SQL Polyglot

Author: abesticode
Version: 0.0.1
Type: Tool Plugin

Description

SQL Polyglot is a powerful Dify plugin that provides comprehensive SQL parsing, transpiling, analysis, optimization, and execution capabilities. Built on top of the SQLGlot library, this plugin enables AI agents to work with SQL across 31+ database dialects.

Features

Tool	Description
🔄 SQL Dialect Converter	Convert SQL between 31+ database dialects
📝 SQL Beautifier	Format SQL with proper indentation
🔍 SQL Metadata Extractor	Extract tables, columns, joins from SQL
✅ SQL Syntax Checker	Validate SQL syntax and detect errors
⚡ SQL Query Optimizer	Optimize SQL for better performance
🚀 SQL on JSON Executor	Execute SQL queries on JSON data

Installation

1. Install the plugin from the Dify Marketplace
2. No credentials required - SQLGlot runs locally

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Usage Examples

🔄 SQL Dialect Converter

Convert SQL from one dialect to another.

Parameters:

• sql (required): The SQL query to transpile
• source_dialect (optional): Source dialect (mysql, postgres, bigquery, etc.)
• target_dialect (required): Target dialect
• pretty (optional): Format output with indentation (default: true)

Example 1: MySQL to PostgreSQL

Input SQL: SELECT DATE_FORMAT(created_at, '%Y-%m-%d') FROM users
Source Dialect: mysql
Target Dialect: postgres

Output: SELECT TO_CHAR(created_at, 'YYYY-MM-DD') FROM users

Example 2: Spark to BigQuery

Input SQL: SELECT CAST(col AS STRING) FROM table
Source Dialect: spark
Target Dialect: bigquery

Output: SELECT CAST(col AS STRING) FROM table

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📝 SQL Beautifier

Beautify SQL with proper indentation and styling.

Parameters:

• sql (required): The SQL query to format
• dialect (optional): SQL dialect for parsing
• identify (optional): Quote all identifiers (default: false)
• normalize (optional): Normalize identifiers to lowercase (default: false)

Example 1: Basic Formatting

Input SQL: select u.id,u.name,count(o.id) as order_count from users u left join orders o on u.id=o.user_id where u.active=1 group by u.id,u.name order by order_count desc

Output:
SELECT
  u.id,
  u.name,
  COUNT(o.id) AS order_count
FROM users AS u
LEFT JOIN orders AS o
  ON u.id = o.user_id
WHERE
  u.active = 1
GROUP BY
  u.id,
  u.name
ORDER BY
  order_count DESC

Example 2: With Quote Identifiers (identify=true)

Input SQL: SELECT id, name FROM users
identify: true

Output: SELECT "id", "name" FROM "users"

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🔍 SQL Metadata Extractor

Extract metadata from SQL queries.

Parameters:

• sql (required): The SQL query to analyze
• dialect (optional): SQL dialect for parsing

Example:

Input SQL: 
SELECT u.name, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.active = 1
GROUP BY u.name
ORDER BY order_count DESC

Output:
{
  "query_type": "Select",
  "tables": [
    {"name": "users", "alias": "u"},
    {"name": "orders", "alias": "o"}
  ],
  "columns": [
    {"name": "name", "table": "u"},
    {"name": "id", "table": "o"},
    {"name": "id", "table": "u"},
    {"name": "user_id", "table": "o"},
    {"name": "active", "table": "u"}
  ],
  "joins": [
    {"type": "LEFT", "table": "orders", "on_condition": "u.id = o.user_id"}
  ],
  "functions": [
    {"name": "Count", "sql": "COUNT(o.id)"}
  ],
  "where_conditions": ["u.active = 1"],
  "group_by": ["u.name"],
  "order_by": [{"expression": "order_count", "desc": true}]
}

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✅ SQL Syntax Checker

Check SQL syntax for errors.

Parameters:

• sql (required): The SQL query to validate
• dialect (optional): SQL dialect to validate against

Example 1: Valid SQL

Input SQL: SELECT id, name FROM users WHERE active = 1

Output: ✓ SQL is valid! Found 1 statement(s).
Statement types: Select

Example 2: Invalid SQL (Missing Parenthesis)

Input SQL: SELECT * FROM users WHERE (id = 1 AND name = 'John'

Output: ✗ SQL Validation Failed: Expecting ). Line 1, Col: 45.
  SELECT * FROM users WHERE (id = 1 AND name = 'John'
                                              ~

Note: Validate SQL checks SYNTAX only (parentheses, keywords, structure), not SEMANTIC (function compatibility with dialect).

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⚡ SQL Query Optimizer

Optimize SQL queries using various techniques.

Parameters:

• sql (required): The SQL query to optimize
• dialect (optional): SQL dialect for parsing
• schema (optional): JSON object defining table schemas for type-aware optimization

Example 1: Boolean Simplification

Input SQL: SELECT * FROM users WHERE active = TRUE OR active = FALSE

Output: SELECT * FROM users

Example 2: Constant Folding

Input SQL: SELECT * FROM users WHERE created_at > '2024-01-01' + INTERVAL '1' MONTH

Output: SELECT * FROM users WHERE created_at > '2024-02-01'

Example 3: With Schema (Advanced)

Input SQL: 
SELECT A OR (B OR (C AND D))
FROM x
WHERE Z = '2024-01-01' + INTERVAL '1' MONTH OR 1 = 0

Schema: {"x": {"A": "BOOLEAN", "B": "BOOLEAN", "C": "BOOLEAN", "D": "BOOLEAN", "Z": "DATE"}}

Output:
SELECT
  A OR B OR (C AND D)
FROM x
WHERE
  Z = CAST('2024-02-01' AS DATE)

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🚀 SQL on JSON Executor

Execute SQL queries against JSON data tables.

Parameters:

• sql (required): The SQL query to execute
• tables (required): JSON object containing table data
• dialect (optional): SQL dialect for parsing

Example 1: Simple SELECT

Input SQL: SELECT * FROM users WHERE age > 25

Tables:
{
  "users": [
    {"id": 1, "name": "Alice", "age": 30},
    {"id": 2, "name": "Bob", "age": 22},
    {"id": 3, "name": "Charlie", "age": 28}
  ]
}

Output:
| id | name    | age |
|----|---------|-----|
| 1  | Alice   | 30  |
| 3  | Charlie | 28  |

Example 2: JOIN with Aggregation

Input SQL: 
SELECT u.name, COUNT(o.product) as total_orders, SUM(o.amount) as total_spent
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY u.name

Tables:
{
  "users": [
    {"id": 1, "name": "Alice"},
    {"id": 2, "name": "Bob"},
    {"id": 3, "name": "Charlie"}
  ],
  "orders": [
    {"user_id": 1, "product": "Laptop", "amount": 1500},
    {"user_id": 1, "product": "Mouse", "amount": 50},
    {"user_id": 2, "product": "Keyboard", "amount": 100}
  ]
}

Output:
| name    | total_orders | total_spent |
|---------|--------------|-------------|
| Alice   | 2            | 1550        |
| Bob     | 1            | 100         |
| Charlie | 0            | NULL        |

Note: Execute SQL is for small datasets and testing purposes, not for production database queries.

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Supported SQL Dialects

Dialect	Name	Status
athena	Amazon Athena	✅ Official
bigquery	Google BigQuery	✅ Official
clickhouse	ClickHouse	✅ Official
databricks	Databricks	✅ Official
doris	Apache Doris	✅ Community
drill	Apache Drill	✅ Community
duckdb	DuckDB	✅ Official
hive	Apache Hive	✅ Official
mysql	MySQL	✅ Official
oracle	Oracle	✅ Official
postgres	PostgreSQL	✅ Official
presto	Presto	✅ Official
redshift	Amazon Redshift	✅ Official
snowflake	Snowflake	✅ Official
spark	Apache Spark	✅ Official
sqlite	SQLite	✅ Official
starrocks	StarRocks	✅ Official
teradata	Teradata	✅ Community
trino	Trino	✅ Official
tsql	T-SQL (SQL Server)	✅ Official

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License

Apache License 2.0

Acknowledgments

This plugin is powered by SQLGlot, an excellent SQL parser and transpiler library created by Toby Mao.