[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Tech with Mak [@techNmak](/creator/twitter/techNmak) on x 10.5K followers Created: 2025-07-13 05:49:03 UTC More indexes ≠ better performance. Pro Tip ➺ Don't Index Everything. Why? ➟ Each index adds overhead for data modifications (inserts, updates, deletes). ➟ Focus on columns frequently used in WHERE, JOIN, ORDER BY or GROUP BY clauses. Database Index Types - [1.] Clustered Index ◾ Physically reorders data rows based on the index key. ◾ One per table. ◾ Significantly speeds up range queries (e.g., find all orders between date X and Y) and queries on the indexed column(s). ◾ Can slow down data modifications (inserts, updates, deletes) due to reordering. [2.] Non-Clustered Index ◾ Creates a separate structure with pointers to data rows, sorted by the index key. ◾ Multiple allowed per table. ◾ Efficient for equality searches (e.g., find the customer with ID 123). ◾ Can be used for covering indexes, where all the required columns are included in the index. [3.] Unique Index ◾ Ensures that all values in a column or a combination of columns are unique. ◾ Can be clustered or non-clustered. ◾ Prevents duplicate values, ensuring data integrity. ◾ Often used on alternate keys (other than the primary key) that must be unique. [4.] Filtered Index ◾ Indexes only a subset of rows based on a filter predicate. ◾ Improves performance for queries that frequently filter on the specified condition. ◾ Reduces index size compared to indexing the entire table. [5.] Full-Text Index ◾ Enables efficient searching within text data (e.g., documents, articles) based on keywords and phrases. ◾ Uses specialized indexing and search algorithms (e.g., inverted indexes). Supports linguistic features like stemming and thesaurus. [6.] Composite Index ◾ An index created on multiple columns. ◾ Can be clustered or non-clustered. ◾ Optimizes queries that filter or sort on multiple columns. ◾ The order of columns in the index definition is crucial for performance. [7.] Covering Index ◾ Includes all columns needed for a query in the index itself, avoiding the need to access the base table. ◾ Can be non-clustered. ◾ Significantly speeds up queries that only need data from the indexed columns. [8.] Index with Included Columns ◾ Similar to a covering index, but only includes non-key columns. ◾ Non-clustered. ◾ Can improve query performance if the additional columns are frequently used. [9.] XML Index ◾ Indexes XML data for efficient querying using XPath or XML-specific functions. ◾ Available in databases that support XML data types. ◾ Enables querying specific elements or attributes within XML documents. [10.] Partial Index ◾ Indexes only a subset of rows that meet a specific condition. ◾ Similar to filtered indexes but with more flexibility in defining filter conditions. ◾ Can improve query performance for niche use cases. [11.] Bitmap Index ◾ Efficiently indexes columns with a low cardinality (few unique values). ◾ Stores bitmaps indicating which rows contain a particular value. Follow - @techNmak  XXXXXX engagements  **Related Topics** [mak](/topic/mak) [Post Link](https://x.com/techNmak/status/1944272843992445320)
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Tech with Mak @techNmak on x 10.5K followers
Created: 2025-07-13 05:49:03 UTC
More indexes ≠ better performance.
Pro Tip ➺ Don't Index Everything.
Why? ➟ Each index adds overhead for data modifications (inserts, updates, deletes). ➟ Focus on columns frequently used in WHERE, JOIN, ORDER BY or GROUP BY clauses.
Database Index Types -
[1.] Clustered Index ◾ Physically reorders data rows based on the index key. ◾ One per table. ◾ Significantly speeds up range queries (e.g., find all orders between date X and Y) and queries on the indexed column(s). ◾ Can slow down data modifications (inserts, updates, deletes) due to reordering.
[2.] Non-Clustered Index ◾ Creates a separate structure with pointers to data rows, sorted by the index key. ◾ Multiple allowed per table. ◾ Efficient for equality searches (e.g., find the customer with ID 123). ◾ Can be used for covering indexes, where all the required columns are included in the index.
[3.] Unique Index ◾ Ensures that all values in a column or a combination of columns are unique. ◾ Can be clustered or non-clustered. ◾ Prevents duplicate values, ensuring data integrity. ◾ Often used on alternate keys (other than the primary key) that must be unique.
[4.] Filtered Index ◾ Indexes only a subset of rows based on a filter predicate. ◾ Improves performance for queries that frequently filter on the specified condition. ◾ Reduces index size compared to indexing the entire table.
[5.] Full-Text Index ◾ Enables efficient searching within text data (e.g., documents, articles) based on keywords and phrases. ◾ Uses specialized indexing and search algorithms (e.g., inverted indexes). Supports linguistic features like stemming and thesaurus.
[6.] Composite Index ◾ An index created on multiple columns. ◾ Can be clustered or non-clustered. ◾ Optimizes queries that filter or sort on multiple columns. ◾ The order of columns in the index definition is crucial for performance.
[7.] Covering Index ◾ Includes all columns needed for a query in the index itself, avoiding the need to access the base table. ◾ Can be non-clustered. ◾ Significantly speeds up queries that only need data from the indexed columns.
[8.] Index with Included Columns ◾ Similar to a covering index, but only includes non-key columns. ◾ Non-clustered. ◾ Can improve query performance if the additional columns are frequently used.
[9.] XML Index ◾ Indexes XML data for efficient querying using XPath or XML-specific functions. ◾ Available in databases that support XML data types. ◾ Enables querying specific elements or attributes within XML documents.
[10.] Partial Index ◾ Indexes only a subset of rows that meet a specific condition. ◾ Similar to filtered indexes but with more flexibility in defining filter conditions. ◾ Can improve query performance for niche use cases.
[11.] Bitmap Index ◾ Efficiently indexes columns with a low cardinality (few unique values). ◾ Stores bitmaps indicating which rows contain a particular value.
Follow - @techNmak
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Related Topics mak