Hash Table In Java
Contents
What is a hash table Java?
Conclusion –
This article covered the characteristics of the Java Hashtable class, its constructors, methods, and actual use cases. Hash tables are used to store and retrieve data (or records) quickly. Hashtables store the records in buckets using hash keys. Java Hashtable implements the Serializable and Cloneable interfaces but not the random access interface. Java Hashtable does not support null for both keys and values. In a hashtable, every method is synchronized. Hence, the Hashtable object is threadsafe. Although Hashtable is threadsafe, it performs poorly when used with multiple threads.
What is the difference between HashMap and hash table in Java?
HashMap is nonsyncronized and is not thread safe while HashTable is thread safe and is synchronized. HashMap allows one null key and values can be null whereas HashTable doesn’t allow null key or value. HashMap is faster than HashTable. HashMap iterator is failsafe where HashTable iterator is not failsafe. HashMap extends AbstractMap class where HashTable extends Dictionary class.
Is HashMap and Hashtable same?
 Differences between HashMap and HashTable in Java and store key and value pairs in a hash table. When using a Hashtable or HashMap, we specify an object that is used as a key and the value that you want to be linked to that key. The key is then hashed, and the resulting hash code is used as the index at which the value is stored within the table. Now let us discuss with the help of an example. Hashmap vs Hashtable
 HashMap is nonsynchronized. It is not threadsafe and can’t be shared between many threads without proper synchronization code whereas Hashtable is synchronized. It is threadsafe and can be shared with many threads.
 HashMap allows one null key and multiple null values whereas Hashtable doesn’t allow any null key or value.
 HashMap is generally preferred over HashTable if thread synchronization is not needed.
 class Ideone
 HashMap hm= new HashMap ();
 hm.put( 100, “Amit” );
 hm.put( 104, “Amit” );
 hm.put( 101, “Vijay” );
 hm.put( 102, “Rahul” );
 }
 }
 Last Updated : 23 Jan, 2022
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 Obsolete: The Hashtable class is considered obsolete and its use is generally discouraged. This is because it was designed prior to the introduction of the Collections framework and does not implement the Map interface, which makes it difficult to use in conjunction with other parts of the framework.
 Limited functionality: The Hashtable class provides basic keyvalue data structure functionality, but does not provide the full range of functionality that is available in the Map interface and its implementations.
 Poor performance: The Hashtable class is synchronized, which can result in slower performance compared to other implementations of the Map interface, such as HashMap or ConcurrentHashMap.
 The Hash table does not allow null value/key.
 When using this you specify an object that is used as a key & the value that you want to associate with the key.
 It is an array of an index.
 Each index is known as a bucket/slot.
 It restrains values based on keys & the place of bucket recognized by calling the Hash code () method.
 To find an item in a list you do the first approach i.e.
 Linear search this involves checking each item, it will take more time.
 Suppose you have retrieved a value and its index number of an array, then you will look up a value very quickly.
 In fact, a time will be taken less if you know the index number is independent of the size & position of the array.
 Name = Mia
 M 77
 i 105
 a 97
 Sum = 279
 Modulus = 279 % 5 = 4
 Mia ⇾ M (77) + i(105) + a(97) = 279 % 5 = 4
 Tim ⇾ T (84) + i(105) + m(77) = 298 % 5 = 3
 Leo ⇾ L(76) + e(101) + n(110) = 287 % 5 = 2
 Som ⇾ S(83) + o(111) + m(77) = 271 % 5 = 1
 Beb ⇾ B(66) + e(101) + b(98) = 265 % 5 0
 Find ASCII for Ada = (65+100+97) = 262
 262 % 11 = 9
 MyData = Array (9)
 Folding method divides key into equal parts then adds the parts together
 Phone No.018767242947 becomes 01+87+67+24+29+47= 255
 Depending on the size of a table, may then divide by some constant and take the remainder.
 There are lots of Hash functions some appropriate to another depending on nature.
 Minimize collisions
 Uniform distribution of hash values
 Easy to calculate
 Resolve any collisions
 Reflexive: For a single object say a, a.equal(a) return true.
 Symmetric: For two different objects, a.equal(y) return true if and only if y.equal(a) returns true.
 Transitive: For multiple objects a, b, c if a.equal(b) returns true & b.equals(c) returns true then a.equals(c) should return true.
 If the object properties are modified means used in equal() method implementation this will result in different and multiple of a.equal(b) will return equal outcome.
 Object class equal() will return true only when two references will mark similar objects.
 public class HashCodeExample
 }
 A base class has attributes for objects for user define.
 By using @override the override hashCode() function can be predefined.
 In a similar way, the override equal() function can be predefined.
 Declare an object of the base class and use hash code and equals function so that objects would be equal in the main function.
 During the execution of the application, if is invoked more than once on the same Object then it must consistently return the same Integer value, provided no information used in equals(Object) comparison on the Object is modified. It is not necessary that this Integer value remain the same from one execution of the application to another execution of the same application.
 If two objects are equal, according to the equals(Object) method, then hashCode() method must produce the same Integer on each of the two Objects.
 If two Objects are unequal, according to the equals(Object) method, It is not necessary the Integer value produced by hashCode() method on each of the two Objects will be distinct. It can be the same but producing the distinct Integer on each of the two Objects is better for improving the performance of hashing based Collections like, HashTable, etc.
 public class GFG
 }
 public class HashMapExample }
 Implementation: HashMap implements and HashSet implements interface.
 Duplicates: HashSet doesn’t allow duplicate values. HashMap stores key, value pairs and it does not allow duplicate keys. If the key is duplicate then the old key is replaced with the new value.
 Number of objects during storing objects: HashMap requires two objects put(K key, V Value) to add an element to HashMap object, while HashSet requires only one object add(Object o)
 Dummy value: In HashMap no concept of dummy value, HashSet internally uses HashMap to add elements. In HashSet, the argument passed in add(Object) method serves as key K. Java internally associates dummy value for each value passed in add(Object) method.
 Storing or Adding mechanism: HashMap internally uses hashing to store or add objects, HashSet internally uses HashMap object to store or add the objects.
 Speed: HashSet is slower than HashMap.
 Insertion HashMap uses the put() method for storing data, While in HashSet use add() method for add or storing data.
 Last Updated : 06 Apr, 2023
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 The HashMap class is roughly equivalent to Hashtable, except that it is non synchronized and permits nulls. ( HashMap allows null values as key and value whereas Hashtable doesn’t allow nulls).
 HashMap does not guarantee that the order of the map will remain constant over time.
 HashMap is non synchronized whereas Hashtable is synchronized.
 Iterator in the Hashtable is failsafe because enumerator for the Hashtable is not throw ConcurrentModificationException if any other Thread modifies the map structurally by adding or removing any element except Iterator ‘s own remove() method. But this is not a guaranteed behavior and will be done by JVM on best effort.
 Synchronized means only one thread can modify a hash table at one point of time. Basically, it means that any thread before performing an update on a hashtable will have to acquire a lock on the object while others will wait for lock to be released.
 Failsafe is relevant from the context of iterators. If an iterator has been created on a collection object and some other thread tries to modify the collection object “structurally”, a concurrent modification exception will be thrown. It is possible for other threads though to invoke “set” method since it doesn’t modify the collection “structurally”. However, if prior to calling “set”, the collection has been modified structurally, ” IllegalArgumentException ” will be thrown.
 Structurally modification means deleting or inserting element which could effectively change the structure of map.
 Syncronized →Being synchronized means that operation is thread safe, so only one thread can access the table at a time.
 Fail safe – Failfast means when you try to modify the content when you are iterating, it will throw ConcurrentModification Exception and fail immediately.
 For HashMap Itration:
 In universities, each student is assigned a unique roll number that can be used to retrieve information about them.
 In libraries, each book is assigned a unique number that can be used to determine information about the book, such as its exact position in the library or the users it has been issued to etc.
 However, in cases where the keys are large and cannot be used directly as an index, you should use hashing,
 In hashing, large keys are converted into small keys by using hash functions,
 The values are then stored in a data structure called hash table,
 The idea of hashing is to distribute entries (key/value pairs) uniformly across an array.
 An element is converted into an integer by using a hash function. This element can be used as an index to store the original element, which falls into the hash table.
 The element is stored in the hash table where it can be quickly retrieved using hashed key. hash = hashfunc(key) index = hash % array_size
 Easy to compute: It should be easy to compute and must not become an algorithm in itself.
 Uniform distribution: It should provide a uniform distribution across the hash table and should not result in clustering.
 Less collisions: Collisions occur when pairs of elements are mapped to the same hash value. These should be avoided. Note : Irrespective of how good a hash function is, collisions are bound to occur. Therefore, to maintain the performance of a hash table, it is important to manage collisions through various collision resolution techniques.
 Implementation of hash tables with separate chaining (open hashing)
 Assumption
 Hash function will return an integer from 0 to 19.
 index = index % hashTableSize index = (index + 1) % hashTableSize index = (index + 2) % hashTableSize
 index = (index + 3) % hashTableSize
 and so on
 There are no more than 20 elements in the data set.
 Hash function will return an integer from 0 to 19.
 Data set must have unique elements.
 Let us assume that the hashed index for an entry is index and at index there is an occupied slot. The probe sequence will be as follows:
 index = index % hashTableSize index = (index + 1 2 ) % hashTableSize index = (index + 2 2 ) % hashTableSize index = (index + 3 2 ) % hashTableSize
 and so on
 Implementation of hash table with quadratic probing
 Assumption
 There are no more than 20 elements in the data set.
 Hash function will return an integer from 0 to 19.
 Data set must have unique elements.
 index = (index + 1 * indexH) % hashTableSize; index = (index + 2 * indexH) % hashTableSize;
 and so on
 Here, indexH is the hash value that is computed by another hash function.
 Implementation of hash table with double hashing
 Assumption
 There are no more than 20 elements in the data set.
 Hash functions will return an integer from 0 to 19.
 Data set must have unique elements.
 Associative arrays : Hash tables are commonly used to implement many types of inmemory tables. They are used to implement associative arrays (arrays whose indices are arbitrary strings or other complicated objects).
 Database indexing : Hash tables may also be used as diskbased data structures and database indices (such as in dbm).
 Caches : Hash tables can be used to implement caches i.e. auxiliary data tables that are used to speed up the access to data, which is primarily stored in slower media.
 Object representation : Several dynamic languages, such as Perl, Python, JavaScript, and Ruby use hash tables to implement objects.
 Hash Functions are used in various algorithms to make their computing faster
 Hashtable is used for fast lookup of data, storing data in dynamic ways, or just storing it in compact ways, which makes it more efficient compared to other solutions, such as arrays or linked lists,
 A Hashtable is often used in programming languages, such as Java, to store data in a way that is easy to retrieve.
 Hashing is an example of a spacetime tradeoff,
 If memory is infinite, the entire key can be used directly as an index to locate its value with a single memory access.
 On the other hand, if infinite time is available, values can be stored without regard for their keys, and a binary search or linear search can be used to retrieve the element.
S. No.  Hashmap  Hashtable 

1.  No method is synchronized.  Every method is synchronized. 
2.  Multiple threads can operate simultaneously and hence hashmap’s object is not threadsafe.  At a time only one thread is allowed to operate the Hashtable’s object. Hence it is threadsafe. 
3.  Threads are not required to wait and hence relatively performance is high.  It increases the waiting time of the thread and hence performance is low. 
4.  Null is allowed for both key and value.  Null is not allowed for both key and value. Otherwise, we will get a null pointer exception. 
5.  It is introduced in the 1.2 version.  It is introduced in the 1.0 version. 
6.  It is nonlegacy.  It is a legacy. 
Now you must be wondering why HashTable doesn’t allow null and HashMap do ? The answer is simple. In order to successfully store and retrieve objects from a HashTable, the objects used as keys must implement the hashCode method and the equals method. Since null is not an object, it can’t implement these methods.
import java.util.*; import java.lang.*; import java.io.*;
System.out.println( “Hash map” ); for (Map.Entry m:hm.entrySet()) 
Output Hash table 103 Rahul 102 Ravi 101 Vijay Hash map 100 Amit 101 Vijay 102 Rahul 104 Amit This article is compiled by Aditya Goel, Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
: Differences between HashMap and HashTable in Java
Why Hashtable is not used in Java?
Disadvantages of Hashtable: –
What does hash () do in Java?
Overview – Hashing in Java is a technique for mapping data to a secret key, that can be used as a unique identifier for data. It employs a function that generates those keys from the data; this function is known as the Hashfunction, and the output of this function (keys) is known as Hashvalues,
How to create a hash key in Java?
In hashing there is a hash function that maps keys to some values. But these hashing function may lead to collision that is two or more keys are mapped to same value. Chain hashing avoids collision. The idea is to make each cell of hash table point to a linked list of records that have same hash function value.
Let’s create a hash function, such that our hash table has ‘N’ number of buckets. To insert a node into the hash table, we need to find the hash index for the given key. And it could be calculated using the hash function. Example: hashIndex = key % noOfBuckets Insert : Move to the bucket corresponds to the above calculated hash index and insert the new node at the end of the list.
Delete : To delete a node from hash table, calculate the hash index for the key, move to the bucket corresponds to the calculated hash index, search the list in the current bucket to find and remove the node with the given key (if found). Please refer Hashing  Set 2 (Separate Chaining) for details.
How to generate hash using sha256 in Java?
In Java, we can use MessageDigest to get a SHA256 or SHA3256 hashing algorithm to hash a string. MessageDigest md = MessageDigest. getInstance(‘SHA3256’); byte result = md. digest(input);
Why HashMap is faster than HashTable?
HashMap is not synchronized, therefore it’s faster and uses less memory than Hashtable. Generally, unsynchronized objects are faster than synchronized ones in a single threaded application.
Why is HashMap used instead of HashTable?
HashTable – A hashtable is a class that is a part of the Java collection framework. It implements a hash table, and it stores the data in key value pair (key, value), Inside a hashtable, we can store any non null object like a key or a value, that is, null values and keys are not allowed in hashtable.
To store and retrieve objects successfully from a hashtable, the objects used as a key must implement the hashCode method and the equals method, that is the object which is used as a key is hashed, and the resulting hashCode which we get after hashing the object key is used as an index to get the value which is associated with the object key.
Although HashMap and HashTable are similar, there are certainly notable differences between them, let us discuss them. Syntax : Example : Output :
How HashTable works internally in Java?
How HashTable Works Internally in Java? is a kind of Hash map but is synchronized. Hash map is non–synchronized, permits one null key & multiple null values, notthread safe i.e. cannot share between many threads without proper synchronization, the key/values pairs are stored in Hashtable.
The Hash table of Java holds a class that has unique elements. Working of Hashtable Hash table intrinsically contains a slot/bucket in which the storage of key and value pair. It uses the key’s hash code to discover which bucket the key/value of a set should map.
But how do you know which element of the array contains the value you are looking for. The answer is by calculating the value itself the index number is somewhat related to data. Let’s proliferate the array, take a name and find its ASCII code, for example, we will take a name say Mia find ASCII code for each letter & add the ASCII code and divide the combined ASCII number with the size of an element in this it is 5, we will take a reminder of the element that is 4 and there we will put the name Mia i.e.
And now we can add more names to the Bucket as shown below:
Index number = sum of ASCII codes % Size of array Let’s retrieve an item say Ada we perform the same calculation,
This is a fast array lookup. Rather than storing individual items or data hash tables are used to store the keyvalue pairs. For example, Ada would be the key for which want to calculate the Index and DOB of the corresponding value, for this reason, a hash table of keyvalue pair is sometimes referred to as a hash map in fact if an objectoriented approach is taken each person in an instance of a class and the key would be of many properties, by populating an array with objects you will effectively store muchrelated data you like for each bucket A hashing algorithm also is known as a Hash function. The hash function is a function that generates a table when given a key. This is a function to obtain a bucket location from the Key’s hash code. It every time returns a digit for an object. Calculation applied to a key to transforming it into an address.
Collisions in a Hashtable So far you have seen, there are a lot of a hash table with data that is very conveniently causing any problem say that is unrealistic sometimes if you apply a hash function in two different cases it generates the same index number for both but both items can’t go in the same place this is called Collision.
Two identical objects every time have the same digits while two unequal objects might not always have dissimilar digits. Putting an object to the hash table, there is a possibility that dissimilar objects might have an equal hash code called Collision. For rectifying this array of lists is used by a hash table.
The set mapped to array index/single bucket and is kept in an index & index reference is stored in array index. Let’s load up the array again this time with a different set of data. As shown below if Mia is occupying position 4 and Leo to position 2 and if the other names too have the same entry at position 2 then automatically the next bucket will be filled with that entry for avoiding collision as shown below. As the reminder for an ASCII code of Rae is 5 but is put in slot 8 because due to collision the 2 items figure out to be in the same slot but for avoiding this the Rae is shifted to other slot and as the other slot is again occupied so it is shifted to the 8th position.
Resolving a collision by putting the item somewhere else then its calculated address is called OPEN ADDRESSING because every location is open to an item. This can use a variety of techniques to decide where to place an item this particular addressing technique is called LINEAR PROBING if the calculated address is occupied then the linear search is used to find the next available bucket if the linear probing comes to an end and still cannot find a position it might cycle around the beginning of array & continue searching from there.
The more items there are in a hash table the more chances of a collision. One way to deal with this is making the hash table bigger for the total amount of data you are expecting perhaps such that happen 70% of the table is occupied the ratio between the no of items stored and the size of an array is known as Load Factor Load Factor = T otal number of items stored / Size of the array Another way to do with collisions is known as chains sometimes referred to as CLOSED ADDRESSING.
Methods like hashCode () & equal () to find exact element match equal(): Object class define as a given language of Java. In which objects indicate whether few objects pass as an argument that is “equal to” the present instance. The different or two objects can be similar only if they are put on an equal memory address.
Note: For any nonnull reference value a, a.equals(null) should return false. hashCode(): In this object gives back an integer depiction of an object’s memory address. By using this method a random integer will get returned that is unique for a particular instance.
import java.util.*;

Output 144 Steps
Contract of hash code() and equal()
Note: Equal objects must produce the same hash code as long as they are equal, however unequal objects need not produce distinct hash codes.
Why is HashMap faster?
hashmap lookups are instant (“constant time”) – Let’s look at this if statement from our fast program: if y in set1: result.append(y) You might think that this check – if y in set1 – would be slower if the set1 contains 10 million elements than it is if set1 contains 1000 elements.
Can HashMap have duplicate keys?
 Difference between HashMap and HashSet – GeeksforGeeks HashSet is an implementation of Set Interface which does not allow duplicate value. The main thing is, objects that are stored in HashSet must override equals() for check for equality, and methods for no duplicate value are stored in our set. HashMap is an implementation of, which maps a key to value. Duplicate keys are not allowed in a Map. Basically, Map Interface has two implementation classes HashMap and the main difference is TreeMap maintains an order of the objects but HashMap will not. HashMap allows null values and null keys. Both HashSet and HashMap are not synchronized. Now let us formulate the difference between and as provided in a tabular manner below as follows:
Basic  HashSet  HashMap 

Implements  Set interface  Map interface 
Duplicates  No  Yes duplicates values are allowed but no duplicate key is allowed 
Dummy values  Yes  No 
Objects required during an add operation  1  2 
Adding and storing mechanism  HashMap object  technique 
Speed  It is comparatively slower than HashMap  It is comparatively faster than HashSet because of hashing technique has been used here. 
Null  Have a single null value  Single null key and any number of null values 
Insertion Method  Only one value is required for the insertion process. Add() function is used for insertion  Two values are required for the insertion process. Put() function is used for insertion. 
Data storage  The data is stored as objects.  The data is stored as keyvalue pair. 
Complexity  O(n)  O(1) 
Let us grasp understanding by peeking into internal working with help of clean java programs. Example 1: HashSet
import java.util.HashSet;

Output Before adding duplicate values After adding duplicate values After adding null values Example 2: HashMap
import java.util.HashMap;

Output: HashMap object output : After inserting duplicate key : From the above two outputs after going through an understanding of their internal working, now we can talk about conceptual differences which are as follows:
Let us wrap up with an example HashSet is a set, e.g., HashMap is a key > value pair(key to value) map, e.g.
: Difference between HashMap and HashSet – GeeksforGeeks
Why HashMap is not synchronized in Java?
Hashmap doesn’t uses hashtable in any way. hashmap is not synchronized because it was never meant to be, it’s simple implementation of maintaining a bucket of hashcode and a linkedlist assigned to that hashcode with keyvalue pair as nodes.
Why Hashtable is fail safe?
Difference between HashMap and HashTable in Java
Note on Some Important Terms
Parameter  Hashmap  Hashtable 

Synchronized  Not Syncronized  Syncronized 
Null key and Value  Allows one null key and any number of null values  Not allow null keys or values 
Fail fast  Iterator in Hashmap is fail fast  Enumeration in Hashtable is not fail fast 
ThreadSafe  No  Yes 
Extends  Extends AbstractMap class  Extends Dictionary class 
Performance  Faster then Hashtable  Slower then Hashmap 
Note – You can synchonized a HashMap with the help of Collections.synchonizedMap(hashmap) Map map=Collections.synchonizedMap(hashmap)
Set keys = hashMap.keySet(); for (Object key : key) For HashTable Enumeration: Enumeration keys = hashTable.keys(); for (Enumeration e = v.elements() ; e.hasMoreElements() ; e.nextElement()) > >> Dinesh Rajput is the chief editor of a website Dineshonjava, a technical blog dedicated to the Spring and Java technologies. It has a series of articles related to Java technologies. Dinesh has been a Spring enthusiast since 2008 and is a Pivotal Certified Spring Professional, an author of a book Spring 5 Design Pattern, and a blogger. He has more than 10 years of experience with different aspects of Spring and Java design and development. His core expertise lies in the latest version of Spring Framework, Spring Boot, Spring Security, creating REST APIs, Microservice Architecture, Reactive Pattern, Spring AOP, Design Patterns, Struts, Hibernate, Web Services, Spring Batch, Cassandra, MongoDB, and Web Application Design and Architecture. He is currently working as a technology manager at a leading product and web development company. He worked as a developer and tech lead at the Bennett, Coleman & Co. Ltd and was the first developer in his previous company, Paytm. Dinesh is passionate about the latest Java technologies and loves to write technical blogs related to it. He is a very active member of the Java and Spring community on different forums. When it comes to the Spring Framework and Java, Dinesh tops the list! : Difference between HashMap and HashTable in Java
Why are hash tables bad?
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Page ID 47921 Although hash table lookups use constant time on average, the time spent can be significant. Evaluating a good hash function can be a slow operation. In particular, if simple array indexing can be used instead, this is usually faster. Hash tables in general exhibit poor locality of reference—that is, the data to be accessed is distributed seemingly at random in memory.
Because hash tables cause access patterns that jump around, this can trigger microprocessor cache misses that cause long delays. Compact data structures such as arrays, searched with linear search, may be faster if the table is relatively small and keys are cheap to compare, such as with simple integer keys.
According to Moore’s Law, cache sizes are growing exponentially and so what is considered “small” may be increasing. The optimal performance point varies from system to system; for example, a trial on Parrot shows that its hash tables outperform linear search in all but the most trivial cases (one to three entries).
More significantly, hash tables are more difficult and errorprone to write and use. Hash tables require the design of an effective hash function for each key type, which in many situations is more difficult and timeconsuming to design and debug than the mere comparison function required for a selfbalancing binary search tree.
In openaddressed hash tables it’s even easier to create a poor hash function. Additionally, in some applications, a black hat with knowledge of the hash function may be able to supply information to a hash which creates worstcase behavior by causing excessive collisions, resulting in very poor performance (i.e., a denial of service attack).
Why use Hashtable over array?
Comparison of an Array and Hash table in terms of Storage structure and Access time complexity and are two of the most widely used data structures in computer science, both serving as efficient solutions for storing and accessing data in Java. They have different storage structures and time complexities, making them suitable for different use cases.
Arrays in Java are fixedsized, contiguous blocks of memory that store elements in a linear fashion. Each element in an array can be accessed by its index, which is simply the position of the element in the array. This means that arrays are great for scenarios where fast random access is required, as accessing an element in an array has a constant time complexity in Java. Hash Tables in Java, on the other hand, are implemented using the HashMap class, which uses a hash function to map keys to indices in an array, also known as buckets, where the actual values are stored. The hash function is used to calculate an index for each key and store the corresponding value in the corresponding bucket. Hash tables are more flexible than arrays, as they allow for dynamic resizing to accommodate new data, but they also have more overhead in terms of memory usage.
Access Time Complexity: refers to the amount of time it takes to access a specific piece of data within a data structure
In terms of access time complexity, arrays in Java have a constant time complexity for accessing elements by index, meaning that the time it takes to access an element does not increase as the size of the array grows. This is because the index of each element is known, and the corresponding value can be accessed directly. Hash Tables in Java, on the other hand, have an average constant time complexity for accessing elements by key, but in the worstcase scenario, the time complexity can be linear due to hash collisions. occur when two or more keys map to the same index in the hash table, causing a linear search through the list of values stored at that index to find the desired value. To mitigate the impact of hash collisions, the should distribute the keys evenly across the indices in the hash table.
Operations:  Array Time Complexity  Hash Table (HashMap) Time Complexity 

Index Access  O(1) (Constant)  N/A 
Key Access  N/A  O(1) Average, O(n) Worst Case 
Lookup  O(n) (Linear)  O(1) Average, O(n) Worst Case 
Search  O(n) (Linear)  O(n) Worst Case 
Insertion  O(n) (Linear)  O(1) Average, O(n) Worst Case 
Deletion  O(n) (Linear)  O(1) Average, O(n) Worst Case 
In conclusion, both arrays and hash tables have their strengths and weaknesses in Java, making them suitable for different use cases. Arrays are great for scenarios where fast random access and fixedsize storage are required, while hash tables are more suitable for scenarios where fast access to elements by key is required and memory usage is not as much of a concern.
Last Updated : 02 Feb, 2023 Like Article Save Article
: Comparison of an Array and Hash table in terms of Storage structure and Access time complexity
Does Java have hash?
Using a hash function, you can change the value of a given key to something else. A mathematical procedure generates a new value using a hash function. A hash value, or simply a hash, is the outcome of a hash function. In Java, one of the most basic computer science concepts is “hashing”.
What is the best hashing algorithm in Java?
The SHA256 algorithm generates an almost unique, fixedsize 256bit (32byte) hash. This is a oneway function, so the result cannot be decrypted back to the original value. Currently, SHA2 hashing is widely used, as it is considered the most secure hashing algorithm in the cryptographic arena.
Why do we need hash?
Data retrieval – Hashing uses functions or algorithms to map object data to a representative integer value. A hash can then be used to narrow down searches when locating these items on that object data map. For example, in hash tables, developers store data – perhaps a customer record – in the form of key and value pairs.
insert (key, value) get (key) delete (key)
Diagram illustrating how a hash table works.
What is a hash table?
Basics of Hash Tables Tutorials & Notes  Data Structures Hashing is a technique that is used to uniquely identify a specific object from a group of similar objects. Some examples of how hashing is used in our lives include:
In both these examples the students and books were hashed to a unique number. Assume that you have an object and you want to assign a key to it to make searching easy. To store the key/value pair, you can use a simple array like a data structure where keys (integers) can be used directly as an index to store values.
Each element is assigned a key (converted key). By using that key you can access the element in O(1) time. Using the key, the algorithm (hash function) computes an index that suggests where an entry can be found or inserted. Hashing is implemented in two steps:
In this method, the hash is independent of the array size and it is then reduced to an index (a number between 0 and array_size − 1) by using the modulo operator (%). Hash function A hash function is any function that can be used to map a data set of an arbitrary size to a data set of a fixed size, which falls into the hash table.
Need for a good hash function Let us understand the need for a good hash function. Assume that you have to store strings in the hash table by using the hashing technique, To compute the index for storing the strings, use a hash function that states the following: The index for a specific string will be equal to the sum of the ASCII values of the characters modulo 599.
As 599 is a prime number, it will reduce the possibility of indexing different strings (collisions). It is recommended that you use prime numbers in case of modulo. The ASCII values of a, b, c, d, e, and f are 97, 98, 99, 100, 101, and 102 respectively. Since all the strings contain the same characters with different permutations, the sum will 599.
The hash function will compute the same index for all the strings and the strings will be stored in the hash table in the following format. As the index of all the strings is the same, you can create a list on that index and insert all the strings in that list. Here, it will take O(n) time (where n is the number of strings) to access a specific string. This shows that the hash function is not a good hash function. Let’s try a different hash function. The index for a specific string will be equal to sum of ASCII values of characters multiplied by their respective order in the string after which it is modulo with 2069 (prime number). Hash table A hash table is a data structure that is used to store keys/value pairs. It uses a hash function to compute an index into an array in which an element will be inserted or searched. By using a good hash function, hashing can work well. Under reasonable assumptions, the average time required to search for an element in a hash table is O(1), Let us consider string S. You are required to count the frequency of all the characters in this string. string S = “ababcd” The simplest way to do this is to iterate over all the possible characters and count their frequency one by one. The time complexity of this approach is O(26*N) where N is the size of the string and there are 26 possible characters. void countFre(string S) } Output a 2 b 2 c 1 d 1 e 0 f 0 z 0 Let us apply hashing to this problem. Take an array frequency of size 26 and hash the 26 characters with indices of the array by using the hash function. Then, iterate over the string and increase the value in the frequency at the corresponding index for each character. The complexity of this approach is O(N) where N is the size of the string. int Frequency; int hashFunc(char c) void countFre(string S) for(int i = 0;i < 26;++i) cout << (char)(i+'a') << ‘ ‘ << Frequency << endl; } Output a 2 b 2 c 1 d 1 e 0 f 0 z 0 Collision resolution techniques Separate chaining (open hashing) Separate chaining is one of the most commonly used collision resolution techniques. It is usually implemented using linked lists. In separate chaining, each element of the hash table is a linked list. The cost of a lookup is that of scanning the entries of the selected linked list for the required key. If the distribution of the keys is sufficiently uniform, then the average cost of a lookup depends only on the average number of keys per linked list.
For this reason, chained hash tables remain effective even when the number of table entries (N) is much higher than the number of slots. For separate chaining, the worstcase scenario is when all the entries are inserted into the same linked list. The lookup procedure may have to scan all its entries, so the worstcase cost is proportional to the number (N) of entries in the table.
In the following image, CodeMonk and Hashing both hash to the value 2, The linked list at the index 2 can hold only one entry, therefore, the next entry (in this case Hashing ) is linked (attached) to the entry of CodeMonk,
vector hashTable; int hashTableSize=20; Insert void insert(string s) Search void search(string s) } cout << s << " is not found!" << endl; } Linear probing (open addressing or closed hashing) In open addressing, instead of in linked lists, all entry records are stored in the array itself. When a new entry has to be inserted, the hash index of the hashed value is computed and then the array is examined (starting with the hashed index). If the slot at the hashed index is unoccupied, then the entry record is inserted in slot at the hashed index else it proceeds in some probe sequence until it finds an unoccupied slot. The probe sequence is the sequence that is followed while traversing through entries. In different probe sequences, you can have different intervals between successive entry slots or probes. When searching for an entry, the array is scanned in the same sequence until either the target element is found or an unused slot is found. This indicates that there is no such key in the table. The name "open addressing" refers to the fact that the location or address of the item is not determined by its hash value. Linear probing is when the interval between successive probes is fixed (usually to 1). Let's assume that the hashed index for a particular entry is index, The probing sequence for linear probing will be:
Hash collision is resolved by open addressing with linear probing. Since CodeMonk and Hashing are hashed to the same index i.e.2, store Hashing at 3 as the interval between successive probes is 1, Implementation of hash table with linear probing Assumption
string hashTable; int hashTableSize = 21; Insert void insert(string s) Search void search(string s) Quadratic Probing Quadratic probing is similar to linear probing and the only difference is the interval between successive probes or entry slots. Here, when the slot at a hashed index for an entry record is already occupied, you must start traversing until you find an unoccupied slot.
string hashTable; int hashTableSize = 21; Insert void insert(string s) hashTable = s; } Search void search(string s) //Is the element present in the hash table if(hashTable == s) cout << s << " is found!" << endl; else cout << s << " is not found!" << endl; } Double hashing Double hashing is similar to linear probing and the only difference is the interval between successive probes. Here, the interval between probes is computed by using two hash functions. Let us say that the hashed index for an entry record is an index that is computed by one hashing function and the slot at that index is already occupied. You must start traversing in a specific probing sequence to look for an unoccupied slot. The probing sequence will be:
string hashTable; int hashTableSize = 21; Insert void insert(string s) Search void search(string s) Applications
Contributed by: Prateek Garg : Basics of Hash Tables Tutorials & Notes  Data Structures
When to use hash table Java?
What is a Hashtable in Java? – A Hashtable is a data structure used to preserve data represented as keyvalue pairs. Despite its similarities with a map, it is more efficient when dealing with huge data sets. This explains why a Hashtable is a great choice in applications where performance is important.
A Hashtable can store large amounts of data quickly and easily, making it ideal for use in applications where speed is important. A Hashtable works by storing data in a table, with each piece of data having a unique key. You can retrieve data from a Hashtable using its key.
What is meant by hash table?
What are Hash Tables? – Hash tables are a type of data structure in which the address/ index value of the data element is generated from a hash function. This enables very fast data access as the index value behaves as a key for the data value. In other words, hash tables store keyvalue pairs but the key is generated through a hashing function.
What is hash table and why it is used?
“Rehash” redirects here. For the South Park episode, see Rehash (South Park),
Hash table  

Type  Unordered associative array  
Invented  1953  
Time complexity in big O notation  

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A small phone book as a hash table In computing, a hash table, also known as hash map, is a data structure that implements an associative array, also called dictionary, which is an abstract data type that maps keys to values, A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value can be found.
During lookup, the key is hashed and the resulting hash indicates where the corresponding value is stored. Ideally, the hash function will assign each key to a unique bucket, but most hash table designs employ an imperfect hash function, which might cause hash collisions where the hash function generates the same index for more than one key.
Such collisions are typically accommodated in some way. In a welldimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation.
: 458 In many situations, hash tables turn out to be on average more efficient than search trees or any other table lookup structure. For this reason, they are widely used in many kinds of computer software, particularly for associative arrays, database indexing, caches, and sets,