$regexFind (aggregation)

Operator Fields

Returns

If the operator does not find a match, the result of the operator is a null.

If the operator finds a match, the result of the operator is a document that contains:

• the first matching string in the input,

• the code point index (not byte index) of the matching string in the input, and

• An array of the strings that corresponds to the groups captured by the matching string. Capturing groups are specified with unescaped parenthesis () in the regex pattern.

{ "match" : <string>, "idx" : <num>, "captures" : <array of strings> }

$regexFind and Collation

$regexFind ignores the collation specified for the collection, db.collection.aggregate(), and the index, if used.

For example, the create a sample collection with collation strength 1 (i.e. compare base character only and ignore other differences such as case and diacritics):

db.createCollection( "myColl", { collation: { locale: "fr", strength: 1 } } )

Insert the following documents:

db.myColl.insertMany([
   { _id: 1, category: "café" },
   { _id: 2, category: "cafe" },
   { _id: 3, category: "cafE" }
])

Using the collection's collation, the following operation performs a case-insensitive and diacritic-insensitive match:

db.myColl.aggregate( [ { $match: { category: "cafe" } } ] )

The operation returns the following 3 documents:

{ "_id" : 1, "category" : "café" }
{ "_id" : 2, "category" : "cafe" }
{ "_id" : 3, "category" : "cafE" }

However, the aggregation expression $regexFind ignores collation; that is, the following regular expression pattern matching examples are case-sensitive and diacritic sensitive:

db.myColl.aggregate( [ { $addFields: { resultObject: { $regexFind: { input: "$category", regex: /cafe/ }  } } } ] )
db.myColl.aggregate(
   [ { $addFields: { resultObject: { $regexFind: { input: "$category", regex: /cafe/ }  } } } ],
   { collation: { locale: "fr", strength: 1 } }           // Ignored in the $regexFind
)

Both operations return the following:

{ "_id" : 1, "category" : "café", "resultObject" : null }
{ "_id" : 2, "category" : "cafe", "resultObject" : { "match" : "cafe", "idx" : 0, "captures" : [ ] } }
{ "_id" : 3, "category" : "cafE", "resultObject" : null }

To perform a case-insensitive regex pattern matching, use the i Option instead. See i Option for an example.

captures Output Behavior

If your regex pattern contains capture groups and the pattern finds a match in the input, the captures array in the results corresponds to the groups captured by the matching string. Capture groups are specified with unescaped parentheses () in the regex pattern. The length of the captures array equals the number of capture groups in the pattern and the order of the array matches the order in which the capture groups appear.

Create a sample collection named contacts with the following documents:

db.contacts.insertMany([
  { "_id": 1, "fname": "Carol", "lname": "Smith", "phone": "718-555-0113" },
  { "_id": 2, "fname": "Daryl", "lname": "Doe", "phone": "212-555-8832" },
  { "_id": 3, "fname": "Polly", "lname": "Andrews", "phone": "208-555-1932" },
  { "_id": 4, "fname": "Colleen", "lname": "Duncan", "phone": "775-555-0187" },
  { "_id": 5, "fname": "Luna", "lname": "Clarke", "phone": "917-555-4414" }
])

The following pipeline applies the regex pattern /(C(ar)*)ol/ to the fname field:

db.contacts.aggregate([
  {
    $project: {
      returnObject: {
        $regexFind: { input: "$fname", regex: /(C(ar)*)ol/ }
      }
    }
  }
])

The regex pattern finds a match with fname values Carol and Colleen:

{ "_id" : 1, "returnObject" : { "match" : "Carol", "idx" : 0, "captures" : [ "Car", "ar" ] } }
{ "_id" : 2, "returnObject" : null }
{ "_id" : 3, "returnObject" : null }
{ "_id" : 4, "returnObject" : { "match" : "Col", "idx" : 0, "captures" : [ "C", null ] } }
{ "_id" : 5, "returnObject" : null }

The pattern contains the capture group (C(ar)*) which contains the nested group (ar). The elements in the captures array correspond to the two capture groups. If a matching document is not captured by a group (e.g. Colleen and the group (ar)), $regexFind replaces the group with a null placeholder.

As shown in the previous example, the captures array contains an element for each capture group (using null for non-captures). Consider the following example which searches for phone numbers with New York City area codes by applying a logical or of capture groups to the phone field. Each group represents a New York City area code:

db.contacts.aggregate([
  {
    $project: {
      nycContacts: {
        $regexFind: { input: "$phone", regex: /^(718).*|^(212).*|^(917).*/ }
      }
    }
  }
])

For documents which are matched by the regex pattern, the captures array includes the matching capture group and replaces any non-capturing groups with null:

{ "_id" : 1, "nycContacts" : { "match" : "718-555-0113", "idx" : 0, "captures" : [ "718", null, null ] } }
{ "_id" : 2, "nycContacts" : { "match" : "212-555-8832", "idx" : 0, "captures" : [ null, "212", null ] } }
{ "_id" : 3, "nycContacts" : null }
{ "_id" : 4, "nycContacts" : null }
{ "_id" : 5, "nycContacts" : { "match" : "917-555-4414", "idx" : 0, "captures" : [ null, null, "917" ] } }

$regexFind and Its Options

To illustrate the behavior of the $regexFind operator as discussed in this example, create a sample collection products with the following documents:

db.products.insertMany([
   { _id: 1, description: "Single LINE description." },
   { _id: 2, description: "First lines\nsecond line" },
   { _id: 3, description: "Many spaces before     line" },
   { _id: 4, description: "Multiple\nline descriptions" },
   { _id: 5, description: "anchors, links and hyperlinks" },
   { _id: 6, description: "métier work vocation" }
])

By default, $regexFind performs a case-sensitive match. For example, the following aggregation performs a case-sensitive $regexFind on the description field. The regex pattern /line/ does not specify any grouping:

db.products.aggregate([
   { $addFields: { returnObject: { $regexFind: { input: "$description", regex: /line/ } } } }
])

The operation returns the following:

{ "_id" : 1, "description" : "Single LINE description.", "returnObject" : null }
{ "_id" : 2, "description" : "First lines\nsecond line", "returnObject" : { "match" : "line", "idx" : 6, "captures" : [ ] } }
{ "_id" : 3, "description" : "Many spaces before     line", "returnObject" : { "match" : "line", "idx" : 23, "captures" : [ ] } }
{ "_id" : 4, "description" : "Multiple\nline descriptions", "returnObject" : { "match" : "line", "idx" : 9, "captures" : [ ] } }
{ "_id" : 5, "description" : "anchors, links and hyperlinks", "returnObject" : null }
{ "_id" : 6, "description" : "métier work vocation", "returnObject" : null }

The following regex pattern /lin(e|k)/ specifies a grouping (e|k) in the pattern:

db.products.aggregate([
   { $addFields: { returnObject: { $regexFind: { input: "$description", regex: /lin(e|k)/ } } } }
])

The operation returns the following:

{ "_id" : 1, "description" : "Single LINE description.", "returnObject" : null }
{ "_id" : 2, "description" : "First lines\nsecond line", "returnObject" : { "match" : "line", "idx" : 6, "captures" : [ "e" ] } }
{ "_id" : 3, "description" : "Many spaces before     line", "returnObject" : { "match" : "line", "idx" : 23, "captures" : [ "e" ] } }
{ "_id" : 4, "description" : "Multiple\nline descriptions", "returnObject" : { "match" : "line", "idx" : 9, "captures" : [ "e" ] } }
{ "_id" : 5, "description" : "anchors, links and hyperlinks", "returnObject" : { "match" : "link", "idx" : 9, "captures" : [ "k" ] } }
{ "_id" : 6, "description" : "métier work vocation", "returnObject" : null }

In the return option, the idx field is the code point index and not the byte index. To illustrate, consider the following example that uses the regex pattern /tier/:

db.products.aggregate([
   { $addFields: { returnObject: { $regexFind: { input: "$description", regex: /tier/ } } } }
])

The operation returns the following where only the last record matches the pattern and the returned idx is 2 (instead of 3 if using a byte index)

{ "_id" : 1, "description" : "Single LINE description.", "returnObject" : null }
{ "_id" : 2, "description" : "First lines\nsecond line", "returnObject" : null }
{ "_id" : 3, "description" : "Many spaces before     line", "returnObject" : null }
{ "_id" : 4, "description" : "Multiple\nline descriptions", "returnObject" : null }
{ "_id" : 5, "description" : "anchors, links and hyperlinks", "returnObject" : null }
{ "_id" : 6, "description" : "métier work vocation",
             "returnObject" : { "match" : "tier", "idx" : 2, "captures" : [ ] } }

// Specify i as part of the regex field
{ $regexFind: { input: "$description", regex: /line/i } }
// Specify i in the options field
{ $regexFind: { input: "$description", regex: /line/, options: "i" } }
{ $regexFind: { input: "$description", regex: "line", options: "i" } }

db.products.aggregate([
   { $addFields: { returnObject: { $regexFind: { input: "$description", regex: /line/i } } } }
])

{ "_id" : 1, "description" : "Single LINE description.", "returnObject" : { "match" : "LINE", "idx" : 7, "captures" : [ ] } }
{ "_id" : 2, "description" : "First lines\nsecond line", "returnObject" : { "match" : "line", "idx" : 6, "captures" : [ ] } }
{ "_id" : 3, "description" : "Many spaces before     line", "returnObject" : { "match" : "line", "idx" : 23, "captures" : [ ] } }
{ "_id" : 4, "description" : "Multiple\nline descriptions", "returnObject" : { "match" : "line", "idx" : 9, "captures" : [ ] } }
{ "_id" : 5, "description" : "anchors, links and hyperlinks", "returnObject" : null }
{ "_id" : 6, "description" : "métier work vocation", "returnObject" : null }

// Specify m as part of the regex field
{ $regexFind: { input: "$description", regex: /line/m } }
// Specify m in the options field
{ $regexFind: { input: "$description", regex: /line/, options: "m" } }
{ $regexFind: { input: "$description", regex: "line", options: "m" } }

db.products.aggregate([
   { $addFields: { returnObject: { $regexFind: { input: "$description", regex: /^s/im } } } }
])

{ "_id" : 1, "description" : "Single LINE description.", "returnObject" : { "match" : "S", "idx" : 0, "captures" : [ ] } }
{ "_id" : 2, "description" : "First lines\nsecond line", "returnObject" : { "match" : "s", "idx" : 12, "captures" : [ ] } }
{ "_id" : 3, "description" : "Many spaces before     line", "returnObject" : null }
{ "_id" : 4, "description" : "Multiple\nline descriptions", "returnObject" : null }
{ "_id" : 5, "description" : "anchors, links and hyperlinks", "returnObject" : null }
{ "_id" : 6, "description" : "métier work vocation", "returnObject" : null }

// Specify x in the options field
{ $regexFind: { input: "$description", regex: /line/, options: "x" } }
{ $regexFind: { input: "$description", regex: "line", options: "x" } }

db.products.aggregate([
   { $addFields: { returnObject: { $regexFind: { input: "$description", regex: /lin(e|k) # matches line or link/, options:"x" } } } }
])

{ "_id" : 1, "description" : "Single LINE description.", "returnObject" : null }
{ "_id" : 2, "description" : "First lines\nsecond line", "returnObject" : { "match" : "line", "idx" : 6, "captures" : [ "e" ] } }
{ "_id" : 3, "description" : "Many spaces before     line", "returnObject" : { "match" : "line", "idx" : 23, "captures" : [ "e" ] } }
{ "_id" : 4, "description" : "Multiple\nline descriptions", "returnObject" : { "match" : "line", "idx" : 9, "captures" : [ "e" ] } }
{ "_id" : 5, "description" : "anchors, links and hyperlinks", "returnObject" : { "match" : "link", "idx" : 9, "captures" : [ "k" ] } }
{ "_id" : 6, "description" : "métier work vocation", "returnObject" : null }

// Specify s in the options field
{ $regexFind: { input: "$description", regex: /m.*line/, options: "s" } }
{ $regexFind: { input: "$description", regex: "m.*line", options: "s" } }

db.products.aggregate([
   { $addFields: { returnObject: { $regexFind: { input: "$description", regex:/m.*line/, options: "si"  } } } }
])

{ "_id" : 1, "description" : "Single LINE description.", "returnObject" : null }
{ "_id" : 2, "description" : "First lines\nsecond line", "returnObject" : null }
{ "_id" : 3, "description" : "Many spaces before     line", "returnObject" : { "match" : "Many spaces before     line", "idx" : 0, "captures" : [ ] } }
{ "_id" : 4, "description" : "Multiple\nline descriptions", "returnObject" : { "match" : "Multiple\nline", "idx" : 0, "captures" : [ ] } }
{ "_id" : 5, "description" : "anchors, links and hyperlinks", "returnObject" : null }
{ "_id" : 6, "description" : "métier work vocation", "returnObject" : null }

Use $regexFind to Parse Email from String

Create a sample collection feedback with the following documents:

db.feedback.insertMany([
   { "_id" : 1, comment: "Hi, I'm just reading about MongoDB -- aunt.arc.tica@example.com"  },
   { "_id" : 2, comment: "I wanted to concatenate a string" },
   { "_id" : 3, comment: "How do I convert a date to string? cam@mongodb.com" },
   { "_id" : 4, comment: "It's just me. I'm testing.  fred@MongoDB.com" }
])

The following aggregation uses the $regexFind to extract the email from the comment field (case insensitive).

db.feedback.aggregate( [
    { $addFields: {
       "email": { $regexFind: { input: "$comment", regex: /[a-z0-9_.+-]+@[a-z0-9_.+-]+\.[a-z0-9_.+-]+/i } }
    } },
    { $set: { email: "$email.match"} }
] )

First Stage

The stage uses the $addFields stage to add a new field email to the document. The new field contains the result of performing the $regexFind on the comment field:

{ "_id" : 1, "comment" : "Hi, I'm just reading about MongoDB -- aunt.arc.tica@example.com", "email" : { "match" : "aunt.arc.tica@example.com", "idx" : 38, "captures" : [ ] } }
{ "_id" : 2, "comment" : "I wanted to concatenate a string", "email" : null }
{ "_id" : 3, "comment" : "I can't find how to convert a date to string. cam@mongodb.com", "email" : { "match" : "cam@mongodb.com", "idx" : 46, "captures" : [ ] } }
{ "_id" : 4, "comment" : "It's just me. I'm testing.  fred@MongoDB.com", "email" : { "match" : "fred@MongoDB.com", "idx" : 28, "captures" : [ ] } }

Second Stage

The stage use the $set stage to reset the email to the current "$email.match" value. If the current value of email is null, the new value of email is set to null.

{ "_id" : 1, "comment" : "Hi, I'm just reading about MongoDB -- aunt.arc.tica@example.com", "email" : "aunt.arc.tica@example.com" }
{ "_id" : 2, "comment" : "I wanted to concatenate a string" }
{ "_id" : 3, "comment" : "I can't find how to convert a date to string. cam@mongodb.com", "email" : "cam@mongodb.com" }
{ "_id" : 4, "comment" : "It's just me. I'm testing.  fred@MongoDB.com", "email" : "fred@MongoDB.com" }

Apply $regexFind to String Elements of an Array

Create a sample collection contacts with the following documents:

db.contacts.insertMany([
   { "_id" : 1, name: "Aunt Arc Tikka", details: [ "+672-19-9999", "aunt.arc.tica@example.com" ] },
   { "_id" : 2, name: "Belle Gium",  details: [ "+32-2-111-11-11", "belle.gium@example.com" ] },
   { "_id" : 3, name: "Cam Bo Dia",  details: [ "+855-012-000-0000", "cam.bo.dia@example.com" ] },
   { "_id" : 4, name: "Fred", details: [ "+1-111-222-3333" ] }
])

The following aggregation uses the $regexFind to convert the details array into an embedded document with an email and phone fields:

db.contacts.aggregate( [
   { $unwind: "$details" },
   { $addFields: {
      "regexemail": { $regexFind: { input: "$details", regex: /^[a-z0-9_.+-]+@[a-z0-9_.+-]+\.[a-z0-9_.+-]+$/, options: "i" } },
      "regexphone": { $regexFind: { input: "$details", regex: /^[+]{0,1}[0-9]*\-?[0-9_\-]+$/ } }
   } },
   { $project: { _id: 1, name: 1, details: { email: "$regexemail.match", phone: "$regexphone.match" } } },
   { $group: { _id: "$_id", name: { $first: "$name" }, details: { $mergeObjects: "$details"} } },
   { $sort: { _id: 1 } }
])

First Stage

The stage $unwinds the array into separate documents:

{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : "+672-19-9999" }
{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : "aunt.arc.tica@example.com" }
{ "_id" : 2, "name" : "Belle Gium", "details" : "+32-2-111-11-11" }
{ "_id" : 2, "name" : "Belle Gium", "details" : "belle.gium@example.com" }
{ "_id" : 3, "name" : "Cam Bo Dia", "details" : "+855-012-000-0000" }
{ "_id" : 3, "name" : "Cam Bo Dia", "details" : "cam.bo.dia@example.com" }
{ "_id" : 4, "name" : "Fred", "details" : "+1-111-222-3333" }

Second Stage

The stage uses the $addFields stage to add new fields to the document that contains the result of the $regexFind for phone number and email:

{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : "+672-19-9999", "regexemail" : null, "regexphone" : { "match" : "+672-19-9999", "idx" : 0, "captures" : [ ] } }
{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : "aunt.arc.tica@example.com", "regexemail" : { "match" : "aunt.arc.tica@example.com", "idx" : 0, "captures" : [ ] }, "regexphone" : null }
{ "_id" : 2, "name" : "Belle Gium", "details" : "+32-2-111-11-11", "regexemail" : null, "regexphone" : { "match" : "+32-2-111-11-11", "idx" : 0, "captures" : [ ] } }
{ "_id" : 2, "name" : "Belle Gium", "details" : "belle.gium@example.com", "regexemail" : { "match" : "belle.gium@example.com", "idx" : 0, "captures" : [ ] }, "regexphone" : null }
{ "_id" : 3, "name" : "Cam Bo Dia", "details" : "+855-012-000-0000", "regexemail" : null, "regexphone" : { "match" : "+855-012-000-0000", "idx" : 0, "captures" : [ ] } }
{ "_id" : 3, "name" : "Cam Bo Dia", "details" : "cam.bo.dia@example.com", "regexemail" : { "match" : "cam.bo.dia@example.com", "idx" : 0, "captures" : [ ] }, "regexphone" : null }
{ "_id" : 4, "name" : "Fred", "details" : "+1-111-222-3333", "regexemail" : null, "regexphone" : { "match" : "+1-111-222-3333", "idx" : 0, "captures" : [ ] } }

Third Stage

The stage use the $project stage to output documents with the _id field, the name field and the details field. The details field is set to a document with email and phone fields, whose values are determined from the regexemail and regexphone fields, respectively.

{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : { "phone" : "+672-19-9999" } }
{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : { "email" : "aunt.arc.tica@example.com" } }
{ "_id" : 2, "name" : "Belle Gium", "details" : { "phone" : "+32-2-111-11-11" } }
{ "_id" : 2, "name" : "Belle Gium", "details" : { "email" : "belle.gium@example.com" } }
{ "_id" : 3, "name" : "Cam Bo Dia", "details" : { "phone" : "+855-012-000-0000" } }
{ "_id" : 3, "name" : "Cam Bo Dia", "details" : { "email" : "cam.bo.dia@example.com" } }
{ "_id" : 4, "name" : "Fred", "details" : { "phone" : "+1-111-222-3333" } }

Fourth Stage

The stage uses the $group stage to groups the input documents by their _id value. The stage uses the $mergeObjects expression to merge the details documents.

{ "_id" : 3, "name" : "Cam Bo Dia", "details" : { "phone" : "+855-012-000-0000", "email" : "cam.bo.dia@example.com" } }
{ "_id" : 4, "name" : "Fred", "details" : { "phone" : "+1-111-222-3333" } }
{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : { "phone" : "+672-19-9999", "email" : "aunt.arc.tica@example.com" } }
{ "_id" : 2, "name" : "Belle Gium", "details" : { "phone" : "+32-2-111-11-11", "email" : "belle.gium@example.com" } }

Fifth Stage

The stage uses the $sort stage to sort the documents by the _id field.

{ "_id" : 1, "name" : "Aunt Arc Tikka", "details" : { "phone" : "+672-19-9999", "email" : "aunt.arc.tica@example.com" } }
{ "_id" : 2, "name" : "Belle Gium", "details" : { "phone" : "+32-2-111-11-11", "email" : "belle.gium@example.com" } }
{ "_id" : 3, "name" : "Cam Bo Dia", "details" : { "phone" : "+855-012-000-0000", "email" : "cam.bo.dia@example.com" } }
{ "_id" : 4, "name" : "Fred", "details" : { "phone" : "+1-111-222-3333" } }

Use Captured Groupings to Parse User Name

Create a sample collection employees with the following documents:

db.employees.insertMany([
   { "_id" : 1, name: "Aunt Arc Tikka", "email" : "aunt.tica@example.com" },
   { "_id" : 2, name: "Belle Gium", "email" : "belle.gium@example.com" },
   { "_id" : 3, name: "Cam Bo Dia", "email" : "cam.dia@example.com" },
   { "_id" : 4, name: "Fred"  }
])

The employee email has the format <firstname>.<lastname>@example.com. Using the captured field returned in the $regexFind results, you can parse out user names for employees.

db.employees.aggregate( [
    { $addFields: {
       "username": { $regexFind: { input: "$email", regex: /^([a-z0-9_.+-]+)@[a-z0-9_.+-]+\.[a-z0-9_.+-]+$/, options: "i" } },
    } },
    { $set: { username: { $arrayElemAt:  [ "$username.captures", 0 ] } } }
] )

First Stage

The stage uses the $addFields stage to add a new field username to the document. The new field contains the result of performing the $regexFind on the email field:

{ "_id" : 1, "name" : "Aunt Arc Tikka", "email" : "aunt.tica@example.com", "username" : { "match" : "aunt.tica@example.com", "idx" : 0, "captures" : [ "aunt.tica" ] } }
{ "_id" : 2, "name" : "Belle Gium", "email" : "belle.gium@example.com", "username" : { "match" : "belle.gium@example.com", "idx" : 0, "captures" : [ "belle.gium" ] } }
{ "_id" : 3, "name" : "Cam Bo Dia", "email" : "cam.dia@example.com", "username" : { "match" : "cam.dia@example.com", "idx" : 0, "captures" : [ "cam.dia" ] } }
{ "_id" : 4, "name" : "Fred", "username" : null }

Second Stage

The stage use the $set stage to reset the username to the zero-th element of the "$username.captures" array. If the current value of username is null, the new value of username is set to null.

{ "_id" : 1, "name" : "Aunt Arc Tikka", "email" : "aunt.tica@example.com", "username" : "aunt.tica" }
{ "_id" : 2, "name" : "Belle Gium", "email" : "belle.gium@example.com", "username" : "belle.gium" }
{ "_id" : 3, "name" : "Cam Bo Dia", "email" : "cam.dia@example.com", "username" : "cam.dia" }
{ "_id" : 4, "name" : "Fred", "username" : null }