SQL to MongoDB Query Converter
New| SQL | MongoDB |
|---|---|
| WHERE a = 1 | { a: 1 } |
| WHERE a != 1 | { a: { $ne: 1 } } |
| WHERE a > 1 | { a: { $gt: 1 } } |
| WHERE a IN (1,2) | { a: { $in: [1,2] } } |
| WHERE a LIKE '%x%' | { a: { $regex: ".*x.*" } } |
| ORDER BY a DESC | .sort({ a: -1 }) |
| LIMIT 10 | .limit(10) |
| SELECT DISTINCT a | .distinct("a") |
Convert SQL SELECT queries to MongoDB find() syntax. Supports WHERE, AND, OR, LIKE, IN, ORDER BY, LIMIT, and DISTINCT. Instant browser-based conversion.
How to Use SQL to MongoDB Query Converter
- Enter a SQL SELECT query in the left text area.
- The MongoDB equivalent is generated automatically.
- Use the example buttons to try common patterns.
- Copy the MongoDB query with the copy button.
- Refer to the syntax table for supported operators.
What is SQL to MongoDB Conversion?
SQL to MongoDB conversion translates relational database queries written in Structured Query Language into MongoDB's document-oriented query syntax. SQL databases like PostgreSQL, MySQL, and SQL Server use a declarative query language with clauses like SELECT, WHERE, ORDER BY, and LIMIT. MongoDB uses a JavaScript-based query API with methods like find(), filter objects, and projection documents. This tool automates the translation so developers transitioning between these paradigms can work productively without memorizing both syntaxes.
How the Conversion Works
The converter parses your SQL SELECT statement and maps each clause to its MongoDB equivalent. The WHERE clause becomes a filter object using MongoDB query operators: = maps to $eq, > to $gt, < to $lt, LIKE to $regex, and IN to $in. SELECT columns become a projection object, ORDER BY translates to .sort(), and LIMIT maps directly. AND/OR logical operators are converted to $and/$or arrays. The table name in the FROM clause becomes the collection name.
Common Use Cases
- Learning MongoDB query syntax when coming from a SQL background
- Migrating applications from relational databases to MongoDB
- Prototyping queries during database evaluations
- Teaching and training teams on NoSQL query patterns
- Quick reference when switching between SQL and MongoDB projects
Relational vs Document Model
SQL databases store data in tables with fixed schemas, enforcing relationships through foreign keys and JOINs. MongoDB stores data as flexible JSON-like documents (BSON) within collections, allowing nested objects and arrays. This means data that would require multiple tables and JOINs in SQL can often be embedded in a single MongoDB document. However, this flexibility requires careful schema design to avoid data duplication and ensure query efficiency. Simple single-table queries translate cleanly between the two systems, but complex JOINs and aggregations require MongoDB's aggregation pipeline, which has a fundamentally different approach.
For related developer tools, try the JSON to YAML Converter or the JSON Formatter. You can also validate your query results with the JSON Validator.