Indexing and hashing are two fundamental techniques used in database systems for efficient data retrieval. Let’s explore each technique and compare them:
Indexing:
Indexing involves creating auxiliary data structures, known as indexes, to facilitate efficient retrieval of data based on certain attributes (keys) in a database table. There are various indexing techniques, including:
- Primary Index: An index built on the primary key of a table, typically organized as a B+ tree or a clustered index, allowing for direct access to rows based on their primary key values.
- Secondary Index: An index built on non-primary key attributes, providing alternate access paths to the data based on other attributes. Secondary indices can be organized as B+ trees, B trees, or other structures.
- Bitmap Index: An index structure that uses bitmap vectors to represent the presence or absence of values for a given attribute across rows in a table. Bitmap indices are efficient for low cardinality attributes.
- Hash Index: An index based on hashing, where hash functions are used to map keys to index entries. Hash indices are particularly efficient for exact match queries but not well-suited for range queries.
Hashing:
Hashing is a technique that involves applying a hash function to a key to compute an index or address where the associated value (record) can be stored or retrieved. There are different hashing techniques, including:
- Hash Tables: A data structure that implements hashing, typically using an array where each slot (bucket) corresponds to a hash value. Collisions (multiple keys hashing to the same slot) can be resolved using techniques like chaining or open addressing.
- Extendible Hashing: A dynamic hashing technique where the directory size grows or shrinks dynamically as the number of entries increases or decreases. Extendible hashing reduces the probability of collisions by dynamically adjusting the directory size.
- Linear Hashing: A dynamic hashing technique similar to extendible hashing but with a linear directory structure. It dynamically splits or merges buckets to accommodate new entries or reduce fragmentation.
Comparisons:
- Search Complexity: Hashing typically offers constant-time search complexity O(1) for exact match queries, while indexing techniques like B+ trees provide logarithmic search complexity O(log n), where n is the number of entries.
- Range Queries: Indexing techniques are well-suited for range queries, as they maintain the sorted order of keys, allowing efficient range scans. Hashing is not well-suited for range queries.
- Updates and Inserts: Hashing may require rehashing and redistribution of data when the hash table needs to be resized due to changes in the number of entries. Indexing structures like B+ trees handle updates and inserts more efficiently without requiring redistribution of data.
- Space Overhead: Hashing may require more space overhead due to potential collisions and bucket overflows, while indexing structures may have less space overhead, especially for sparse data.
- Ordering: Indexing structures maintain the ordering of keys, allowing for ordered traversal and range scans. Hashing does not maintain any inherent order.
 both indexing and hashing techniques offer efficient data retrieval mechanisms, each with its advantages and trade-offs. Hashing is efficient for exact match queries and has constant search complexity but may not be suitable for range queries. Indexing structures are well-suited for range queries and offer logarithmic search complexity but may have higher space overhead and less efficient updates compared to hashing. The choice between indexing and hashing depends on factors such as the access patterns of the data, query requirements, and space constraints.