Query Your Data

The new Data Explorer user interface is available in Public Preview and includes new features that weren't present in the old interface.

You can type MongoDB filter documents into the query bar to display only documents which match the specified criteria. To learn more about querying documents, see Query Documents in the MongoDB manual.

Set Query Filter

In the Filter field, enter a filter document between the curly braces. You can use all the MongoDB query operators except the $text and $expr operators.
Example
The following filter returns documents that have a title value of Jurassic Park:
{ "title": "Jurassic Park" }
Click Find to run the query and view the updated results.
click to enlarge

Examples

The examples on this page use a small example dataset. To import the sample data into your MongoDB deployment, perform the following steps:

Copy the following documents to your clipboard:

[
   {
      "name": "Andrea Le",
      "email": "andrea_le@fake-mail.com",
      "school": {
         "name": "Northwestern"
      },
      "version": 5,
      "scores": [ 85, 95, 75 ],
      "dateCreated": { "$date": "2003-03-26" }
   },
   {
      "email": "no_name@fake-mail.com",
      "version": 4,
      "scores": [ 90, 90, 70 ],
      "dateCreated": { "$date": "2001-04-15" }
   },
   {
      "name": "Greg Powell",
      "email": "greg_powell@fake-mail.com",
      "version": 1,
      "scores": [ 65, 75, 80 ],
      "dateCreated": { "$date": "1999-02-10" }
   }
]

In Atlas, use the left navigation panel to select the database and the collection you want to import the data to.
Click the Documents tab.
Click Add Data and select Insert Document.
Set the View to JSON ({}).
Paste the JSON documents from your clipboard into the modal.
Click Insert.

Note

If you do not have a MongoDB deployment or if you want to query a larger sample data set, see Sample Data for Atlas Clusters for instructions on creating a free-tier cluster with sample data. The following example queries filter the sample documents provided on this page.

Match by a Single Condition

The following query filter finds all documents where the value of name is "Andrea Le":

{ name: "Andrea Le" }

The query returns the following document:

{
   "_id": { "$oid": "5e349915cebae490877d561d" },
   "name": "Andrea Le",
   "email": "andrea_le@fake-mail.com",
   "school": {
      "name": "Northwestern"
   },
   "version": 5,
   "scores": [ 85, 95, 75 ],
   "dateCreated": { "$date": "2003-03-26" }
}

Match by Multiple Conditions ($and)

The following query filter finds all documents where scores array contains the value 75, and the name is Greg Powell:

{ $and: [ { scores: 75, name: "Greg Powell" } ] }

The query returns the following document:

{
   "_id": { "$oid":"5a9427648b0beebeb69579cf" },
   "name": "Greg Powell",
   "email": "greg_powell@fake-mail.com",
   "version": 1,
   "scores": [ 65, 75, 80 ],
   "dateCreated": { "$date": "1999-02-10" }
}

Match by Multiple Possible Conditions ($or)

The following query filter uses the $or operator to find documents where version is 4, or name is Andrea Le:

{ $or: [ { version: 4 }, { name: "Andrea Le" } ] }

The query returns the following documents:

[
   {
      "_id": { "$oid": "5e349915cebae490877d561d" },
      "name": "Andrea Le",
      "email": "andrea_le@fake-mail.com",
      "school": {
         "name": "Northwestern"
      },
      "version": 5,
      "scores": [ 85, 95, 75 ],
      "dateCreated": { "$date": "2003-03-26" }
   },
   {
      "_id": { "$oid":"5e349915cebae490877d561e" },
      "email": "no_name@fake-mail.com",
      "version": 4,
      "scores": [ 90, 90, 70 ],
      "dateCreated": { "$date": "2001-04-15" }
   }
]

Match by Exclusion ($not)

The following query filter uses the $not operator to find all documents where the value of the name field is not equal to "Andrea Le", or the name field does not exist:

{ name: { $not: { $eq: "Andrea Le" } } }

The query returns the following documents:

[
   {
      "_id": { "$oid":"5e349915cebae490877d561e" },
      "email": "no_name@fake-mail.com",
      "version": 4,
      "scores": [ 90, 90, 70 ],
      "dateCreated": { "$date": "2001-04-15" }
   },
   {
      "_id": { "$oid":"5a9427648b0beebeb69579cf" },
      "name": "Greg Powell",
      "email": "greg_powell@fake-mail.com",
      "version": 1,
      "scores": [ 65, 75, 80 ],
      "dateCreated": { "$date": "1999-02-10" }
   }
]

Tip

For a complete list of logical query operators, see Logical Query Operators.

Match with Comparison Operators

The following query filter uses the $lte operator to find all documents where version is less than or equal to 4:

{ version: { $lte: 4 } }

The query returns the following documents:

[
   {
      "_id": { "$oid":"5e349915cebae490877d561e" },
      "email": "no_name@fake-mail.com",
      "version": 4,
      "scores": [ 90, 90, 70 ],
      "dateCreated": { "$date": "2001-04-15" }
   },
   {
      "_id": { "$oid":"5a9427648b0beebeb69579cf" },
      "name": "Greg Powell",
      "email": "greg_powell@fake-mail.com",
      "version": 1,
      "scores": [ 65, 75, 80 ],
      "dateCreated": { "$date": "1999-02-10" }
   }
]

Tip

For a complete list of comparison operators, see Comparison Query Operators.

Match by Date

The following query filter uses the $gt operator and Date() method to find all documents where the dateCreated field value is later than June 22nd, 2000:

{ dateCreated: { $gt: new Date('2000-06-22') } }

The query returns the following documents:

[
   {
      "_id": { "$oid": "5e349915cebae490877d561d" },
      "name": "Andrea Le",
      "email": "andrea_le@fake-mail.com",
      "school": {
         "name": "Northwestern"
      },
      "version": 5,
      "scores": [ 85, 95, 75 ],
      "dateCreated": { "$date": "2003-03-26" }
   },
   {
      "_id": { "$oid": "5e349915cebae490877d561e" },
      "email": "no_name@fake-mail.com",
      "version": 4,
      "scores": [ 90, 90, 70 ],
      "dateCreated": { "$date": "2001-04-15" }
   }
]

Match by Array Conditions

The following query filter uses the $elemMatch operator to find all documents where at least one value in the scores array is greater than 80 and less than 90:

{ scores: { $elemMatch: { $gt: 80, $lt: 90 } } }

The query returns the following document because one of the values in the scores array is 85:

{
   "_id": { "$oid": "5e349915cebae490877d561d" },
   "name": "Andrea Le",
   "email": "andrea_le@fake-mail.com",
   "school": {
      "name": "Northwestern"
   },
   "version": 5,
   "scores": [ 85, 95, 75 ],
   "dateCreated": { "$date": "2003-03-26" }
}

For more query examples, see Query Documents in the MongoDB manual.

Match by Substring

The following query filter uses the $regex operator to find all documents where the value of email includes the term "andrea_le":

{ email: { $regex: "andrea_le" } }

The query returns the following document:

{
   "_id": { "$oid": "5e349915cebae490877d561d" },
   "name": "Andrea Le",
   "email": "andrea_le@fake-mail.com",
   "school": {
      "name": "Northwestern"
   },
   "version": 5,
   "scores": [ 85, 95, 75 ],
   "dateCreated": { "$date": "2003-03-26" }
}

Match by Embedded Field

The following query filter finds the document with the school.name subfield of "Northwestern":

{ "school.name": "Northwestern" }

The query returns the following document:

{
   "_id": { "$oid": "5e349915cebae490877d561d" },
   "name": "Andrea Le",
   "email": "andrea_le@fake-mail.com",
   "school": {
      "name": "Northwestern"
   },
   "version": 5,
   "scores": [ 85, 95, 75 ],
   "dateCreated": { "$date": "2003-03-26" }
}

For more query examples, see Query Documents in the MongoDB manual.

Supported Data Types in the Query Bar

The Atlas Filter supports using the mongosh representation of the MongoDB Extended JSON BSON data types.

Example

The following filter returns documents where start_date is greater than than the BSON Date 2017-05-01:

{ "start_date": {$gt: new Date('2017-05-01')} }

By specifying the Date type in both start_date and the $gt comparison operator, Atlas performs the greater than comparison chronologically, returning documents with start_date later than 2017-05-01.

Without the Date type specification, Atlas compares the start_dates as strings lexicographically, instead of comparing the values chronologically.

Clear the Query

To clear the query bar and the results of the query, click Reset.

How Does the Atlas Query Compare to MongoDB and SQL Queries?

$filter corresponds to the WHERE clause in a SQL SELECT statement.

Example

You have 3,235 articles. You would like to see all articles that Joe Bloggs wrote.

Atlas Filter Option

{ author : { $eq : "Joe Bloggs" } }

MongoDB Aggregation

db.article.aggregate( { $match: { "author": "Joe Bloggs" } } )

SQL

SELECT * FROM article
WHERE author = "Joe Bloggs";

Back

Delete Multiple

Set Returned Fields