our experience with teaching this method at undergraduate level shows that even with a relatively small database, the designer can quickly become overwhelmed by the sheer volume of usage data involved. In addition, Beynon-Davies' method does not produce any conclusions as to which physical tuning methods should be implemented---rather it provides much of the information required to enable these decisions to be made. Beynon-Davies' method is thus more a notation for summarising the physical usage patterns of a database, rather than a notation for physical modelling per se. % The closest model that demonstrates the link between conceptual, logical % and physical design work is by Beynon-Davies \cite{BeDa-P-1992-PDD}. He % suggests a method that can be used for the physical design process. The % paper defines physical modelling as ``the transformation of the logical % model into a definition of the physical model suitable for a specific % software/hardware configuration'' \cite{BeDa-P-1992-PDD}. % % According to Beynon-Davies one of the first steps that must be taken to % move from logical to physical database design is to establish estimates % of the average and maximum number of instances per entity (volume % analysis). Volatility analysis is represented in the same way. Similar % to volume analysis, volatility analysis cannot be used as a measure if a % table is continually growing. It is most effective if the size of the % table is static. Figure 9 summarizes Beynon-Davies's final modelling % method; he does not model all the individual annotations but generalizes % the entity relationship diagrams into a general model. % % It is understandable that the reason behind this generalization is to % minimize complexity and since individual annotations are developed in % the beginning, references can be made to them if needed. This might be % effective for small systems that have a limited number of tables. % However, for medium to large-scale projects, Beynon-Davies suggests that % a systematic analysis of volatility and usage should be done. This % analysis would constitute a map of the database application that would % be maintained by the DBA. This meets the objective of providing an % overview of all the performance tuning techniques and data in place. \section{A New Physical Notation} \label{sec-notation} Both of the notations discussed in the previous sections are limited in their ability to graphically model the physical level of a database. Ambler's notation lacks clarity and is thus potentially confusing, while Beynon-Davies' notation only summarises the physical usage patterns of a database rather than providing an actual physical level database model. We have therefore adopted aspects from both approaches to devise a graphical notation that enables database designers to graphically model the common physical database tuning techniques discussed in Sect.~\ref{sec-techniques}. The notations that we have adopted for this notation are shown in Fig.~\ref{fig-notation}. Some of these are adapted from other notations, while some we have created ourselves. The symbols have been chosen to be intuitive and simple to draw, so as to produce diagrams that are as clear and uncluttered as possible. Physical models may be developed using this notation either with or without a prior Beynon-Davies style analysis. \begin{figure} \centering \subfigure[Table]{\label{fig-notation-table}\includegraphics[scale=0.9]{notation-table}} \hfill \subfigure[Indexes]{\label{fig-notation-index}\includegraphics[scale=0.9]{notation-index}} \hfill \subfigure[Hashing]{\label{fig-notation-hash}\includegraphics[scale=0.9]{notation-hash}} \\ \subfigure[Clustering]{\label{fig-notation-cluster}\includegraphics[scale=0.9]{notation-cluster}} \hfill \subfigure[Partitoning]{\label{fig-notation-partition}\includegraphics[scale=0.9]{notation-partition}} \hfill \subfigure[Replication]{\label{fig-notation-replica}\includegraphics[scale=0.9]{notation-replica}} \caption{Proposed notations for physical tuning techniques} \label{fig-notation} \end{figure} A physical table is represented by a simple box, as shown in Fig.~\ref{fig-notation-table}. This is similar to most logical and conceptual level ERD notations. The fields of the physical table may be included, with or without physical data types, as appropriate. Indexes are represented by a small tree-like symbol within a table, as shown in Fig.~\ref{fig-notation-index}. The index key is listed next to this symbol. Composite keys are indicated by a grouping symbol. Hashing is represented in a similar way, but uses an ``H'' symbol instead of a tree symbol (see Fig.~\ref{fig-notation-hash}). Clustering is represented by nesting one table inside another, as shown in Fig.~\ref{fig-notation-cluster} (adapted from \cite{BeDa-P-1992-PDD}). The cluster key is indicated by an asterisk (*) attached to the appropriate field(s). Tables may be nested to as many levels as required in order to represent more complex clustering schemes. This notation is intuitive, and clearly indicates the field(s) on which the records are clustered. Partitioning is represented by splitting a table into either vertical or horizontal partitions according to the style of partitioning, as shown in Fig.~\ref{fig-notation-partition} (adapted from \cite{Silb-A-2002-4E}). Once again, the notation is intuitive, and allows the partition definitions to be easily indicated. Replication is indicated by placing a diagonal bar across the bottom-right corner of the table to be replicated, along with the total number of replicas, as shown in Fig.~\ref{fig-notation-replica}. This is adapted from a similar notation used in data flow diagrams \cite{Gane-C-1979}. This notation could also be used to indicate replication of individual table partitions, for DBMSs that permit this combination. % The graphical notations use in this model is not complex and cluttered. % This is to enable a database designer to simplify the physical design in % a notation that will be easy to understand but comprehensive. Some of % the graphical notations use in this model was adapted from other models % and a few were modified by the author to suit the model. % % The table below illustrates some of the graphical representations that % were available to choose from. Consider the entity-relationship diagram shown in Fig.~\ref{fig-ERD}, which uses Martin notation \cite{Mart-J-1990-IE2} to depict a database for a consumer electronics manufacturer. A corresponding Beynon-Davies' composite usage map based on the fourteen most significant transactions is shown in Fig.~\ref{fig-Beynon-Davies}. The arrows represent physical access paths, while the number attached to each access path indicates the number of disk accesses per hour along that path. The diagram clearly highlights some potential performance problem areas in the database, for example: \begin{itemize} \item There are many disk accesses on the access paths between the Sale\_head/Sale\_line and the Order\_head/Order\_line tables. Since these are tables that will normally be accessed together, both could perhaps be candidates for clustering (depending on the mix of update versus read operations). \item The appear to be multiple transactions acessing the Staff table. This could imply a need for partitioning. \item There is an extremely high access rate on the Customer table. Further examination, however, reveals that this rate only occurs for a short period once per month, and that the transaction in question only requires read access. Replication of the Customer table could therefore be a suitable solution. \end{itemize} \begin{figure} \includegraphics[width=\columnwidth,keepaspectratio]{ERD} \caption{ERD of the example database} \label{fig-ERD} \end{figure} \begin{figure} \includegraphics[width=\columnwidth,keepaspectratio]{Beynon-Davies} \caption{Beynon-Davies composite usage map for the example database} \label{fig-Beynon-Davies} \end{figure} The suggestions above can be represented as a physical model using our proposed modelling notation, as shown in Fig.~\ref{fig-physical-model} (some details have been omitted to save space). Note that we have placed indexes on all primary keys as a matter of course. \begin{figure} % \includegraphics[angle=90,scale=0.9]{Physical-Model} \includegraphics[width=\columnwidth,keepaspectratio]{Physical-Model} \caption{Physical database model for the example database} \label{fig-physical-model} \end{figure} \section{Future Research} \label{sec-future} - symbols for different types of index (reverse-key, index-organised tables, R-trees, bitmap indexes). - physical placement information, i.e., what devices partitions go on, etc. - evaluation of the notation with undergraduate students \& comparison with Beynon-Davies (preliminary results by time of publication). \section{Conclusion} \label{sec-conclusion} \bibliographystyle{splncs} \bibliography{ER2005} \end{document}

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Physical_modelling.tex

\documentclass{CRPITStyle}

\usepackage{harvard}
\usepackage[hang]{subfigure}
\usepackage{graphicx}

\pagestyle{empty}
\thispagestyle{empty}

\title{A Graphical Notation for Physical Database Modelling}
\author{Antonia Pillay \and Nigel Stanger}
\affiliation{Department of Information Science, \\
	University of Otago, \\
	PO Box 56, Dunedin, New Zealand \\
	Email:~\texttt{nstanger@infoscience.otago.ac.nz}}

\hyphenation{da-ta-base da-ta-ba-ses co-pies}

\begin{document}

\maketitle

\begin{abstract}
For database systems, physical level design is often as important as
conceptual level design, as it is the physical level that ultimately
determines the performance of a database system. It is therefore
somewhat surprising that there have been relatively few prior attempts
to devise an abstract graphical notation for physical level modelling.
Such a notation would provide database designers and administrators with
a means to consistently model the physical structure of new and existing
databases, thus enabling them to make more proactive and informed tuning
decisions, compared to the more typical situation where database
monitoring tools are used to identify and remedy performance problems in
a reactive and ad hoc manner. In this paper we propose an abstract
graphical notation for physical level database modelling, and compare it
with earlier work in this area.
\end{abstract}

\section{Introduction}

As with most information systems, the design and implementation of a
database goes through several phases, including conceptual, logical and
physical modelling \cite{BeDa-P-2003}. These three phases are of
particular interest, as they embody the progression from higher to lower
levels of abstraction \cite{Tsic-D-1978}. Conceptual models are
typically highly abstract, using techniques such as entity-relationship
modelling. Logical models represent the database structure in a form
that is closer to the physical representation, yet still sufficiently
abstract to isolate applications from the physical representation
\cite{Codd-EF-1970}, and are expressed using formalisms such as the
relational model. A logical model for a database can be derived by
transforming the corresponding conceptual model.

Physical models represent the database structure in terms of the
internal physical storage implementation of a specific database
management system (DBMS) such as Oracle or DB2. A physical model for a
database can be derived by transforming the corresponding logical model
\cite{Bato-DS-1985,Conn-TM-2002}. Because of their low level of
abstraction, physical level database models have tended to not be
expressed using graphical notations, unlike models at higher levels of
abstraction.

Physical level modelling, however, is equally as important as, if not
\emph{more} important than the higher levels, because it is the physical
level that determines the performance of a database \cite{BeDa-P-2003}.
It is therefore somewhat surprising that there have been relatively few
attempts to devise a graphical physical modelling notation, because such
a notation can provide several advantages
\cite{BeDa-P-1992-PDD,Conn-TM-2002,Tuft-ER-1997,Will-J-1992}:
\begin{itemize}

\item it can reduce complexity and thus improve understandability;

\item it can provide a more complete and integrated display of
	performance tuning techniques in a database;

\item database developers can be more confident about the design
	decisions that they make for the performance of the database;

\item potential database performance problem areas are more easily
	visualised using a graphical notation; and

\item a specific methodology is developed and used, thus enabling
	developers to resolve physical performance issues more
	systematically.

\end{itemize}

Many of these benefits are embodied in modern database performance
monitoring tools, which provide higher-level visualisations of a
database's internals in order to easily identify and highlight
performance problems. Such tools, however, are primarily
\emph{monitoring} tools rather than \emph{design} tools. They may
therefore unintentionally encourage database administrators (DBAs) into
a \emph{reactive} mode of continually ``tweaking'' the database to
resolve performance issues, rather than a \emph{proactive} mode of
anticipating and designing for expected usage. It may also be difficult
for a DBA using such tools to gain a clear and comprehensive overview of
all the tuning techniques that are in use within a particular database
\cite{Core-MJ-1997-OracleDW}.

It could be argued that physical level structures vary so much from one
DBMS to another that it is futile to even consider a generic physical
modelling notation. However, examination of the major DBMS products in
use today reveals that they share many of the same physical tuning
techniques. The specific implementation of (for example) clustering may
vary in detail from platform to platform, but the feature is in general
largely similar across all implementations. We provide a brief overview
of commonly used physical tuning techniques in
Section~\ref{sec-techniques}.

In this paper we propose a graphical notation for physical database
modelling. In Section~\ref{sec-previous} we discuss two earlier
approaches upon which our work is partially based.
Section~\ref{sec-notation} introduces our notation, and
Section~\ref{sec-future} discusses possible future work. The paper
concludes in Section~\ref{sec-conclusion}.

\section{Common Physical Tuning Techniques}
\label{sec-techniques}

Database management is generally an I/O bound task, so the main
performance bottleneck in most databases will be the performance of
tertiary storage devices such as disk drives. Retrieving data from a
hard disk is theoretically about six orders of magnitude slower than
retrieving data from RAM\footnote{On the order of milliseconds
(\(10^{-3}\)) for disk versus nanoseconds (\(10^{-9}\)) for RAM.}. The
aim of any physical tuning strategy must therefore be to minimise the
impact of slow tertiary storage, either by directly reducing the number
of physical disk accesses required, or by parallelising access to disk
in order to reduce contention.

These considerations have led to the development of five general
physical tuning techniques, which are implemented with slight variations
by most modern mainstream DBMS products:

\begin{description}

\item[Indexes] reduce the number of physical disk accesses required
	to retrieve a specific record \cite{Roti-S-1996}, most typically by
	building a B+-tree \cite{Knut-DE-1997-Art} based on some key value
	within the data. Without any indexes, a DBMS often has little choice
	but to perform a sequential scan in order to locate a specific
	record. If we assume one disk access per database block, a
	sequential scan has average and worst case performance of \(K/2b\)
	and \((K - 1)/b\) disk accesses required, respectively, where \(K\)
	is the number of records and \(b\) is the number of records per
	database block. By comparison, a B+-tree has a worst case
	performance of \(\log_{n/2}(K)\) \cite{Silb-A-2002-4E}, where
	\(n\) is the number of key values per index node.

\item[Hashing] is a method of quickly locating specific records by
	passing a key value to a \emph{hash function}. This function ideally
	returns a unique physical location of a hash bucket containing the
	requested physical record. Hashing schemes typically require only
	one or two physical disk accesses to retrieve a specific record, and
	perform best for exact key matches on very large tables. Hashing
	generally performs poorly for queries that require the retrieval of
	many records.

\item[Clustering] minimises disk access by ensuring that related
	records (such as an order header and its associated order lines) are
	physically adjacent on disk. This usually means that related records
	will be stored in the same database block, and can thus be retrieved
	with a single disk access. Clustering can, however, be expensive to
	maintain in a high update environment, because of the frequent
	``reclustering'' required.

\item[Partitioning] provides parallel access paths to data by
	physically splitting a table into disjoint parts (either vertically
	by columns or horizontally by rows) and placing them on separate
	disks. This is particularly advantageous when multiple users wish to
	access different subsets of a set of records, because it provides a
	separate physical access path to each of the partitions.
	Partitioning can also reduce the number of disk accesses required,
	because there are fewer records to scan in each partition than if
	the table were not partitioned.

\item[Replication] provides parallel access paths to data by making
	multiple copies of the same records and placing them on separate
	disks. This is particularly advantageous when multiple users need to
	access the same sets of records, but is more complex to manage due
	to the need to keep replicas synchronised.

\end{description}

These techniques are normally applied to different parts of a database
to achieve different effects. In order to choose an appropriate physical
tuning technique, the DBA must consider various factors that may benefit
only some users of the database, or may improve the performance of the
database as a whole. While most of the techniques can be combined to
varying degrees, simply applying all techniques is usually not optimal,
because each technique excels under different conditions. That is, what
are optimal conditions for one technique may be the exact opposite for
another, so the DBA needs to be able to model all available information
in order to develop an appropriate physical design.

\section{Prior Physical Modelling Techniques}
\label{sec-previous}

To achieve an effective physical design requires a large amount of
information, particularly with regard to the predicted or actual volume
and usage of data within the database \cite{BeDa-P-2003}. Incorporating
this information into a graphical model can provide a more concise and
clearer overview of the physical aspects of a database system. In this
section we briefly discuss two previous efforts at modelling such
information in a graphical manner.

\subsection{\emph{Agile Modeling} (Ambler)}

Ambler proposed a physical modelling notation based on the Unified
Modelling Language (UML), as part of a broader effort to produce a
``traditional'' style data modelling profile for the UML
\cite{Ambl-SW-2003-ADT,Ambl-SW-2004-ObjPrimer3}. Ambler and others have
argued the need for such a profile for some time
\cite{Ambl-SW-1998-BOA,Naib-EJ-2001-UMLDD}.

Ambler's notation focuses on the physical modelling of relational
databases. The notation uses class boxes without stereotypes to
represent physical tables, while indexes are represented by class boxes
with the stereotype \verb|<<index>>|, as shown in
Figure~\ref{fig-Ambler}. There appear to be no stereotypes for other
physical tuning techniques such as partitioning, although these could be
easily incorporated.

\begin{figure*}[htb]
	\fbox{\parbox[b]{.99\linewidth}{%
		\vskip 0.5cm%
		\centerline{\includegraphics[scale=0.9]{Ambler}}%
		\vskip 0.5cm%
	}}
	\caption{Ambler's physical modelling notation (adapted from \protect\cite{Ambl-SW-2003-ADT})}
	\label{fig-Ambler}
\end{figure*}

Ambler's approach suffers from two serious disadvantages. First, the
notation is very limited in the types of symbol used. All physical level
constructs are represented by identical class boxes, which in a complex
diagram could make distinguishing them difficult. This limitation
probably arises from the constraints on developing a new notation within
the existing UML framework.

Second, his approach appears to consistently confuse the conceptual and
physical levels of abstraction: the same notations are used to represent
not only physical but also conceptual elements \cite{Ambl-SW-2003-ADT}.
This confusion is illustrated by the inclusion of a view (a non-physical
construct) in Figure~\ref{fig-Ambler}.

In summary, while Ambler's notation graphically models the physical
level of a database, the similarity of the graphical symbols and the
evident confusion between the physical and logical levels diminish its
usefulness.

\subsection{\emph{Physical Design Using an Entity Model} (Beynon-Davies)}

Beynon-Davies proposed a method for analysing and modelling the physical
usage patterns of a database \cite{BeDa-P-1992-PDD}. In his method,
various aspects of the physical performance of a database are measured,
such as the size and expected growth rates of tables (volume analysis),
the volatility of tables, and the frequency of transactions (usage
analysis). The data obtained from these analyses are then used to
annotate a logical level entity-relationship diagram (ERD) of the
database, producing what is known as a \emph{composite usage map} (see
Figure~\ref{fig-Beynon-Davies} for an example).

Beynon-Davies' method provides a very good mechanism for representing
the usage statistics of a database in a coherent manner, but is rather
complex and time-consuming to undertake without some form of automation.
Our experience with teaching this method at undergraduate level shows
that even with a relatively small database, the designer can quickly
become overwhelmed by the sheer volume of usage data involved.

In addition, Beynon-Davies' method does not produce any conclusions as
to which physical tuning methods should be implemented---rather it
summarises the information required to facilitate these decisions.
Beynon-Davies' method is thus more a notation for summarising the
physical usage patterns of a database, rather than a notation for
physical modelling per se.

\section{A New Physical Notation}
\label{sec-notation}

Both of the notations discussed in the previous section are limited in
their ability to graphically model the physical level of a database.
Ambler's notation lacks clarity and is thus potentially confusing, while
Beynon-Davies' notation only summarises the physical usage patterns of a
database rather than providing an actual physical level model. We have
therefore adopted aspects from both approaches to devise a graphical
notation that enables database designers to graphically model the common
physical database tuning techniques discussed in
Section~\ref{sec-techniques}.

The symbols that we have adopted for this notation are shown in
Figure~\ref{fig-notation}. Some of these symbols are adapted from other
notations, while some we have created ourselves. The symbols have been
chosen to be intuitive and simple to draw, so as to produce diagrams
that are as clear and uncluttered as possible. Physical models may be
constructed using this notation either with or without a prior
Beynon-Davies style analysis.

\begin{figure*}[htb]
	\fbox{\parbox[b]{.99\linewidth}{%
		\vskip 0.5cm%
		\mbox{}%
		\hfill%
		\subfigure[Physical\newline{}table]{\label{fig-notation-table}\includegraphics[scale=0.9]{notation-table}}%
		\hfill%
		\subfigure[Indexes]{\label{fig-notation-index}\includegraphics[scale=0.9]{notation-index}}%
		\hfill%
		\subfigure[Hashing]{\label{fig-notation-hash}\includegraphics[scale=0.9]{notation-hash}}%
		\hfill%
		\subfigure[Replication]{\label{fig-notation-replica}\includegraphics[scale=0.9]{notation-replica}}
		\hfill%
		\mbox{} \\%
		\mbox{}%
		\hfill%
		\subfigure[Clustering]{\label{fig-notation-cluster}\includegraphics[scale=0.9]{notation-cluster}}%
		\hfill%
		\subfigure[Horizontal\newline{}partitioning]{\label{fig-notation-partition-h}\includegraphics[scale=0.9]{notation-partition-h}}%
		\hfill%
		\subfigure[Vertical partitioning]{\label{fig-notation-partition-v}\includegraphics[scale=0.9]{notation-partition-v}}%
		\hfill%
		\mbox{}%
		\vskip 0.5cm%
	}}
	\caption{Symbols representing physical tuning techniques}
	\label{fig-notation}
\end{figure*}

A physical table is represented by a simple box, as shown in
Figure~\ref{fig-notation-table}. This is similar to most
entity-relationship modelling notations. The fields of the physical
table may be included, with or without physical data types, as
appropriate. It could be argued that a different symbol should be used
to avoid confusion between, for example, physical tables and conceptual
entities. These constructs belong to different levels of abstraction,
however, so they should not both appear on the same diagram.
Consequently there is no real potential for confusion.

B-tree indexes are indicated by the symbol ``\(\bigtriangleup\)''
(representing a tree-like structure) within a table, as shown in
Figure~\ref{fig-notation-index}. The index key is listed next to this
symbol. Composite keys are indicated by a grouping symbol, and a unique
index is indicated by a subscript ``1'', that is,
\(\bigtriangleup_{1}\). Bitmap indexes and hashing are represented in a
similar way, using the symbols
``\(\smash{\vdots\vdots\vdots}\)''\rule{0pt}{1.02\baselineskip} and
``\#'', respectively (see Figure~\ref{fig-notation-index}
and~\ref{fig-notation-hash}). These symbols could easily be extended to
cater for other types of index, such as R-trees.

Clustering is represented by nesting one table inside another, as shown
in Figure~\ref{fig-notation-cluster} (adapted from
\cite{BeDa-P-1992-PDD}). The cluster key is indicated by an asterisk (*)
attached to the appropriate field(s). Tables may be nested to as many
levels as required in order to represent more complex clustering
schemes. This notation is intuitive, and clearly indicates the field(s)
on which the records are clustered. (Note that the physical relationship
connecting the clustered tables has been omitted in
Figure~\ref{fig-notation-cluster}.)

Partitioning is represented by splitting a table into either vertical or
horizontal partitions according to the style of partitioning, as shown
in Figure~\ref{fig-notation-partition-h} and
\ref{fig-notation-partition-v} (adapted from \cite{Silb-A-2002-4E}).
Once again, the notation is intuitive, and allows the partition
definitions to be easily indicated.

Replication is indicated by placing a diagonal bar across the
lower-right corner of the table to be replicated, along with the total
number of replicas, as shown in Figure~\ref{fig-notation-replica}. This
is adapted from a similar notation used in data flow diagrams
\cite{Gane-C-1979}. This notation could also be used to indicate
replication of individual table partitions, for DBMSs that permit this
combination. We considered an alternative symbol that looked like a
``stack'' of tables, on the basis that it might be useful to
differentiate between different replicas. All the replicas are
identical, however, which diminishes the usefulness of such a symbol,
and the symbol was also more complex to draw.

Consider the entity-relationship diagram shown in Figure~\ref{fig-ERD},
which uses Martin notation \cite{Mart-J-1990-IE2} to depict a database
for a consumer electronics manufacturer (this was a case study used in
an undergraduate advanced database course). A corresponding
Beynon-Davies' composite usage map based on fourteen significant
transactions \cite{Pill-A-2005-DP} is shown in
Figure~\ref{fig-Beynon-Davies} (some details have been omitted for
clarity). The arrows represent physical access paths, while the number
attached to each access path indicates the number of disk accesses per
hour along that path. The diagram clearly highlights some potential
performance problem areas in the database, for example:
\begin{itemize}

\item There are many disk accesses per hour along the access paths
	between the \textsf{Sale\_head}/\textsf{Sale\_line} and the
	\textsf{Order\_head}/\textsf{Order\_line} tables. Since each pair of
	tables will normally be accessed together, both pairs could perhaps
	be candidates for clustering (depending on the mix of update versus
	read operations).

\item There appear to be multiple transactions accessing the
	\textsf{Staff} table along different access paths. This could imply
	a need for partitioning.

\item There is an extremely high access rate on the
	\textsf{Customer} table. Further investigation, however, reveals
	that this rate only occurs for a short period once per month, and
	that the transaction in question only requires read access.
	Replication of the \textsf{Customer} table could therefore be a
	suitable solution to ensure that this short, intense and
	intermittent transaction does not interfere with normal day-to-day
	transaction processing.
	
\end{itemize}

\begin{figure*}[htbp]
	\fbox{\parbox[b]{.99\linewidth}{%
		\vskip 0.5cm%
		\centerline{\includegraphics[scale=0.9]{ERD}}%
		\vskip 0.5cm%
	}}
	\caption{Logical level ERD of the example database}
	\label{fig-ERD}
\end{figure*}

\begin{figure*}[htbp]
	\fbox{\parbox[b]{.99\linewidth}{%
		\vskip 0.5cm%
		\centerline{\includegraphics[scale=0.9]{Beynon-Davies}}%
		\vskip 0.5cm%
	}}
	\caption{Beynon-Davies composite usage map for the example database}
	\label{fig-Beynon-Davies}
\end{figure*}

The suggestions above can be represented as a physical model using our
modelling notation, as shown in Figure~\ref{fig-physical-model} (some
details have been omitted to save space). Note that we have placed
indexes on all primary keys as a matter of course, as almost all DBMS
products do this automatically.

\begin{figure*}[htb]
	\fbox{\parbox[b]{.99\linewidth}{%
		\vskip 0.5cm%
		\centerline{\includegraphics[scale=0.875]{Physical-Model}}%
		\vskip 0.5cm%
	}}
	\caption{Physical level model of the example database using the new notation}
	\label{fig-physical-model}
\end{figure*}

\section{Future Work}
\label{sec-future}

The current notation, while it covers the most common aspects of
physical modelling, is not complete and could be extended in various
ways, for example:
\begin{itemize}

\item The current notation only caters for B-tree indexes, bitmap
	indexes and hashing. An obvious extension is to define symbols for
	other types of index, such as R-trees. We also plan to investigate
	symbols for index variations such as reverse-key and integrated
	indexes.
	
	\item Many DBMS products support partitioning of indexes in addition
	to tables. Our notation does not cater for this, but could possibly
	be modified so that indexes are modelled separately from their
	associated physical tables (similar to Ambler's notation). We would
	need to evaluate whether the benefits gained from such an extension
	would outweigh the additional complexity introduced.
	
	\item There is currently no way to specify physical placement
	information, for example, which devices different partitions should
	be placed on.
	
	\item The clustering notation effectively assumes that there are at
	least two tables being clustered, but most DBMS products support
	single-table clusters, which in effect physically sort the contents
	of the table on the value of the cluster key.
	
\end{itemize}

We are currently evaluating the efficacy of the notation with
undergraduate students in an advanced database course. We will then
compare this with using Beynon-Davies' method alone.

\section{Conclusion}
\label{sec-conclusion}

In this paper we have described a graphical notation for physical
database modelling, which enables database administrators (DBAs) to
model the physical structure of new and existing databases in a more
abstract manner. This will enable them to make more proactive and
informed tuning decisions, compared to existing database monitoring
tools, which tend to encourage a more reactive approach to database
tuning. The notation uses simple and intuitive symbols to represent
major physical database structures, and can easily represent complex
physical schemas.

The notation is to be evaluated with undergraduate students in an
advanced database course; the results of this evaluation will be
compared with other physical modelling methods.

\section*{Acknowledgements}

The authors would like to thank the students of \emph{INFO 321: Database
Systems} 2004 and 2005 for participating in the development and evaluation
of this new notation.

\bibliographystyle{agsm}

\bibliography{Physical_modelling}

\end{document}

\documentclass{llncs}

\usepackage{subfigure}
\usepackage{graphicx}

\title{A Graphical Notation for \\ Physical Database Modelling}
\author{Antonia Pillay\inst{1} \and Nigel Stanger\inst{2}}
\institute{???? Malaysia \and Department of Information Science, University of Otago, Dunedin, New Zealand \email{nstanger@infoscience.otago.ac.nz}}

\begin{document}

\maketitle

\begin{abstract}
In this paper we describe a graphical notation for physical database
modelling. This notation provides database administrators (DBAs) with a
means to model the physical structure of new and existing databases,
thus enabling them to make more proactive and informed tuning decisions,
compared to existing database monitoring tools.
\end{abstract}

\section{Introduction}

Physical models represent the database structure in terms of the
physical storage implementation of a specific database management system
(DBMS) such as Oracle or DB2. A physical model for a database can be
derived by transforming the corresponding logical model
\cite{Bato-DS-1985,Conn-TM-2002}. Because of their low abstraction
level, physical level database models have tended to not be expressed
using graphical notations, unlike models at higher levels of
abstraction.

Physical level modelling, however, is equally as important as, if not
\emph{more} important than the higher levels, because it is the physical
level that determines the performance of a database \cite{BeDa-P-2003}.
It is somewhat surprising that there have been relatively few attempts
to devise a graphical physical modelling notation, because such a
notation can provide several advantages
\cite{Conn-TM-2002,BeDa-P-1992-PDD,Will-J-1992}:
\begin{itemize}

\item it can reduce complexity and thus improve understandability
	\cite{Tuft-ER-1997};

\item it can provide a more complete and integrated display of
	performance tuning techniques in a database;

\item database developers can be more confident about the design
	decisions that they make for the performance of the database;

\item database performance problems are more easily visualised
	using a graphical notation; and

\item a specific methodology is developed and used, thus enabling
	developers to resolve physical performance issues more
	systematically.

\end{itemize}

These benefits are embodied in modern database performance monitoring
tools, which provide higher-level visualisations of a database's
internals in order to easily identify and highlight performance
problems. Such tools, however, are primarily \emph{monitoring} tools
rather than \emph{design} tools. They may therefore unintentionally
encourage DBAs into a \emph{reactive} mode of continually ``tweaking''
the database to resolve performance issues, rather than a
\emph{proactive} mode of anticipating and designing for expected usage.
It may also be difficult for a DBA using such tools to gain a clear and
comprehensive overview of all the tuning techniques that are in use
within a particular database \cite{Core-MJ-1997-OracleDW}.

In this paper we propose a graphical notation for physical database
modelling. In Sect.~\ref{sec-techniques}, we provide a brief overview of
commonly used physical tuning techniques. We then discuss in
Sect.~\ref{sec-previous} two earlier approaches upon which our work is
partially based. Sect.~\ref{sec-notation} introduces our proposed
notation, and Sect.~\ref{sec-future} discusses possible future work. The
paper concludes in Sect.~\ref{sec-conclusion}.

\section{Physical Tuning Techniques}
\label{sec-techniques}

Database management is generally an I/O bound task, so the main
performance bottleneck in most databases will be the speed of offline
storage such as disk drives. Retrieving data from a hard disk is
theoretically about six orders of magnitude slower than retrieving data
from RAM\footnote{On the order of milliseconds (\(10^{-3}\)) for disk
versus nanoseconds (\(10^{-9}\)) for RAM.}. The aim of any physical
tuning strategy must therefore be to minimise the impact of slow
storage, either by directly reducing the number of physical disk
accesses required, or by parallelising access to disk in order to reduce
contention.

These considerations have led to the development of five general
physical tuning techniques, which are implemented to various degrees by
most modern mainstream DBMS products:

\begin{description}

\item[Indexes] reduce the number of physical disk accesses required
	to retrieve a specific record, most typically by building a B+-tree
	\cite{Knut-DE-1997-Art} based on some key value. Without any
	indexes, a DBMS often has little choice but to perform a sequential
	scan in order to locate a specific record.

% A common database operation is to request a specific data record
% identified by a some key value. When such a request occurs, the DBMS
% must locate this record on disk (assuming that it is not already cached
% elsewhere). If we assume that the records are randomly ordered with
% respect to the chosen key value, then the DBMS has no option but to
% perform a sequential scan of all the records, which will require on
% average\(n/2b\) disk accesses, where \(n\) is the number of records and
% \(b\) is the number of records per database block (the worst case is
% \((n - 1)(b)\)). Sorting the records on the key value will obviously
% help for searches based on that key value, but will not help for
% searches based on a different key value.
% 
% One solution is to build a separate index structure that associates key
% values with their corresponding physical record. Because indexes are
% stored separately, we can provide multiple indexes on different key
% values for the same set of records. In addition, by choosing an
% appropriate data structure for the index, we can improve the efficiency
% of searching and thus reduce the number of physical disk accesses
% required \cite{Roti-S-1996}. Most modern DBMSs use some variant of the
% \emph{B+-tree} structure \cite{Knut-DE-1997-Art}. This structure has
% average and worst case performance of (formula) and (formula),
% respectively.
% 
% Indexes are a ``pure'' disk access minimisation technique, that is, they
% reduce the number of physical disk accesses required, but do not provide
% any form of paralellism. B-tree indexes perform well for a wide range of
% typical database queries (ref), but can suffer in high update
% environments due to the need to also update the index.

\item[Hashing] is a method of quickly locating specific records by
	passing a key value to a \emph{hash function}. This function returns
	the physical location of a hash bucket, which contains a pointer to
	the associated physical record (check). Hashing schemes typically
	require only a single disk access to retrieve a specific record.

% Hashing is another pure disk access minimisation technique that performs
% well for exact key queries on very large tables. Rather than build a
% separate index structure, a key value is passed to a \emph{hash
% function}. This function returns the physical location of a hash bucket,
% which contains a pointer to the associated physical record (check).
% Hashing schemes typically require only a single disk access to retrieve
% a specific record, but perform poorly for queries that require the
% retrieval of multiple records.

% Clustering is a disk access minimisation technique that can be applied
% when two different record types are logically related, and thus commonly
% retrieved together; for example, an order header and its order lines.
% Without any further optimisation, such an operation could at worst
% require one disk access for the order header and one disk access for
% each of its order lines.
% 
% To improve this situation, we \emph{cluster} the two record types, so
% that related physical records are physically adjacent to each other
% \cite{Chan-N-2003-clustering}. This usually means that all the records
% will be stored in the same database block, and can thus all be retrieved
% with a single disk access. Clustering can, however, be expensive to
% maintain in a high update environment.

\item[Partitioning] provides parallel access paths to data by
	physically splitting a table into disjoint parts and placing them on
	separate disks. This is particularly advantageous when multiple
	users wish to access different subsets of a set of records, because
	it provides a separate physical access path to each of the
	partitions.
% 	Partitioning can also reduce the number of disk accesses
% 	required, because there are fewer records to scan in each partition
% 	than if the table were not partitioned.

% In a typical untuned database, all data will be stored on the same disk.
% If the database is heavily accessed, then contention for the single I/O
% channel to this disk becomes a major bottleneck. Partitioning reduces
% this contention by splitting the database into disjoint partitions, each
% of which is assigned to a different disk. This approach is particularly
% advantageous in situations where multiple users wish to access different
% subsets of a set of records, because it provides a disjoint physical
% access paths to each of the partitions. This reduces I/O channel
% contention and thus improves performance. Partitioning is thus primarily
% an access parallelism technique.
% 
% Partitioning can also provide some disk access minimisation benefits,
% because the amount of data to be searched in each partition is smaller
% than if the database were not partitioned.

% Replication is an access parallelism technique in which multiple copies
% of the same records are placed on different disks, thus providing
% multiple physical access paths to the same data.

\end{description}

These techniques are normally applied to different parts of a database
to achieve different effects. In order to choose an appropriate physical
tuning technique, the DBA must consider various factors that may benefit
only some users of the database, or may improve the performance of the
database as a whole. While most of the techniques can be combined to
varying degrees, simply applying all techniques is usually not optimal,
because each technique excells under different conditions. That is, what
are optimal conditions for one technique may be the exact opposite for
another, so the DBA needs to be able to model all the available
information in order to develop an appropriate physical design.

\section{Prior Physical Modelling Techniques}
\label{sec-previous}

\subsection{Agile Modeling (Ambler)}

Ambler proposed a physical modelling notation based on the Unified
Modeling Language (UML), as part of a larger effort to produce a
``traditional'' style data modelling profile for the UML
\cite{Ambl-SW-2003-ADT,Ambl-SW-2004-ObjPrimer3}. Ambler and others have
argued the need for such a profile for some time
\cite{Ambl-SW-1998-BOA,Naib-EJ-2001-UMLDD}.

Ambler's notation focuses on the physical modelling of relational
databases. The notation uses class boxes without stereotypes to
represent physical tables, while indexes are represented by class boxes
with the stereotype \verb|<<index>>|, as illustrated in
Fig.~\ref{fig-Ambler}. There appear to be no stereotypes for other
physical tuning techniques such as partitioning, although these could be
easily incorporated.

\begin{figure}
	\includegraphics[width=\columnwidth,keepaspectratio]{Ambler}
	\caption{Ambler's physical modelling notation (adapted from \cite{Ambl-SW-2003-ADT})}
	\label{fig-Ambler}
\end{figure}

Ambler's approach suffers from two serious disadvantages. First, the
notation is very limited in the types of symbol used. All physical
level constructs are represented by class boxes, which in a complex
diagram could make distinguishing them difficult. This limitation
probably arises from the constraints on developing a new notation within
the existing UML framework.

Second, his approach appears to consistently confuse the logical and
physical levels of abstraction: the same notations are used to represent
not only physical but also logical and conceptual elements
\cite{Ambl-SW-2003-ADT}. This confusion is illustrated by the inclusion
of a view (a non-physical construct) in Fig.~\ref{fig-Ambler}.

In summary, while Ambler's notation does graphically model the physical
level of a database, the similarity of the graphical symbols and the
evident confusion between the physical and logical levels diminish its
usefulness.

% . He states that since Unified
% Modelling Language (UML) does not cover data (i.e., ER) modelling yet,
% he presents the solution in this paper. Ambler has argued for some time
% for the presence of data modelling in UML \cite{Ambl-SW-1998-BOA}, and
% has suggested various ways that it should be done. It is enlightening to
% know that Ambler is not alone in his quest for adopting an industry
% standard in data modelling. Other methodologies like
% \cite{Naib-EJ-2001-UMLDD} have recognized the need as well. This model
% type is built on the practice of UML 2.0 of separating core methodology.
% Ambler admits that the methodology that he has presented is not perfect
% and it focuses on the physical modelling of a relational database. In
% this model he also tries to stress style issues that according to him
% are not appropriate for a proper UML profile \cite{Ambl-SW-2003-ADT}.

% This model suggests that a class box without a stereotype in a physical
% database design is a table. It also represents views that have
% dependencies on the table structures. Tables, entities and views are all
% modelled using class boxes. The class boxes that appear on the logical
% and conceptual model are entities so the stereotype is optional.
% Similarly, it is assumed that any class box without a stereotype on a
% physical data model is a table. Ambler's methodology seems to be limited
% in terms of annotations. All representations whether tables or indices
% are modelled using class boxes. This representation can sometimes be
% confusing and exact identification of tables and indices in the system
% has to be known by the database designer. In this model, relationships
% are modelled using associations that are derived from the logical data
% model. He further discusses his methodology for modelling attributes and
% columns, keys, constraints and triggers, stored procedures and sections.
% Ambler states that requirements for something should be identified
% before it is built. He states the requirements of each model, e.g.,
% requirements that are needed to model entities and tables, requirements
% that are needed to model relationships, etc.
% 
% An example of Ambler's notation is shown in Fig.~\ref{fig-Ambler}.

% Even though a lot of detail is covered in his suggestion, Ambler's
% methodology somehow seems relatively weak. It is much too general in
% annotations and has no specific description. The methodology seems to
% consistently confuse the logical and physical design. For example: both
% are modelled using class boxes but are later explained as representing
% different objects. However, this model will definitely be useful as a
% stepping-stone to work towards an official UML data modelling profile.

\subsection{Physical Design Using an Entity Model (Beynon-Davies)}

Beynon-Davies' method provides a very good mechanism for representing
the usage statistics of database in a coherent manner, but is somewhat
complex to execute without some form of automation. Our experience with
teaching this method at undergraduate level shows that even with a
relatively small database, the designer can quickly become overwhelmed
by the sheer volume of usage data involved.

In addition, Beynon-Davies' method does not produce any conclusions as
to which physical tuning methods should be implemented---rather it
provides much of the information required to enable these decisions to
be made. Beynon-Davies' method is thus more a notation for summarising
the physical usage patterns of a database, rather than a notation for
physical modelling per se.

% The closest model that demonstrates the link between conceptual, logical
% and physical design work is by Beynon-Davies \cite{BeDa-P-1992-PDD}. He
% suggests a method that can be used for the physical design process. The
% paper defines physical modelling as ``the transformation of the logical
% model into a definition of the physical model suitable for a specific
% software/hardware configuration'' \cite{BeDa-P-1992-PDD}.
% 
% According to Beynon-Davies one of the first steps that must be taken to
% move from logical to physical database design is to establish estimates
% of the average and maximum number of instances per entity (volume
% analysis). Volatility analysis is represented in the same way. Similar
% to volume analysis, volatility analysis cannot be used as a measure if a
% table is continually growing. It is most effective if the size of the
% table is static. Figure 9 summarizes Beynon-Davies's final modelling
% method; he does not model all the individual annotations but generalizes
% the entity relationship diagrams into a general model.
% 
% It is understandable that the reason behind this generalization is to
% minimize complexity and since individual annotations are developed in
% the beginning, references can be made to them if needed. This might be
% effective for small systems that have a limited number of tables.
% However, for medium to large-scale projects, Beynon-Davies suggests that
% a systematic analysis of volatility and usage should be done. This
% analysis would constitute a map of the database application that would
% be maintained by the DBA. This meets the objective of providing an
% overview of all the performance tuning techniques and data in place.

\section{A New Physical Notation}
\label{sec-notation}

Both of the notations discussed in the previous sections are limited in
their ability to graphically model the physical level of a database.
Ambler's notation lacks clarity and is thus potentially confusing, while
Beynon-Davies' notation only summarises the physical usage patterns of a
database rather than providing an actual physical level database model.
We have therefore adopted aspects from both approaches to devise a
graphical notation that enables database designers to graphically model
the common physical database tuning techniques discussed in
Sect.~\ref{sec-techniques}.

The notations that we have adopted for this notation are shown in
Fig.~\ref{fig-notation}. Some of these are adapted from other notations,
while some we have created ourselves. The symbols have been chosen to be
intuitive and simple to draw, so as to produce diagrams that are as
clear and uncluttered as possible. Physical models may be developed
using this notation either with or without a prior Beynon-Davies style
analysis.

\begin{figure}
	\centering
	\subfigure[Table]{\label{fig-notation-table}\includegraphics[scale=0.9]{notation-table}}
	\hfill
	\subfigure[Indexes]{\label{fig-notation-index}\includegraphics[scale=0.9]{notation-index}}
	\hfill
	\subfigure[Hashing]{\label{fig-notation-hash}\includegraphics[scale=0.9]{notation-hash}}	\\
	\subfigure[Clustering]{\label{fig-notation-cluster}\includegraphics[scale=0.9]{notation-cluster}}
	\hfill
	\subfigure[Partitoning]{\label{fig-notation-partition}\includegraphics[scale=0.9]{notation-partition}}
	\hfill
	\subfigure[Replication]{\label{fig-notation-replica}\includegraphics[scale=0.9]{notation-replica}}
	\caption{Proposed notations for physical tuning techniques}
	\label{fig-notation}
\end{figure}

A physical table is represented by a simple box, as shown in
Fig.~\ref{fig-notation-table}. This is similar to most logical and
conceptual level ERD notations. The fields of the physical table may be
included, with or without physical data types, as appropriate.

Indexes are represented by a small tree-like symbol within a table, as
shown in Fig.~\ref{fig-notation-index}. The index key is listed next to
this symbol. Composite keys are indicated by a grouping symbol. Hashing
is represented in a similar way, but uses an ``H'' symbol instead of a
tree symbol (see Fig.~\ref{fig-notation-hash}).

Clustering is represented by nesting one table inside another, as shown
in Fig.~\ref{fig-notation-cluster} (adapted from
\cite{BeDa-P-1992-PDD}). The cluster key is indicated by an asterisk (*)
attached to the appropriate field(s). Tables may be nested to as many
levels as required in order to represent more complex clustering
schemes. This notation is intuitive, and clearly indicates the field(s)
on which the records are clustered.

Partitioning is represented by splitting a table into either vertical or
horizontal partitions according to the style of partitioning, as shown
in Fig.~\ref{fig-notation-partition} (adapted from
\cite{Silb-A-2002-4E}). Once again, the notation is intuitive, and
allows the partition definitions to be easily indicated.

Replication is indicated by placing a diagonal bar across the
bottom-right corner of the table to be replicated, along with the total
number of replicas, as shown in Fig.~\ref{fig-notation-replica}. This is
adapted from a similar notation used in data flow diagrams
\cite{Gane-C-1979}. This notation could also be used to indicate
replication of individual table partitions, for DBMSs that permit this
combination.

% The graphical notations use in this model is not complex and cluttered.
% This is to enable a database designer to simplify the physical design in
% a notation that will be easy to understand but comprehensive. Some of
% the graphical notations use in this model was adapted from other models
% and a few were modified by the author to suit the model.
% 
% The table below illustrates some of the graphical representations that
% were available to choose from.

Consider the entity-relationship diagram shown in Fig.~\ref{fig-ERD},
which uses Martin notation \cite{Mart-J-1990-IE2} to depict a database
for a consumer electronics manufacturer. A corresponding Beynon-Davies'
composite usage map based on the fourteen most significant transactions
is shown in Fig.~\ref{fig-Beynon-Davies}. The arrows represent physical
access paths, while the number attached to each access path indicates
the number of disk accesses per hour along that path. The diagram
clearly highlights some potential performance problem areas in the
database, for example:
\begin{itemize}

\item There are many disk accesses on the access paths between the
	Sale\_head/Sale\_line and the Order\_head/Order\_line tables. Since
	these are tables that will normally be accessed together, both could
	perhaps be candidates for clustering (depending on the mix of update
	versus read operations).

\item The appear to be multiple transactions acessing the Staff
	table. This could imply a need for partitioning.
	
	\item There is an extremely high access rate on the Customer table.
	Further examination, however, reveals that this rate only occurs for
	a short period once per month, and that the transaction in question
	only requires read access. Replication of the Customer table could
	therefore be a suitable solution.
	
\end{itemize}

\begin{figure}
	\includegraphics[width=\columnwidth,keepaspectratio]{ERD}
	\caption{ERD of the example database}
	\label{fig-ERD}
\end{figure}

\begin{figure}
	\includegraphics[width=\columnwidth,keepaspectratio]{Beynon-Davies}
	\caption{Beynon-Davies composite usage map for the example database}
	\label{fig-Beynon-Davies}
\end{figure}

The suggestions above can be represented as a physical model using our
proposed modelling notation, as shown in Fig.~\ref{fig-physical-model}
(some details have been omitted to save space). Note that we have placed
indexes on all primary keys as a matter of course.

\begin{figure}
% 	\includegraphics[angle=90,scale=0.9]{Physical-Model}
	\includegraphics[width=\columnwidth,keepaspectratio]{Physical-Model}
	\caption{Physical database model for the example database}
	\label{fig-physical-model}
\end{figure}

\section{Future Research}
\label{sec-future}

- symbols for different types of index (reverse-key, index-organised
tables, R-trees, bitmap indexes).

- physical placement information, i.e., what devices partitions go on, etc.

- evaluation of the notation with undergraduate students \& comparison
with Beynon-Davies (preliminary results by time of publication).

\section{Conclusion}
\label{sec-conclusion}

\bibliographystyle{splncs}

\bibliography{ER2005}

\end{document}

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