SQL Indexing

Indexing improves query performance by reducing the number of disk I/O operations required to retrieve data. When a query is executed, the database engine can use the index to quickly locate the rows that match the query criteria, instead of scanning the entire table. This reduces the amount of data that needs to be read from disk, resulting in faster query execution.

There are several types of indexes in SQL, including:

1. B-tree index: This is the most common type of index, which stores data in a balanced tree structure. It is efficient for both equality and range queries.

2. Bitmap index: This type of index uses a bitmap to represent the presence or absence of a value in a column. It is useful for columns with a small number of distinct values.

3. Hash index: This type of index uses a hash function to map values to index keys. It is efficient for equality queries, but not for range queries.

4. Full-text index: This type of index is used for searching text-based data, such as documents or web pages. It allows for efficient keyword searches.

While indexing can greatly improve query performance, it also has some drawbacks:

1. Increased storage space: Indexes require additional storage space to store the index data structures. This can be a concern for large databases with limited storage capacity.

2. Increased maintenance overhead: Indexes need to be updated whenever the indexed data is modified, which can add overhead to write operations. This can be a concern for heavily updated tables.

3. Index fragmentation: Over time, indexes can become fragmented, which can degrade query performance. Regular index maintenance is required to keep indexes optimized.

4. Increased complexity: Having too many indexes on a table can make the database schema more complex and harder to manage.

While indexing can be beneficial for query performance, there are situations where using an index may not be appropriate:

1. Small tables: For very small tables, the overhead of maintaining an index may outweigh the performance benefits.

2. Frequent write operations: If a table is heavily updated, the overhead of updating the index on each write operation may outweigh the performance benefits of using the index.

3. Columns with low selectivity: If a column has a small number of distinct values, using an index may not provide significant performance improvement.

4. Queries that return a large portion of the table: If a query retrieves a large portion of the table's data, using an index may not be efficient, as it may require scanning the entire index and then accessing the corresponding data pages.

In SQL, a clustered index determines the physical order of data in a table. Each table can have only one clustered index, and it determines the storage order of the table's rows. When a table has a clustered index, the data is physically sorted and stored based on the values of the indexed column(s). This means that the data is stored in the same order as the clustered index, which can improve the performance of queries that retrieve data in the order defined by the index.

On the other hand, a non-clustered index is a separate structure that contains a copy of a portion of the table's data, along with a reference to the location of the full row. Unlike a clustered index, a table can have multiple non-clustered indexes. Non-clustered indexes are typically used to improve the performance of queries that involve filtering, sorting, or joining data based on columns that are not part of the clustered index.

When executing a query, the database optimizer analyzes the query and the available indexes to determine the most efficient index to use. It takes into account various factors such as the selectivity of the index (how many rows match a particular value), the size of the index, and the cost of accessing the data using the index.

The optimizer uses statistics about the data distribution in the table to estimate the selectivity of the indexes. It then compares the estimated cost of using each index and chooses the one with the lowest cost. The cost is usually based on the number of disk I/O operations required to access the data using the index.

It's important to note that the database optimizer may not always choose the optimal index, especially if the statistics are outdated or if the query is complex. In such cases, it may be necessary to manually specify the index to use or to update the statistics to help the optimizer make better decisions.

Index cardinality refers to the uniqueness or distinctness of values in an index. It represents the number of unique values in the indexed column(s) compared to the total number of rows in the table. A high cardinality means that the index has a large number of unique values, while a low cardinality means that there are relatively few unique values.

The cardinality of an index is an important factor in determining its selectivity and usefulness. A high cardinality index is more selective, as it can narrow down the search to a smaller subset of data. This can result in better query performance, especially when filtering or joining data based on the indexed column(s).

On the other hand, a low cardinality index may not be very selective, as it may have many duplicate values. In such cases, using the index may not significantly improve query performance, as it may still need to scan a large portion of the table to retrieve the desired data.

It's important to consider the cardinality of an index when designing database schemas and choosing the appropriate columns to index.

A clustered index determines the physical order of data in a table. The data is physically sorted and stored on disk based on the values of the indexed column(s) in ascending or descending order. This means that the data in a table with a clustered index is physically stored in the same order as the clustered index key values.

No, a table can have only one clustered index. The clustered index determines the physical order of data in a table, so having multiple clustered indexes would create conflicts in determining the physical order.

Advantages of using a clustered index:

• Improved query performance for range-based queries that match the clustered index key
• Elimination of the need for a separate index structure, as the clustered index itself serves as the data structure
• Efficient retrieval of data in the order defined by the clustered index

Disadvantages of using a clustered index:

• Slower performance for insert, update, and delete operations, as the physical order of data needs to be maintained
• Increased storage requirements, as the data is physically sorted and stored based on the clustered index key values
• Limited flexibility in choosing the clustering key, as it should be unique and stable

A non-clustered index differs from a clustered index in the way it organizes and stores data. In a clustered index, the data rows are physically sorted and stored in the order of the indexed column(s). This means that a table can have only one clustered index. On the other hand, a non-clustered index does not affect the physical order of the data rows. It is stored separately and contains a copy of the indexed columns along with a pointer to the actual data rows. A table can have multiple non-clustered indexes.

In SQL Server, a table can have up to 999 non-clustered indexes. However, it is important to note that having too many indexes on a table can negatively impact performance, as each index requires additional storage space and maintenance overhead.

Advantages of using a non-clustered index include faster data retrieval for queries that involve the indexed columns, improved query performance for filtering and sorting operations, and the ability to cover queries by including all the required columns in the index. Disadvantages of using a non-clustered index include increased storage space requirements, additional maintenance overhead for index updates, and potential performance degradation for insert, update, and delete operations on the indexed columns.

A composite index differs from a single-column index in that it is created on multiple columns instead of just one. This means that the index is built using a combination of values from multiple columns, allowing for more specific and targeted searches. In contrast, a single-column index is created on a single column and can only be used to search or sort based on the values in that specific column.

A composite index is typically used when there is a need to search or sort data based on multiple columns. It is especially useful when queries involve conditions or sorting on multiple columns simultaneously. By creating a composite index on these columns, the database can optimize the query execution and improve performance.

Advantages of using a composite index include improved query performance for queries involving multiple columns, reduced disk space usage compared to creating separate indexes for each column, and better index utilization for certain types of queries.

However, there are also some disadvantages to consider. Creating and maintaining a composite index can be more complex and time-consuming compared to a single-column index. Additionally, the composite index may not be as effective for queries that only involve one of the indexed columns, as the index is optimized for searches and sorts involving all the indexed columns.