A tree is a self-balancing tree data

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Improve the performance of indexed queries. Data structures that provide support for indexes Database indexes are smaller auxiliary data structures that a database uses to store subsets of table data. They are a collection of key-value pairs that point the columns used to create the index to the records in a specific table. However, the data structures used to define the index are more complex making them just as fast. The default data structure used when defining indexes is a tree. structure that maintains sorted data. Every update to the tree by inserting an update or deleting rebalances the tree. This video provides an excellent conceptual overview of tree data structures. Each write to an indexed column in the database context updates the associated index.

Time Complexity of the Tree A sequen photo editing servies tial scan has linear time complexity. This means that the time it takes to retrieve a record scales linearly with the number of records you have. If you are unfamiliar with the concept of representation check out What is representation. Trees on the other hand have logarithmic time complexity. This means that the cost of retrieving records grows significantly slower as the size of the data grows. Using indexes to improve query performance You can use attribute functions to add indexes to fields in your schema. Accepts multiple arguments such as a list of fields to index and the name of the index to be created in the database.



Supports more arguments. You can learn more in the reference. Update the model by adding an index to the field After making the changes create and run another migration to update the database schema with the index Copy the resulting migration Next navigate to the file again and send the request to You will notice a significant improvement in the response time . In my case the response time was down to milliseconds. Your query now has logarithmic time complexity and the search time is more scalable than initially. Bonus Adding .