Types of indexing

1. Single-level indexing: In single-level indexing, only one index is created for a table or database. This index is used to quickly locate specific rows or records within the table.

2. Multi-level indexing: In multi-level indexing, multiple indexes are created for a table or database. These indexes are organized in a hierarchical structure, with each level providing a more specific search path to locate data.

3. Clustered indexing: In clustered indexing, the physical order of the data in the table is the same as the order of the index. This means that the data is physically stored in the same order as the index, making searches faster.

4. Non-clustered indexing: In non-clustered indexing, the physical order of the data in the table is different from the order of the index. This means that the data is physically stored separately from the index, and searches may take longer.

5. Unique indexing: Unique indexing ensures that each value in the indexed column is unique, preventing duplicate entries. This type of indexing is commonly used for primary keys in databases.

6. Composite indexing: Composite indexing involves creating an index on multiple columns in a table. This allows for more specific and efficient searches that involve multiple criteria.

7. Bitmap indexing: Bitmap indexing is a technique that uses bitmaps to represent the presence or absence of values in a column. This type of indexing is efficient for columns with a low cardinality (few distinct values).

8. Full-text indexing: Full-text indexing is used to index and search text data, such as documents or articles. This type of indexing allows for more advanced search capabilities, such as keyword searches and proximity searches.

1. Basic Indexing

- Term Indexing: Builds an index of unique words (terms) found in a document.
- Inverted Indexing: Organizes terms in an inverted index, where each term is mapped to a list of documents containing it.

2. Advanced Indexing

- Attribute Indexing: Indexes specific attributes of documents, such as author, date, or category.
- Clustering Indexing: Groups documents into clusters based on their similarity.
- Full-Text Indexing: Indexes the entire text content of documents.
- Geospatial Indexing: Indexes geographical data, such as location, distance, and proximity.
- Temporal Indexing: Indexes time-related information, such as date ranges or intervals.
- Hierarchical Indexing: Organizes terms or documents into a hierarchical structure.
- Nested Indexing: Indexes documents that contain nested structures, such as XML documents.

3. Specialized Indexing

- Exact-Match Indexing: Indexes terms in their exact form, without stemming or stop word removal.
- Stemming Indexing: Reduces words to their root forms for improved search results.
- Stop Word Removal Indexing: Excludes common words, such as "the," "and," and "of," from the index to enhance search relevance.
- Synonym Indexing: Indexes synonyms to expand search results.
- Phonetic Indexing: Indexes words based on their phonetic pronunciations.

4. NoSQL Indexing

- B-Tree Indexing: Uses a balanced binary search tree to organize data for efficient retrieval.
- Hash Indexing: Uses a hash function to map keys to specific locations in a database.
- Bloom Filter Indexing: A probabilistic data structure that quickly checks for the presence of specific items in a dataset.

5. Other Types

- Reverse Indexing: Inverts the relationship in inverted indexing, mapping documents to terms.
- Matrix Indexing: Represents documents and terms as a matrix, with entries indicating their relevance.
- Partitioned Indexing: Divides the index into multiple parts for better performance and scalability.
- Sharded Indexing: Distributes the index across multiple servers for improved fault tolerance and scalability.