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MongoDB

MongoDB is a document database. SnoutData connects to it like any other database: its collections show up in the explorer, you can query them with SQL or a native aggregation pipeline, edit documents in the results grid, and manage collections and indexes.

Connect​

  1. Open the connections sidebar and choose New connection.
  2. Pick the MongoDB driver.
  3. Provide either a full connection string (mongodb://... or mongodb+srv://...) or a host, port, and authentication database with a username and password.
  4. Click Test, then Save.

Flag the connection as read-only or production if you want to block writes. See Security.

Browse collections​

Expand the connection to see its collections, listed like tables. SnoutData infers a collection's fields by sampling documents, so each collection shows its top-level field names and types. Views appear too. Because documents in a collection can differ, the field list is a best-effort picture of the shape, not a fixed schema.

Query with SQL​

Write SQL against a collection and SnoutData transcribes it to an aggregation pipeline. The common shapes are supported:

SELECT name, email
FROM users
WHERE country = 'US'
ORDER BY created_at DESC
LIMIT 50;

WHERE (including =, !=, comparisons, IN, IS NULL, and LIKE), projections, ORDER BY, LIMIT / OFFSET, and GROUP BY with COUNT / SUM / AVG / MIN / MAX all translate to the matching pipeline stages. Anything the offline compiler cannot represent can be translated by the AI assistant after a confirmation prompt (see querying beyond SQL).

:::tip See the pipeline for your SQL Select a SQL statement, right-click, and choose Get Pipeline Translation to insert the equivalent aggregation pipeline as a comment. It is a quick way to learn the pipeline for a query you already know, or to start a native pipeline from it. :::

Write a native pipeline​

For the full power of MongoDB, write the aggregation pipeline directly. On a MongoDB connection the editor toolbar shows a SQL / Pipeline switch and a collection picker. Flip to Pipeline and write a JSON array of stages:

[
  { "$match": { "country": "US" } },
  { "$group": { "_id": "$plan", "users": { "$sum": 1 } } },
  { "$sort": { "users": -1 } }
]

The pipeline editor has schema-aware completion for stage operators, the collection's field names, and collection names (for $lookup). Run it like any query; results land in the same grid. Switching between SQL and Pipeline keeps both buffers, so you can move back and forth.

Edit documents​

On a non-production, writable connection you can edit data straight from the results grid:

  • Edit a cell to update that field on the document (updateOne keyed on _id).
  • Insert a new document, or delete one.

Edited values are coerced to the field's inferred type (numbers, booleans, and so on). Editing requires each row to have an _id. A few limits to know:

  • Date, nested-object, and array cells edit as raw strings (an explicit "set to current time" produces a real date).
  • A save with several changes is applied one operation at a time, not as a single transaction, so a failure partway through can leave some changes applied. SnoutData notes this before you confirm.

Manage collections and indexes​

A MongoDB connection's Properties tab shows the collection's inferred fields and its indexes, where you can:

  • Create or drop a collection (the per-database + in the sidebar is New collection).
  • Create or drop a secondary index. On a new or empty collection you can type the field names to index by hand.

Each change shows its native command and asks for confirmation before it runs.

Ask the assistant​

On a MongoDB connection the AI assistant proposes a native aggregation pipeline instead of SQL, grounded in the connection's collections and fields. Insert it into a pipeline tab and run it. In agentic mode the assistant can run a read-only pipeline for you and read the results.