Behind the scenes, SQL queries use various data structures to optimize data retrieval, manage storage, and ensure efficient querying. The most common data structures used in relational databases for handling SQL queries are:

1. B-tree (Balanced Tree)

Usage: B-trees are the most commonly used data structure for indexing, especially for clustered and non-clustered indexes.

How it works: A B-tree is a self-balancing tree structure that keeps data sorted and allows for efficient insertion, deletion, and searching in logarithmic time.

In SQL: When you create an index (e.g., a B-tree index), the database organizes the index keys in this structure. The B-tree allows fast lookup, range queries (e.g., BETWEEN), and ordered traversal.

Example: A query like SELECT * FROM Employees WHERE EmployeeID = 101 can quickly retrieve the row using a B-tree index on EmployeeID.

1. Hash Tables

Usage: Hash tables are often used in hash indexing or hash joins for looking up data quickly based on an exact match.

How it works: A hash table stores data in key-value pairs, where the key is hashed to generate an address in memory. This allows for nearly constant time complexity (O(1)) for lookups.

In SQL: Hash indexing is used for equality comparisons. Hash joins (common in query execution plans) use hash tables to join large datasets efficiently.

Example: A query like SELECT * FROM Employees WHERE Department = 'HR' might use a hash table to quickly find rows with the matching department.

1. Heaps

Usage: A heap is a data structure that represents an unordered collection of data. It is used when there is no clustered index on a table, meaning the data is stored in no particular order.

How it works: Data is simply appended in no specific order. This makes insertions fast but makes searching slower as the database needs to scan the entire table (a full table scan) to find the desired data.

In SQL: A table without a clustered index is referred to as a heap, and queries on such tables might not be as efficient, especially for large datasets.

Example: A query like SELECT * FROM Employees on a table without any index may result in a full table scan due to the heap structure.

1. Bitmaps

Usage: Bitmap indexes use bitmaps (arrays of bits) to index data, which are especially useful for columns with low cardinality (few distinct values).

How it works: A bitmap index uses a string of bits to represent the presence or absence of a value. Bitwise operations on these strings allow for fast querying and filtering of data.

In SQL: Bitmap indexes are used for queries involving multiple conditions (AND/OR operations) on low-cardinality columns.

Example: A query like SELECT * FROM Employees WHERE Gender = 'Male' AND Department = 'Sales' can benefit from bitmap indexes on the Gender and Department columns.

1. Tries (Prefix Trees)

Usage: Tries are used for fast prefix-based searching, particularly in full-text search and when performing pattern matching queries.

How it works: A trie is a tree-like structure where each node represents a character of a string. Searching for a prefix (e.g., "Jo") is efficient because it follows the tree structure.

In SQL: Tries are used in text search engines within databases for quickly locating entries that match a pattern or a prefix.

Example: A query like SELECT * FROM Employees WHERE FirstName LIKE 'Jo%' can be optimized using a trie structure for quick retrieval.

1. Graphs (Execution Plan Trees)

Usage: When an SQL query is executed, the database generates an execution plan, which is internally represented as a graph of operations.

How it works: Each node in the graph represents an operation (e.g., table scan, index lookup, join), and the edges represent the flow of data between operations.

In SQL: The execution plan is built using a graph, and the database engine follows this graph to execute the query optimally.

Example: A complex query with multiple joins, filters, and sorting will have an execution plan tree that outlines how the database will retrieve the data.

1. Queues

Usage: Queues are used in the processing of SQL queries that require tasks to be performed in sequence, such as sorting or result processing in parallel execution.

How it works: A queue stores data in a first-in, first-out (FIFO) order, ensuring that the operations are processed in the correct sequence.

In SQL: Parallel execution plans can use queues to manage the flow of intermediate results before final aggregation or sorting.

Example: A query like SELECT * FROM Employees ORDER BY Salary might use a queue to handle the sorting process, ensuring that results are delivered in the correct order.

Summary

B-tree: Used for most index types (clustered and non-clustered) to enable fast lookups and range queries.

Hash Tables: Used for equality comparisons and hash joins.

Heaps: Represent unordered data storage when no clustered index exists.

Bitmaps: Used in bitmap indexes for columns with low cardinality.

Tries: Used for prefix-based searches and pattern matching.

Graphs: Represent query execution plans to optimize how queries are processed.

Queues: Used to manage the order of operations in sorting and parallel execution plans.

These data structures work together to optimize the performance of SQL queries, enabling faster data retrieval and efficient storage.