Hash Table vs STL Map

This article compares and contrasts the Hash table and an STL Map. How does the hash table work? Which data structure options can be used instead of a hash table if the number of inputs is small?

HASH TABLE

A hash table stores a value by calling a hash function on a key.

• The values are not kept in sorted order.
• Furthermore, because hash tables use the key to locate the index that will hold the value, an insert or lookup can be performed in amortised O(1) time (assuming few collisions in the hash table).
• Potential collisions must also be handled in a hash table. This is frequently accomplished through chaining, which is the creation of a linked list of all the values whose keys map to a specific index.

Hash Table Implementation: A hash table is traditionally implemented using an array of linked lists. When we want to insert a key/value pair, we use the hash function to map the key to an index in the array. The value is then placed at that position in the linked list. The linked list elements at a specific index of the array do not have the same key. Instead, hash function(key) is the same for all of these values. As a result, in order to retrieve the value for a specific key, we must store both the exact key and the value in each node. To summarise, a hash table will be implemented using an array of linked lists, with each node holding two pieces of data: the value and the original key. In addition, the following design criteria should be considered:

• To ensure that the keys are evenly distributed, we want to use a good hash function. If they are not evenly distributed, we will have a lot of collisions and the speed with which we can find an element will slow down.
• We will still have collisions regardless of how good the hash function is, so we need a method to handle them. This is frequently accomplished through the use of a linked list, but it is not the only method.
• We may also want to include methods for dynamically increasing or decreasing the size of the hash table based on capacity. For example, if the number of elements to table size ratio exceeds a certain threshold, we may want to increase the size of the hash table. This entails creating a new hash table and copying the entries from the old table to the new table. We want to avoid performing this procedure too frequently because it is costly.

STL MAP

The container map is an associative container that is part of the C++ standard library. This class is defined in the namespace std's header file "map." Internal STL Map Implementation: It is implemented as a red-black self-balancing tree. The two most common self-balancing trees are probably red-black trees and AVL trees. To rebalance the tree after an insertion/update, both algorithms employ the concept of rotations, in which the tree's nodes are rotated. While insert/delete operations are O(log n) in both algorithms, in the case of Red-Black tree re-balancing rotation is an O(1) operation while with AVL this is an O(log n) operation, making the RB tree more efficient in this aspect of the re-balancing sage and one of the possible reasons why it is more commonly used.

Hash table vs STL map: What is the Difference?

• Null Keys: An STL Map can have one null key and multiple null values, whereas a hash table cannot have a null key or value.
• Thread synchronisation: If thread synchronisation is not required, a map is generally preferred over a hash table. The hash table has been synchronised.
• STL Maps are not thread safe, whereas Hashmaps are thread safe and can be shared by multiple threads.
• Value Order: Values in an STL map are stored in sorted order, whereas values in a hash table are not stored in sorted order.
• Searching Time: You can use STL Map or binary tree for smaller data (although it takes O (log n) time, the number of inputs may be small enough to make this time negligible) and hash table for larger data.

Let's look at the differences in a table -: