How Can I Systematically Construct SQL Queries from Human-Readable Descriptions?

Constructing SQL queries from human-readable descriptions: a systematic approach

Converting human-readable descriptions into SQL queries often requires the use of heuristics and brainstorming, but there does exist a systematic approach to guide this process.

Step 1: Understand the logical framework

The first step is to recognize the correspondence between natural language expressions and logical expressions. This correspondence extends to relational algebra expressions and SQL expressions. Each table has a predicate that represents a natural language template. Rows in the table satisfy this predicate when populated with column values.

Step 2: Determine the predicate of the required row

The goal is to construct a predicate that describes the desired row. This can be expressed by a logical expression involving a predicate for a given base table.

Step 3: Convert predicate to SQL expression

Once the predicate is determined, it can be converted into a SQL expression using the following operators:

• JOIN: Joins rows based on equality or other conditions.
• WHERE: Filter rows based on conditions.
• CROSS JOIN: Creates a Cartesian product of joined tables.
• DISTINCT: Remove duplicate lines.
• IN: Check if the row exists in the subquery.
• UNION: Combine rows from different tables with matching columns.
• VALUES: Create a temporary table with specific values.

Example:

Suppose we want to retrieve rows where "Bob" likes someone who likes "Carol" but dislikes "Ed".

Natural language predicate:

<code>存在某个x的值，
   [Bob] 喜欢 [x]
并且 [x] 喜欢 [Carol]
并且 [Bob] = 'Bob'
并且 [x] 不喜欢 'Ed'</code>

SQL conversion:

<code class="language-sql">SELECT DISTINCT l1.liker AS person, l2.liked AS liked
FROM Likes AS l1
INNER JOIN Likes AS l2 ON l1.liked = l2.liker
WHERE l1.liker = 'Bob'
AND NOT (l1.liked, 'Ed') IN (SELECT * FROM Likes)</code>

This approach provides a step-by-step guide to systematically building SQL queries based on human-readable descriptions. It helps determine the appropriate syntax and operators to convert natural language statements into logical expressions and ultimately into SQL queries.

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