Advanced MongoDB Aggregation Pipelines

Advanced MongoDB
Aggregation Pipelines
Tom Schreiber

{ "About me" : {
"Name" : "Tom Schreiber",
"Title" : "Senior Consulting Engineer",
"Email" : "tom.schreiber@mongodb.com",
"Twitter" : "@SchreiberTom1",
"Location" : "London, UK"
}
}

"Data should be easy to work with."
"The data should serve you.
You should not serve the data."
"The same (data-) model in your head, in your code, and in your data
- NO impedance mismatch."
Eliot Horowitz
CTO & Co-Founder of MongoDB
MongoDB World’16

MongoDB Aggregation Framework

A Highly Composable Compute Pipeline
$sum$match

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$sum$match

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$sum$match
Notions of computation and monads
Eugenio Moggi∗
Abstract
The λ-calculus is considered an useful mathematical tool in the study of programming
languages, since programs can be identified with λ-terms. However, if one goes further and
uses βη-conversion to prove equivalence of programs, then a gross simplification is introduced
(programs are identified with total functions from values to values), that may jeopardise the
applicability of theoretical results. In this paper we introduce calculi based on a categorical
semantics for computations, that provide a correct basis for proving equivalence of programs,
for a wide range of notions of computation.

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$sum$match
Eugenio Moggi∗
Abstract
A monad defines what it means to chain operations together.  
This allows the programmer to build pipelines that process data in a
series of steps […] 
Monads allow a programming style where programs are written by
putting together highly composable parts […]

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$sum$match
Eugenio Moggi∗
Abstract
A monad defines what it means to chain operations together.  
This allows the programmer to build pipelines that process data in a
series of steps […] 
Monads allow a programming style where programs are written by
putting together highly composable parts […]
Ruby
Module Enumerable:
collect, select, group_by, …
C# / LINQ
IEnumerable<T>:
Select, Where, GroupBy, …
Java
Stream<T>:
collect,filter,groupingBy,…
MongoDB
Aggregation Framework:
$project, $match, $group, …
map, filter, group, …
Haskell
Module Data.List:

(1) are one of the easiest ways to work with your data.
(2) are highly scalable and optimised for performance.
(3) are (much) more capable and powerful than you think!
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$sum$match

1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
The two top paid employees
per departement
1 Alex DE 300
3 Cora DE 200
DE
US
5 Erik US 400
6 Fred US 300
NL
8 Herb NL 600
9 Ivan NL 400
UK
10 Jill UK 500
DB Land

1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
per departement
select id, name, dpt, sal
from emps
where sal =
(select max(sal)
from emps as e
where e.dpt = emps.dpt)
or sal =
(select max(sal)
from emps as e
where e.dpt = emps.dpt
and sal <
(select max(sal)
from emps as e2
where e2.dpt = emps.dpt));
DB Land

1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
per departement
select r.id, r.name, r.dpt, r.sal 
from (select id, name, dept, sal, 
rank() 
OVER (PARTITION BY dpt 
ORDER BY sal DESC)
as rank 
from emps) AS r 
where r.rank <= 2 
order by r.dpt, r.rank
1 Alex DE 300
3 Cora DE 200
5 Erik US 400
6 Fred US 300
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
DB Land

1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
per departement
1 Alex DE 300
3 Cora DE 200
5 Erik US 400
6 Fred US 300
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
1 Alex DE 300
3 Cora DE 200
DE
US
5 Erik US 400
6 Fred US 300
NL
8 Herb NL 600
9 Ivan NL 400
UK
10 Jill UK 500
?
DB Land

1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
DB LandPL Land

PL Land
1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
ID NAME DPT SAL
DB Land

PL Land
{"id"=>10, "name"=>"Jill", "dpt"=>"UK", "sal"=>500}]]
{"id"=> 9, "name"=>"Ivan", "dpt"=>"NL", "sal"=>400},,
{"id"=> 8, "name"=>"Herb", "dpt"=>"NL", "sal"=>600},,
{"id"=> 7, "name"=>"Gina", "dpt"=>"US", "sal"=>200},,
{"id"=> 6, "name"=>"Fred", "dpt"=>"US", "sal"=>300},,
{"id"=> 5, "name"=>"Erik", "dpt"=>"US", "sal"=>400},,
{"id"=> 4, "name"=>"Drew", "dpt"=>"US", "sal"=>200},,
{"id"=> 3, "name"=>"Cora", "dpt"=>"DE", "sal"=>200},,
{"id"=> 2, "name"=>"Bert", "dpt"=>"DE", "sal"=>100},,
[{"id"=> 1, "name"=>"Alex", "dpt"=>"DE", "sal"=>300},,[
DB Land

PL Land
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}collect
group_by
sort_by
take
Ruby
DB Land

PL Land
},
[{"DE"=>
]
]
]
[
[
[
[{"US"=>
[{"NL"=>
[{"UK"=>
},
},
]
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}collect
group_by
sort_by
take
Ruby
DB Land

PL Land
},{"id"=> 9, "name"=>"Ivan", "dpt"=>"NL", "sal"=>400},,
{"id"=> 8, "name"=>"Herb", "dpt"=>"NL", "sal"=>600},,[[{"NL"=>
{"id"=> 5, "name"=>"Erik", "dpt"=>"US", "sal"=>400},,[[{"US"=>
},
[{"DE"=>
},
{"id"=>10, "name"=>"Jill", "dpt"=>"UK", "sal"=>500}]][[{"UK"=> ]
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}collect
group_by
sort_by
take
]
]]
]]
Ruby
DB Land

PL Land
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}takecollect
group_by
sort_by
},{"id"=> 9, "name"=>"Ivan", "dpt"=>"NL", "sal"=>400},,
]
[[{"NL"=>
]
[[{"US"=>
},
[{"DE"=>
] },
{"id"=>10, "name"=>"Jill", "dpt"=>"UK", "sal"=>500}]][[{"UK"=> ]
1 Alex DE 300
3 Cora DE 200
DE
US
5 Erik US 400
6 Fred US 300
NL
8 Herb NL 600
9 Ivan NL 400
UK
10 Jill UK 500
Ruby
DB Land

emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}collect
group_by
sort_by
Ruby
PL Land
emps.sorted(Comparator.comparing(Employee::getDpt)
.thenComparing(Employee::getSal)
.reversed()).collect(Collectors 
.groupingBy(Employee::getDpt, Collectors 
.collectingAndThen(Collectors.toList(),l->l.stream()
.limit(2).collect(
Collectors.toList()))));
emps.OrderBy(e => e.Dpt).ThenByDescending(e => e.Sal)
.GroupBy(e => e.Dpt)
.Select(g => new {Key = g.Key, Emps = g.Take(2)})
C# / LINQ
Java Streams
take
DB Land

PL Land
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
group_by
$sort
Ruby
DB Land

PL Land
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
$sort
$group
Ruby
DB Land

PL Land
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}take
$sort
$group
$project
Ruby
DB Land

PL Land
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}
$sort
$group
$project $slice
Ruby
DB Land

PL Land
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {_id:"$dpt", emps:{$push: "$$CURRENT"}}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
Aggregation Pipeline
DB Land

PL Land
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
DB Land
$project
$slice
$group$sort

MongoDB
PL Land
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
PL Land DB Land
$project
$slice
$group$sort

MongoDB
PL Land
{"_id":10, "name":"Jill", "dpt":"UK", "sal":500}]
{"_id": 9, "name":"Ivan", "dpt":"NL", "sal":400},
{"_id": 8, "name":"Herb", "dpt":"NL", "sal":600},
{"_id": 7, "name":"Gina", "dpt":"US", "sal":200},
{"_id": 6, "name":"Fred", "dpt":"US", "sal":300},
{"_id": 5, "name":"Erik", "dpt":"US", "sal":400},
{"_id": 4, "name":"Drew", "dpt":"US", "sal":200},
{"_id": 3, "name":"Cora", "dpt":"DE", "sal":200},
{"_id": 2, "name":"Bert", "dpt":"DE", "sal":100},
[{"_id": 1, "name":"Alex", "dpt":"DE", "sal":300},
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
DB Land

MongoDB
PL Land
{"_id": 7, "name":"Gina", "dpt":"US", "sal":200},
{"_id": 4, "name":"Drew", "dpt":"US", "sal":200},
{"_id": 2, "name":"Bert", "dpt":"DE", "sal":100},
[{"_id" :"DE",
"emps":
},
{"_id" :"US",
"emps":
},
{"_id" :"NL",
"emps":
},
{"_id" :"UK",
"emps":
]
]
]
}]
[
[
[
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
DB Land

MongoDB
PL Land
[{"dpt" :"DE",
"emps":
},]
{"dpt" :"US",
"emps":
},]
{"dpt" :"NL",
"emps":
},]
{"dpt" :"UK",
"emps": }]
[
[
[
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
DB Land

MongoDB
PL Land
[{"dpt" :"DE",
"emps":
},]
{"dpt" :"US",
"emps":
},]
{"dpt" :"NL",
"emps":
},]
{"dpt" :"UK",
"emps": }]
[
[
[
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
1 Alex DE 300
3 Cora DE 200
DE
US
5 Erik US 400
6 Fred US 300
NL
8 Herb NL 600
9 Ivan NL 400
UK
10 Jill UK 500
DB Land

MongoDB
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
])
$sort
$group
$project $slice
emps.OrderBy(e => e.Dpt).ThenByDescending(e => e.Sal)
.GroupBy(e => e.Dpt)
.Select(g => new {Key = g.Key, Emps = g.Take(2)})
C#
emps.sorted(Comparator.comparing(Employee::getDpt)
.thenComparing(Employee::getSal)
.reversed()).collect(Collectors 
.groupingBy(Employee::getDpt, Collectors 
.collectingAndThen(Collectors.toList(),l->l.stream()
.limit(2).collect(
Collectors.toList()))));
Java
emps. {|e| [e["dpt"], -e["sal"]]}
. {|e| e["dpt"]}
. {|k,v| [k => v. (2)]}collect
group_by
sort_by Ruby
take
DB LandPL Land

Benchmarking
PL Land vs DB Land
2,2 GHz Intel Core i7 CPU
16 GB 1600 MHz DDR3 RAM
SSD-Storage
MongoDB Enterprise 3.4.0-rc0
WiredTiger (12 GB Cache Size,
snappy compressor)
!

λ
PL Land DB Land
# Employees Data Size Index Size Runtime
1M 50 MB 20 MB 6 s + 7 s
10M 500 MB 200 MB 133 s + 99 s
100M 5 GB 2 GB 2 h - OOM

PL Land DB Land
Q
# Employees Data Size Index Size Runtime Runtime
1M 50 MB 20 MB 6 s + 7 s 100 ms
10M 500 MB 200 MB 133 s + 99 s 1 s
100M 5 GB 2 GB 2 h - OOM 5 m

PL Land DB Land
Q
100M 5 GB 2 GB 2 h - OOM 5 m

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
]
$slice
)
$project
$group
$sort
$project
$group
$sort
5 GB

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
]
$slice
)
$project
$group
$sort
$project
$group
$sort
5 GB"errmsg":"Exceeded memory limit for $group,
but didn't allow external sort. 
Pass allowDiskUse:true to opt in."

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
]
$slice
)
$project
$group
$sort
$project
$group
$sort
5 GB
,{allowDiskUse:true}

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
db.emps.aggregate([
{ : {dpt:1, sal:-1}},
{ : {dpt:"$_id", emps:{ : ["$emps",2]}}}
]
$slice
)
$project
$group
$sort
$project
$group
$sort
5 GB
,{allowDiskUse:true}
!
CPU Load

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
$project
$group
$sort
5 GB
mongod --dbpath ~/data/shard1 --port 40001
…
!

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
$project
$group
$sort
5 GB
…
!
sh.addShard("Toms-MacBook-Pro.local:40001")
…

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
$project
$group
$sort
5 GB
…
!
…
sh.shardCollection("DB.emps", {dpt:1})

!DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
$project
$group
$sort
5 GB
…
!
…
sh.shardCollection("DB.emps", {dpt:1})
"Micro-Sharding"
0.5 GB0.5 GB0.5 GB0.5 GB0.5 GB
0.5 GB 0.5 GB 0.5 GB 0.5 GB 0.5 GB

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
$project
$group
$sort
5 GB

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
$project
$group
$sort
0.5 GB0.5 GB 0.5 GB0.5 GB
0.5 GB0.5 GB0.5 GB0.5 GB0.5 GB
0.5 GB
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort

DB Land
100M 5 GB 2 GB 2 h - OOM 5 m
$project
$group
$sort
0.5 GB0.5 GB 0.5 GB0.5 GB
0.5 GB0.5 GB0.5 GB0.5 GB0.5 GB
0.5 GB
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
$project
$group
$sort
6 s
!
CPU Load

DB Land
{"name":"Cora", "dpt":"DE", "sal":200},
{"name":"Alex", "dpt":"DE", "sal":300},
[{"dpt" :"DE",
"emps":
},]
{"name":"Fred", "dpt":"US", "sal":300},
{"name":"Erik", "dpt":"US", "sal":400},
{"dpt" :"US",
"emps":
},]
{"name":"Ivan", "dpt":"NL", "sal":400},
{"name":"Herb", "dpt":"NL", "sal":600},
{"dpt" :"NL",
"emps":
},]
{"name":"Jill", "dpt":"UK", "sal":500}]
{"dpt" :"UK",
"emps" }]
[
[
[
db.emps.aggregate([
[
{$sort: {dpt:1,sal:-1}},
{$group: {_id:"$dpt",emps:{$push:{name:"$name",sal:"$sal"}}}},
{$project: {dpt:"$_id",emps:{$slice :["$emps",2]}}}
])

DB Land
[{"dpt" :"DE",
"emps":
},]
{"dpt" :"US",
"emps":
},]
{"dpt" :"NL",
"emps":
},]
{"dpt" :"UK",
"emps" }]
[
[
[
db.emps.aggregate([
[
{$match: {dpt:"DE"}},
])

DB Land
[{"dpt" :"DE",
"emps":
},]
{"dpt" :"US",
"emps":
},]
{"dpt" :"NL",
"emps":
},]
{"dpt" :"UK",
"emps" }]
[
[
[
db.emps.aggregate([
]
[
,{explain:true})

db.emps.aggregate([
]
,{explain:true})
DB Land

{ 
"stages": [ 
{ 
"$cursor": { 
"query": { 
"dpt": "DE" 
}, 
"sort": { 
"dpt": 1, 
"sal": -1 
}, 
"fields": { 
"dpt": 1, 
"name": 1, 
db.emps.aggregate([
]
,{explain:true})
DB Land

{ 
"stages": [ 
{ 
"$cursor": { 
"query": { 
"dpt": "DE" 
}, 
"sort": { 
"dpt": 1, 
"sal": -1 
}, 
"fields": { 
"dpt": 1, 
"name": 1, 
"sal": 1, 
"_id": 0 
}, 
db.emps.aggregate([
]
,{explain:true})
DB Land

{ 
"stages": [ 
{ 
"$cursor": { 
"query": { 
"dpt": "DE" 
}, 
"sort": { 
"dpt": 1, 
"sal": -1 
}, 
"fields": { 
"dpt": 1, 
"name": 1, 
"sal": 1, 
"_id": 0 
}, 
db.emps.aggregate([
]
,{explain:true})
DB Land
$cursor

], 
"name": [ 
"[MinKey, MaxKey]" 
] 
} 
} 
}, 
"rejectedPlans": [] 
} 
} 
}, 
{ 
"$group": { 
"_id": "$dpt", 
"emps": { 
"$push": { 
"name": "$name", 
"sal": "$sal" 
} 
} 
} 
}, 
{ 
db.emps.aggregate([
]
,{explain:true})
DB Land
$cursor

], 
"name": [ 
] 
} 
} 
}, 
"rejectedPlans": [] 
} 
} 
}, 
{ 
"$group": { 
"_id": "$dpt", 
"emps": { 
"$push": { 
"name": "$name", 
"sal": "$sal" 
} 
} 
} 
}, 
{ 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$cursor

"_id": "$dpt", 
"emps": { 
"$push": { 
"name": "$name", 
"sal": "$sal" 
} 
} 
} 
}, 
{ 
"$project": { 
"_id": false, 
"dpt": "$_id", 
"emps": { 
"$slice": [ 
"$emps", 
{ 
"$const": 2 
} 
] 
} 
} 
} 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$cursor

"_id": "$dpt", 
"emps": { 
"$push": { 
"name": "$name", 
"sal": "$sal" 
} 
} 
} 
}, 
{ 
"$project": { 
"_id": false, 
"dpt": "$_id", 
"emps": { 
"$slice": [ 
"$emps", 
{ 
"$const": 2 
} 
] 
} 
} 
} 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor

"queryPlanner": { 
"plannerVersion": 1, 
"namespace": "DB.emps", 
"indexFilterSet": false, 
"parsedQuery": { 
"dpt": { 
"$eq": "DE" 
} 
}, 
"winningPlan": { 
"stage": "PROJECTION", 
"transformBy": { 
"dpt": 1, 
"name": 1, 
"sal": 1, 
"_id": 0 
}, 
"inputStage": { 
"stage": "IXSCAN", 
"keyPattern": { 
"dpt": 1, 
"sal": -1, 
"name": 1 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor

"parsedQuery": { 
"dpt": { 
"$eq": "DE" 
} 
}, 
"winningPlan": { 
"transformBy": { 
"dpt": 1, 
"name": 1, 
"sal": 1, 
"_id": 0 
}, 
"inputStage": { 
"keyPattern": { 
"dpt": 1, 
"sal": -1, 
"name": 1 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor
IXSCAN
Index on 
"dpt":1,
"sal":-1,
"name":1
PROJECTION

"multiKeyPaths": { 
"dpt": [], 
"sal": [], 
"name": [] 
}, 
"isUnique": false, 
"isSparse": false, 
"isPartial": false, 
"indexVersion": 2, 
"direction": "forward", 
"indexBounds": { 
"dpt": [ 
"["DE", "DE"]" 
], 
"sal": [ 
"[MaxKey, MinKey]" 
], 
"name": [ 
] 
} 
} 
}, 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor
IXSCAN
Index on 
"dpt":1,
"sal":-1,
"name":1
PROJECTION

"dpt": [], 
"sal": [], 
"name": [] 
}, 
"indexBounds": { 
"dpt": [ 
"["DE", "DE"]" 
], 
"sal": [ 
], 
"name": [ 
] 
} 
} 
}, 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor
IXSCAN
Index on 
"dpt":1,
"sal":-1,
"name":1
PROJECTION
Index used
for sorting

"dpt": [], 
"sal": [], 
"name": [] 
}, 
"indexBounds": { 
"dpt": [ 
"["DE", "DE"]" 
], 
"sal": [ 
], 
"name": [ 
] 
} 
} 
}, 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor
IXSCAN
Index on 
"dpt":1,
"sal":-1,
"name":1
PROJECTION
Index used
for sorting
Pipeline
Reordering:
Index used
for matching

"parsedQuery": { 
"dpt": { 
"$eq": "DE" 
} 
}, 
"winningPlan": { 
"transformBy": { 
"dpt": 1, 
"name": 1, 
"sal": 1, 
"_id": 0 
}, 
"inputStage": { 
"keyPattern": { 
"dpt": 1, 
"sal": -1, 
"name": 1 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor
IXSCAN
Index on 
"dpt":1,
"sal":-1,
"name":1
PROJECTION
Index used
for sorting
Pipeline
Reordering:
Index used
for matching

"parsedQuery": { 
"dpt": { 
"$eq": "DE" 
} 
}, 
"winningPlan": { 
"transformBy": { 
"dpt": 1, 
"name": 1, 
"sal": 1, 
"_id": 0 
}, 
"inputStage": { 
"keyPattern": { 
"dpt": 1, 
"sal": -1, 
"name": 1 
db.emps.aggregate([
]
,{explain:true})
DB Land
$group
$project
$cursor
IXSCAN
Index on 
"dpt":1,
"sal":-1,
"name":1
PROJECTION
Index used
for sorting
Pipeline
Reordering:
Index used
for matching
Projection
Optimization:
Query turned to
Covered Query

DB Land

1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
DB Land
_id name dpt sal

1 Alex DE 300
2 Bert DE 100
3 Cora DE 200
4 Drew US 200
5 Erik US 400
6 Fred US 300
7 Gina US 200
8 Herb NL 600
9 Ivan NL 400
10 Jill UK 500
DB Land
_id name dpt sal
10
3
1
6
10
5
5
10
8
bid

10 Jill UK 500
_id name dpt sal bid
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
2 Bert DE 100 3
{$match: {name:"Bert"}},
db.emps.aggregate([
])
Bert’s bosses
{$graphLookup: {from:"emps", startWith:"$bid", connectFromField:"bid", connectToField:"_id", 
dpthField:"lvl", as:"bosses"}}
"bid" "_id""$bid"
"bosses"
$match $graphLookup

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
2 Bert DE 100 3
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ }2 Bert DE 100 3
"bosses":[, ]
3 Cora DE 200 1
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ }2 Bert DE 100 3
"bosses":[, ]
3 Cora DE 200 1
"
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ }2 Bert DE 100 3
"bosses":[, ]3 Cora DE 200 1
"lvl":0,{ }
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ }2 Bert DE 100 3
"lvl":0,{ }
1 Alex DE 300 10
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ }2 Bert DE 100 3
"lvl":0,{ }
1 Alex DE 300 10
"
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{
}
2 Bert DE 100 3
"bosses":[,
]
3 Cora DE 200 1
"lvl":0,{ }
1 Alex DE 300 10
"lvl":1,{ }
,
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{
}
2 Bert DE 100 3
"bosses":[,
]
3 Cora DE 200 1
"lvl":0,{ }
1 Alex DE 300 10
"lvl":1,{ }
,
10 Jill UK 500
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{
}
2 Bert DE 100 3
"bosses":[,
]
3 Cora DE 200 1
"lvl":0,{ }
1 Alex DE 300 10
"lvl":1,{ }
,
10 Jill UK 500
"
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{
}
2 Bert DE 100 3
"bosses":[,
]
3 Cora DE 200 1
"lvl":0,{ }
1 Alex DE 300 10
"lvl":1,{ }
,
10 Jill UK 500
"lvl":2,{ }
,
db.emps.aggregate([
])
Bert’s bosses
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
Bert’s bosses
{
}
2 Bert DE 100 3
"bosses":[,
]
3 Cora DE 200 1
"lvl":0,{ }
1 Alex DE 300 10
"lvl":1,{ }
,
10 Jill UK 500
"lvl":2,{ }
,
db.emps.aggregate([
])
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
Bert’s bosses
db.emps.aggregate([
])
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
The two employees bosseswith most
"bid" "_id""$bid"
"bosses"
])
db.emps.aggregate([
,
{$sort:{"bosses.leve:-1}}, {$limit:2}"bosses.lvl"
$limit$graphLookup $sort

8 Herb NL 600 10
10 Jill UK 500
5 Erik US 400 105 Erik US 400 10
10 Jill UK 500
6 Fred US 300 5
1 Alex DE 300 10
3 Cora DE 200 1
10 Jill UK 50010 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
{ ,"bosses":[[
{ ,"bosses":[
{ ,"bosses":[
{ ,"bosses":[
{ ,"bosses":[
{ ,"bosses":[
{ ,"bosses":[
{ ,"bosses":[
{ ,"bosses":[
{ ,"bosses":[]}]
]},
]},
]},
]},
]},
]},
]},
]},
]},
])
db.emps.aggregate([
"bid" "_id""$bid"
"bosses" ,

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}[
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[]}]
3 Cora DE 200 1
1 Alex DE 300 10
6 Fred US 300 5
10 Jill UK 500
5 Erik US 400 10
5 Erik US 400 10
10 Jill UK 500
8 Herb NL 600 10
]},
]},
]},
]},
]},
]},
]},
]},
]},
])
db.emps.aggregate([
"bid" "_id""$bid"
"bosses" ,

10 Jill UK 50010 Jill UK 50010 Jill UK 500
5 Erik US 400 10
10 Jill UK 500
1 Alex DE 300 10
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}[
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[{ ,"lvl":0}
{ ,"bosses":[]}]
3 Cora DE 200 1
1 Alex DE 300 10
6 Fred US 300 5
10 Jill UK 500
5 Erik US 400 10
5 Erik US 400 10
10 Jill UK 500
8 Herb NL 600 10
]},
]},
]},
]},
]},
]},
]},
]},
]},
])
db.emps.aggregate([
"bid" "_id""$bid"
"bosses" ,

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}[
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"bosses":[{ ,"lvl":0}
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"bosses":[]}]
3 Cora DE 200 1 1 Alex DE 300 10
1 Alex DE 300 10 10 Jill UK 500
6 Fred US 300 5 5 Erik US 400 10
10 Jill UK 500
5 Erik US 400 10 10 Jill UK 500
5 Erik US 400 10 10 Jill UK 500
10 Jill UK 500
8 Herb NL 600 10 10 Jill UK 500
]},
]},
]},
]},
]},
]},
]},
]},
]},
])
db.emps.aggregate([
"bid" "_id""$bid"
"bosses" ,

10 Jill UK 50010 Jill UK 50010 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}[
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"bosses":[{ ,"lvl":0}
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"bosses":[]}]
3 Cora DE 200 1 1 Alex DE 300 10
1 Alex DE 300 10 10 Jill UK 500
6 Fred US 300 5 5 Erik US 400 10
10 Jill UK 500
5 Erik US 400 10 10 Jill UK 500
5 Erik US 400 10 10 Jill UK 500
10 Jill UK 500
8 Herb NL 600 10 10 Jill UK 500
]},
]},
]},
]},
]},
]},
]},
]},
]},
])
db.emps.aggregate([
"bid" "_id""$bid"
"bosses" ,

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0} }] ,[
{ ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,
10 Jill UK 500
{ ,"bosses":[{ ,"lvl":0}
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"bosses":[{ ,"lvl":0}
{ ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},
{ ,"bosses":[]}]
3 Cora DE 200 1 1 Alex DE 300 10 10 Jill UK 500
1 Alex DE 300 10 10 Jill UK 500
6 Fred US 300 5 5 Erik US 400 10 10 Jill UK 500
10 Jill UK 500
5 Erik US 400 10 10 Jill UK 500
5 Erik US 400 10 10 Jill UK 500
10 Jill UK 500
8 Herb NL 600 10 10 Jill UK 500
}] ,
}] ,}] ,
]},
]},
]},
]},
]},
]},
])
db.emps.aggregate([
"bid" "_id""$bid"
"bosses" ,

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
[
1 Alex DE 300 10{ ,"bosses":[{ ,"lvl":0} }] ,10 Jill UK 500
10 Jill UK 500{ ,"bosses":[]}]
5 Erik US 400 10{ ,"bosses":[{ ,"lvl":0}10 Jill UK 500 }] ,
8 Herb NL 600 10{ ,"bosses":[{ ,"lvl":0}10 Jill UK 500 }] ,}] ,
2 Bert DE 100 3 { ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,3 Cora DE 200 1 1 Alex DE 300 10 10 Jill UK 500 ]},
4 Drew US 200 6 { ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,6 Fred US 300 5 5 Erik US 400 10 10 Jill UK 500 ]},
6 Fred US 300 5 { ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},5 Erik US 400 10 10 Jill UK 500 ]},
7 Gina US 200 5 { ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},5 Erik US 400 10 10 Jill UK 500 ]},
3 Cora DE 200 1 { ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},1 Alex DE 300 10 10 Jill UK 500 ]},
9 Ivan NL 400 8 { ,"lvl":1}{ ,"bosses":[{ ,"lvl":0},8 Herb NL 600 10 10 Jill UK 500 ]},
])
db.emps.aggregate([
"bid" "_id""$bid"
"bosses" ,

"bid" "_id""$bid"
"bosses"
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
[ 2 Bert DE 100 3 { ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,3 Cora DE 200 1 1 Alex DE 300 10 10 Jill UK 500 ]},
4 Drew US 200 6 { ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,6 Fred US 300 5 5 Erik US 400 10 10 Jill UK 500 ]}]
])
db.emps.aggregate([
,

dpthField:"lvl", as:"bosses"}} ,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
[ 2 Bert DE 100 3 { ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,3 Cora DE 200 1 1 Alex DE 300 10 10 Jill UK 500 ]},
4 Drew US 200 6 { ,"lvl":1} { ,"lvl":2}{ ,"bosses":[{ ,"lvl":0}, ,6 Fred US 300 5 5 Erik US 400 10 10 Jill UK 500 ]}]
"bid" "_id""$bid"
"bosses"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"bid" "_id""$bid"
"bosses"

{$sort:{"bosses.leve:-1}}, {$limit:2}
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"bid" "_id""$bid"
"bosses"
The two employeesbosses with most
"bosses.lvl"

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"bid""_id""$bid"
"bosses"
"bosses.lvl"

,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"emps"
"bosses.lvl"
"$_id" "_id" "bid"

,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"bosses.lvl"
"$_id" "_id" "bid"
"emps" ,

,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"

,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"
{$addFields: {hdcnt:{$size:"$emps"}}},

,
{$sort:{"bosses:-1}}, {$limit:2}
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"

,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"
$limit$graphLookup $sort$addFields

[{ ,"emps":[],
"hdcnt":0}]
9 Ivan NL 400 8
[{ ,"emps":[],
"hdcnt":0}
7 Gina US 200 5
[{ ,"emps":[],
"hdcnt":0}
4 Drew US 200 6
[{ ,"emps":[],
"hdcnt":0}
2 Bert DE 100 3
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
8 Herb NL 600 10 9 Ivan NL 400 8
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
6 Fred US 300 5 4 Drew US 200 6
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
,
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"

[ 5 Erik US 400 10 { ,"lvl":0} { ,"lvl":1}{ ,"emps":[{ ,"lvl":0}, ,7 Gina US 200 5 6 Fred US 300 5 4 Drew US 200 6 ],
"hdcnt":3}
[{ ,"emps":[],
"hdcnt":0}
7 Gina US 200 5
[{ ,"emps":[],
"hdcnt":0}
4 Drew US 200 6
[{ ,"emps":[],
"hdcnt":0}
2 Bert DE 100 3
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
3 Cora DE 200 1 2 Bert DE 100 3
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
8 Herb NL 600 10 9 Ivan NL 400 8
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
6 Fred US 300 5 4 Drew US 200 6
[ { ,"lvl":1}],{ ,"emps":[{ ,"lvl":0},
"hdcnt":2},
1 Alex DE 300 10 3 Cora DE 200 1 2 Bert DE 100 3
, 10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
,
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"

[ 10 Jill UK 500{ ,"emps":[ { ,"lvl":0} { ,"lvl":0}{ ,"lvl":0}, , ,
{ ,"lvl":1} { ,"lvl":1}{ ,"lvl":1}, , ,
{ ,"lvl":2} { ,"lvl":2}{ ,"lvl":1}, , 4 Drew US 200 6 ],2 Bert DE 100 3
1 Alex DE 300 10 5 Erik US 400 10 8 Herb NL 600 10
3 Cora DE 200 1 7 Gina US 200 5 6 Fred US 300 5
9 Ivan NL 400 8
"hdcnt":9},
"hdcnt":3}
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
3 Cora DE 200 1 2 Bert DE 100 3
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
8 Herb NL 600 10 9 Ivan NL 400 8
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
6 Fred US 300 5 4 Drew US 200 6
[ { ,"lvl":1}],{ ,"emps":[{ ,"lvl":0},
"hdcnt":2},
,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
,
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"

{ ,"lvl":1} { ,"lvl":1}{ ,"lvl":1}, , ,
9 Ivan NL 400 8
"hdcnt":9},
"hdcnt":3}
[{ ,"emps":[{ ,"lvl":0}],
"hdcnt":1},
3 Cora DE 200 1 2 Bert DE 100 3
[ { ,"lvl":1}],{ ,"emps":[{ ,"lvl":0},
"hdcnt":2},
,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
,
db.emps.aggregate([
])
"$_id" "_id" "bid"
"emps" ,
"hdcnt"

,
db.emps.aggregate([
"$_id" "_id" "bid"
"emps" ,
"hdcnt" ,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ ,"lvl":1} { ,"lvl":1}{ ,"lvl":1}, , ,
9 Ivan NL 400 8
"hdcnt":9},
"hdcnt":3}
},
]}

{$addFields: {tsal:{$reduce: {
input: "$emps",
initialValue: 0,
in: {$add: ["$$value", "$$this.sal"]}}}}}])
,
db.emps.aggregate([
"$_id" "_id" "bid"
"emps" ,
"hdcnt" ,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ ,"lvl":1} { ,"lvl":1}{ ,"lvl":1}, , ,
9 Ivan NL 400 8
"hdcnt":9},
"hdcnt":3}
},
]}

input: "$emps",
initialValue: 0,
,
db.emps.aggregate([
"$_id" "_id" "bid"
"emps" ,
"hdcnt" ,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ ,"lvl":1} { ,"lvl":1}{ ,"lvl":1}, , ,
9 Ivan NL 400 8
"hdcnt":9},
"hdcnt":3}
},
]}

input: "$emps",
initialValue: 0,
,
db.emps.aggregate([
"$_id" "_id" "bid"
"emps" ,
"hdcnt" ,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ ,"lvl":1} { ,"lvl":1}{ ,"lvl":1}, , ,
9 Ivan NL 400 8
"hdcnt":9},
"hdcnt":3}
},
]}
$addFields
$reduce

input: "$emps",
initialValue: 0,
,
db.emps.aggregate([
"$_id" "_id" "bid"
"emps" ,
"hdcnt" ,
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
{ ,"lvl":1} { ,"lvl":1}{ ,"lvl":1}, , ,
9 Ivan NL 400 8
"hdcnt":9},
"hdcnt":3}
},
]}
, "tsal":2700
, "tsal":700

10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6
10 Jill UK 500
5 Erik US 400 10
7 Gina US 200 5
6 Fred US 300 5
9 Ivan NL 400 8
8 Herb NL 600 10
1 Alex DE 300 10
3 Cora DE 200 1
2 Bert DE 100 3
4 Drew US 200 6

MongoDB
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500

MongoDB
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
Munich
Hamburg
Essen
Chicago
Boston
Orlando
Austin
Utrecht
Arnheim
London
loc

MongoDB
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
Munich
Hamburg
Essen
Chicago
Boston
Orlando
Austin
Utrecht
Arnheim
London
_id name dpt sal bid loc

1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
Munich
Hamburg
Essen
Chicago
Boston
Orlando
Austin
Utrecht
Arnheim
London

Chicago
Boston
Austin
Orlando
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
Munich
Hamburg
Essen
Chicago
Boston
Orlando
Austin
Utrecht
Arnheim
London

Chicago
Boston
Austin
Orlando
✈
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
Munich
Hamburg
Essen
Chicago
Boston
Orlando
Austin
Utrecht
Arnheim
London

Chicago
Boston
Austin
Orlando
$
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
Munich
Hamburg
Essen
Chicago
Boston
Orlando
Austin
Utrecht
Arnheim
London

Chicago
Boston
Austin
Orlando
1 Alex DE 300 10
2 Bert DE 100 3
3 Cora DE 200 1
4 Drew US 200 6
5 Erik US 400 10
6 Fred US 300 5
7 Gina US 200 5
8 Herb NL 600 10
9 Ivan NL 400 8
10 Jill UK 500
Munich
Hamburg
Essen
Chicago
Boston
Orlando
Austin
Utrecht
Arnheim
London
$

Chicago
Boston
Austin
Orlando
Boston 980 Chicago
Boston 2000 Austin
Boston 1300 Orlando
Chicago 980 Boston
Chicago 1150 Austin
Chicago 1200 Orlando
Austin 2000 Boston
Austin 1150 Chicago
Austin 1100 Orlando
Orlando 1300 Boston
Orlando 1200 Chicago
Orlando 1100 Austin
start enddist

Orlando 1300 BostonBoston 980 ChicagoChicago 1200 OrlandoOrlando 1100 AustinAustin 2000 BostonBoston 980 ChicagoChicago 1150 AustinAustin 1100 OrlandoOrlando 1300 BostonBoston 1300 OrlandoOrlando 1100 AustinAustin 1150 ChicagoChicago 980 BostonBoston 1300 OrlandoOrlando 1200 ChicagoChicago 1150 AustinAustin 2000 BostonBoston 2000 AustinAustin 1100 OrlandoOrlando 1200 ChicagoChicago 980 BostonBoston 2000 AustinAustin 1150 ChicagoChicago 1200 Orlando
…,{$graphLookup:{from:"dists",startWith:"$end",connectFromField:"end",connectToField:"start", 
dpthField:"lvl", as:"routes"}}, …, {$sort:{"tdist":1}}, {$limit:1})]
start enddist
Boston 980 Chicago
Boston 2000 Austin
Boston 1300 Orlando
Chicago 980 Boston
Chicago 1150 Austin
Chicago 1200 Orlando
Austin 2000 Boston
Austin 1150 Chicago
Austin 1100 Orlando
Orlando 1300 Boston
Orlando 1200 Chicago
Orlando 1100 Austin
Chicago
Boston
Austin
Orlando
$limit$graphLookup $sort...

5280Boston 980 Chicago Chicago 1200 Orlando Orlando 1100 Austin Austin 2000 Boston
4530Boston 980 Chicago Chicago 1150 Austin Austin 1100 Orlando Orlando 1300 Boston
4530Boston 1300 Orlando Orlando 1100 Austin Austin 1150 Chicago Chicago 980 Boston
5650Boston 1300 Orlando Orlando 1200 Chicago Chicago 1150 Austin Austin 2000 Boston
5280Boston 2000 Austin Austin 1100 Orlando Orlando 1200 Chicago Chicago 980 Boston
5650Boston 2000 Austin Austin 1150 Chicago Chicago 1200 Orlando Orlando 1300 Boston
start enddist start enddist start enddist start enddist tdist
Chicago
Boston
Austin
Orlando

5280Boston 980 Chicago Chicago 1200 Orlando Orlando 1100 Austin Austin 2000 Boston
4530Boston 1300 Orlando Orlando 1100 Austin Austin 1150 Chicago Chicago 980 Boston
5650Boston 1300 Orlando Orlando 1200 Chicago Chicago 1150 Austin Austin 2000 Boston
5280Boston 2000 Austin Austin 1100 Orlando Orlando 1200 Chicago Chicago 980 Boston
5650Boston 2000 Austin Austin 1150 Chicago Chicago 1200 Orlando Orlando 1300 Boston
Chicago
Boston
Austin
Orlando

Chicago
Boston
Austin
Orlando

Chicago
Boston
Austin
Orlando
✈

Chicago
Boston
Austin
Orlando
$

MongoDB
_id name dpt sal expertise

MongoDB
5 results for mongodb
Alex
location: DE
salary: 300
expertise: MongoDB, Ruby
Bert
location: DE
salary: 100
expertise: MongoDB, Java
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
expertise: MongoDB, Scala
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
https://www.awesomeempview.com/search/f?type=all&key…
Search | EmpView
mongodb
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔

3 results for oracle
Fred
location: US
salary: 300
expertise: Oracle, Cobol
Gina
location: US
salary: 200
expertise: Oracle, Pascal
Ivan
location: NL
salary: 400
expertise: Oracle, Fortran
Search | EmpView
Location
All
US
NL
✔
Expertise
All
Oracle
Cobol
Pascal
Fortran
✔
Salary
All
100 - 200
200 - 400
✔
oracle
(2)
(1)
(3)
(1)
(1)
(1)
(1)
(2)
MongoDB

MongoDB
Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
db.emps.aggregate([ 
{ $match: { $text : { $search : "mongodb" }}}, 
{ $sortByCount: "$dpt"} 
])

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{"_id":"DE", "count":3},
{"_id":"NL", "count":1},
{"_id":"US", "count":1}
{ $match: { $text : { $search : "mongodb" }}}, 
{ $sortByCount: "$dpt"} 
])

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
db.emps.aggregate( [ 
{$match: { $text : {$search : "mongodb" }}}, 
{$unwind: "$expertise"}, 
{$sortByCount: "$expertise"} 
])
{"_id":"MongoDB","count":5},
{"_id":"Java","count":2},
{"_id":"Ruby","count":2},
{"_id":"Scala","count":1}

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{$bucket: { 
groupBy: "$sal", 
boundaries: [100, 201, 401, 601, Infinity]}} 
])
{"_id":100,"count":2},
{"_id":201,"count":2},
{"_id":401,"count":1}

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{$bucket: { 
groupBy: "$sal", 
boundaries: [100, 201, 401, 601, Infinity], 
output: { 
count: {$sum: 1}, 
matches: {$push: "$$CURRENT"} 
}}} 
])
{"_id":100,"count":2,"matches":[
]},
]},
]}

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{$bucketAuto: { 
groupBy: "$sal", 
buckets: 4}} 
])
{"_id":{"min":100,"max":200},
"count":1},
{"_id":{"min":200,"max":300},
"count":1},
{"_id":{"min":300,"max":400},
“count":1},
{"_id":{"min":400,"max":600},
"count":2}

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{$match: { $text : {$search : 
{$facet: { 
"Location": [ 
{$sortByCount: "$dpt"} 
], 
"Expertise":[ 
], 
"Salary":[ 
{$bucket: { 
groupBy: "$sal", 
]}}])
{"Location":[ 
{"_id":"DE","count":3}, 
{"_id":"NL","count":1}, 
{"_id":"US","count":1}], 
 
"Expertise":[ 
{"_id":"MongoDB","count":5}, 
{"_id":"Java","count":2}, 
{"_id":"Ruby","count":2}, 
{"_id":"Scala","count":1}], 
 
"Salary":[ 
{"_id":100,"count":2}, 
{"_id":201,"count":2}, 
{"_id":401,"count":1}]}
}}},"mongodb"

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{"Location":[ 
{"_id":"DE","count":3}, 
{"_id":"NL","count":1}, 
{"_id":"US","count":1}], 
 
"Expertise":[ 
 
"Salary":[ 
{"_id":100,"count":2}, 
{"_id":201,"count":2}, 
{"_id":401,"count":1}]}
{$facet: { 
"Location": [ 
], 
"Expertise":[ 
], 
"Salary":[ 
{$bucket: { 
groupBy: "$sal", 
]}}])
}}},"mongodb"

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{"Location":[ 
{"_id":"DE","count":3}, 
{"_id":"NL","count":1}, 
{"_id":"US","count":1}], 
 
"Expertise":[ 
 
"Salary":[ 
{"_id":100,"count":2}, 
{"_id":201,"count":2}, 
{"_id":401,"count":1}]}
{$facet: { 
"Location": [ 
], 
"Expertise":[ 
], 
"Salary":[ 
{$bucket: { 
groupBy: "$sal", 
]}}])
}}},""mongodb" "ruby""

Alex
location: DE
salary: 300
Bert
location: DE
salary: 100
Cora
location: DE
salary: 200
Erik
location: US
salary: 400
Herb
location: NL
salary: 600
Location
All
DE
NL
Salary
All
100 - 200
200 - 400
400 - 600
Expertise
All
MongoDB
Java
Scala
✔
(1)
(2)
(1)
(2)
(2)
(1)
✔
3( )
( )5
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{$facet: { 
"Location": [ 
], 
"Expertise":[ 
], 
"Salary":[ 
{$bucket: { 
groupBy: "$sal", 
]}}])
{"Location":[ 
{"_id":"DE","count":1}, 
{"_id":"US","count":1}], 
 
"Expertise":[ 
{"_id":"Ruby","count":2}], 
 
"Salary":[ 
{"_id":201,"count":2}]}

MongoDB
Alex
location: DE
salary: 300
Erik
location: US
salary: 400
Location
All
DE
Salary
All
200 - 400
Expertise
All
MongoDB
✔
(2)
✔
1(
( )2
Ruby (2)
US (1)
✔
Search | EmpView
mongodb
{$facet: { 
"Location": [ 
], 
"Expertise":[ 
], 
"Salary":[ 
{$bucket: { 
groupBy: "$sal", 
]}}])
{"Location":[ 
{"_id":"DE","count":1}, 
{"_id":"US","count":1}], 
 
"Expertise":[ 
{"_id":"Ruby","count":2}], 
 
"Salary":[ 
{"_id":201,"count":2}]})

PL Land
nats =
nats. (nats. {|n| nats. (n-1)
. (1)
. {|p| n % p != 0}
. })
drop
take
map
all?
mapzip
[1,2,3,4,5,6,7,8,9,10]
Ruby

PL Land
[[1, true ],
[2, true ],
[3, true ],
[4, false],
[5, true ],
[6, false],
[7, true ],
[8, false],
[9, false],
[10,false]]
nats =
nats. (nats. {|n| nats. (n-1)
. (1)
. {|p| n % p != 0}
. })
drop
take
map
all?
mapzip
[1,2,3,4,5,6,7,8,9,10]
Ruby

PL Land
drop
take
map
all?
mapzip
[1,2,3,4,5,6,7,8,9,10]
Ruby

PL Land
drop
take
map
all?
mapzip
$range: [1, 11, 1]
Ruby

PL Land
drop
take
map
all?
map$zip
$range: [1, 11, 1]
Ruby

PL Land
drop
take
map
all?
map$zip
$range: [1, 11, 1]
["A", "B", "C"]
[10, 20, 30]
Ruby

PL Land
drop
take
map
all?
map$zip
$range: [1, 11, 1]
["A", "B", "C"]
[10, 20, 30]
[["A",10], ["B",20], ["C",30]]
Ruby

PL Land
drop
take
map
all?
$map$zip
$range: [1, 11, 1]
Ruby

PL Land
drop
map
all?
$slice$map$zip
$range: [1, 11, 1]
Ruby

PL Land
map
all?
$slice
$slice
$map$zip
$range: [1, 11, 1]
Ruby

PL Land
all?
$slice
$slice
$map
$map$zip
$range: [1, 11, 1]
Ruby

PL Land
$slice
$slice
$map
$allElementsTrue
$map$zip
$range: [1, 11, 1]
Ruby

PL Land
$slice
$slice
$map
$allElementsTrue
$map
$zip
$range: [1, 11, 1]
Ruby

$let: {vars: {nats: { }}, in: { 
: {inputs: ["$$nats", { 
: {input: "$$nats", as: "n", in: { 
: [{ 
: {input: {
: [{
: ["$$nats", {$subtract: ["$$n", 1]}]},
1, {$size: "$$nats"}]}, as: "p", in: {
$ne: [{$mod: ["$$n", "$$p"]}, 0]}}}]}}}]}}}
PL Land
$slice
$slice
$map
$allElementsTrue
$map
$zip
: [1, 11, 1]$range

: [{ 
: {input: {
: [{
: ["$$nats", {$subtract: ["$$n", 1]}]},
1, {$size: "$$nats"}]}, as: "p", in: {
$ne: [{$mod: ["$$n", "$$p"]}, 0]}}}]}}}]}}}
PL Land
$slice
$slice
$map
$allElementsTrue
$map
$zip
: [1, 11, 1]$range
$allElementsTrue$map$slice$slice
$map
$range $zip

MongoDB
PL Land
: [{ 
: {input: {
: [{
: ["$$nats", {$subtract: ["$$n", 1]}]},
1, {$size: "$$nats"}]}, as: "p", in: {
$ne: [{$mod: ["$$n", "$$p"]}, 0]}}}]}}}]}}}
$slice
$slice
$map
$allElementsTrue
$map
$zip
: [1, 11, 1]$range
$map
$range $zip

MongoDB
PL Land
[[1, true ],
[2, true ],
[3, true ],
[4, false],
[5, true ],
[6, false],
[7, true ],
[8, false],
[9, false],
[10,false]]
: [{ 
: {input: {
: [{
: ["$$nats", {$subtract: ["$$n", 1]}]},
1, {$size: "$$nats"}]}, as: "p", in: {
$ne: [{$mod: ["$$n", "$$p"]}, 0]}}}]}}}]}}}
$slice
$slice
$map
$allElementsTrue
$map
$zip
: [1, 11, 1]$range
$map
$range $zip

$slice
$slice
$map
$allElementsTrue
$map
$zip
$range

$slice
$map
$allElementsTrue
$zip
$range

$indexStats
$stdDevSamp
$stdDevPop
$sqrt
$abs
$log
$log10
$ln $pow
$exp
$trunc
$ceil
$floor
$arrayElemAt $concatArrays
$isArray
$filter
$avg
$min
$max
$sum
$stdDevPop
$stdDevSamp
$split
$indexOf
$reduce
$reverseArray
$indexOfArray
$type
$isoWeekYear
$isoDayOfWeek
$isoWeek
$in
$switch
$count
$replaceRoot
$addFields
$graphLookup $facet
$bucket $bucketAuto $sortByCount
$out
$redact
$setEquals
$setIntersection$setUnion
$setDifference
$setIsSubset $anyElementTrue
$literal
$size
$geoNear $millisecond $concat
$project $match $limit
$skip
$unwind
$group $sort
$and $or
$not
$cmp
$eq $gt
$gte
$lt
$lte
$ne
$abs
$add
$ceil
$divide
$mod
$multiply
$subtract
$toLower
$toUpper
$strcasecmp
$meta
$let
$dayOfYear
$dayOfMonth
$dayOfWeek
$year
$month
$week
$hour
$minute
$second
$millisecond
$dateToString
$cond
$ifNull
$first
$last
$push
$addToSet
$indexOfBytes
$indexOfCP
$strLenBytes
$strLenCP
$substrBytes
$substrCP
$slice
$map
$allElementsTrue
$zip
$range
$sample$lookup
$dateToString
$collStats

3.0
3.4
3.2
2.6
2.4
2.2
$indexStats
$stdDevSamp
$stdDevPop
$sqrt
$abs
$log
$log10
$ln $pow
$exp
$trunc
$ceil
$floor
$isArray
$filter
$avg
$min
$max
$sum
$stdDevPop
$stdDevSamp
$out
$redact
$setEquals
$setDifference
$literal
$size
$geoNear $millisecond $concat
$skip
$unwind
$group $sort
$and $or
$not
$cmp
$eq $gt
$gte
$lt
$lte
$ne
$abs
$add
$ceil
$divide
$mod
$multiply
$subtract
$toLower
$toUpper
$strcasecmp
$meta
$let
$dayOfYear
$dayOfMonth
$dayOfWeek
$year
$month
$week
$hour
$minute
$second
$millisecond
$dateToString
$cond
$ifNull
$first
$last
$push
$addToSet
$slice
$map
$allElementsTrue
$sample$lookup
$dateToString
$split
$indexOf
$reduce
$reverseArray
$indexOfArray
$type
$isoWeekYear
$isoDayOfWeek
$isoWeek
$in
$switch
$count
$replaceRoot
$addFields
$graphLookup $facet
$indexOfBytes
$indexOfCP
$strLenBytes
$strLenCP
$substrBytes
$substrCP
$zip
$range
$collStats

2.4 $geoNear $millisecond $concat
2.2
$avg
$min
$max
$sum
$stdDevPop
$stdDevSamp
$skip
$unwind
$group $sort
$and $or
$not
$cmp
$eq $gt
$gte
$lt
$lte
$ne
$abs
$add
$ceil
$divide
$mod
$multiply
$subtract
$toLower
$toUpper
$strcasecmp
$meta
$let
$dayOfYear
$dayOfMonth
$dayOfWeek
$year
$month
$week
$hour
$minute
$second
$millisecond
$dateToString
$cond
$ifNull
$first
$last
$push
$addToSet
$map
2.6
$out
$redact
$setEquals
$setDifference
$literal
$size
$allElementsTrue
3.2$indexStats
$stdDevSamp
$stdDevPop
$sqrt
$abs
$log
$log10
$ln $pow
$exp
$trunc
$ceil
$floor
$isArray
$filter
$slice
$sample$lookup
3.0$dateToString
3.4
$indexOf $indexOfArray
$type
$isoWeekYear
$in
$switch
$count
$replaceRoot
$addFields
$graphLookup $facet
$indexOfBytes
$indexOfCP$substrBytes
$substrCP
$zip
$range
$split
$reduce
$reverseArray $isoWeekYear
$isoDayOfWeek
$isoWeek
$strLenBytes
$strLenCP $collStats
Data-Parallel  
Pipeline Execution
Shard 1 Shard 2 Shard 3 Shard N
mongos
Q
Pipeline 
Optimisations
Pipeline Projection Optimization
Pipeline Coalescence Optimization
. . .
Pipeline Reordering
Pipeline Combining

2.4 $geoNear $millisecond $concat
2.2
$avg
$min
$max
$sum
$stdDevPop
$stdDevSamp
$skip
$unwind
$group $sort
$and $or
$not
$cmp
$eq $gt
$gte
$lt
$lte
$ne
$abs
$add
$ceil
$divide
$mod
$multiply
$subtract
$toLower
$toUpper
$strcasecmp
$meta
$let
$dayOfYear
$dayOfMonth
$dayOfWeek
$year
$month
$week
$hour
$minute
$second
$millisecond
$dateToString
$cond
$ifNull
$first
$last
$push
$addToSet
$map
2.6
$out
$redact
$setEquals
$setDifference
$literal
$size
$allElementsTrue
3.2$indexStats
$stdDevSamp
$stdDevPop
$sqrt
$abs
$log
$log10
$ln $pow
$exp
$trunc
$ceil
$floor
$isArray
$filter
$slice
$sample$lookup
3.0$dateToString
3.4
$indexOf $indexOfArray
$type
$isoWeekYear
$in
$switch
$count
$replaceRoot
$addFields
$graphLookup $facet
$indexOfBytes
$indexOfCP$substrBytes
$substrCP
$zip
$range
$split
$reduce
$reverseArray $isoWeekYear
$isoDayOfWeek
$isoWeek
$strLenBytes
$strLenCP $collStats

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