C Program To Implement Dictionary Using Hashing Algorithms [NEW]

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This is intended more as an exercise. I know that there are 3rd party libraries available which one can use. But consider for a moment, that they don't exist. In such a situation what's the quickest way you can implement a dictionary satisfying the above requirements.

Section 6.6 of The C Programming Language presents a simple dictionary (hashtable) data structure. I don't think a useful dictionary implementation could get any simpler than this. For your convenience, I reproduce the code here.

There are different ways to resolve a collision. This tutorial will rely upon a method called Separate Chaining, which aims to create independent chains for all items that have the same hash index. The implementation in this tutorial will create these chains using linked lists.

Below is a simple program (demo.c) that demonstrates using all the functions of the API. It counts the frequencies of unique, space-separated words from standard input, and prints the results (in an arbitrary order, because the iteration order of our hash table is undefined). It ends by printing the total number of unique words.

To mitigate the damage that a hash table or a dictionary attack could do, we salt the passwords. According to OWASP Guidelines, a salt is a value generated by a cryptographically secure function that is added to the input of hash functions to create unique hashes for every input, regardless of the input not being unique. A salt makes a hash function look non-deterministic, which is good as we don't want to reveal duplicate passwords through our hashing.

As we can see, hashing and salting are very complex processes and the security of our systems greatly relies on their successful implementation. While these are no methods to create 100% secure systems, these are methods to create hardy and resilient systems. It's best to leave the creation, maintenance, and operation of such methods and systems to security experts. A misstep in your home-made security strategy may lead to extensive damage to your business, users, and reputation.

You'd want to rely on algorithms such as bcrypt that hash and salt the password for you using strong cryptography. Additionally, you may use a security framework, such as Spring Security for the Java Ecosystem for example. These frameworks offer you abstractions that make the development of your applications safer but also integrate with reliable identity providers, such as Auth0, that make Identity and Access Management much easier.

In previous sections we were able to make improvements in our searchalgorithms by taking advantage of information about where items arestored in the collection with respect to one another. For example, byknowing that a list was ordered, we could search in logarithmic timeusing a binary search. In this section we will attempt to go one stepfurther by building a data structure that can be searched in\\(O(1)\\) time. This concept is referred to as hashing.

A hash table is a collection of items which are stored in such a wayas to make it easy to find them later. Each position of the hash table,often called a slot, can hold an item and is named by an integervalue starting at 0. For example, we will have a slot named 0, a slotnamed 1, a slot named 2, and so on. Initially, the hash table containsno items so every slot is empty. We can implement a hash table by usinga list with each element initialized to the special Python valueNone. Figure 4 shows a hash table of size \\(m=11\\).In other words, there are m slots in the table, named 0 through 10.

One of the great benefits of a dictionary is the fact that given a key,we can look up the associated data value very quickly. In order toprovide this fast look up capability, we need an implementation thatsupports an efficient search. We could use a list with sequential orbinary search but it would be even better to use a hash table asdescribed above since looking up an item in a hash table can approach\\(O(1)\\) performance.

In Listing 2 we use two lists to create aHashTable class that implements the Map abstract data type. Onelist, called slots, will hold the key items and a parallel list,called data, will hold the data values. When we look up a key, thecorresponding position in the data list will hold the associated datavalue. We will treat the key list as a hash table using the ideaspresented earlier. Note that the initial size for the hash table hasbeen chosen to be 11. Although this is arbitrary, it is important thatthe size be a prime number so that the collision resolution algorithmcan be as efficient as possible.

The final methods of the HashTable class provide additionaldictionary functionality. We overload the __getitem__ and__setitem__ methods to allow access using``[]``. This means thatonce a HashTable has been created, the familiar index operator willbe available. We leave the remaining methods as exercises.

Invented over half a century ago, the hash table is a classic data structure that has been fundamental to programming. To this day, it helps solve many real-life problems, such as indexing database tables, caching computed values, or implementing sets. It often comes up in job interviews, and Python uses hash tables all over the place to make name lookups almost instantaneous.

Aside from verifying data integrity and solving the dictionary problem, hash functions help in other fields, including security and cryptography. For example, you typically store hashed passwords in databases to mitigate the risk of data leaks. Digital signatures involve hashing to create a message digest before encryption. Blockchain transactions are another prime example of using a hash function for cryptographic purposes.

First of all, the intent of id() is different from hash(), so other Python distributions may implement identity in alternative ways. Second, memory addresses are predictable without having a uniform distribution, which is both insecure and severely inefficient for hashing. Finally, equal objects should generally produce the same hash code even if they have distinct identities.

Because you can now determine whether a given key has an associated value in your hash table, you might as well implement the in operator to mimic a Python dictionary. Remember to write and cover these test cases individually to respect test-driven development principles:

You create a new hash table and set its capacity using an arbitrary factor. Then, you insert key-value pairs by copying them from the dictionary passed as an argument to the method. You can allow for overriding the default capacity if you want to, so add a similar test case:

Your custom hash table prototype is still missing a couple of nonessential features that the built-in dictionary provides. You may try adding them yourself as an exercise. For example, you could replicate other methods from Python dictionaries, such as dict.clear() or dict.update(). Other than that, you might implement one of the bitwise operators supported by dict since Python 3.9, which allow for a union operation.

At this point, you can implement a hash table from scratch in Python, using different strategies to resolve hash collisions. You know how to make your hash table resize and rehash dynamically to accommodate more data and how to preserve the insertion order of key-value pairs. Along the way, you practiced test-driven development (TDD) by adding features to your hash table step by step.

Division-based implementations can be of particular concern because the division is microprogrammed on nearly all chip architectures. Divide (modulo) by a constant can be inverted to become a multiply by the word-size multiplicative-inverse of the constant. This can be done by the programmer, or by the compiler. Divide can also be reduced directly into a series of shift-subtracts and shift-adds, though minimizing the number of such operations required is a daunting problem; the number of assembly instructions resulting may be more than a dozen, and swamp the pipeline. If the architecture has hardware multiply functional units, the multiply-by-inverse is likely a better approach.

Linear hashing and spiral storage are examples of dynamic hash functions that execute in constant time but relax the property of uniformity to achieve the minimal movement property. Extendible hashing uses a dynamic hash function that requires space proportional to n to compute the hash function, and it becomes a function of the previous keys that have been inserted. Several algorithms that preserve the uniformity property but require time proportional to n to compute the value of H(z,n) have been invented.[clarification needed]

There are several common algorithms for hashing integers. The method giving the best distribution is data-dependent. One of the simplest and most common methods in practice is the modulo division method.

Zobrist hashing was originally introduced as a means of compactly representing chess positions in computer game-playing programs. A unique random number was assigned to represent each type of piece (six each for black and white) on each space of the board. Thus a table of 64x12 such numbers is initialized at the start of the program. The random numbers could be any length, but 64 bits was natural due to the 64 squares on the board. A position was transcribed by cycling through the pieces in a position, indexing the corresponding random numbers (vacant spaces were not included in the calculation), and XORing them together (the starting value could be 0, the identity value for XOR, or a random seed). The resulting value was reduced by modulo, folding or some other operation to produce a hash table index. The original Zobrist hash was stored in the table as the representation of the position.

Python dictionaries are implemented using hash tables. It is an array whose indexes are obtained using a hash function on the keys. The goal of a hash function is to distribute the keys evenly in the array. A good hash function minimizes the number of collisions e.g. different keys having the same hash. Python does not have this kind of hash function. Its most important hash functions (for strings and ints) are very regular in common cases: 153554b96e

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