SQL Formatter

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SQL Formatter & Optimizer

Enterprise-grade SQL formatting includes syntax validation and performance analysis.

Our advanced SQL formatter transforms unreadable SQL queries into nicely structured, standards-compliant code. Supports 12+ SQL languages and provides intelligent query optimization suggestions.

Query Transformation

Original Query

SELECT u.id,u.name,COUNT(o.id) AS orders FROM users u LEFT JOIN orders o ON u.id=o.user_id WHERE u.created_at>'2023-01-01' AND u.status='active' GROUP BY u.id HAVING COUNT(o.id)>5 ORDER BY orders DESC LIMIT 100;
Issues Detected: No indentation, inconsistent capitalization, crowded syntax

Formatted Query (ANSI Standard)

SELECT
    u.id,
    u.name,
    COUNT(o.id) AS orders
FROM
    users u
    LEFT JOIN orders o 
        ON u.id = o.user_id
WHERE
    u.created_at > '2023-01-01'
    AND u.status = 'active'
GROUP BY
    u.id
HAVING
    COUNT(o.id) > 5
ORDER BY
    orders DESC
LIMIT 100;
Optimized: Proper indentation, aligned clauses, semantic spacing

Dialect-Specific Formatting

-- MySQL formatted with backticks
SELECT
    `u`.`user_id`,
    `u`.`username`,
    DATE_FORMAT(`u`.`created_at`, '%Y-%m-%d') AS signup_date
FROM
    `users` `u`
WHERE
    `u`.`is_active` = TRUE
    AND `u`.`signup_date` > NOW() - INTERVAL 30 DAY
LIMIT 50;

Query Intelligence

Security Analysis

  • SQL injection pattern detection
  • Overprivileged operations alert
  • Warnings about the exposure of sensitive data.
  • Parameterization recommendations

Style Configurations

  • Comma placement (before and after)
  • Parenthesis spacing
  • Line break regulations (column limits)
  • CTE Formatting Options
  • Window Function Alignment

Complex Query Example

WITH monthly_sales AS (
    SELECT
        DATE_TRUNC('month', order_date) AS month,
        product_category,
        SUM(amount) AS total_sales,
        COUNT(DISTINCT customer_id) AS unique_customers
    FROM
        orders
    WHERE
        order_date BETWEEN '2023-01-01' AND '2023-12-31'
    GROUP BY
        1, 2
),
category_growth AS (
    SELECT
        product_category,
        month,
        total_sales,
        LAG(total_sales, 1) OVER (PARTITION BY product_category ORDER BY month) AS prev_sales,
        unique_customers
    FROM
        monthly_sales
)
SELECT
    product_category,
    month,
    total_sales,
    ROUND((total_sales - prev_sales) / prev_sales * 100, 2) AS growth_pct,
    unique_customers
FROM
    category_growth
WHERE
    prev_sales IS NOT NULL
ORDER BY
    growth_pct DESC
LIMIT 10;