What are the common use cases and scenarios for using secondary index in DBMS?

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A secondary index is a data structure that allows you to access records in a database table based on a different attribute than the primary key. For example, if you have a table of customers with a primary key of customer_id, you can create a secondary index on the customer_name attribute to quickly find all the records that match a given name. Secondary indexes can improve the performance and flexibility of your queries, but they also have some trade-offs and limitations. In this article, we will explore some of the common use cases and scenarios for using secondary index in DBMS.

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Chitransh Jaiswal

Top Voice in Artificial Intelligence ? | Former Software Developer Intern at Salesforce | Full Stack .NET Developer |…
Khanh-Quan (Kevin) Truong

Senior Insights Analyst at Linkedin. Tokyo University, Master of Computer Science.

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Durgesh Kumar Prajapati

Managing Director - Chrome Gaming | Open Source Contributor - C4GT DMP'24 | Ex Contributor - The Tor Project, OmegaUp &…

1 Benefits of secondary index

One of the main benefits of secondary index is that it can speed up queries that involve filtering, sorting, or joining on attributes other than the primary key. For instance, if you want to find all the customers who live in a certain city, you can use a secondary index on the city attribute to avoid scanning the entire table. Similarly, if you want to sort the customers by their last name, you can use a secondary index on the last_name attribute to avoid sorting the whole table. Moreover, if you want to join the customers table with another table that has a foreign key reference to the customer_name attribute, you can use a secondary index on the customer_name attribute to facilitate the join operation.

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Khanh-Quan (Kevin) Truong

Senior Insights Analyst at Linkedin. Tokyo University, Master of Computer Science.
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Secondary indexes, also known as non-clustered indexes, provide several benefits in a DBMS: 1. Improved query performance: significantly improve the performance of queries that search for data based on columns other than the primary key. By providing a pre-filtered set of rows to scan, secondary indexes can reduce the amount of data that needs to be read from disk, which can improve query performance. 2. Faster sorting and grouping: Secondary indexes can be used to speed up sorting and grouping operations by providing a pre-sorted list of rows. 3. Faster joins: When joining two or more tables, a secondary index can be used to speed up the join operation by providing a smaller set of rows to scan.

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Durgesh Kumar Prajapati

Managing Director - Chrome Gaming | Open Source Contributor - C4GT DMP'24 | Ex Contributor - The Tor Project, OmegaUp & XWiki | City Lead - Lucknow | Google - Arcade Facilitator
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Secondary indexes enhance query performance by enabling filtering, sorting, or joining on attributes other than the primary key. For example, searching for customers in a specific city or sorting by last name benefits from secondary indexes on respective attributes, avoiding full table scans or sorts. Additionally, when joining tables via foreign key references like customer_name, secondary indexes on related attributes optimize join operations.

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SUNNY SINGH

10X LINKEDIN TOP VOICE || FORMER DATA SCIENCE TRAINEE (INEURON.AI) || EX-SME OF CSE(CHEGG) || CHANDIGARH UNIVERSITY ALUMINI || EX-SOFTWARE DEVELOPER- INTERN (JP MORGAN) || EX-DATA ANALYTICS INTERN ( KPMG)
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Secondary indexes in database management systems (DBMS) serve various use cases and scenarios, enhancing query performance and facilitating efficient data retrieval. One common use case is in scenarios where queries frequently filter or sort data based on attributes other than the primary key. Secondary indexes allow DBMS to quickly locate rows based on these secondary attributes, reducing the need for full table scans and improving query response times. Another use case is in supporting join operations, where secondary indexes on join attributes enable faster joining of tables by providing quick access to related rows. Additionally, secondary indexes are valuable for enforcing uniqueness constraints on non-primary key columns.

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Saiyyed Murtaza Ali Naqvi

Senior Software Solutions Architect-Software Consultant
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Secondary indexes in DBMS serve various pivotal purposes. They're vital for enhancing query performance by enabling rapid data retrieval in scenarios like: Filtering Operations: Ideal for refining searches on non-primary key attributes. Join Optimization: Facilitates efficient join operations between tables. Sorting: Enables quick sorting based on indexed attributes. Aggregation: Accelerates aggregation functions, expediting analytical queries. Range Queries: Perfect for speeding up range-based searches on specific attributes. Secondary indexes significantly bolster database performance across diverse use cases and query scenarios.

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2 Types of secondary index

There are two main types of secondary index: sparse and dense. A sparse index has one entry for each distinct value of the indexed attribute, and points to the first record that has that value. A dense index has one entry for each record in the table, and points to the exact location of that record. Sparse indexes are more space-efficient and less costly to maintain, but they require more disk accesses to locate a record. Dense indexes are more time-efficient and less costly to access, but they require more space and more updates.

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Chitransh Jaiswal

Top Voice in Artificial Intelligence ? | Former Software Developer Intern at Salesforce | Full Stack .NET Developer | Problem Solver (Java and CPP)
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Secondary indexes are essential tools for optimizing query performance, and understanding their types—sparse and dense—can help in choosing the right one for your needs. Sparse indexes are more space-efficient because they index only distinct values, reducing the index size and maintenance overhead, but they may require additional disk accesses to find records due to their less granular nature.

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3 Challenges of secondary index

Secondary indexes also introduce some challenges and drawbacks that you need to consider before using them. First, secondary indexes increase the storage space and the memory usage of your database, which can affect the performance and scalability of your system. Second, secondary indexes increase the complexity and the overhead of data manipulation operations, such as insert, update, and delete, which can affect the concurrency and consistency of your database. Third, secondary indexes may not always be up-to-date with the primary data, especially if you use deferred or asynchronous updates, which can affect the accuracy and reliability of your queries.

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Khanh-Quan (Kevin) Truong

Senior Insights Analyst at Linkedin. Tokyo University, Master of Computer Science.
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There are also some challenges that must be considered: 1. Increased storage requirements: Secondary indexes require additional storage space to store the index data. This can increase the overall storage requirements of the system, which can be a concern for large databases. 2. Increased write overhead: Maintaining secondary indexes can be computationally expensive and can increase the write overhead of the system. This can be a concern for systems that require high write throughput.

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4 Best practices for secondary index

To use secondary indexes effectively and efficiently, it is essential to adhere to some best practices and guidelines. When selecting attributes to index, consider the frequency and selectivity of your queries. Avoid indexing attributes that are rarely used, have low cardinality, or have high update rates. Choose the type of index that best suits your needs based on the trade-off between space and time. Sparse indexes are suitable for attributes with high cardinality and low update rates, while dense indexes are better for attributes with low cardinality and high update rates. Regularly monitor and tune your indexes based on changes in data and workload using tools and metrics to measure performance and utilization. Lastly, balance the number and size of your indexes according to available resources and expected benefits; too many or too large indexes can overwhelm the system and decrease performance.

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5 Examples of secondary index

To illustrate some of the use cases and scenarios for using secondary index in DBMS, let's consider examples from different domains and applications. For instance, e-commerce customers can use secondary indexes to search and filter products by category, price, rating, or availability. In social media, secondary indexes enable users to find and follow other users by name, location, interests, or hashtags. Additionally, analysts can use secondary indexes to aggregate and compare data by date, region, segment, or metric.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the common use cases and scenarios for using secondary index in DBMS?

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1 Benefits of secondary index

2 Types of secondary index

3 Challenges of secondary index

4 Best practices for secondary index

5 Examples of secondary index

6 Here’s what else to consider

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