Which SQL statements can it convert?

It handles SELECT, INSERT, UPDATE, and DELETE. A plain SELECT becomes find() with an optional projection, sort, limit, and skip. Add a GROUP BY, JOIN, HAVING, or an aggregate function and it becomes an aggregation pipeline instead. INSERT becomes insertOne() or insertMany(), UPDATE becomes updateMany() with $set, and DELETE becomes deleteMany(). Other statement types return an "unsupported statement" error.

When does it produce an aggregation pipeline instead of find()?

Whenever the SELECT contains a GROUP BY, a JOIN, a HAVING clause, or an aggregate function (COUNT, SUM, AVG, MIN, MAX). In that case the converter emits db.collection.aggregate([...]) with the appropriate stages: WHERE becomes a $match, JOIN becomes a $lookup, GROUP BY and aggregates become a $group, HAVING becomes a post-group $match, and ORDER BY / LIMIT / OFFSET become $sort / $limit / $skip. A SELECT without any of those stays a simple find().

How are WHERE conditions translated?

Each condition is mapped to MongoDB syntax: = becomes a field match, != and <> become $ne, and >, >=, <, <= become $gt, $gte, $lt, $lte. IN (a, b, c) becomes $in with the listed values, and LIKE becomes a case-insensitive $regex with the % and _ wildcards converted to regex. Conditions joined by AND are merged into one filter, while OR produces an $or array. Numeric and boolean literals are coerced to the right JS types.

What output targets are supported?

Three. The Mongo shell target emits db.collection.find()/aggregate() calls you can paste into mongosh. The Node.js driver target emits await db.collection("name").find(...).toArray() style code. The Mongoose target emits Model.find(...) and Model.aggregate(...) using a capitalized model name. The data and structure are identical across targets - only the surrounding API changes.

How are JOINs handled?

A JOIN ... ON a = b is turned into a $lookup stage inside an aggregation pipeline, using the referenced table as the "from" collection and the ON columns as localField and foreignField. The joined documents are placed in a field named after the joined collection. Because MongoDB models relationships differently from SQL, treat the $lookup as a starting point - in practice you often embed related data rather than join it at query time.

Is the generated query ready to run as-is?

For straightforward statements it is runnable in the shell, driver, or Mongoose, and the explanation panel lets you verify the mapping first. Because the parser is regex-based, deeply nested boolean logic, unusual formatting, or vendor-specific SQL may need manual adjustment. The output is meant as an accurate, well-structured draft that gets you most of the way rather than a guaranteed one-to-one translation of every edge case.

SQL to NoSQL Converter

Convert SQL into runnable MongoDB code. SELECT becomes find() or, when it has GROUP BY / JOIN / aggregates, an aggregation pipeline ($match, $group, $lookup). INSERT, UPDATE, and DELETE map to insertOne/Many, updateMany with $set, and deleteMany. WHERE, ORDER BY, LIMIT, and OFFSET are translated for you — live, with a clause-by-clause breakdown and output for the Mongo shell, the Node.js driver, or Mongoose.

About this ToolHow it works, benefits & use cases

Translate SQL into runnable MongoDB code and see exactly how each clause maps over. Paste a SELECT, INSERT, UPDATE, or DELETE and the converter rewrites it live. A simple SELECT becomes db.collection.find() with a projection, .sort(), .limit(), and .skip(); add a GROUP BY, JOIN, HAVING, or an aggregate like COUNT/SUM/AVG and it switches to an aggregation pipeline with $match, $group, and $lookup stages. INSERT becomes insertOne() for one row or insertMany() for several, UPDATE becomes updateMany() with a $set object, and DELETE becomes deleteMany(). WHERE conditions are parsed into a MongoDB filter: = maps to a field match, != and <> to $ne, comparisons to $gt/$gte/$lt/$lte, IN to $in, and LIKE to a case-insensitive $regex, while AND merges fields and OR becomes $or. Pick your output target - the Mongo shell, the Node.js driver, or Mongoose - and the generated code adapts to that API. Alongside the query you get a clause-by-clause breakdown of the mapping, and the whole thing runs in your browser with a shareable URL.

How to Use

1Load a starter with the SELECT, GROUP BY, JOIN, INSERT, UPDATE, or DELETE example buttons, or type your own SQL.
2Paste or edit your SQL query in the input panel - the output updates as you type.
3Choose an output target: Mongo shell, Node.js driver, or Mongoose.
4Read the "How it maps" panel to see what each SQL clause became.
5Copy the MongoDB code, download it as a .js file, or copy the shareable URL.

Key Benefits

Converts all four core statements: SELECT, INSERT, UPDATE, and DELETE
Promotes GROUP BY, JOIN, HAVING, and aggregates to an aggregation pipeline
Maps WHERE to a filter: =, !=, <>, >, >=, <, <=, IN, LIKE, AND, and OR
Turns ORDER BY into a sort, and LIMIT/OFFSET into limit/skip
Outputs for three targets: Mongo shell, Node.js driver, and Mongoose
Shows a clause-by-clause explanation of every mapping
Runs in-browser with a shareable URL and a downloadable .js file

Common Use Cases

Porting a familiar SQL query to MongoDB while learning the shell or driver syntax
Sketching the MongoDB equivalent of a relational query during a migration
Drafting an aggregation pipeline from a GROUP BY query without writing it by hand
Teaching how SQL clauses map to find(), $group, $lookup, and $set
Documenting both representations of a query side by side in a ticket or PR

Output target

Mongo shell query

Live output — copy, download, share, or pipe

db.users.find({
  age: {
    $gt: 21
  },
  status: "active"
}, {
  name: 1,
  email: 1
}).sort({
  name: 1
}).limit(10)

How it maps

WHERE -> filter document
SELECT columns -> projection
ORDER BY -> .sort()
LIMIT -> .limit(10)

Load example

SQL query

93 characters

What it converts

SELECT → find() with projection, .sort(), .limit(), .skip()
GROUP BY / JOIN / aggregates → an aggregation pipeline ($match, $group, $lookup)
INSERT → insertOne() / insertMany()
UPDATE → updateMany() with $set
DELETE → deleteMany()

WHERE mapping

Conditions translate clause by clause: = becomes a field match, !=/<> → $ne, comparisons → $gt/$gte/$lt/$lte, IN → $in, and LIKE→ a case-insensitive $regex. AND merges fields; OR becomes $or.

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SQL to NoSQL Converter

How to Use

Key Benefits

Common Use Cases

How it maps

WHERE mapping

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Frequently Asked Questions - SQL to NoSQL Converter