1.1. Data Structures and Algorithms¶
1.1.1. Data Structures and Algorithms¶
1.1.1.1. Introduction¶
How many cities with more than 250,000 people lie within 500 miles of Dallas, Texas? How many people in my company make over $100,000 per year? Can we connect all of our telephone customers with less than 1,000 miles of cable? To answer questions like these, it is not enough to have the necessary information. We must organize that information in a way that allows us to find the answers in time to satisfy our needs.
Representing information is fundamental to computer science. The primary purpose of most computer programs is not to perform calculations, but to store and retrieve information—usually as fast as possible. For this reason, the study of data structures and the algorithms that manipulate them is at the heart of computer science. And that is what this book is about—helping you to understand how to structure information to support efficient processing.
Any course on Data Structures and Algorithms will try to teach you about three things:
It will present a collection of commonly used data structures and algorithms. These form a programmer’s basic “toolkit”. For many problems, some data structure or algorithm in the toolkit will provide a good solution. We focus on data structures and algorithms that have proven over time to be most useful.
It will introduce the idea of tradeoffs, and reinforce the concept that there are costs and benefits associated with every data structure or algorithm. This is done by describing, for each data structure, the amount of space and time required for typical operations. For each algorithm, we examine the time required for key input types.
It will teach you how to measure the effectiveness of a data structure or algorithm. Only through such measurement can you determine which data structure in your toolkit is most appropriate for a new problem. The techniques presented also allow you to judge the merits of new data structures that you or others might invent.
There are often many approaches to solving a problem. How do we choose between them? At the heart of computer program design are two (sometimes conflicting) goals:
To design an algorithm that is easy to understand, code, and debug.
To design an algorithm that makes efficient use of the computer’s resources.
Ideally, the resulting program is true to both of these goals. We might say that such a program is “elegant.” While the algorithms and program code examples presented here attempt to be elegant in this sense, it is not the purpose of this book to explicitly treat issues related to goal (1). These are primarily concerns for the discipline of Software Engineering. Rather, we mostly focus on issues relating to goal (2).
How do we measure efficiency? Our method for evaluating the efficiency of an algorithm or computer program is called asymptotic analysis. Asymptotic analysis also gives a way to define the inherent difficulty of a problem. Throughout the book we use asymptotic analysis techniques to estimate the time cost for every algorithm presented. This allows you to see how each algorithm compares to other algorithms for solving the same problem in terms of its efficiency.
1.1.1.2. A Philosophy of Data Structures¶
You might think that with ever more powerful computers, program efficiency is becoming less important. After all, processor speed and memory size still continue to improve. Won’t today’s efficiency problem be solved by tomorrow’s hardware?
As we develop more powerful computers, our history so far has always been to use that additional computing power to tackle more complex problems, be it in the form of more sophisticated user interfaces, bigger problem sizes, or new problems previously deemed computationally infeasible. More complex problems demand more computation, making the need for efficient programs even greater. Unfortunately, as tasks become more complex, they become less like our everyday experience. So today’s computer scientists must be trained to have a thorough understanding of the principles behind efficient program design, because their ordinary life experiences often do not apply when designing computer programs.
In the most general sense, a data structure is any data representation and its associated operations. Even an integer or floating point number stored on the computer can be viewed as a simple data structure. More commonly, people use the term “data structure” to mean an organization or structuring for a collection of data items. A sorted list of integers stored in an array is an example of such a structuring. These ideas are explored further in a discussion of Abstract Data Types.
Given sufficient space to store a collection of data items, it is always possible to search for specified items within the collection, print or otherwise process the data items in any desired order, or modify the value of any particular data item. The most obvious example is an unsorted array containing all of the data items. It is possible to perform all necessary operations on an unsorted array. However, using the proper data structure can make the difference between a program running in a few seconds and one requiring many days. For example, searching for a given record in a hash table is much faster than searching for it in an unsorted array.
A solution is said to be efficient if it solves the problem within the required resource constraints. Examples of resource constraints include the total space available to store the data—possibly divided into separate main memory and disk space constraints—and the time allowed to perform each subtask. A solution is sometimes said to be efficient if it requires fewer resources than known alternatives, regardless of whether it meets any particular requirements. The cost of a solution is the amount of resources that the solution consumes. Most often, cost is measured in terms of one key resource such as time, with the implied assumption that the solution meets the other resource constraints.
1.1.1.3. Selecting a Data Structure¶
It should go without saying that people write programs to solve problems. However, sometimes programmers forget this. So it is crucial to keep this truism in mind when selecting a data structure to solve a particular problem. Only by first analyzing the problem to determine the performance goals that must be achieved can there be any hope of selecting the right data structure for the job. Poor program designers ignore this analysis step and apply a data structure that they are familiar with but which is inappropriate to the problem. The result is typically a slow program. Conversely, there is no sense in adopting a complex representation to “improve” a program that can meet its performance goals when implemented using a simpler design.
When selecting a data structure to solve a problem, you should follow these steps.
Analyze your problem to determine the basic operations that must be supported. Examples of basic operations include inserting a data item into the data structure, deleting a data item from the data structure, and finding a specified data item.
Quantify the resource constraints for each operation.
Select the data structure that best meets these requirements.
This three-step approach to selecting a data structure operationalizes a data-centered view of the design process. The first concern is for the data and the operations to be performed on them, the next concern is the representation of those data, and the final concern is the implementation of that representation.
Resource constraints on certain key operations, such as search, inserting data records, and deleting data records, normally drive the data structure selection process. Many issues relating to the relative importance of these operations are addressed by the following three questions, which you should ask yourself whenever you must choose a data structure.
Are all data items inserted into the data structure at the beginning, or are insertions interspersed with other operations? Static applications (where the data are loaded at the beginning and never change) typically get by with simpler data structures to get an efficient implementation, while dynamic applications often require something more complicated.
Can data items be deleted? If so, this will probably make the implementation more complicated.
Are all data items processed in some well-defined order, or is searching for specific data items allowed? “Random access” search generally requires more complex data structures.
Each data structure has associated costs and benefits. In practice, it is hardly ever true that one data structure is better than another for use in all situations. If one data structure or algorithm is superior to another in all respects, the inferior one will usually have long been forgotten. For nearly every data structure and algorithm presented in this book, you will see examples of where it is the best choice. Some of the examples might surprise you.
A data structure requires a certain amount of space for each data item it stores, a certain amount of time to perform a single basic operation, and a certain amount of programming effort. Each problem has constraints on available space and time. Each solution to a problem makes use of the basic operations in some relative proportion, and the data structure selection process must account for this. Only after a careful analysis of your problem’s characteristics can you determine the best data structure for the task.
Example 1.2.1
A bank must support many types of transactions with its customers, but we will examine a simple model where customers wish to open accounts, close accounts, and add money or withdraw money from accounts. We can consider this problem at two distinct levels: (1) the requirements for the physical infrastructure and workflow process that the bank uses in its interactions with its customers, and (2) the requirements for the database system that manages the accounts.
The typical customer opens and closes accounts far less often than accessing the account. Customers are willing to spend many minutes during the process of opening or closing the account, but are typically not willing to wait more than a brief time for individual account transactions such as a deposit or withdrawal. These observations can be considered as informal specifications for the time constraints on the problem.
It is common practice for banks to provide two tiers of service. Human tellers or automated teller machines (ATMs) support customer access to account balances and updates such as deposits and withdrawals. Special service representatives are typically provided (during restricted hours) to handle opening and closing accounts. Teller and ATM transactions are expected to take little time. Opening or closing an account can take much longer (perhaps up to an hour from the customer’s perspective).
From a database perspective, we see that ATM transactions do not modify the database significantly. For simplicity, assume that if money is added or removed, this transaction simply changes the value stored in an account record. Adding a new account to the database is allowed to take several minutes. Deleting an account need have no time constraint, because from the customer’s point of view all that matters is that all the money be returned (equivalent to a withdrawal). From the bank’s point of view, the account record might be removed from the database system after business hours, or at the end of the monthly account cycle.
When considering the choice of data structure to use in the database system that manages customer accounts, we see that a data structure that has little concern for the cost of deletion, but is highly efficient for search and moderately efficient for insertion, should meet the resource constraints imposed by this problem. Records are accessible by unique account number (sometimes called an exact-match query). One data structure that meets these requirements is the hash table. Hash tables allow for extremely fast exact-match search. A record can be modified quickly when the modification does not affect its space requirements. Hash tables also support efficient insertion of new records. While deletions can also be supported efficiently, too many deletions lead to some degradation in performance for the remaining operations. However, the hash table can be reorganized periodically to restore the system to peak efficiency. Such reorganization can occur offline so as not to affect ATM transactions.
Example 1.2.2
A company is developing a database system containing information about cities and towns in the United States. There are many thousands of cities and towns, and the database program should allow users to find information about a particular place by name (another example of an exact-match query). Users should also be able to find all places that match a particular value or range of values for attributes such as location or population size. This is known as a range query.
A reasonable database system must answer queries quickly enough to satisfy the patience of a typical user. For an exact-match query, a few seconds is satisfactory. If the database is meant to support range queries that can return many cities that match the query specification, the user might tolerate the entire operation to take longer, perhaps on the order of a minute. To meet this requirement, it will be necessary to support operations that process range queries efficiently by processing all cities in the range as a batch, rather than as a series of operations on individual cities.
The hash table suggested in the previous example is inappropriate for implementing our city database, because it cannot perform efficient range queries. The B$^+$-tree supports large databases, insertion and deletion of data records, and range queries. However, a simple linear index would be more appropriate if the database is created once, and then never changed, such as an atlas distributed on a CD or accessed from a website.
