Static hashing is a technique that is used to map keys to values in a way that is both efficient and easy to update. The idea is to use a hash function to map the keys to a small number of values, and then use a simple array to store the values. The advantage of this approach is that it is very efficient to look up values, and it is also very easy to update the values in the array.
What are the types of static hashing?
There are two types of static hashing, table-based hashing and block-based hashing.
Table-based hashing is where a table is used to store the data to be hashed. The table is then divided into a number of buckets, each of which contains a certain number of entries. When a piece of data is to be hashed, the table is consulted to determine which bucket it should go into.
Block-based hashing is where the data to be hashed is divided into a number of blocks, each of which is hashed separately. The results of the hashing are then concatenated to form the final hash value.
What are the advantages and disadvantages of static hashing?
The main advantage of static hashing is that it can be used to distribute data across a cluster of servers more evenly. This can improve performance by reducing the amount of data that needs to be transferred between servers.
The main disadvantage of static hashing is that it can lead to data fragmentation. This can make it more difficult to query data and can cause performance issues. What is the main disadvantage of static hashing? The main disadvantage of static hashing is that it can lead to inefficient use of resources. When data is spread across multiple servers, each server must maintain its own copy of the hash table. This can lead to increased overhead and decreased performance.
What is dynamic hashing with example?
Dynamic hashing is a hashing technique in which the hash function is not fixed, but is instead computed dynamically based on the data being stored. This can be used to improve the performance of a hash table by reducing the number of collisions.
For example, consider a hash table with 10 slots and a hash function that maps each key to a slot by taking the key modulo 10. If the keys being stored are integers in the range 1-1000, then the distribution of keys will be fairly even and the number of collisions will be low. However, if the keys are integers in the range 1-1000000, then most of the keys will map to the same slot (slot 1), and the number of collisions will be high.
In a dynamic hash table, the hash function would be changed dynamically to map each key to a different slot. For example, the hash function could be changed to map each key to a slot by taking the key modulo 100. This would distribute the keys more evenly and reduce the number of collisions.
What are the problems with static hashing?
The main problem with static hashing is that it can lead to inefficient use of resources, since all data must be stored in a fixed number of buckets. This can result in empty buckets (if the data is not evenly distributed) or full buckets (if the data is too evenly distributed).
Another problem with static hashing is that it can be difficult to add or remove nodes from the system, since all data must be redistributed to maintain the fixed number of buckets.
Finally, static hashing can lead to increased contention as more nodes are added to the system, since all nodes must compete for the same set of buckets.