Let us continue our System Design series! In this post, we’ll dive into the concept of indexing in databases. Indexing is a technique that allows for faster data retrieval by organizing and optimizing the way data is stored.
What is Indexing?
Indexing is like creating a shortcut for finding the right data quickly. Instead of scanning the entire database, the index helps you jump directly to the location of the data you need, improving the performance of your queries.
Real-World Example:
Let’s go back to our bookstore analogy. Imagine you want to find a particular book in a large bookstore. Instead of going through every single shelf, you check the index of book categories at the store’s entrance. This index tells you exactly which aisle to head to for a particular genre or author, saving you time. The bookstore’s index improves efficiency and helps customers find their desired books faster.
Actual Example in a Book:
Another familiar example of indexing is in the back of a physical book. The index section lists important terms, topics, or names and provides the exact page numbers where you can find them. Instead of flipping through the entire book, you refer to the index, which quickly directs you to the right page.
Types of Indexes in Databases
In databases, there are several types of indexes that help structure and optimize data retrieval.
- Primary Index: The system creates a primary index on the primary key of a table, which uniquely identifies each record.
- Secondary Index: A secondary index is created on non-primary key columns, allowing you to search based on fields other than the primary key.
- Composite Index: A composite index is an index that involves multiple columns. It’s useful when you need to search for data based on multiple attributes.
- Hash Index: A hash index uses a hash function to convert the index values into a specific address in memory. This type of index is very efficient for equality searches but less effective for range-based queries.
Challenges of Indexing
While indexing improves query performance, it also introduces a few challenges that need to be considered in system design.
- Extra Write Operations:
- Every time you insert, update, or delete data, the corresponding index also needs to be updated. This can add overhead to write operations, making them slower.
- Unused Indexes:
- Over time, certain indexes may become unused, yet they still consume storage and processing power. Regular auditing is required to identify and remove such indexes.
- Complexity of Managing Indexes:
- As the number of indexes grows, the database becomes harder to manage. Too many indexes can slow down the system, as every write operation needs to update all relevant indexes.
Video Explanation
