Free SQL to MongoDB Converter

SQL to MongoDB is a free online tool that converts SQL queries into equivalent MongoDB query syntax, helping developers transition between databases.

Yes, it is completely free with no registration required. All conversion happens client-side in your browser.

SQL to MongoDB supports SELECT, INSERT, UPDATE, DELETE queries and converts them to their MongoDB equivalents including find, insertOne, updateMany, and deleteMany.

Absolutely. The SQL to MongoDB Converter processes everything client-side in your browser. No data is uploaded to or stored on any server. Your content remains private on your device at all times.

Yes, the SQL to MongoDB Converter is fully responsive and works on smartphones and tablets. You can use it on any device with a modern web browser -- no app download required.

No account or registration is needed. Simply open the SQL to MongoDB Converter in your browser and start using it immediately. There are no sign-up walls or usage restrictions.

The SQL to MongoDB Converter supports a wide range of popular formats and languages. Check the tool interface for the full list of supported options.

Simply enter your input in the provided field, adjust any settings to your preference, and the tool will process it instantly. You can then copy the result to your clipboard or download it.

The SQL to MongoDB Converter works in all modern browsers including Chrome, Firefox, Safari, Edge, and Opera. For the best experience, use the latest version of your preferred browser.

A SQL WHERE clause becomes the filter object MongoDB passes to find(), or a $match stage when the query needs an aggregation pipeline. This converter parses your conditions one at a time and translates each operator: = becomes a direct key-value pair, while >, <, >=, and <= become $gt, $lt, $gte, and $lte, and != or <> becomes $ne. Multiple conditions joined by AND are wrapped in $and, and conditions joined by OR are wrapped in $or, so the boolean logic is preserved. Special predicates are handled too: IN and NOT IN map to $in and $nin, BETWEEN x AND y becomes a $gte/$lte range, and IS NULL and IS NOT NULL map to null and $ne: null. Paste your WHERE-filtered SELECT above to see the exact filter document it produces.

MongoDB has no LIKE operator, so this tool converts LIKE into a case-insensitive $regex with the $options: "i" flag. The key is how it handles the SQL wildcard %: a prefix pattern like 'John%' anchors the regex at the start (^John), a suffix pattern like '%son' anchors it at the end (son$), and a contains pattern like '%admin%' drops both anchors to match anywhere in the string. The SQL single-character wildcard _ is translated to the regex dot. NOT LIKE is wrapped in $not around the same regex so the match is negated. Because regex matching can be slower than exact lookups on large collections, treat anchored prefix patterns as the most index-friendly. Try the "SELECT with LIKE" sample query in the tool to see a real pattern conversion.

MongoDB does not join tables the way SQL does, so this converter rewrites a JOIN as an aggregation pipeline built around $lookup. It reads your FROM table, the joined table, and the ON equality condition, then emits a $lookup stage that pulls matching documents from the second collection, followed by a $unwind stage to flatten the joined array into individual documents. INNER and LEFT joins with a single ON equality are supported. Any WHERE conditions are folded into a $match stage, ORDER BY becomes $sort, LIMIT becomes $limit, and a selected column list becomes a $project stage, all placed in the correct pipeline order. The output is labeled with an Aggregation badge so the multi-stage structure is obvious. Because document modeling often replaces joins entirely, review whether embedding would suit your schema better, then load the JOIN sample to experiment.

GROUP BY queries are converted into an aggregate() call with a $group stage. The columns you group by become the stage's _id (a single field for one column, or a compound object for several), and your aggregate functions map directly to MongoDB accumulators: COUNT(*) becomes $sum: 1, SUM becomes $sum, AVG becomes $avg, and MIN and MAX become $min and $max. Column aliases written with AS carry through as the output field names. A WHERE clause is placed in a $match stage before $group, and a HAVING clause becomes a second $match after $group, mirroring SQL's filter-after-aggregate ordering. ORDER BY and LIMIT are appended as $sort and $limit stages. Try the "GROUP BY with SUM" sample to see how aliases and HAVING render in the pipeline.

The output panel offers the same converted query in three formats through a tab switch, so you can copy code that matches your environment. Shell gives you the raw mongosh syntax, like db.users.find({ age: { $gt: 25 } }), ideal for pasting straight into the Mongo shell or Compass. Node.js wraps the result in a runnable scaffold using the official mongodb driver, with MongoClient setup, a connection from your MONGODB_URI environment variable, and the matching collection method call. Python produces an equivalent scaffold using PyMongo, converting method names to snake_case such as insert_one and update_many. The scaffolds are starting templates, so you fill in the database name and final filter before running. Pick the tab you need, then click Copy to put the formatted code on your clipboard.