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How do you handle hash table collisions?

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Hash tables are one of the most commonly used data structures in programming, because they offer fast and efficient access to data by using a key-value mapping. However, hash tables are not perfect, and sometimes they face a problem called collision. A collision occurs when two or more keys are mapped to the same index in the hash table, causing a conflict and potentially affecting the performance and correctness of the program. In this article, you will learn how to handle hash table collisions using different methods and techniques.

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Shubham Mishra

Engineer @ Mongodb | Backend, AI, Machine Learning, Full Stack | Scalable tech
Muhammad Ezzat

Software Engineer @Siemens | My opinions only represent me

1 What causes collisions?

Collisions are inevitable in hash tables, because there is a finite number of indices in the hash table, and an infinite number of possible keys. Therefore, no matter how good the hash function is, there is always a chance that two keys will have the same hash value. The likelihood of collisions depends on the size of the hash table, the number of keys, and the quality of the hash function. A good hash function should distribute the keys uniformly and randomly across the hash table, minimizing the chance of collisions.

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Shubham Mishra

Engineer @ Mongodb | Backend, AI, Machine Learning, Full Stack | Scalable tech
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In my experience with Java, its internal implementation of hash tables utilizes linked lists to manage collisions. Essentially, when two keys have the same hash value, they're stored in the same bucket using a linked list. Coming to hash table collisions in general: they're unavoidable given the limited indices in the table versus infinite possible keys. The efficacy of the hash function is vital, it should spread keys uniformly across the table, thus minimizing collision risks. The more uniform and random the distribution, the better the hash function.

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2 How to avoid collisions?

One way to avoid collisions is to choose a large enough hash table size, and a good hash function, that reduce the load factor of the hash table. The load factor is the ratio of the number of keys to the number of indices in the hash table, and it indicates how full the hash table is. A low load factor means less collisions, but more space wasted. A high load factor means more collisions, but less space wasted. A general rule of thumb is to keep the load factor below 0.7, and to resize the hash table when it exceeds this threshold.

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3 How to resolve collisions?

Another way to handle collisions is to resolve them when they occur, using different strategies. The two main strategies are separate chaining and open addressing. Separate chaining means that each index in the hash table points to a linked list of key-value pairs, and when a collision occurs, the new pair is added to the end of the list. This way, each index can store multiple pairs, but the downside is that it requires extra space and time to traverse the list. Open addressing means that each index in the hash table can store only one pair, and when a collision occurs, the new pair is probed for another index until an empty one is found. This way, no extra space is needed, but the downside is that it requires more computation and can cause clustering and deletion issues.

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4 How to choose a collision resolution strategy?

The choice of a collision resolution strategy depends on several factors, such as the expected load factor, the number and distribution of keys, the frequency of insertion and deletion operations, and the available memory and computation resources. Generally speaking, separate chaining is preferred when the load factor is high, the keys are not uniform or random, or the insertion and deletion operations are frequent. Open addressing is preferred when the load factor is low, the keys are uniform or random, or the insertion and deletion operations are rare.

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Muhammad Ezzat

Software Engineer @Siemens | My opinions only represent me
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The key that makes separate chaining works is storing the whole key-value pair in the implemented linked list as a node. Thus, on looking up value "V" for key "K", the process goes as follows: 1- "K" is hashed through our hash function. 2- The array index is calculated from the hash code. 3- The linked list of the computed index is retrieved. 4- The linked list is searched using a preferred algorithm. 5- If "K" was found in any node "V" is retrieved. Else, depending on your implementation; an exception could be thrown or a new node is allocated for K & V. So in essence, we go back to our standard searching process; but in a MUCH smaller search space.

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5 How to implement collision resolution methods?

There are different ways to implement collision resolution methods in code, depending on the programming language and the data structure used. For example, in Python, you can use a dictionary as a hash table, and it will automatically handle collisions using separate chaining. In Java, you can use a HashMap or a Hashtable class, which also use separate chaining by default, but you can customize the hash function and the equality method for your keys. In C++, you can use a map or an unordered_map class, which use separate chaining or open addressing respectively, and you can also provide your own hash function and equality comparator for your keys.

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Shubham Mishra

Engineer @ Mongodb | Backend, AI, Machine Learning, Full Stack | Scalable tech
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From my experience, leveraging predefined classes or data structures for hash tables, like Python's dictionary or Java's HashMap, is usually more efficient and safer than custom implementations. They come with optimized collision resolution methods built-in. Whether it's separate chaining in Java's HashMap or open addressing in C++'s unordered_map, these solutions are tailored for general use cases. When needed, they even offer customization options, like defining your own hash function, making the process both robust and flexible.

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6 How to test collision resolution methods?

One way to test collision resolution methods is to measure their performance and compare them against each other. You can use different metrics, such as the average time and space complexity, the worst-case scenario, and the practical results on real-world data sets. You can also use different tools, such as benchmarking libraries, profiling tools, and debugging tools, to analyze and optimize your code. You can also use different test cases, such as random keys, skewed keys, duplicate keys, and malicious keys, to simulate different situations and challenges.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you handle hash table collisions?

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1 What causes collisions?

2 How to avoid collisions?

3 How to resolve collisions?

4 How to choose a collision resolution strategy?

5 How to implement collision resolution methods?

6 How to test collision resolution methods?

7 Here’s what else to consider

Programming

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How do you handle hash table collisions?

1

2

3

4

5

6

7

1 What causes collisions?

2 How to avoid collisions?

3 How to resolve collisions?

4 How to choose a collision resolution strategy?

5 How to implement collision resolution methods?

6 How to test collision resolution methods?

7 Here’s what else to consider

Programming

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