CarbunqleX - SQL Parser and Modeler

🚀 Overview

CarbunqleX enhances the reusability and maintainability of raw SQL queries by deeply analyzing their Abstract Syntax Tree (AST). This allows for powerful transformations while preserving query semantics. With CarbunqleX, you can:

Modify selection columns
Inject JOIN and WHERE conditions dynamically
Transform queries into different SQL statements (CREATE TABLE AS, INSERT INTO, UPDATE, DELETE)

💡 Key Features

Advanced CTE Handling

CarbunqleX offers flexible Common Table Expression (CTE) processing. Traditionally, CTEs exist only within the WITH clause and cannot be referenced in WHERE or JOIN conditions. However, CarbunqleX detects existing CTEs and lifts them to the top level, making them accessible in places where they would otherwise be restricted. This enables highly flexible query modifications.

Intelligent Search Condition Injection

Unlike conventional SQL libraries, CarbunqleX automatically determines the most appropriate insertion point for search conditions, even within complex queries involving subqueries and CTEs. This ensures optimal filtering while preserving query integrity. For example, when dealing with GROUP BY, conditions are inserted before aggregation to ensure correctness.

Lightweight and Easy to Use

Minimal dependencies – Works directly with raw SQL
No special setup or DBMS required – Purely operates on query strings
Seamless ORM integration – Works alongside existing ORM frameworks

📦 Installation

Carbunqlex can be installed from NuGet. To install using the package manager, use the following command:

NuGet\Install-Package Carbunqlex

📖 Documentation

The complete code for the sample used here can be found at Sample.csproj

1. Parsing a SQL Query

Let's start by parsing a simple SQL query into an AST using QueryAstParser.Parse. We will then convert it back to SQL with ToSql and inspect its structure using ToTreeString.

using Carbunqlex;
using Carbunqlex.Parsing;

var sql = "SELECT a.table_a_id, a.value FROM table_a AS a";
var query = QueryAstParser.Parse(sql);

// Convert back to SQL
Console.WriteLine("* SQL");
Console.WriteLine(query.ToSql());

// View AST structure (useful for debugging)
Console.WriteLine("* AST");
Console.WriteLine(query.ToTreeString());

🔍 Expected SQL Output

SELECT a.table_a_id, a.value FROM table_a AS a

This makes it easy to convert between them.

2. Modifying the WHERE Clause

Now, let's modify the query by injecting a WHERE condition.

var query = QueryAstParser.Parse("SELECT a.table_a_id, a.value FROM table_a AS a");

// Inject a filter condition
query.Where("value", w => w.Equal(1));

Console.WriteLine(query.ToSql());

🔍 Expected SQL Output

SELECT a.table_a_id, a.value
FROM table_a AS a
WHERE a.value = 1;

CarbunqleX allows you to inject conditions while still maintaining the integrity of your SQL.

3. Handling CTEs and Subqueries

Let's try to insert a WHERE condition into a query that contains a CTE and a subquery.

var query = QueryAstParser.Parse("""
    WITH regional_sales AS (
        SELECT orders.region, SUM(orders.amount) AS total_sales
        FROM orders
        GROUP BY orders.region
    ), top_regions AS (
        SELECT rs.region
        FROM regional_sales rs
        WHERE rs.total_sales > (SELECT SUM(x.total_sales)/10 FROM regional_sales x)
    )
    SELECT orders.region, orders.product, SUM(orders.quantity) AS product_units, SUM(orders.amount) AS product_sales
    FROM orders
    WHERE orders.region IN (SELECT x.region FROM top_regions x)
    GROUP BY orders.region, orders.product
""");

query.Where("region", w => w.Equal("'east'"));

🔍 Modified SQL Output

WITH regional_sales AS (
    SELECT orders.region, SUM(orders.amount) AS total_sales
    FROM orders
    WHERE orders.region = 'east'
    GROUP BY orders.region
),
 top_regions AS (
    SELECT rs.region
    FROM regional_sales rs
    WHERE rs.total_sales > (SELECT SUM(x.total_sales)/10 FROM regional_sales x)
)
SELECT orders.region, orders.product, SUM(orders.quantity) AS product_units, SUM(orders.amount) AS product_sales
FROM orders
WHERE orders.region IN (SELECT x.region FROM top_regions x)
GROUP BY orders.region, orders.product;

CarbunqleX will intelligently place the condition in the deepest related query. It will also dynamically merge CTEs.

4. Standardizing filtering

Next, we will introduce a more advanced use case that dynamically filters data based on user permissions. We define the areas that users can access as reusable functions.

🔧 Define a Subquery Function

private QueryNode BuildRegionScalarQueryByUser(int userId)
{
    return QueryAstParser.Parse("""
        WITH user_permissions AS (
            SELECT rrp.region
            FROM region_reference_permission rrp
            WHERE rrp.user_id = :user_id
        )
        SELECT up.region FROM user_permissions up
    """)
    .AddParameter(":user_id", userId);
}

🔍 Apply the Function in a Query

var query = QueryAstParser.Parse("...");
query.Where("region", w => w.Exists(BuildRegionScalarQueryByUser(1)));

🔍 Expected SQL Output

WITH user_permissions AS (
    SELECT rrp.region
    FROM region_reference_permission AS rrp
    WHERE rrp.user_id = :user_id
), regional_sales AS (
    SELECT orders.region, SUM(orders.amount) AS total_sales
    FROM orders
    WHERE EXISTS (
        SELECT * 
        FROM (SELECT up.region FROM user_permissions AS up) AS x 
        WHERE orders.region = x.region
    )
    GROUP BY orders.region
), top_regions AS (
    SELECT rs.region
    FROM regional_sales AS rs
    WHERE rs.total_sales > (SELECT SUM(x.total_sales) / 10 FROM regional_sales AS x)
)
SELECT orders.region, orders.product, SUM(orders.quantity) AS product_units, SUM(orders.amount) AS product_sales
FROM orders
WHERE orders.region IN (SELECT x.region FROM top_regions AS x)
GROUP BY orders.region, orders.product

By making filtering a function, we can express queries that are highly maintainable and versatile.

5. Modify columns

This feature is useful for enforcing constraints such as closing date control in accounting by ensuring a column value does not fall below a specified threshold.

var query = QueryAstParser.Parse("SELECT s.sale_date, s.sales_amount FROM sales AS s");

// Ensure sale_date is at least '2024-01-01'
query.ModifyColumn("sale_date", c => c.Greatest(new DateTime(2024, 1, 1)));

Console.WriteLine(query.ToSql());

🔍 Expected SQL Output

SELECT GREATEST(s.sale_date, '2024-01-01 00:00:00') AS sale_date, s.sales_amount 
FROM sales AS s;

In this way, column processing can be made common.

6. Manage UNION queries as modular components

This approach allows you to manage UNION queries as separate components, improving maintainability and modularity.

var query1 = QueryAstParser.Parse("SELECT id FROM table_a");
var query2 = QueryAstParser.Parse("SELECT id FROM table_b");

// Create a UNION ALL query and wrap it as a distinct subquery
var distinctQuery = query1.UnionAll(query2).ToSubQuery().Distinct();

Console.WriteLine(distinctQuery.ToSql());

🔍 Expected SQL Output

SELECT DISTINCT * FROM (SELECT id FROM table_a UNION ALL SELECT id FROM table_b) AS d;

Now managing huge union queries is not scary.

7. Filtering using outer joins

You can also dynamically insert outer join conditions to perform filtering.

var query = QueryAstParser.Parse("""  
  WITH regional_sales AS (  
      SELECT orders.region, SUM(orders.amount) AS total_sales  
      FROM orders  
      GROUP BY orders.region  
  ), top_regions AS (  
      SELECT rs.region  
      FROM regional_sales rs  
      WHERE rs.total_sales > (SELECT SUM(x.total_sales)/10 FROM regional_sales x)  
  )  
  SELECT orders.region,  
         orders.product,  
         SUM(orders.quantity) AS product_units,  
         SUM(orders.amount) AS product_sales  
  FROM orders   
  WHERE orders.region IN (SELECT x.region FROM top_regions x)  
  GROUP BY orders.region, orders.product  
""");

// Set isCurrentOnly: false to use the join as a filter condition  
query.From("region", isCurrentOnly: false, static from =>
{
    from.LeftJoin("top_regions", "tp");
    from.EditQuery(q =>
    {
        q.Where("tp.region is null");
    });
});

// left join top_regions as tp on orders.region = tp.region where tp.region is null  
var expected = "with regional_sales as (select orders.region, SUM(orders.amount) as total_sales from orders left join top_regions as tp on orders.region = tp.region where tp.region is null group by orders.region), top_regions as (select rs.region from regional_sales as rs where rs.total_sales > (select SUM(x.total_sales) / 10 from regional_sales as x)) select orders.region, orders.product, SUM(orders.quantity) as product_units, SUM(orders.amount) as product_sales from orders where orders.region in (select x.region from top_regions as x) group by orders.region, orders.product";

8. Create a table from a select query

You can convert a select query into a CREATE TABLE statement. This is very useful when you want to create a new table based on the results of a query.

var query = QueryAstParser.Parse("SELECT a.table_a_id, 1 AS value FROM table_a AS a");

// Generate a CREATE TABLE query, specifying that the table is temporary
var createTableQuery = query.ToCreateTableQuery("table_b", isTemporary: true);

// Print the generated SQL query
Console.WriteLine(createTableQuery.ToSql());

🔍 Expected SQL output

CREATE TEMPORARY TABLE table_b AS SELECT a.table_a_id, 1 AS value FROM table_a AS a;

9. Manage Update Queries with Select Queries

The effects of insert, update, and delete queries can only be seen after they are executed, making it difficult to preview the expected results in advance.

CarbunqleX allows you to express these queries as select queries. Select queries simplify the debugging and validation process by allowing you to preview changes without actually modifying tables.

var query = QueryAstParser.Parse("SELECT a.table_a_id, 1 AS value FROM table_a AS a");

// Generate an INSERT INTO query for table_b with a RETURNING clause
var insertTableQuery = query.ToInsertQuery("table_b", hasReturning: true);

// Print the generated SQL query
Console.WriteLine(insertTableQuery.ToSql());

🔍 Expected SQL output

INSERT INTO table_b(table_a_id, value)
SELECT a.table_a_id, 1 AS value FROM table_a AS a
RETURNING *;

The above example demonstrates how to express an insert query using a select query, but similar techniques can also be applied to update and delete queries.

For update queries:

var query = QueryAstParser.Parse("SELECT a.table_a_id, 1 AS value FROM table_a AS a");

var updateQuery = query.ToUpdateQuery("table_b", new[] { "table_a_id" });

var expected = "UPDATE table_b SET value = q.value FROM (SELECT a.table_a_id, 1 AS value FROM table_a AS a) AS q WHERE table_b.table_a_id = q.table_a_id";

For delete queries:

var query = QueryAstParser.Parse("SELECT a.table_a_id, 1 AS value FROM table_a AS a");

var deleteQuery = query.ToDeleteQuery("table_b", new[] { "table_a_id" });

var expected = "DELETE FROM table_b WHERE table_b.table_a_id IN (SELECT q.table_a_id FROM (SELECT a.table_a_id, 1 AS value FROM table_a AS a) AS q)";

10. Converting a Query to Return JSON (Postgres-only)

In PostgreSQL, you can transform a query to return JSON using functions like json_build_object and row_to_json.

var query = QueryAstParser.Parse("""
    SELECT 
        posts.post_id, 
        posts.title, 
        posts.content, 
        posts.created_at, 
        users.user_id, 
        users.name AS user_name, 
        blogs.blog_id, 
        blogs.name AS blog_name, 
        organizations.organization_id, 
        organizations.name AS organization_name
    FROM posts
    INNER JOIN users ON posts.user_id = users.user_id
    INNER JOIN blogs ON posts.blog_id = blogs.blog_id
    INNER JOIN organizations ON blogs.organization_id = organizations.organization_id
    WHERE posts.post_id = 1
""");

query.NormalizeSelectClause()
    .Serialize("organizations", objectName: "organization")
    .Serialize("users", objectName: "user")
    .Serialize("blogs", objectName: "blog", include: ["organization"])
    .Serialize("posts", objectName: "post", include: ["user", "blog"])
    .ToJson();

🔍 Expected SQL output

SELECT row_to_json(d) 
FROM (
    SELECT json_build_object(
        'post_id', posts.post_id, 
        'title', posts.title, 
        'content', posts.content, 
        'created_at', posts.created_at, 
        'user', json_build_object(
            'user_id', users.user_id, 
            'user_name', users.name
        ), 
        'blog', json_build_object(
            'blog_id', blogs.blog_id, 
            'blog_name', blogs.name, 
            'organization', json_build_object(
                'organization_id', organizations.organization_id, 
                'organization_name', organizations.name
            )
        )
    ) AS post 
    FROM posts
    INNER JOIN users ON posts.user_id = users.user_id
    INNER JOIN blogs ON posts.blog_id = blogs.blog_id
    INNER JOIN organizations ON blogs.organization_id = organizations.organization_id
    WHERE posts.post_id = :post_id
) AS d 
LIMIT 1;

🔍 Query Output

{
    "post": {
        "post_id": 1,
        "title": "Understanding AI",
        "content": "This is a post about AI.",
        "created_at": "2025-02-18T20:25:21.974106",
        "user": {
            "user_id": 1,
            "user_name": "Alice"
        },
        "blog": {
            "blog_id": 1,
            "blog_name": "AI Insights",
            "organization": {
                "organization_id": 1,
                "organization_name": "Tech Corp"
            }
        }
    }
}

📌 Conclusion

CarbunqleX makes raw SQL more maintainable, reusable, and dynamically modifiable without sacrificing performance. Its AST-based transformations provide a powerful way to manipulate queries at scale, making it an essential tool for advanced SQL users.

Name		Name	Last commit message	Last commit date
Latest commit History 174 Commits
.github/workflows		.github/workflows
demo		demo
src/Carbunqlex		src/Carbunqlex
tests/Carbunqlex.Tests		tests/Carbunqlex.Tests
.editorconfig		.editorconfig
.gitignore		.gitignore
Carbunqlex.sln		Carbunqlex.sln
LICENSE		LICENSE
README.md		README.md

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CarbunqleX - SQL Parser and Modeler

🚀 Overview

💡 Key Features

Advanced CTE Handling

Intelligent Search Condition Injection

Lightweight and Easy to Use

📦 Installation

📖 Documentation

1. Parsing a SQL Query

🔍 Expected SQL Output

2. Modifying the WHERE Clause

🔍 Expected SQL Output

3. Handling CTEs and Subqueries

🔍 Modified SQL Output

4. Standardizing filtering

🔧 Define a Subquery Function

🔍 Apply the Function in a Query

🔍 Expected SQL Output

5. Modify columns

🔍 Expected SQL Output

6. Manage UNION queries as modular components

🔍 Expected SQL Output

7. Filtering using outer joins

8. Create a table from a select query

🔍 Expected SQL output

9. Manage Update Queries with Select Queries

🔍 Expected SQL output

10. Converting a Query to Return JSON (Postgres-only)

🔍 Expected SQL output

🔍 Query Output

📌 Conclusion

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Repository files navigation

CarbunqleX - SQL Parser and Modeler

🚀 Overview

💡 Key Features

Advanced CTE Handling

Intelligent Search Condition Injection

Lightweight and Easy to Use

📦 Installation

📖 Documentation

1. Parsing a SQL Query

🔍 Expected SQL Output

2. Modifying the WHERE Clause

🔍 Expected SQL Output

3. Handling CTEs and Subqueries

🔍 Modified SQL Output

4. Standardizing filtering

🔧 Define a Subquery Function

🔍 Apply the Function in a Query

🔍 Expected SQL Output

5. Modify columns

🔍 Expected SQL Output

6. Manage UNION queries as modular components

🔍 Expected SQL Output

7. Filtering using outer joins

8. Create a table from a select query

🔍 Expected SQL output

9. Manage Update Queries with Select Queries

🔍 Expected SQL output

10. Converting a Query to Return JSON (Postgres-only)

🔍 Expected SQL output

🔍 Query Output

📌 Conclusion

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