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Separate Chaining in Data Structure

What is Separate Chaining?

Separate chaining is a technique used in data structures such as hash tables to handle collisions, which occur when two or more keys map to the same hash value. When a collision occurs in a hash table that uses separate chaining, instead of overwriting the existing value or finding another slot for the new value, the new value is simply added to a list or chain of values associated with that hash value.

For example, suppose we have a hash table with the following key-value pairs:


If we use a hash function that maps keys 1 and 3 to the same hash value, a collision will occur. With separate chaining, we would create a linked list or chain for this hash value, and add the values for keys 1 and 3 to that list:

Hash valueValues

When we need to look up a value in the hash table, we use the hash function to determine the hash value for the key and then search the linked list or chain associated with that hash value for the key's value.

Separate chaining is a simple and effective way to handle collisions in hash tables, and is commonly used in implementations of hash tables in programming languages and libraries.

How to avoid collision in separate chaining in DS

  • In separate chaining, collisions can be avoided or minimized by carefully choosing the hash function and the size of the hash table.
  • The hash function should be designed to distribute the keys as uniformly as possible across the hash table, so that the probability of two keys colliding is reduced. A good hash function takes into account the characteristics of the data being hashed, such as its length, distribution, and randomness, and produces a hash value that is as evenly distributed as possible.
  • In addition, the size of the hash table should be chosen appropriately to minimize collisions. If the hash table is too small, there will be more collisions, and if it is too large, it will waste memory. A common rule of thumb is to make the hash table size about twice the number of elements to be stored in it, although the optimal size depends on the specific application and data being stored.
  • Another way to reduce collisions is to use a technique called open addressing, which stores multiple values in the same hash table slot by probing for the next available slot until an empty slot is found. However, this technique requires more complex logic for insertion and deletion, and can suffer from performance issues if the table becomes too full.

Advantages of Separate Chaining

Separate chaining has several advantages in data structures such as hash tables:

  • Memory efficiency: Separate chaining allows multiple values to be stored in a single hash table slot, which can save memory compared to other collision resolution techniques like open addressing.
  • Performance: When the hash function is well-designed and the hash table is appropriately sized, separate chaining can provide fast average-case performance for insertions, deletions, and lookups.
  • Flexibility: Separate chaining can handle arbitrary numbers of collisions, since each slot in the hash table can hold multiple values. This makes it easy to adapt to changing data sets or hash functions.
  • Simplicity: Separate chaining is a simple and easy-to-understand collision resolution technique, which makes it a popular choice for hash table implementations in many programming languages and libraries.
  • Robustness: Separate chaining can handle situations where the number of elements in the hash table is unknown or may grow or shrink over time. This makes it a suitable technique for a wide range of applications, from small to large-scale projects.

Disadvantage of separate chaining

While separate chaining has several advantages, it also has some potential drawbacks:

  • Memory overhead:Separate chaining can require additional memory overhead to store the pointers or references to the linked lists or chains that hold the values for each hash table slot. This overhead can be significant for small hash tables with few collisions.
  • Cache inefficiency: The use of linked lists or chains in separate chaining can result in poor cache performance, as the elements in a linked list are typically not stored contiguously in memory. This can lead to slower access times and reduced performance, particularly for large hash tables.
  • Performance degradation with high load factors: As the load factor of the hash table (the ratio of the number of elements to the size of the table) increases, the probability of collisions also increases. When the load factor becomes too high, the performance of separate chaining can degrade significantly, as the length of the linked lists or chains becomes longer and the time required searching to them increases.
  • Inefficiency for small tables: Separate chaining can be less efficient than other collision resolution techniques, such as linear or quadratic probing, for small hash tables with few collisions. This is because the memory overhead and pointer indirection required by separate chaining can be relatively high compared to the number of elements being stored.
  • Vulnerability to denial-of-service attacks: In certain situations, attackers can intentionally craft input data to cause a hash table using separate chaining to have a high number of collisions, leading to a denial-of-service attack. This can be mitigated by using techniques such as hash function randomization or universal hashing to make the probability of collisions less predictable.