Hashing Algorithms: Comparing Collision Resolution Techniques for Hash Tables 💡🔍

Welcome to this overview of hashing algorithms and collision resolution techniques for hash tables! If you’re new to this topic, don’t worry - we’ll take it one step at a time and break down the complex concepts to make them more approachable.

What are Hashing Algorithms? 🤔

Hashing algorithms are a set of functions that take in data and produce a fixed-size output (called a hash). These algorithms are often used in computer science to quickly store, access, and retrieve data in hash tables.

How Hashing Works 🧠

Let’s say we have a set of data (for example, a list of names), and we want to quickly search for a specific name in that list. If we were to search linearly through the list, we would have to potentially check every element in the list until we found the one we were looking for - this could be slow and inefficient if the list was very large or if we needed to perform frequent searches.

Instead, we can use a hashing algorithm to transform each name into a unique hash (a short string of characters that is guaranteed to be different from the hash of any other name). We then store each name and its corresponding hash in a hash table. When we need to search for a specific name, we run that name through the same hashing algorithm, which returns a hash that we can quickly look up in the table to find the corresponding name.

Common Hashing Algorithms 📝

There are many different hashing algorithms, each with its own strengths and weaknesses. Some common hashing algorithms include:

• MD5
• SHA-1
• SHA-2 (which includes SHA-256 and SHA-512)
• CRC32

Each algorithm has a different output size and computational complexity, which can affect their speed and security.

Collision Resolution Techniques 🤝

When we use hashing algorithms to store data in a hash table, we run into the possibility of collisions - that is, two different inputs producing the same hash. This can happen for a number of reasons, such as the hash function not being able to produce a wide enough range of outputs, or two different inputs having similar characteristics.

To deal with collisions, we use collision resolution techniques - that is, ways of determining what to do when two keys hash to the same value. There are two main approaches to collision resolution:

Separate Chaining 🧩

In separate chaining, each hash table entry contains a linked list of all the key-value pairs that hash to that entry. When a new key collides with an existing one, we simply add it to the end of the linked list. When we search for a key, we hash it and follow the linked list until we find a matching key (or determine that the key isn’t in the table).

Separate chaining is simple to implement and works well for small to moderate-sized hash tables with low to moderate collision rates. However, it can become inefficient if the collision rate is very high, as the linked lists can become very long and slow to traverse.

Open Addressing 🔍

In open addressing, when a collision occurs, we attempt to find another empty slot in the hash table to place the new key. There are a few different ways to determine where to search for that slot:

• Linear probing: we search for the next consecutive slot in the table until we find an empty slot.
• Quadratic probing: we search for the next slot using a quadratic probing sequence (i.e., we try the next slot, then skip one, then try the next one, then skip two, etc.).
• Double hashing: we use a second hash function to determine how many slots to skip each time we search for an empty slot.

Open addressing can be more efficient than separate chaining for large hash tables or hash tables with high collision rates, as there is less overhead of linking lists. However, it requires careful implementation to avoid clustering (where keys tend to “clump” together in certain areas of the hash table).

Choosing a Collision Resolution Technique 👨‍💻

When deciding which collision resolution technique to use, there are a few factors to consider:

• The expected size and shape of the hash table
• The expected number of keys to be inserted
• The characteristics of the key data (i.e., how likely it is to produce collisions)
• The desired speed and memory usage of the hash table

Generally, separate chaining is preferred for small to medium-sized hash tables with low to moderate collision rates, while open addressing is preferred for larger tables or tables with high collision rates.

Conclusion 🎉

We hope this overview of hashing algorithms and collision resolution techniques has been helpful! While this is a complex topic, it’s an important one to understand if you plan on working with hash tables in your code. By choosing the right hashing algorithm and collision resolution technique, you can build efficient and effective data structures that can quickly store and retrieve data.