Open In App

Secondary Indexing in Databases

Last Updated : 14 Feb, 2023
Improve
Improve
Like Article
Like
Save
Share
Report

Pre-requisites: Primary Indexing in Databases

Databases are a critical component of modern applications, storing vast amounts of data and serving as a source of information for various functions. One of the primary challenges in managing databases is providing efficient access to the stored data. To meet this challenge, database management systems use various techniques, including indexing, to improve the performance of data retrieval operations. Indexing is a method that creates a separate structure, referred to as an index, from the data stored in a database. The purpose of an index is to allow for fast access to data without having to search through the entire dataset. There are several types of indexes, including primary indexes and secondary indexes.

What is Secondary Indexing in Databases?

Secondary indexing is a database management technique used to create additional indexes on data stored in a database. The main purpose of secondary indexing is to improve the performance of queries and to simplify the search for specific records within a database. A secondary index provides an alternate means of accessing data in a database, in addition to the primary index. The primary index is typically created when the database is created and is used as the primary means of accessing data in the database. Secondary indexes, on the other hand, can be created and dropped at any time, allowing for greater flexibility in managing the database.

Benefits 

  • Improved Query Performance: Secondary indexes can improve the performance of queries by reducing the amount of data that needs to be scanned to find the desired records. With a secondary index, the database can directly access the required records, rather than having to scan the entire table.
  • Flexibility: Secondary indexes provide greater flexibility in managing a database, as they can be created and dropped at any time. This allows for a more dynamic approach to database management, as the needs of the database can change over time.
  • Simplified Search: Secondary indexes simplify the search for specific records within a database, making it easier to find the desired data.
  • Reduced Data Storage Overhead: Secondary indexes use a compact data structure that requires less space to store compared to the original data. This means that you can store more data in a database while reducing the amount of storage space required.

Types of Secondary Indexes

  • B-tree Index: A B-tree index is a type of index that stores data in a balanced tree structure. B-tree indexes are commonly used in relational databases and provide efficient search, insert, and delete operations.
  • Hash Index: A hash index is a type of index that uses a hash function to map data to a specific location within the index. Hash indexes are commonly used in non-relational databases, such as NoSQL databases, and provide fast access to data.
  • Bitmap Index: A bitmap index is a type of index that uses a bitmap to represent the data in a database. Each bit in the bitmap represents a specific record in the database, and the value of the bit indicates whether the record is present or not. Bitmap indexes are commonly used in data warehousing and business intelligence applications, as they provide efficient access to large amounts of data.

When to Use Secondary Indexing

Secondary indexing should be used in database management systems when there is a need to improve the performance of data retrieval operations that search for data based on specific conditions. Secondary indexing is particularly useful in the following scenarios:

  • Queries with Complex Search Criteria: Secondary indexes can be used to support complex queries that search for data based on multiple conditions. By creating a secondary index based on the columns used in the search criteria, database management systems can access the data more efficiently.
  • Large Data Sets: Secondary indexing can be beneficial for large data sets where the time and resources required for data retrieval operations can be significant. By creating a secondary index, database management systems can access the data more quickly, reducing the time and resources required for data retrieval operations.
  • Frequently Accessed Data: Secondary indexing should be used for frequently accessed data to reduce the time and resources required for data retrieval operations. This is because secondary indexes provide a fast and efficient way to access data stored in a database.
  • Sorting and Aggregating Data: Secondary indexing can be used to support sorting and aggregating data based on specific columns. By creating a secondary index based on the columns used for sorting and aggregating, database management systems can access the data more efficiently, reducing the time and resources required for data retrieval operations.
  • Data Structure: The data structure of a database can also affect the decision to use secondary indexing. For example, if the data is structured as a B-tree, a B-tree index may be the most appropriate type of secondary index.

Conclusion
 

Secondary indexing is an essential technique used in database management systems to improve the performance of data retrieval operations. By creating a separate index structure based on specific columns, database management systems can access data more quickly and efficiently, reducing the time and resources required for data retrieval operations.

Secondary indexing provides several benefits, including improved query performance, increased flexibility, and reduced data storage overhead. It is particularly useful in scenarios where there is a need to support complex search criteria, access large data sets, and sort and aggregate data based on specific columns. However, it’s important to consider the trade-offs when using secondary indexing, as it can also add additional overhead in terms of storage and update operations. The number and size of secondary indexes should be carefully managed to minimize the impact on database performance.


Similar Reads

Clustering Indexing in Databases
Pre-requisites: Primary Indexing in Databases, indexing Databases are a crucial component of modern computing, providing a structured way to store, manage, and retrieve vast amounts of data. As the size of databases increases, it becomes increasingly important to have an efficient indexing mechanism that can quickly search and retrieve data. Cluste
4 min read
Indexing in Databases - Set 1
Indexing improves database performance by minimizing the number of disc visits required to fulfill a query. It is a data structure technique used to locate and quickly access data in databases. Several database fields are used to generate indexes. The main key or candidate key of the table is duplicated in the first column, which is the Search key.
9 min read
Primary Indexing in Databases
Indexing is a technique used to reduce access cost or I/O cost, now the question arrives what is access cost? Access cost is defined as the number of secondary memory blocks which is transferred from secondary memory to main memory in order to access required data. In this article, we are going to discuss every point about primary indexing. What is
5 min read
Difference between Primary and Secondary Data
1. Primary Data : When a data is collected from beginning to end for the first time by an institution or researcher, such data is called primary data, it is the original data, i.e. the data which is first It is completely renewed, it is called primary data. It is collected from scratch a lot of money is spent on collecting primary data. At the same
2 min read
Partial, Unique, Secondary, Composite and Surrogate keys in DBMS
Partial Key : The set of attributes that are used to uniquely identify a weak entity set is called the Partial key. Only a bunch of the tuples can be identified using the partial keys. The partial Key of the weak entity set is also known as a discriminator. It is just a part of the key as only a subset of the attributes can be identified using it.
2 min read
Secondary Indexes on SET Collection in Cassandra
In this article, we will discuss the overview of Secondary Indexes on SET Collection in Cassandra and then will implement the exercise and will see how it actually works, and then finally will conclude the importance of Secondary Indexes on SET Collection. Let's discuss it one by one. Pre-requisite - Concept of indexing in Apache CassandraCollectio
2 min read
Secondary Indexes on MAP Collection in Cassandra
In this article, we will discuss the overview of Secondary Indexes on MAP Collection in Cassandra and then will implement the exercise and will see how it actually works, and then finally will conclude the importance of Secondary Indexes on MAP Collection. Let's discuss it one by one. Pre-requisite - Concept of indexing in Apache CassandraCollectio
4 min read
Secondary Indexes on LIST Collection in Cassandra
In this article, we will discuss the overview of Secondary Indexes on LIST Collection in Cassandra and then will implement the exercise and will see how it actually works, and then finally will conclude the importance of Secondary Indexes on LIST Collection. Let's discuss it one by one. Pre-requisite - Concept of indexing in Apache CassandraCollect
2 min read
Concept of indexing in Apache Cassandra
Prerequisite - Introduction to Apache Cassandra Index: As we can access data using attributes which having the partition key. For Example, if Emp_id is a column name for Employee table and if it is partition key of that table then we can filter or search data with the help of partition key. In this case we can used WHERE clause to define condition
5 min read
Local Indexing and Materialized views in Cassandra 3.0
Prerequisite - Concept of Indexing, Concept of Materialized Views In this article, we will see how we can do local indexing and how it works and how materialized views works internally. Let's discuss one by one. First, we need to create a table. let's consider a table Team_data in which id, name, address are the fields. Let's have a look. CREATE TA
2 min read
Article Tags :