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$graphLookup (aggregation)

Changed in version 3.4.

Definition

$graphLookup

Performs a recursive search on a collection, with options for restricting the search by recursion depth and query filter.

The $graphLookup search process is summarized below:

1. Input documents flow into the $graphLookup stage of an aggregation operation.

2. $graphLookup targets the search to the collection designated by the from parameter (see below for full list of search parameters).

3. For each input document, the search begins with the value designated by startWith.

4. $graphLookup matches the startWith value against the field designated by connectToField in other documents in the from collection.

5. For each matching document, $graphLookup takes the value of the connectFromField and checks every document in the from collection for a matching connectToField value. For each match, $graphLookup adds the matching document in the from collection to an array field named by the as parameter.

   This step continues recursively until no more matching documents are found, or until the operation reaches a recursion depth specified by the maxDepth parameter. $graphLookup then appends the array field to the input document. $graphLookup returns results after completing its search on all input documents.

$graphLookup has the following prototype form:

copy

{
   $graphLookup: {
      from: <collection>,
      startWith: <expression>,
      connectFromField: <string>,
      connectToField: <string>,
      as: <string>,
      maxDepth: <number>,
      depthField: <string>,
      restrictSearchWithMatch: <document>
   }
}

$graphLookup takes a document with the following fields:

Field	Description
from	Target collection for the $graphLookup operation to search, recursively matching the connectFromField to the connectToField. The from collection cannot be sharded and must be in the same database as any other collections used in the operation. For information, see Sharded Collections.
startWith	Expression that specifies the value of the connectFromField with which to start the recursive search. Optionally, startWith may be array of values, each of which is individually followed through the traversal process.
connectFromField	Field name whose value $graphLookup uses to recursively match against the connectToField of other documents in the collection. If the value is an array, each element is individually followed through the traversal process.
connectToField	Field name in other documents against which to match the value of the field specified by the connectFromField parameter.
as	Name of the array field added to each output document. Contains the documents traversed in the $graphLookup stage to reach the document. Note Documents returned in the as field are not guaranteed to be in any order.
maxDepth	Optional. Non-negative integral number specifying the maximum recursion depth.
depthField	Optional. Name of the field to add to each traversed document in the search path. The value of this field is the recursion depth for the document, represented as a NumberLong. Recursion depth value starts at zero, so the first lookup corresponds to zero depth.
restrictSearchWithMatch	Optional. A document specifying additional conditions for the recursive search. The syntax is identical to query filter syntax. Note You cannot use any aggregation expression in this filter. For example, a query document such as copy { lastName: { $ne: "$lastName" } } will not work in this context to find documents in which the lastName value is different from the lastName value of the input document, because "$lastName" will act as a string literal, not a field path.

Considerations

Sharded Collections

The collection specified in from cannot be sharded. However, the collection on which you run the aggregate() method can be sharded. That is, in the following:

copy

db.collection.aggregate([
   { $graphLookup: { from: "fromCollection", ... } }
])

• The collection can be sharded.
• The fromCollection cannot be sharded.

To join multiple sharded collections, consider:

• Modifying client applications to perform manual lookups instead of using the $graphLookup aggregation stage.
• If possible, using an embedded data model that removes the need to join collections.

Max Depth

Setting the maxDepth field to 0 is equivalent to a non-recursive $graphLookup search stage.

Memory

The $graphLookup stage must stay within the 100 megabyte memory limit. If allowDiskUse: true is specified for the aggregate() operation, the $graphLookup stage ignores the option. If there are other stages in the aggregate() operation, allowDiskUse: true option is in effect for these other stages.

See aggregration pipeline limitations for more information.

Views and Collation

If performing an aggregation that involves multiple views, such as with $lookup or $graphLookup, the views must have the same collation.

Examples

Within a Single Collection

A collection named employees has the following documents:

copy

{ "_id" : 1, "name" : "Dev" }
{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }
{ "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" }
{ "_id" : 4, "name" : "Andrew", "reportsTo" : "Eliot" }
{ "_id" : 5, "name" : "Asya", "reportsTo" : "Ron" }
{ "_id" : 6, "name" : "Dan", "reportsTo" : "Andrew" }

The following $graphLookup operation recursively matches on the reportsTo and name fields in the employees collection, returning the reporting hierarchy for each person:

copy

db.employees.aggregate( [
   {
      $graphLookup: {
         from: "employees",
         startWith: "$reportsTo",
         connectFromField: "reportsTo",
         connectToField: "name",
         as: "reportingHierarchy"
      }
   }
] )

The operation returns the following:

copy

{
   "_id" : 1,
   "name" : "Dev",
   "reportingHierarchy" : [ ]
}
{
   "_id" : 2,
   "name" : "Eliot",
   "reportsTo" : "Dev",
   "reportingHierarchy" : [
      { "_id" : 1, "name" : "Dev" }
   ]
}
{
   "_id" : 3,
   "name" : "Ron",
   "reportsTo" : "Eliot",
   "reportingHierarchy" : [
      { "_id" : 1, "name" : "Dev" },
      { "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }
   ]
}
{
   "_id" : 4,
   "name" : "Andrew",
   "reportsTo" : "Eliot",
   "reportingHierarchy" : [
      { "_id" : 1, "name" : "Dev" },
      { "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }
   ]
}
{
   "_id" : 5,
   "name" : "Asya",
   "reportsTo" : "Ron",
   "reportingHierarchy" : [
      { "_id" : 1, "name" : "Dev" },
      { "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" },
      { "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" }
   ]
}
{
   "_id" : 6,
   "name" : "Dan",
   "reportsTo" : "Andrew",
   "reportingHierarchy" : [
      { "_id" : 1, "name" : "Dev" },
      { "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" },
      { "_id" : 4, "name" : "Andrew", "reportsTo" : "Eliot" }
   ]
}

The following table provides a traversal path for the document { "_id" : 5, "name" : "Asya", "reportsTo" : "Ron" }:

Start value	The reportsTo value of the document: copy { ... "reportsTo" : "Ron" }
Depth 0	copy { "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" }
Depth 1	copy { "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }
Depth 2	copy { "_id" : 1, "name" : "Dev" }

The output generates the hierarchy Asya -> Ron -> Eliot -> Dev.

Across Multiple Collections

Like $lookup, $graphLookup can access another collection in the same database.

In the following example, a database contains two collections:

• A collection airports with the following documents:

  copy

  { "_id" : 0, "airport" : "JFK", "connects" : [ "BOS", "ORD" ] }
  { "_id" : 1, "airport" : "BOS", "connects" : [ "JFK", "PWM" ] }
  { "_id" : 2, "airport" : "ORD", "connects" : [ "JFK" ] }
  { "_id" : 3, "airport" : "PWM", "connects" : [ "BOS", "LHR" ] }
  { "_id" : 4, "airport" : "LHR", "connects" : [ "PWM" ] }
  

• A collection travelers with the following documents:

  copy

  { "_id" : 1, "name" : "Dev", "nearestAirport" : "JFK" }
  { "_id" : 2, "name" : "Eliot", "nearestAirport" : "JFK" }
  { "_id" : 3, "name" : "Jeff", "nearestAirport" : "BOS" }
  

For each document in the travelers collection, the following aggregation operation looks up the nearestAirport value in the airports collection and recursively matches the connects field to the airport field. The operation specifies a maximum recursion depth of 2.

copy

db.travelers.aggregate( [
   {
      $graphLookup: {
         from: "airports",
         startWith: "$nearestAirport",
         connectFromField: "connects",
         connectToField: "airport",
         maxDepth: 2,
         depthField: "numConnections",
         as: "destinations"
      }
   }
] )

The operation returns the following results:

copy

{
   "_id" : 1,
   "name" : "Dev",
   "nearestAirport" : "JFK",
   "destinations" : [
      { "_id" : 3,
        "airport" : "PWM",
        "connects" : [ "BOS", "LHR" ],
        "numConnections" : NumberLong(2) },
      { "_id" : 2,
        "airport" : "ORD",
        "connects" : [ "JFK" ],
        "numConnections" : NumberLong(1) },
      { "_id" : 1,
        "airport" : "BOS",
        "connects" : [ "JFK", "PWM" ],
        "numConnections" : NumberLong(1) },
      { "_id" : 0,
        "airport" : "JFK",
        "connects" : [ "BOS", "ORD" ],
        "numConnections" : NumberLong(0) }
   ]
}
{
   "_id" : 2,
   "name" : "Eliot",
   "nearestAirport" : "JFK",
   "destinations" : [
      { "_id" : 3,
        "airport" : "PWM",
        "connects" : [ "BOS", "LHR" ],
        "numConnections" : NumberLong(2) },
      { "_id" : 2,
        "airport" : "ORD",
        "connects" : [ "JFK" ],
        "numConnections" : NumberLong(1) },
      { "_id" : 1,
        "airport" : "BOS",
        "connects" : [ "JFK", "PWM" ],
        "numConnections" : NumberLong(1) },
      { "_id" : 0,
        "airport" : "JFK",
        "connects" : [ "BOS", "ORD" ],
        "numConnections" : NumberLong(0) } ]
}
{
   "_id" : 3,
   "name" : "Jeff",
   "nearestAirport" : "BOS",
   "destinations" : [
      { "_id" : 2,
        "airport" : "ORD",
        "connects" : [ "JFK" ],
        "numConnections" : NumberLong(2) },
      { "_id" : 3,
        "airport" : "PWM",
        "connects" : [ "BOS", "LHR" ],
        "numConnections" : NumberLong(1) },
      { "_id" : 4,
        "airport" : "LHR",
        "connects" : [ "PWM" ],
        "numConnections" : NumberLong(2) },
      { "_id" : 0,
        "airport" : "JFK",
        "connects" : [ "BOS", "ORD" ],
        "numConnections" : NumberLong(1) },
      { "_id" : 1,
        "airport" : "BOS",
        "connects" : [ "JFK", "PWM" ],
        "numConnections" : NumberLong(0) }
   ]
}

The following table provides a traversal path for the recursive search, up to depth 2, where the starting airport is JFK:

Start value	The nearestAirport value from the travelers collection: copy { ... "nearestAirport" : "JFK" }
Depth 0	copy { "_id" : 0, "airport" : "JFK", "connects" : [ "BOS", "ORD" ] }
Depth 1	copy { "_id" : 1, "airport" : "BOS", "connects" : [ "JFK", "PWM" ] } { "_id" : 2, "airport" : "ORD", "connects" : [ "JFK" ] }
Depth 2	copy { "_id" : 3, "airport" : "PWM", "connects" : [ "BOS", "LHR" ] }

With a Query Filter

The following example uses a collection with a set of documents containing names of people along with arrays of their friends and their hobbies. An aggregation operation finds one particular person and traverses her network of connections to find people who list golf among their hobbies.

A collection named people contains the following documents:

copy

{
  "_id" : 1,
  "name" : "Tanya Jordan",
  "friends" : [ "Shirley Soto", "Terry Hawkins", "Carole Hale" ],
  "hobbies" : [ "tennis", "unicycling", "golf" ]
}
{
  "_id" : 2,
  "name" : "Carole Hale",
  "friends" : [ "Joseph Dennis", "Tanya Jordan", "Terry Hawkins" ],
  "hobbies" : [ "archery", "golf", "woodworking" ]
}
{
  "_id" : 3,
  "name" : "Terry Hawkins",
  "friends" : [ "Tanya Jordan", "Carole Hale", "Angelo Ward" ],
  "hobbies" : [ "knitting", "frisbee" ]
}
{
  "_id" : 4,
  "name" : "Joseph Dennis",
  "friends" : [ "Angelo Ward", "Carole Hale" ],
  "hobbies" : [ "tennis", "golf", "topiary" ]
}
{
  "_id" : 5,
  "name" : "Angelo Ward",
  "friends" : [ "Terry Hawkins", "Shirley Soto", "Joseph Dennis" ],
  "hobbies" : [ "travel", "ceramics", "golf" ]
}
{
   "_id" : 6,
   "name" : "Shirley Soto",
   "friends" : [ "Angelo Ward", "Tanya Jordan", "Carole Hale" ],
   "hobbies" : [ "frisbee", "set theory" ]
 }

The following aggregation operation uses three stages:

• $match matches on documents with a name field containing the string "Tanya Jordan". Returns one output document.
• $graphLookup connects the output document’s friends field with the name field of other documents in the collection to traverse Tanya Jordan's network of connections. This stage uses the restrictSearchWithMatch parameter to find only documents in which the hobbies array contains golf. Returns one output document.
• $project shapes the output document. The names listed in connections who play golf are taken from the name field of the documents listed in the input document’s golfers array.

copy

db.people.aggregate( [
  { $match: { "name": "Tanya Jordan" } },
  { $graphLookup: {
      from: "people",
      startWith: "$friends",
      connectFromField: "friends",
      connectToField: "name",
      as: "golfers",
      restrictSearchWithMatch: { "hobbies" : "golf" }
    }
  },
  { $project: {
      "name": 1,
      "friends": 1,
      "connections who play golf": "$golfers.name"
    }
  }
] )

The operation returns the following document:

copy

{
   "_id" : 1,
   "name" : "Tanya Jordan",
   "friends" : [
      "Shirley Soto",
      "Terry Hawkins",
      "Carole Hale"
   ],
   "connections who play golf" : [
      "Joseph Dennis",
      "Tanya Jordan",
      "Angelo Ward",
      "Carole Hale"
   ]
}

Additional Resource

Webinar: Working with Graph Data in MongoDB

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