// translate sql queries to nosql syntax instantly
Convert SQL queries to MongoDB, Firestore, and DynamoDB syntax instantly. Free browser-based tool — no sign-up, no install required.
Ready to convert
Paste a SQL query and click ConvertSelect MongoDB, Firestore, or DynamoDB as your target NoSQL database.
Enter your SELECT, INSERT, UPDATE, or DELETE query in the input panel.
Click Convert and copy the generated NoSQL syntax to your project.
This tool translates standard SQL queries into their equivalent NoSQL syntax. Whether you're migrating a relational database to a document store or just learning NoSQL patterns, this converter bridges the gap between SQL's declarative syntax and NoSQL's programmatic query APIs.
The converter supports SELECT, INSERT INTO, UPDATE, and DELETE FROM statements. It handles WHERE conditions, ORDER BY, LIMIT, OFFSET, and basic JOIN operations.
Basic INNER JOIN, LEFT JOIN, and RIGHT JOIN are supported. However, complex multi-table joins may require manual refinement as NoSQL databases handle relationships differently than relational databases.
No. All conversion happens entirely in your browser using JavaScript. Your SQL queries never leave your machine — complete privacy guaranteed.
The tool generates Node.js MongoDB driver syntax compatible with the official mongodb npm package and Mongoose. The output uses db.collection.find(), insertOne(), updateOne(), and deleteOne() patterns.
Firestore output uses the Firebase v9 modular SDK syntax with getDocs(), addDoc(), updateDoc(), and deleteDoc() calls wrapped with collection() and query() helpers.
The DynamoDB converter generates standard DocumentClient-compatible syntax. It handles string, number, and boolean types automatically based on the SQL query values. Complex type inference may need manual review.
Currently the tool focuses on DML statements (SELECT, INSERT, UPDATE, DELETE). Stored procedures, views, triggers, and DDL (CREATE TABLE, ALTER) are not supported as they have no direct NoSQL equivalent.
Basic GROUP BY with COUNT, SUM, AVG, MIN, MAX are translated to MongoDB aggregation pipeline stages. Firestore and DynamoDB aggregate support is limited and will include comments explaining limitations.
SQL to NoSQL conversion is the process of translating structured query language statements — designed for relational databases like MySQL, PostgreSQL, and SQLite — into equivalent query syntax for document-oriented, key-value, or wide-column NoSQL databases like MongoDB, Firestore, and DynamoDB.
Relational databases have dominated data storage for decades, offering powerful declarative query capabilities through SQL. However, the rise of cloud-native applications, real-time systems, and massive scale has driven widespread adoption of NoSQL databases. Developers migrating between these paradigms frequently need to translate existing SQL logic into NoSQL equivalents — a process that can be time-consuming and error-prone when done manually.
There are many reasons a team might migrate from SQL to a NoSQL system:
MongoDB is a document database that stores data as BSON (Binary JSON) documents. The core conceptual shift from SQL to MongoDB involves understanding that tables become collections, rows become documents, and columns become fields.
A SQL SELECT * FROM users WHERE age > 25 translates to MongoDB's db.users.find({ age: { $gt: 25 } }). MongoDB's query operators like $gt, $lt, $eq, $in, and $regex correspond to SQL's comparison operators and LIKE clause.
SQL's ORDER BY becomes MongoDB's .sort(), LIMIT maps to .limit(), and OFFSET maps to .skip(). The aggregation pipeline handles more complex operations like GROUP BY, with stages like $match, $group, $sort, and $project corresponding to SQL's WHERE, GROUP BY, ORDER BY, and SELECT clauses.
Google Firestore is a serverless NoSQL document database built into Firebase. Its query model differs from both SQL and MongoDB in important ways. Firestore organizes data into collections and documents, with support for subcollections enabling hierarchical data structures.
Firestore queries are built using a composable query() function with where(), orderBy(), and limit() constraints. The modern Firebase v9 modular SDK uses a functional API: const q = query(collection(db, "users"), where("age", ">", 25), orderBy("name"), limit(10)).
One key limitation of Firestore is that it does not support full-text search, complex joins, or OR queries across different fields without multiple queries. These constraints must be considered when migrating from SQL.
AWS DynamoDB is a fully managed key-value and document database. Its query model is fundamentally different from SQL — you must define a partition key (and optionally a sort key) upfront, and queries are always scoped to a single partition.
A SQL SELECT with a WHERE clause on a primary key translates to DynamoDB's GetItem or Query operations. Filtering across the entire table requires a Scan operation, which reads every item and is expensive at scale. This means SQL's flexible WHERE conditions often need architectural rethinking when moving to DynamoDB.
DynamoDB uses the DocumentClient API, with methods like get(), put(), update(), and delete() for single-item operations, and query() and scan() for multi-item retrieval with FilterExpression and KeyConditionExpression.
Automated conversion tools like this one are excellent starting points, but successful migration requires careful consideration of data modeling, access patterns, and database-specific constraints:
This browser-based tool parses your SQL query using JavaScript to extract the table name, selected columns, WHERE conditions, ORDER BY clauses, LIMIT/OFFSET values, and JOIN specifications. It then maps each SQL component to the corresponding NoSQL API call for your chosen target database.
All processing happens entirely in your browser — no SQL query data is ever sent to any server. This makes the tool safe for use with proprietary or sensitive database schema information. The output is ready-to-use code that you can paste directly into your application, with explanatory comments where manual review may be needed.