Dynamic hashing is a technique used in relational database management systems (RDBMS) to handle hash collisions and provide efficient access to data. It allows the hash index to dynamically grow or shrink as the data set changes, without sacrificing performance. The primary goal of dynamic hashing is to evenly distribute the data and minimize collisions.

Here are the key concepts and steps involved in dynamic hashing:

1. Initial Configuration:

- Initially, the hash index starts with a minimal number of buckets or hash slots.
- The number of initial buckets is typically chosen to minimize collisions.

2. Hash Function:

- A hash function is used to compute a hash code from the search key.
- The hash code determines the bucket or slot where the record will be stored.

3. Collision Resolution:

- When a collision occurs (i.e., multiple records mapping to the same hash code), dynamic hashing uses a collision resolution technique to handle it.
- The most commonly used technique is called "Extendible Hashing."

4. Extendible Hashing:

- Extendible hashing maintains a global directory and local directories.
- The global directory contains pointers to the local directories.
- Initially, the global directory and local directories are sparsely populated.
- Each local directory is associated with a fixed number of hash slots or buckets.
- When a collision occurs, the local directory associated with the hash code is dynamically expanded.
- Expansion involves splitting the existing bucket into two and redistributing the records based on an additional bit in the hash code.
- The global directory is updated to point to the newly created local directory.
- This process allows for the efficient redistribution of records and minimizes the likelihood of collisions.

5. Splitting and Merging:

- Splitting: When a bucket becomes full due to repeated collisions, it is split into two, and the records are redistributed based on an additional bit in the hash code.
- Merging: When a bucket becomes sparsely populated, it may be merged with an adjacent bucket to reduce the number of buckets and improve space utilization.
- The splitting and merging operations are performed dynamically based on the distribution of records and collision patterns.

Dynamic hashing provides several benefits in an RDBMS:

- Efficient Space Utilization: Dynamic hashing allows for the dynamic allocation and reallocation of storage space based on the data set. It ensures optimal space utilization by adjusting the number of buckets as needed.

- Collision Minimization: By dynamically redistributing records and expanding the hash index, dynamic hashing minimizes collisions and improves search performance.

- Scalability: Dynamic hashing can easily handle growing or shrinking data sets without requiring extensive restructuring of the hash index.

It's important to note that the choice of hashing technique, including dynamic hashing, depends on factors such as the expected data distribution, the characteristics of the workload, and the specific requirements of the database application. Performance tuning and optimization may also be necessary to achieve the best results.

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