dct-js2sql

name

dct-js2sql

description

Use this skill when the user wants to convert JSON Schema to SQL CREATE TABLE statements, transform schema definitions to database DDL, create SQL tables from JSON Schema files, or generate database schemas from API specifications. Triggers include "json schema to sql", "convert schema to sql", "create table from json schema", "json schema ddl", "schema conversion", or when working with OpenAPI, JSON Schema, or API specifications that need database tables.

DCT JS2SQL - JSON Schema to SQL

Convert JSON Schema files to DuckDB CREATE TABLE statements.

When to Use

Use this skill when you need to:

• Convert API specifications to database schemas
• Generate SQL from JSON Schema definitions
• Create tables from OpenAPI schemas
• Transform schema files to DDL
• Migrate from document to relational models

Installation

which dct || go build -o dct && chmod +x ./dct

Usage

dct js2sql <schema_file> [flags]

Arguments

• schema_file: Path to JSON Schema file

Flags

• -t, --table <name>: Table name (default: "test")
• -o, --output <file>: Output to file instead of stdout

Examples

Basic conversion:

dct js2sql schema.json

With custom table name:

dct js2sql api-schema.json -t api_events

Save to file:

dct js2sql user-schema.json -t users -o create_users.sql

Supported JSON Schema Features

Primitive Types

Maps JSON Schema types to DuckDB types:

JSON Schema	DuckDB
string	varchar
integer	integer
number	double
boolean	boolean

Arrays

Arrays with item types:

{
  "type": "array",
  "items": {
    "type": "string"
  }
}

Output: array(varchar)

Objects/Structs

Nested objects become row types:

{
  "type": "object",
  "properties": {
    "name": {"type": "string"},
    "age": {"type": "integer"}
  }
}

Output: row(name varchar, age integer)

References ($ref)

Supports local $ref references:

{
  "$ref": "#/definitions/User"
}

Example Schema Conversions

Simple Schema

Input:

{
  "type": "object",
  "properties": {
    "id": {"type": "integer"},
    "name": {"type": "string"},
    "email": {"type": "string"},
    "active": {"type": "boolean"}
  }
}

Output:

create table test (
    id integer,
    name varchar,
    email varchar,
    active boolean
);

Complex Schema with Arrays

Input:

{
  "type": "object",
  "properties": {
    "order_id": {"type": "integer"},
    "items": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "product_id": {"type": "integer"},
          "name": {"type": "string"},
          "price": {"type": "number"},
          "quantity":  
        
      
    
  

Output:

create table test (
    order_id integer,
    items array(row(product_id integer, name varchar, price double, quantity integer))
);

Schema with References

Input:

{
  "definitions": {
    "Address": {
      "type": "object",
      "properties": {
        "street": {"type": "string"},
        "city": {"type": "string"}
      }
    }
  },
  "type": "object",
  "properties": {
    "name": {"type": "string"},
    "address": {"$ref": "#/definitions/Address"}
  }
}

Output:

create table test (
    name varchar,
    address row(street varchar, city varchar)
);

Best Practices

• Review generated SQL for type accuracy
• Add constraints (PRIMARY KEY, NOT NULL) manually after generation
• Consider normalizing nested arrays to separate tables
• Test the SQL in your target database
• Use meaningful table names with -t flag

Integration Examples

With DuckDB

# Convert and create table
dct js2sql schema.json -t events | duckdb mydb.duckdb

# Save and review first
dct js2sql schema.json -o schema.sql
# Review schema.sql
duckdb mydb.duckdb < schema.sql

In Data Pipeline

#!/bin/bash
# Convert all JSON schemas in a directory
for schema in schemas/*.json; do
    table=$(basename "$schema" .json)
    dct js2sql "$schema" -t "$table" > "sql/${table}.sql"
done

Limitations

• Supports JSON Schema Draft 7 features
• Complex validations (min/max, patterns) are not converted to constraints
• External $ref references (URLs) are not resolved
• Database-specific features (indexes, triggers) must be added manually

Related Skills

• dct-flattify: Convert actual JSON data (not schemas) to SQL
• dct-infer: Generate SQL from data files rather than schemas
• dct-peek: Preview JSON Schema files

Schema Sources

Works with schemas from:

• OpenAPI specifications
• JSON Schema Store
• API documentation
• Data validation frameworks
• TypeScript-to-JSON-Schema converters