Database Query Execution Process and SQL Parsing

Problem Description: Please explain in detail the core execution steps that a SQL query statement undergoes internally within a database, from submission to returning results. Focus on elaborating the specific workflow, purpose, and key technical implementations of the SQL parsing phase (including lexical analysis and syntactic analysis).

Solution Process:

This question aims to examine your understanding of the underlying mechanism of how a database kernel "understands" and executes a SQL command. We can break down the entire execution process into several core stages. Today, we focus on the most front-end part: SQL Parsing.

Step 1: Overall Process Overview

A SQL statement (e.g., SELECT name FROM users WHERE id = 1;) is not executed immediately after being received by the database. It needs to go through a precise "translation" and "planning" process. The general workflow is as follows:

1. Parsing: Converts the textual SQL string into an internal structure that the database can operate on.
2. Optimization: Generates an efficient execution plan for the query (e.g., choosing which index to use, deciding the order of table joins).
3. Execution: The execution engine follows the plan generated by the optimizer to interact with the storage engine, retrieve data, and return results.

Parsing is the starting point of all this, ensuring that subsequent steps can work based on an accurate, structured representation of the query.

Step 2: Parsing Phase Details - Lexical Analysis

• Purpose: To break down the continuous character stream of a SQL statement into individual, indivisible units with specific meanings, called Tokens (or lexical units).
• Process: This is similar to how we instinctively split an English sentence into individual words when reading. The database's lexical analyzer (Lexer) is a specialized "scanner" that reads the SQL string character by character from left to right.
  • It identifies which character combinations form a keyword (e.g., SELECT, FROM, WHERE).
  • Which ones form an identifier (e.g., table name users, column names name, id).
  • Which ones are operators (e.g., =).
  • Which ones are constants (e.g., the number 1).
  • Simultaneously, it ignores unnecessary whitespace, line breaks, and comments.
• Key Technology: Typically implemented using Finite State Automata. The analyzer matches and generates Tokens based on predefined rules (regular expressions).
• Example: For the SQL statement SELECT name FROM users WHERE id = 1;
  • Input: Character stream S,E,L,E,C,T, ,n,a,m,e, ...
  • Output: A sequence of Tokens, which might be represented as:
    [KEYWORD: SELECT], [IDENTIFIER: name], [KEYWORD: FROM], [IDENTIFIER: users], [KEYWORD: WHERE], [IDENTIFIER: id], [OPERATOR: =], [CONSTANT: 1], [TERMINATOR: ;]

Step 3: Parsing Phase Details - Syntactic Analysis (Parsing)

• Purpose: Based on lexical analysis, it checks whether the Token sequence conforms to the grammatical rules of SQL and builds a tree-like representation that reflects the hierarchical structure of the SQL statement. This tree is called the Abstract Syntax Tree (AST) or Parse Tree.
• Process: The syntactic analyzer (Parser) acts like a "grammar teacher," checking the order of the Token sequence against a set of predefined context-free grammar rules. This grammar defines the legal structures of the SQL language (e.g., a SELECT statement must consist of a SELECT clause, a FROM clause, etc.).
  • It verifies if the Token sequence is legal. For example, whether SELECT is followed by an expression or column name, not a FROM keyword. If a sequence like SELECT FROM WHERE ... appears, the analyzer will immediately report a syntax error.
  • While verifying, it recursively builds the AST according to the grammar rules. Nodes in the AST represent syntactic structures, such as "Query," "Select List," "Table Reference," "Conditional Expression," etc. Leaf nodes are usually Tokens generated by lexical analysis (e.g., identifiers, constants).
• Key Technology: Commonly uses top-down (e.g., recursive descent parsing) or bottom-up (e.g., LR parsing) parsing algorithms.
• Example: For the Token sequence above, the syntactic analyzer would build an AST with a structure roughly as follows:
  • Root Node: SelectStmt
    • Child Node 1: Projection (corresponding to SELECT name)
      • Grandchild Node: ColumnRef: name
    • Child Node 2: FromClause (corresponding to FROM users)
      • Grandchild Node: TableRef: users
    • Child Node 3: WhereClause (corresponding to WHERE id = 1)
      • Grandchild Node: BinaryExpr: =
        • Great-Grandchild Node: ColumnRef: id
        • Great-Grandchild Node: Constant: 1

Step 4: Significance of the Parsing Phase and Subsequent Steps

• Significance: The core task of the SQL parsing phase is to ensure the query statement is syntactically correct and to transform it from a human-friendly text format into a computer-friendly, structured tree format (AST). This AST is the foundation for all subsequent processing.
• Subsequent Steps: After generating the AST, the parsing phase is often not yet complete. There is usually a Semantic Analysis step (sometimes grouped into the parsing phase). The semantic analyzer checks the semantic correctness of the AST, for example:
  • Does the users table exist?
  • Does this table have name and id columns?
  • Does the current user have permission to query this table?
    These checks require querying the database's system catalog (data dictionary). Only after passing semantic analysis will this structurally correct AST be handed over to the query optimizer, entering the stage of generating an efficient execution plan.

Summary

The first step in database SQL query execution is parsing. The parser acts like a meticulous translator. First, through lexical analysis, it "deconstructs" the SQL string into meaningful "words" (Tokens). Then, through syntactic analysis, it "assembles" these "words" into a grammatically correct "sentence" (Abstract Syntax Tree) according to grammatical rules. This process ensures the database can accurately and flawlessly understand the user's query intent, laying a solid foundation for subsequent optimization and execution.