How Does Database Indexing Speed Up Data Retrieval?

How database index improves data retrieval efficiency

In the field of database management, indexes play a vital role in optimizing data retrieval and speeding up query execution. By understanding the fundamentals of database indexing, developers can effectively take advantage of whatever specific database system they are using.

Why indexes are essential

Large data sets stored on disk devices are organized into discrete blocks of data. These data blocks represent the smallest unit of I/O operations and can be accessed as a whole. Each time a query requires data, the database must retrieve the entire data block in which the data resides.

When searching for data based on unsorted fields, the database uses a linear search, which requires checking each block of data in turn. This process requires on average (N 1)/2 data block accesses, where N is the total number of data blocks occupied by the table.

However, by sorting the data based on a specific field and creating an index, binary search is possible. Binary search significantly reduces the number of data block accesses to approximately log2 N. This efficiency gain is especially beneficial when working with large data sets.

Indexing mechanism

Database indexing involves creating a separate data structure that stores the indexed field values ​​along with pointers to their corresponding records. These index entries are sorted, allowing binary searches to be used, which perform far better than linear searches.

For example, consider a table with five million rows, each containing the id, firstName, lastName, and emailAddress fields. The id field is defined as the primary key, ensuring unique values, while firstName is an unsorted non-key field.

Without an index, a query searching for a specific firstName value would require a linear search of the entire table, requiring 1,000,000 block accesses.

By creating an index on the firstName field, the database can perform a binary search on the index, reducing the number of data block accesses to approximately 20. The reduction in the number of data block accesses significantly improves query performance.

Best Indexing Practices

While indexing can significantly improve performance, it requires additional storage space for the index structure. Therefore, careful consideration is required when choosing which fields to index.

Indexes should be created for fields that are frequently used in query filters and conditions. Index fields used only for results display may cause unnecessary space consumption and performance overhead during insertions and deletions.

In addition, the cardinality of the indexed field affects the effectiveness of the index. High cardinality fields with many different values ​​optimize index performance by effectively dividing the data into smaller subsets. Conversely, low-cardinality fields with limited unique values ​​may invalidate the index.

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