Can you implement a map with a tree what about with a list

Here we've got all sorted Cats from Boris to Snowy in alphabetical order. Sure we can do the same with a HashMap, but we should code all the logic of sorting and so on.

HashMap is not ordered, while TreeMap sorts by key. How items are stored depends on the hash function of the keys and seems to be chaotic. TreeMap, which implements not only Map but also NavigableMap automatically sorts pairs by their keys natural orders according to their compareTo method or an externally supplied Comparator.

HashMap is faster and provides average constant time performance O 1 for the basic operations get and put , if the hash function disperses the elements properly among the buckets. It usually works as is, but in reality sometimes collisions happen. In this case HashMap handles collision using a linked list to store collided elements and performance reduces up to O n. To improve the performance in case of frequent collisions, in JDK 8 is used balanced tree instead of linked list.

JDK8 switches to balanced tree in case of more than 8 entries in one bucket, it improves the worst-case performance from O n to O log n. According to its structure, HashMap requires more memory than just to keep its elements. The performance of a hash map depends on two parameters — Initial Capacity and Load Factor.

The Initial Capacity is a quantity of buckets of a new created HashMap. The load factor measures a percentage of fullness. The default initial capacity is 16 and default load factor is 0. We can change these values.

Thus, HashMap almost always works faster than TreeMap. The larger the object that's stored, the faster HashMap will be in comparison to TreeMap. However, a TreeMap uses the optimal amount of memory to hold its items, unlike a HashMap. HashMap allow you to store one null key and multiple null values.

It keeps entry with a null key in index[0] of an internal bucket. HashMap also allows storing many null values. In output we'll get a HashMap with three elements, first with a null key and value, second is an "ordinary" one, and the third with a null value as well. TreeMap sorts elements in natural order and doesn't allow null keys because compareTo method throws NullPointerException if compared with null. If you are using TreeMap with user-defined Comparator , work with null entries depends on the implementation of compare method.

HashMap is a general purpose Map implementation. It provides a performance of O 1 , while TreeMap provides a performance of O log n to add, search, and remove items.

Hence, HashMap is usually faster. A TreeMap uses memory way more effective so it is a good Map implementation for you if you are not sure of elements quantity that have to be stored in memory. Can they be implemented using a BST? What is the storage complexity of a BST? Under which conditions will these operations be faster using a BST , compared with just using, say, a linked list? Add a comment. Active Oldest Votes. Improve this answer.

Jack Jack k 27 27 gold badges silver badges bronze badges. Now the term is rather smokey since some languages pretend to call their maps array because they allow to map any kind of key with a value , but they are not array, they are maps. Then you have to take into account the memory complexity, you have to take into account the fact that and hashmap needs rehashing when going over its load factor and most importantly a BST can manage ordering, while a hashmap can't.

You can improve worst-case with balanced trees but that's not the point. Specified in sense of "provable through coputational complexity theory", another issue which is not the point of the answer and is irrelevant to the OP.

Show 1 more comment. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Having allocated massive amounts of memory is impractical. So, we can automatically have the hash map resize itself based on a load factor. The load factor is the measurement of how full is a hash map.

We can get the load factor by dividing the number of items by the bucket size. Pay special attention to lines 96 to We create a new HashMap with doubled capacity.

Take notice that after we add the 12th item, the load factor gets beyond 0. Also, you can see how the number of collisions improves from 2 to 0! We have a decent hash function that produces different outputs for different data. Two distinct data will never return the same code.

Also, we have a rehash function that automatically grows the capacity as needed. Inserting an element on a HashMap requires two things: a key and a value. We could use our DecentHashMap data structure that we develop or use the built-in as follows:.

Also, Map s keeps the order of insertion. Behind the scenes, the Map. So, similar to Array. Insert an element in HashMap runtime is O 1. If rehash is needed, then it will take O n. Our implementation with rehash functionality will keep collisions to the minimum.

This is the HashMap. But, we know there will be collisions. If the initial capacity is too small and the hash function is terrible like NaiveHashMap. HashMap access operation has a runtime of O 1 on average and worst-case of O n. Advanced Note: Another idea to reduce the time to get elements from O n to O log n is to use a binary search tree instead of an array. Editing HashMap. In the case of many collisions, we could face an O n as a worst-case.

However, with our rehash operation, we can mitigate that risk. HashMap edits and delete operations has a runtime of O 1 on average and worst-case of O n. How can we implement a Set Array without duplicates? We could use an array and check if an element is there before inserting a new one. But the running time of checking if a value is already there is O n.

Can we do better than that? We develop the Map with an amortized run time of O 1! We could use the JavaScript built-in Set. We are going to use the optimized HashMap with rehash functionality. We used HashMap. Checking if an element is already there can be done using the hashMap. Most operations would be an amortized constant time except for getting the entries , O n. Note: The JS built-in Set. You can see the Set.

You should be able to use MySet and the built-in Set interchangeably for these examples. From our Set implementation using a HashMap, we can sum up the time complexity as follows very similar to the HashMap :. The linked list is the first data structure that we are going to implement without using an array. Instead, we will use a node that holds a value and points to the next element.

When we have a chain of nodes where each one points to the next one, we a Singly Linked list. If it is the first element, then adding to the root is O 1. However, finding the last item is O n. Now, removing an element from the end of the list has a similar code. We have to find the current before last and make its next reference null. The runtime again is O n because we have to iterate until the second-last element and remove the reference to the last line Adding and removing elements from the beginning is a constant time because we hold a reference to the first element:.

Removing an element anywhere in the list leverage the removeLast and removeFirst. However, if the removal is in the middle, then we assign the previous node to the next one. That removes any reference from the current node, this is removed from the list:. Note that index is a zero-based index: 0 will be the first element, 1 second, and so on. When we have a chain of nodes where each one points to the next one, we have a Singly Linked list. When we have a linked list where each node leads to the next and the previous element, we have a Doubly Linked List.

Doubly linked list nodes have double references next and previous. We are also going to keep track of the list first and the last element. Adding and removing from the start of the list is simple since we have this. Adding and removing from the end of the list is a little tricky. If you checked in the Singly Linked List, both operations took O n since we had to loop through the list to find the last element.

Now, we have the last reference:. Again, we have to be careful about updating the references and handling exceptional cases such as only one element. Using a doubly-linked list, we no longer have to iterate through the whole list to get the 2nd last element.

We can use directly this. Did you remember that for the Queue, we had to use two arrays? We can now change that implementation and use a doubly-linked list instead. The runtime will be O 1 for insert at the start and deleting at the end. Adding an element on anywhere on the list leverages our addFirst and addLast functions as you can see below:.

If we have an insertion in the middle of the Array, then we have to update the next and previous reference of the surrounding elements. Doubly linked lists are a significant improvement compared to the singly linked list! We improved from O n to O 1 by:.

Stacks is a data structure where the last entered data is the first to come out. As you can see, it is easy since we are using the built-in Array.

Both have a runtime of O 1. The first element in a is the last to get out. We can also implement Stack using a linked list instead of an array. The runtime will be the same. Queues are a data structure where the first data to get in is also the first to go out.

A naive implementation would be this one using Array. Think of how you can implement a Queue only using Array. When we remove something for the first time, the output array is empty. So, we insert the content of input backward like ['b', 'a']. Then we pop elements from the output array.