Concrete Collections in Java

Table 9.1 shows the collections in the Java library and briefly describes the purpose of each collection class. (For simplicity, we omit the thread-safe
collections that will be discussed in Chapter 12.) All classes in Table 9.1 im­plement the Collection interface, with the exception of the classes with names ending in Map. Those classes implement the Map interface instead. We will discuss maps in Section 9.4, “Maps,” on p. 519.

Figure 9.5 Classes in the collections framework

1. Linked Lists

We already used arrays and their dynamic cousin, the ArrayList class, for many examples in this book. However, arrays and array lists suffer from a major drawback. Removing an element from the middle of an array is expensive since all array elements beyond the removed one must be moved toward the beginning of the array (see Figure 9.6). The same is true for inserting elements in the middle.

Another well-known data structure, the linked list, solves this problem. Where an array stores object references in consecutive memory locations, a linked list stores each object in a separate link. Each link also stores a reference to the next link in the sequence. In the Java programming language, all linked lists are actually doubly linked; that is, each link also stores a reference to its predecessor (see Figure 9.7).

Removing an element from the middle of a linked list is an inexpensive operation—only the links around the element to be removed need to be updated (see Figure 9.8).

Perhaps you once took a data structures course in which you learned how to implement linked lists. You may have bad memories of tangling up the links when removing or adding elements in the linked list. If so, you will be pleased to learn that the Java collections library supplies a class LinkedList ready for you to use.

The following code example adds three elements and then removes the second one:

var staff = new LinkedList<String>();




Iterator<String> iter = staff.iterator();

String first =; // visit first element

String second =; // visit second element

iter.remove(); // remove last visited element

There is, however, an important difference between linked lists and generic collections. A linked list is an ordered collection in which the position of the objects matters. The LinkedList.add method adds the object to the end of the list.

But you will often want to add objects somewhere in the middle of a list. This position-dependent add method is the responsibility of an iterator, since iterators describe positions in collections. Using iterators to add elements makes sense only for collections that have a natural ordering. For example, the set data type that we discuss in the next section does not impose any or­dering on its elements. Therefore, there is no add method in the Iterator inter­face. Instead, the collections library supplies a subinterface ListIterator that contains an add method:

interface ListIterator<E> extends Iterator<E>


void add(E element);


Unlike Collection.add, this method does not return a boolean—it is assumed that the add operation always modifies the list.

In addition, the ListIterator interface has two methods that you can use for traversing a list backwards.

E previous()

boolean hasPrevious()

Like the next method, the previous method returns the object that it skipped over.

The listIterator method of the LinkedList class returns an iterator object that implements the ListIterator interface.

ListIterator<String> iter = staff.listIterator();

The add method adds the new element before the iterator position. For example, the following code skips past the first element in the linked list and adds “Juliet” before the second element (see Figure 9.9):

var staff = new LinkedList<String>();




ListIterator<String> iter = staff.listIterator();; // skip past first element


If you call the add method multiple times, the elements are simply added in the order in which you supplied them. They are all added in turn before the current iterator position.

When you use the add operation with an iterator that was freshly returned from the listIterator method and that points to the beginning of the linked list, the newly added element becomes the new head of the list. When the iterator has passed the last element of the list (that is, when hasNext returns false), the added element becomes the new tail of the list. If the linked list has n elements, there are n + 1 spots for adding a new element. These spots correspond to the n + 1 possible positions of the iterator. For example, if a linked list contains three elements, A, B, and C, there are four possible positions (marked as |) for inserting a new element:





Finally, a set method replaces the last element, returned by a call to next or previous, with a new element. For example, the following code replaces the first element of a list with a new value:

ListIterator<String> iter = tist.tistIterator();

String otdVatue =; // returns first element

iter.set(newVatue); // sets first element to newValue

As you might imagine, if an iterator traverses a collection while another iter­ator is modifying it, confusing situations can occur. For example, suppose an iterator points before an element that another iterator has just removed. The iterator is now invalid and should no longer be used. The linked list iter­ators have been designed to detect such modifications. If an iterator finds that its collection has been modified by another iterator or by a method of the collection itself, it throws a ConcurrentModificationException. For example, consider the following code:

List<String> list = . . .;

ListIterator<String> iter1 = list.listIterator();

ListIterator<String> iter2 = list.listIterator();;

iter1.remove();; // throws ConcurrentModificationException

The call to throws a ConcurrentModificationException since iter2 detects that the list was modified externally.

To avoid concurrent modification exceptions, follow this simple rule: You can attach as many iterators to a collection as you like, provided that all of them are only readers. Alternatively, you can attach a single iterator that can both read and write.

Concurrent modification detection is done in a simple way. The collection keeps track of the number of mutating operations (such as adding and remov­ing elements). Each iterator keeps a separate count of the number of mutating operations that it was responsible for. At the beginning of each iterator method, the iterator simply checks whether its own mutation count equals that of the collection. If not, it throws a ConcurrentModificationException.

Now you have seen the fundamental methods of the LinkedList class. Use a ListIterator to traverse the elements of the linked list in either direction and to add and remove elements.

As you saw in Section 9.2, “Interfaces in the Collections Framework,” on p. 492, many other useful methods for operating on linked lists are declared in the Collection interface. These are, for the most part, implemented in the AbstractCollection superclass of the LinkedList class. For example, the toString method invokes toString on all elements and produces one long string of the format [A, B, C]. This is handy for debugging. Use the contains method to check whether an element is present in a linked list. For example, the call staff.contains(“Harry”) returns true if the linked list already contains a string equal to the string “Harry”.

The library also supplies a number of methods that are, from a theoretical perspective, somewhat dubious. Linked lists do not support fast random access. If you want to see the nth element of a linked list, you have to start at the beginning and skip past the first n – 1 elements. There is no shortcut. For that reason, programmers don’t usually use linked lists in situations where elements need to be accessed by an integer index.

Nevertheless, the LinkedList class supplies a get method that lets you access a particular element:

LinkedList<String> list = . . .;

String obj = list.get(n);

Of course, this method is not very efficient. If you find yourself using it, you are probably using a wrong data structure for your problem.

You should never use this illusory random access method to step through a linked list. The code

for (int i = 0; i < list.size(); i++)

do something with list.get(i);

is staggeringly inefficient. Each time you look up another element, the search starts again from the beginning of the list. The LinkedList object makes no effort to cache the position information.

The list iterator interface also has a method to tell you the index of the current position. In fact, since Java iterators conceptually point between elements, it has two of them: The nextIndex method returns the integer index of the element that would be returned by the next call to next; the previousIndex method returns the index of the element that would be returned by the next call to previous. Of course, that is simply one less than nextIndex. These methods are efficient—an iterator keeps a count of its current position. Finally, if you have an integer index n, then tist.tistIterator(n) returns an iterator that points just before the element with index n. That is, calling next yields the same element as tist.get(n); obtaining that iterator is inefficient.

If you have a linked list with only a handful of elements, you don’t have to be overly paranoid about the cost of the get and set methods. But then, why use a linked list in the first place? The only reason to use a linked list is to minimize the cost of insertion and removal in the middle of the list. If you have only a few elements, you can just use an ArrayList.

We recommend that you simply stay away from all methods that use an integer index to denote a position in a linked list. If you want random access into a collection, use an array or ArrayList, not a linked list.

The program in Listing 9.1 puts linked lists to work. It simply creates two lists, merges them, then removes every second element from the second list, and finally tests the removeAtt method. We recommend that you trace the pro­gram flow and pay special attention to the iterators. You may find it helpful to draw diagrams of the iterator positions, like this:

Note that the call


prints all elements in the linked list a by invoking the toString method in AbstractCottection.

2. Array Lists

In the preceding section, you saw the List interface and the LinkedList class that implements it. The List interface describes an ordered collection in which the position of elements matters. There are two protocols for visiting the elements: through an iterator and by random access with methods get and set. The latter is not appropriate for linked lists, but of course get and set make a lot of sense for arrays. The collections library supplies the familiar ArrayList class that also implements the List interface. An ArrayList encapsulates a dynamically reallocated array of objects.

3. Hash Sets

Linked lists and arrays let you specify the order in which you want to arrange the elements. However, if you are looking for a particular element and don’t remember its position, you need to visit all elements until you find a match. That can be time consuming if the collection contains many elements. If you don’t care about the ordering of the elements, there are data structures that let you find elements much faster. The drawback is that those data structures give you no control over the order in which the elements appear. These data structures organize the elements in an order that is convenient for their own purposes.

A well-known data structure for finding objects quickly is the hash table. A hash table computes an integer, called the hash code, for each object. A hash code is somehow derived from the instance fields of an object, preferably in such a way that objects with different data yield different codes. Table 9.2 lists a few examples of hash codes that result from the hashCode method of the String class.

If you define your own classes, you are responsible for implementing your own hashCode method—see Chapter 5 for more information. Your implementation needs to be compatible with the equals method: If a.equats(b), then a and b must have the same hash code.

What’s important for now is that hash codes can be computed quickly and that the computation depends only on the state of the object that needs to be hashed, not on the other objects in the hash table.

In Java, hash tables are implemented as arrays of linked lists. Each list is called a bucket (see Figure 9.10). To find the place of an object in the table, compute its hash code and reduce it modulo the total number of buckets. The resulting number is the index of the bucket that holds the element. For example, if an object has hash code 76268 and there are 128 buckets, then the object is placed in bucket 108 (because the remainder 76268 % 128 is 108). Perhaps you are lucky and there is no other element in that bucket. Then, you simply insert the element into that bucket. Of course, sometimes you will hit a bucket that is already filled. This is called a hash collision. Then, compare the new object with all objects in that bucket to see if it is already present. If the hash codes are reasonably randomly distributed and the number of buckets is large enough, only a few comparisons should be necessary.

NOTE: As of Java 8, the buckets change from linked lists into balanced binary trees when they get full. This improves performance if a hash function was poorly chosen and yields many collisions, or if malicious code tries to flood a hash table with many values that have identical hash codes.

If you want more control over the performance of the hash table, you can specify the initial bucket count. The bucket count gives the number of buckets used to collect objects with identical hash values. If too many elements are inserted into a hash table, the number of collisions increases and retrieval performance suffers.

If you know how many elements, approximately, will eventually be in the table, you can set the bucket count. Typically, you should set it to somewhere between 75% and 150% of the expected element count. Some researchers believe that it is a good idea to make the bucket count a prime number to prevent a clustering of keys. The evidence for this isn’t conclusive, however. The standard library uses bucket counts that are powers of 2, with a default of 16. (Any value you supply for the table size is automatically rounded to the next power of 2.)

Of course, you do not always know how many elements you need to store, or your initial guess may be too low. If the hash table gets too full, it needs to be rehashed. To rehash the table, a table with more buckets is created, all elements are inserted into the new table, and the original table is discarded. The load factor determines when a hash table is rehashed. For example, if the load factor is 0.75 (which is the default) and the table is more than 75% full, it is automatically rehashed with twice as many buckets. For most applications, it is reasonable to leave the load factor at 0.75.

Hash tables can be used to implement several important data structures. The simplest among them is the set type. A set is a collection of elements without duplicates. The add method of a set first tries to find the object to be added, and adds it only if it is not yet present.

The Java collections library supplies a HashSet class that implements a set based on a hash table. You add elements with the add method. The contains method is redefined to make a fast lookup to see if an element is already present in the set. It checks only the elements in one bucket and not all elements in the collection.

The hash set iterator visits all buckets in turn. Since hashing scatters the ele­ments around in the table, they are visited in a seemingly random order. You would only use a HashSet if you don’t care about the ordering of the elements in the collection.

The sample program at the end of this section (Listing 9.2) reads words from, adds them to a set, and finally prints out the first twenty words in the set. For example, you can feed the program the text from Alice in Wonder­land (which you can obtain from by launching it from a command shell as

java SetTest < atice30.txt

The program reads all words from the input and adds them to the hash set. It then iterates through the unique words in the set and finally prints out a count. (Alice in Wonderland has 5,909 unique words, including the copyright notice at the beginning.) The words appear in random order.

4. Tree Sets

The TreeSet class is similar to the hash set, with one added improvement. A tree set is a sorted collection. You insert elements into the collection in any order. When you iterate through the collection, the values are automatically presented in sorted order. For example, suppose you insert three strings and then visit all elements that you added.

var sorter = new TreeSet<String>();




for (String s : sorter) System.out.println(s);

Then, the values are printed in sorted order: Amy Bob Cart. As the name of the class suggests, the sorting is accomplished by a tree data structure. (The cur­rent implementation uses a red-black tree. For a detailed description of red- black trees see, for example, Introduction to Algorithms by Thomas Cormen, Charles Leiserson, Ronald Rivest, and Clifford Stein, The MIT Press, 2009.) Every time an element is added to a tree, it is placed into its proper sorting position. Therefore, the iterator always visits the elements in sorted order.

Adding an element to a tree is slower than adding it to a hash table—see Table 9.3 for a comparison. But it is still much faster than checking for dupli­cates in an array or linked list. If the tree contains n elements, then an average of log2 n comparisons are required to find the correct position for the new element. For example, if the tree already contains 1,000 elements, adding a new element requires about 10 comparisons.

If you look back at Table 9.3, you may well wonder if you should always use a tree set instead of a hash set. After all, adding elements does not seem to take much longer, and the elements are automatically sorted. The answer depends on the data that you are collecting. If you don’t need the data sorted, there is no reason to pay for the sorting overhead. More important, with some data it is much more difficult to come up with a sort order than a hash function. A hash function only needs to do a reasonably good job of scram­bling the objects, whereas a comparison function must tell objects apart with complete precision.

To make this distinction more concrete, consider the task of collecting a set of rectangles. If you use a TreeSet, you need to supply a Comparator<Rectangte>. How do you compare two rectangles? By area? That doesn’t work. You can have two different rectangles with different coordinates but the same area. The sort order for a tree must be a total ordering. Any two elements must be comparable, and the comparison can only be zero if the elements are equal. There is such a sort order for rectangles (the lexicographic ordering on its coordinates), but it is unnatural and cumbersome to compute. In contrast, a hash function is already defined for the Rectangle class. It simply hashes the coordinates.

The program in Listing 9.3 builds two tree sets of Item objects. The first one is sorted by part number, the default sort order of Item objects. The second set is sorted by description, using a custom comparator.

5. Queues and Deques

As we already discussed, a queue lets you efficiently add elements at the tail and remove elements from the head. A double-ended queue, or deque, lets you efficiently add or remove elements at the head and tail. Adding elements in the middle is not supported. Java 6 introduced a Deque interface. It is imple­mented by the ArrayDeque and LinkedList classes, both of which provide deques whose size grows as needed. In Chapter 12, you will see bounded queues and deques.

6. Priority Queues

A priority queue retrieves elements in sorted order after they were inserted in arbitrary order. That is, whenever you call the remove method, you get the smallest element currently in the priority queue. However, the priority queue does not sort all its elements. If you iterate over the elements, they are not necessarily sorted. The priority queue makes use of an elegant and efficient data structure called a heap. A heap is a self-organizing binary tree in which the add and remove operations cause the smallest element to gravitate to the root, without wasting time on sorting all elements.

Just like a TreeSet, a priority queue can either hold elements of a class that implements the Comparable interface or a Comparator object you supply in the constructor.

A typical use for a priority queue is job scheduling. Each job has a priority. Jobs are added in random order. Whenever a new job can be started, the highest priority job is removed from the queue. (Since it is traditional for priority 1 to be the “highest” priority, the remove operation yields the minimum element.)

Listing 9.5 shows a priority queue in action. Unlike iteration in a TreeSet, the iteration here does not visit the elements in sorted order. However, removal always yields the smallest remaining element.

Source: Horstmann Cay S. (2019), Core Java. Volume I – Fundamentals, Pearson; 11th edition.

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