How SQL Server Relational Engine works in processing SQL queries

The SQL Server Relational Engine, also known as the Query Processor, is responsible for interpreting and executing SQL queries. It optimizes queries and coordinates their execution to return the requested data efficiently. Here’s an overview of how SQL Server processes SQL queries:

1. SQL Query Lifecycle

A SQL query undergoes several stages in SQL Server’s Relational Engine:

a. Query Parsing

1. Syntax Check: SQL Server checks the query for syntax errors.
2. Parsing: Generates a parse tree (a structural representation of the query) using the query’s syntax.
3. Binding: Ensures that all objects (tables, columns, etc.) in the query exist and are accessible.
   • Example: Ensures a column referenced in the SELECT clause exists in the specified table.

b. Query Optimization

1. Algebrizer Phase:
   • Converts the parse tree into a query processor tree.
   • Resolves object references and validates permissions.
2. Query Optimizer:
   • Generates multiple execution plans for the query.
   • Chooses the most efficient plan based on cost (e.g., I/O, CPU usage).
   • Uses statistics (metadata about data distribution) to make decisions.
   • Example: Deciding between a full table scan or using an index.

c. Query Compilation

• Converts the chosen execution plan into a compiled plan.
• The compiled plan includes instructions for data retrieval, joining, filtering, and sorting.

2. Query Execution

After optimization, the Execution Engine runs the query:

1. Execution Plan: The compiled plan guides how the query interacts with data.
2. Operators: Executes physical operators like scans, seeks, joins, or sorts.
3. Data Retrieval:
   • Accesses data pages stored in the buffer pool or retrieves them from disk if not cached.
   • Example: Fetching rows matching a WHERE condition.

3. Interaction with the Storage Engine

The Relational Engine interacts with the Storage Engine for data access:

1. Access Methods:
   • Determines how to retrieve data (e.g., index scan vs. table scan).
2. Locks and Transactions:
   • Manages locking to ensure ACID compliance.
   • Example: Applying a shared lock during a SELECT operation.
3. Data Retrieval and Updates:
   • Fetches requested rows or modifies data in pages.
   • Applies changes and ensures they are written to the transaction log.

4. Query Results

Once the Execution Engine completes the plan:

1. Data is formatted according to the query’s structure (e.g., column order, aggregations).
2. Results are sent back to the client application.

5. Example

For the query:

SELECT Name FROM Employees WHERE Department = 'HR';

1. Parsing: Ensures Employees table and Name column exist.
2. Optimization: Evaluates using an index on Department vs. a table scan.
3. Execution: Retrieves rows from the Employees table where Department equals 'HR'.
4. Results: Returns a list of employee names in the HR department.

Key Features of the Relational Engine

1. Plan Caching: Reuses execution plans to reduce optimization overhead for repeated queries.
2. Dynamic Optimization: Adapts to changing data patterns using updated statistics.
3. Parallel Execution: Uses multiple threads for query execution on large datasets.

Performance Considerations

1. Ensure indexes are in place for frequently queried columns.
2. Update statistics regularly for optimal query plans.
3. Use query hints sparingly to guide the optimizer in specific scenarios.

The Relational Engine handles query interpretation and execution. The Storage Engine manages data retrieval. By dividing these responsibilities, SQL Server ensures efficient query processing and robust data handling.