CS403 - Database Management Systems - Lecture Handout 23

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Overview of Lecture

Physical Record and De-normalization


In the previous lecture, we have studied different data types and the coding techniques. We have reached now on implementing our database in which relations are now normalized. Now we will make this database efficient form implementation point of view.

Physical Record and Denormalization

Denormalization is a technique to move from higher to lower normal forms of database modeling in order to speed up database access. Denormalization process is applied for deriving a physical data model from a logical form. In logical data base design we group things logically related through same primary key. In physical database design fields are grouped, as they are stored physically and accessed by DBMS. In general it may decompose one logical relation into separate physical records, combine some or do both. There is a valid reason for denormalization that is to enhance the performance. However, there are several indicators, which will help to identify systems, and tables, which are potential denormalization candidates. These are:

Many critical queries and reports exist which rely upon data from more than one table. Often times these requests need to be processed in an on-line environment. Repeating groups exist which need to be processed in a group instead of individually. Many calculations need to be applied to one or many columns before queries can be successfully answered. Tables need to be accessed in different ways by different users during the same timeframe. Certain columns are queried a large percentage of the time. Consider 60% or greater to be a cautionary number flagging denormalization as an option.

We should be aware that each new RDBMS release usually bring enhanced performance and improved access options that may reduce the need for denormalization. However, most of the popular RDBMS products on occasion will require denormalized data structures. There are many different types of denormalized tables, which can resolve the performance problems caused when accessing fully normalized data. Denormalization must balance the need for good system response time with the need to maintain data, while avoiding the various anomalies or problems associated with denormalized table structures. Denormalization goes hand-in-hand with the detailed analysis of critical transactions through view analysis. View analysis must include the specification of primary and secondary access paths for tables that comprise end-user views of the database. A fully normalized database schema can fail to provide adequate system response time due to excessive table join operations

Denormalization Situation 1:

Merge two Entity types into one with one to one relationship. Even if one of the entity type is optional, so joining can lead to wastage of storage, however if two accessed together very frequently their merging might be a wise decision. So those two relations must be merged for better performance, which have one to one relationship.

Denormalization Situation 2:

Many to many binary relationships mapped to three relations. Queries needing data from two participating ETs need joining of three relations that is expensive. Join is an expensive operation from execution point of view. It takes time and lot of resources. Now suppose there are two relations STUDENT and COURSE and there exits a many to many relationship in between them. So there are three relations STUDENT, COURSE and ENROLLED in between them. Now if we want to see that a student has enrolled how many courses. So to get this we will have to join three relations, first the STUDENT and ENROLLED and then joining it with COURSE, which is quite expensive. The relation created against relationship is merged with one of the relation created against participating ETs. Now the join operation will be performed only once. Consider the following many to many relationship:-

EMP (empID, eName,pjId,Sal)

PROJ (pjId,pjName)

WORK (empId.pjId,dtHired,Sal)

This is a many to many relationship in between EMP and PROJ with a relationship of WORK. So now if we by de-normalizing these relations and merge the WORK relation with PROJ relation, which is comparatively smaller one. But in this case it is violating 2NF and anomalies of 2NF would be there. But there would be only one join operation involved by joining two tables, which increases the efficiency

EMP (empID, eName,pjId,Sal)

PROJ (pjId,pjName, empId,dtHired,Sal)

So now it is up to you that you want to weigh the drawbacks and advantages of denormalization.

Denormalization Situation 3:

Reference Data: One to many situation when the ET on side does not participate in any other relationship, then many side ET is appended with reference data rather than the foreign key. In this case the reference table should be merged with the main table.

We can see it with STUDENT and HOBBY relations. One student can have one hobby and one hobby can be adopted by many students. Now in this case the hobby can be merged with the student relation. So in this case although redundancy of data would be there, but there would not be any joining of two relations, which will have a better performance.


De-normalization leads to merging different relations, whereas partitioning splits same relation into two. The general aims of data partitioning and placement in database are to

  1. Reduce workload (e.g. data access, communication costs, search space)
  2. Balance workload
  3. Speed up the rate of useful work (e.g. frequently accessed objects in main memory)

There are two types of partitioning:-

Horizontal Partitioning

Vertical Partitioning

Horizontal Partitioning:

Table is split on the basis of rows, which means a larger table is split into smaller tables. Now the advantage of this is that time in accessing the records of a larger table is much more than a smaller table. It also helps in the maintenance of tables, security, authorization and backup. These smaller partitions can also be placed on different disks to reduce disk contention. Some of the types of horizontal partitioning are as under:-

Range Partitioning:

In this type of partitioning range is imposed on any particular attribute. So in this way different partitions are made on the basis of those ranges with the help of select statement. For Example for those students whose ID is from 1-1000 are in partition 1 and so on. This will improve the overall efficiency of the database. In range partition the partitions may become unbalanced. So in this way few partitions may be overloaded.

Hash Partitioning:

It is a type of horizontal partitioning. In this type particular algorithm is applied and DBMS knows that algorithm. So hash partitioning reduces the chances of unbalanced partitions to a large extent.

List Partitioning:

In this type of partitioning the values are specified for every partition. So there is a specified list for all the partitions. So there is no range involved in this rather there is a list of values.


De-normalization can lead to improved processing efficiency. The objective is to improve system response time without incurring a prohibitive amount of additional data maintenance requirements. This is especially important for client-server systems. Denormalization requires thorough system testing to prove the effect that denormalized table structures have on processing efficiency. Furthermore, unseen ad hoc data queries may be adversely affected by denormalized table structures. Denormalization must be accomplished in conjunction with a detailed analysis of the tables required to support various end-user views of the database. This analysis must include the identification of primary and secondary access paths to data. Similarly before carrying out partitioning of the table thorough analysis of the relations is must.


Critically examine the tables drawn for Examination system and see if there is a requirement of denormalization and partitioning and then carry out the process.