Ashutosh is an Application Developer at Thoughtworks. Apart from his love for Backend Development and DevOps, he has a keen interest in writing technical blogs and articles.
Performance and efficiency are crucial in the area of database management systems (DBMS). The demand for speedy and effective data retrieval increases as databases get bigger and more complicated. Indexing in DBMS is a basic idea that speeds up data retrieval by offering a structured and organized means to access and locate particular data within a database. This blog will focus on indexing in DBMS and its types to help you optimize data retrieval and enhance overall performance.
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The term "indexing" in DBMS refers to the process of adding new index data structures in DBMS to enhance data retrieval and query efficiency. It involves developing a logical mapping between key values and the database's corresponding physical locations.
In a database, the data is typically stored in tables, and each table may contain a large number of records. Without an index, the DBMS would have to scan every row of the database sequentially to find a particular piece of information, which would take time and resources, especially when working with huge datasets. This issue is resolved by indexing, which produces a distinct structure that makes quick data lookup possible.
In DBMS, an index is a key-value pair of two columns- Search Key and Data Reference:
The search key column contains copies of selected columns, such as the primary key or candidate key, from the database table. These selected keys are often stored in a sorted manner to optimize query time by enabling binary search instead of linear search.
The data reference column contains a set of pointers that store the addresses of disk blocks. Each disk block contains the actual data that corresponds to the search key. The data reference column is sometimes referred to as the block pointer since it utilizes block-based addressing points, lines, and polygons, based on their spatial relationships and coordinates. Spatial indexes employ specialized data structures, such as R-trees or quad-trees, to enable fast spatial queries, such as range searches, nearest neighbor searches, and spatial joins. They are well-suited for data types that represent spatial information, such as points, lines, polygons, and more.
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Tree based indexing in DBMS is a widely used technique for efficient data retrieval and storage. Two commonly used tree-based index structures are B-tree indexing and B+ tree indexing. Let us explore each of these types:
B-tree indexing in DBMS is a balanced tree-based indexing structure that organizes data in a hierarchical manner. It has effective search and retrieval functions and is made to manage massive amounts of data. The B-tree index maintains a sorted order of the keys and allows for quick lookup by traversing the tree from the root to the leaf nodes.
The branching factor of a B-tree ensures that the depth of the tree is kept to a minimum, allowing for quicker access. B-tree indexing is commonly used in scenarios where the data size is too large to fit in memory and where efficient range queries and updates are required.
B+ tree indexing is an extension of the B-tree index structure that further optimizes the performance and storage characteristics. It is particularly suitable for disk-based storage systems. In a B+ tree, the keys are stored only in the leaf nodes, while the internal nodes act as navigational pointers.
Leaf nodes are connected in a linked list, allowing for efficient range scans and sequential access. The B+ tree index structure provides better utilization of disk blocks, reduces disk I/O operations, and allows for faster range queries and ordered traversals. B+ tree indexing is commonly used in DBMS for handling large datasets and supporting efficient range-based queries.
Effective database management requires a thorough understanding of the numerous indexing techniques. In this section, you will learn about the different indexing techniques in DBMS, which are divided into categories based on the attributes of the index, the structure of data files, and particular use cases.
In DBMS, indexes can be categorized based on the characteristics of the index attribute they are created on. The Three common types of indexes based on these characteristics are:
a. Primary Index
The primary index is created based on the primary key of a table. It provides a unique, ordered, and one-to-one mapping between the primary key values and the physical locations of the corresponding data records. The primary index enables fast retrieval of specific records based on their primary key values, as it offers direct access to the desired data.Here are the characteristics of a Primary Index in DBMS:
b. Clustered Index
Clustered Index is employed when multiple related records are physically stored together. It is based on ordering the data in a specific manner. In clustered indexing, the index table is created using the key values of the underlying data table. The primary objective is to enhance retrieval speed by grouping columns with similar characteristics. This grouping is accomplished through the creation of indexes, known as the clustering Index. Here are the characteristics of clustered index in DBMS:
c. Secondary Index
Secondary Index in DBMS, also known as non-clustered indexing, is a two-level indexing technique that aims to reduce the mapping size of the primary index. Unlike primary indexing, where the actual data is sorted, secondary indexing points to specific locations where the data is stored without maintaining a sorted order.Here are the characteristics of Secondary Indexing:
Based on the data file, indexes can be further classified into the following types:
a. Dense Index: Dense index is a type of index in which an entry exists for every search key value in the data file. It provides a direct mapping between search key values and their corresponding disk block addresses. In a dense index, the index entries are typically sorted in the order of the search key values.This enables efficient lookup operations as the index allows for direct access to the desired data block based on the search key value. However, dense indexing requires more space to store the index entries as compared to sparse index in DBMS.
b. Sparse Index: Sparse index, on the other hand, does not have an entry for every search key value in the data file. Instead, it contains entries only for selected search key values, usually at specific intervals or predetermined points in the data file. These selected values are referred to as index key values. Sparse indexing helps reduce the size of the index, especially for large data files with a wide range of search key values.
To locate a specific search key value, the sparse index directs the search to the nearest index key value that is less than or equal to the desired value, and then linearly scans the data blocks from that point. Sparse indexing requires less storage space for the index but may involve more disk accesses during the search process compared to dense indexing.
Based on specific scenarios, several types of indexes can be used to address specific data characteristics or query requirements. Some of them are:
It is important to carefully analyze the data properties, query patterns, and performance requirements while building an index in a DBMS. Indexes can considerably improve query performance and boost the overall effectiveness of database operations if the indexed columns and index types are chosen carefully and maintained on a regular basis.
In SQL, you can use the CREATE INDEX statement to create an index. The syntax for creating an index is as follows:
CREATE INDEX index_name
Here, index_name is the name you assign to the index, and table_name specifies the table on which the index is to be created.
You can create a single-column index by providing the column_name representing the column based on which the index is built. The syntax for that is as follows:
CREATE INDEX index_name
ON table_name column_name;
If you need to create an index based on multiple columns, you can use the following syntax:
CREATE INDEX index_name
ON table_name (column_name1, column_name2);
This type of index, known as a composite index, is useful when multiple columns are frequently used as filters in the WHERE clause of queries.
To remove an index, you can utilize the DROP INDEX statement with the following syntax:
DROP INDEX index_name;
Indexing offers several advantages in a database management system. Here are some key benefits of indexing:
While indexing provides several advantages, there are also some limitations and drawbacks to consider:
Indexing plays a crucial role in enhancing the efficiency and performance of DBMS. Indexes provide quicker query processing, increased search performance, and optimized data access by organizing and structuring data in a way that promotes quick and efficient data retrieval.
Indexes require additional storage space and entail maintenance overhead, especially during data modification operations. Therefore, it is essential to strike a balance between the benefits and costs associated with indexing. The Database certification course will help you explore the most popular databases leveraged by organizations worldwide.