Write the query in SQL and get the MongoDB shell command that does the same: find() with a filter and projection, an aggregation pipeline for GROUP BY, or insertMany, updateMany and deleteMany.
Runs entirely in your browser — nothing you enter here is sent to a server.
If you know SQL, you already know what you want from MongoDB — the difficulty is the syntax. MongoDB expresses a query as a document rather than a sentence: conditions are nested objects, operators start with a dollar sign, and anything involving grouping moves into an aggregation pipeline. This converter translates the SQL you can write into the MongoDB you are trying to remember.
| SQL | MongoDB |
|---|---|
| WHERE a = 1 AND b > 2 | { a: 1, b: { $gt: 2 } } |
| WHERE a = 1 OR b = 2 | { $or: [{ a: 1 }, { b: 2 }] } |
| IN (…) / NOT IN (…) | $in / $nin |
| LIKE 'An%' | { $regex: "^An", $options: "i" } |
| BETWEEN 1 AND 5 | { $gte: 1, $lte: 5 } |
| IS NULL / IS NOT NULL | null / { $ne: null } |
| SELECT a, b | projection { a: 1, b: 1, _id: 0 } |
| ORDER BY a DESC LIMIT 10 OFFSET 20 | .sort({ a: -1 }).skip(20).limit(10) |
| COUNT(*) … GROUP BY a | aggregate([{ $group: { _id: "$a", count: { $sum: 1 } } }]) |
Single-table statements translate cleanly: SELECT with conditions, sorting and paging; COUNT, SUM, AVG, MIN and MAX with or without GROUP BY; DISTINCT; and INSERT, UPDATE and DELETE. Joins and subqueries are reported as unsupported rather than guessed at — not because MongoDB cannot do them ($lookup exists) but because a join usually signals a relational design, and in a document database the better answer is often to embed the related data.
A date written as '2026-01-01' is translated as a string. If the field holds real dates, wrap the value: ISODate("2026-01-01").
A pattern anchored at the start ('An%') can use an index; a leading wildcard ('%an') scans the collection, just as in SQL.
{ field: null } matches documents where the field is null and those where it does not exist.
They become updateMany and deleteMany. Without a WHERE the filter is {} — every document.
As a filter document passed to find(). Equality is { field: value }; other comparisons use operators such as $gt, $lte, $ne and $in; AND is several keys in one document; OR is { $or: [ … ] }.
The $group stage of an aggregation pipeline. The grouping column goes in _id, and each aggregate becomes an accumulator such as $sum or $avg. A WHERE clause becomes a $match stage before it.
No. Joins require $lookup in an aggregation pipeline and usually a rethink of the data model, so the converter reports them instead of producing something misleading.
No. The converter only translates text in your browser. Copy the result into mongosh or your driver and run it yourself.
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