The document discusses different explain modes for MongoDB queries and aggregations. It begins with an overview of explain() and query plans, then covers the default "queryPlanner" mode which shows the winning and rejected plans. It also mentions the "executionStats" and "allPlansExecution" modes which provide more runtime statistics. The document aims to help understand how queries and aggregations are executed and troubleshoot performance issues.

1. Deciphering Explain Output
Charlie Swanson

2. Charlie Swanson
Software Engineer - Query Team

3. Goals Of This Talk
Understand how
MongoDB answers
queries
Knowledge
Figure out what's
going on
Debugging
Learn some tricks to
optimize your queries
& aggregations
Best Practices
created by Francielly Constantin Senra from Noun Project
created by Ramon Sandino from Noun Project

4. Non-goals of This Talk
Picking the best
indexes for your
needs
Index Selection
It's mostly extends
naturally
Sharded
ClustersSorry, we don't have
time…
Fixing
Problems
created by ChangHoon Baek from Noun Project
created by David Marioni from Noun Project
created by factor[e] design initiative from Noun Project

5. Overview
Make explain() exciting
Motivation
01 Different Verbosities
Diagnostics
Query
03Why Do You Care?
What is explain()?
02
Similarities & Differences
Aggregation
04

6. Overview
Make explain() exciting
Motivation
01 Different Verbosities
Diagnostics
Query
03Why Do You Care?
What is explain()?
02
Similarities & Differences
Aggregation
04

9. Monitoring the Spread

10. Our Task
MongoDB
created by Christopher Holm-Hansen from Noun Project

11. Overview
Make explain() exciting
Motivation
01 Different Verbosities
Diagnostics
Query
03Why Do You Care?
What is explain()?
02
Similarities & Differences
Aggregation
04

12. 01: What Is explain()?

14. Example Explain Output
> db.foo.find({"nFollowers": {$gte:
1000}}).explain()
{
"queryPlanner" : {
"winningPlan" : {
"stage" : "FETCH",
"inputStage" : {
"stage" : "IXSCAN",
"keyPattern" : {"nFollowers" : 1},
}
},
"rejectedPlans" : [ ]
}
}

15. Why Do You Care?
Your Application MongoDB
created by Mike Ashley from Noun Project

16. Why Do You Care?
Your Application MongoDB
Documents
Please!

17. Why Do You Care?
Your Application MongoDB
Hmm… Let
me think
about
that…

18. Why Do You Care?
Your Application MongoDB
Ah! Here
are your
results!

19. Why Do You Care?
Your Application MongoDB
What took
you so
long?!

20. Overview
Make explain() exciting
Motivation
01 Different Verbosities
Diagnostics
Query
03Why Do You Care?
What is explain()?
02
Similarities & Differences
Aggregation
04

21. Overview of Query Explain
• Query Plans
• explain() Output Format
• Different Verbosities

22. Query Plans
//
db.tweets.ensureIndex({hashtags:
1});
db.tweets.find({hashtags:
"#MDBW16"})
.sort({nFavorites:
-­‐1});
FETCH
INDEX SCAN
(hashtags)
SORT

23. Query Plans
db.tweets.find({hashtags:
"#MDBW16"})
.sort({nFavorites:
-­‐1});
FETCH
INDEX SCAN
{hashtags: 1}
SORT
Returns the IDs of
tweets with a hashtag
of "MDBW16"

24. Query Plans
db.tweets.find({hashtags:
"#MDBW16"})
.sort({nFavorites:
-­‐1});
FETCH
INDEX SCAN
{hashtags: 1}
SORT
Converts from an ID
to a full BSON object

25. Query Plans
db.tweets.find({hashtags:
"#MDBW16"})
.sort({nFavorites:
-­‐1});
FETCH
INDEX SCAN
{hashtags: 1}
SORT
Sorts the BSON
objects by the field
"nFavorites"

26. Query Plans
db.tweets.find({$or:
[
{hashtag:
{"#MongoDB"}},
{username:
{$in:
[
"@MongoDB",
"@MongoDBEng",
"@MongoDBcareers"
]}}
]});
INDEX SCAN
{hashtags: 1}
OR
FETCH
INDEX SCAN
{username: 1}

27. Explain Output
>
db.collection.find(…).explain()
{
"queryPlanner"
:
{
…
"winningPlan"
:
{…},
"rejectedPlans"
:
[…]
},
…
}

28. Explain Output
>
db.collection.find(…).explain()
{queryPlanner:
{
winningPlan:
{
stage:
"SORT",
inputStage:
{
stage:
"FETCH",
inputStage:
{
stage:
"IXSCAN"
}
}
}
}
}}
FETCH
INDEX SCAN
{hashtags: 1}
SORT

29. Explain Output
>
db.collection.find(…).explain()
{
…
stage:
"IXSCAN"
keyPattern:
{hashtags:
1},
indexBounds:
{
a:
[
"[4.0,
inf.0]"
]
},
…
//
Other
index
scan
//
specific
stats.
…
}}
FETCH
INDEX SCAN
keyPattern: {
hashtags: 1
}
indexBounds: […]
…
SORT

30. Explain Output
>
db.collection.find(…).explain()
{
"queryPlanner"
:
{
…
"winningPlan"
:
{
//
Encodes
selected
plan.
},
"rejectedPlans"
:
[…]
},
…
}
FETCH
INDEX
SORT

31. >
db.collection.find(…).explain()
{"queryPlanner"
:
{
"winningPlan"
:
{
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
},
"rejectedPlans"
:
[
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
…
]
}}
Explain Output
FETCH
INDEX
SORT
COLLECTI
ON SCAN
SORT

32. Explain Output
>
db.collection.find(…).explain()
{
"queryPlanner"
:
{
…
"winningPlan"
:
{
//
Encodes
selected
plan.
},
"rejectedPlans"
:
[
//
Encodes
rejected
plan(s).
]
},
…
}
FETCH
INDEX
SORT
COLLECTI
ON SCAN
SORT

33. Applying This Information

34. Is your query using the index you expect?

35. Is your query using the index you expect?
FETCH
SORT
✓ COLLECTION
SORT
✗
INDEX SCAN
keyPattern: {nFollowers:

36. Is your query using an index to provide the sort?

37. Is your query using an index to provide the sort?
FETCH
INDEX SCAN
✓ ✗FETCH
INDEX SCAN
SORT

38. Is your query using an index to provide the sort?
✓SORT_MERGE
INDEX SCAN
FETCH
INDEX SCAN

39. Is your query using an index to provide the
projection?

40. PROJECTI
INDEX SCAN
✓ ✗
PROJECTI
INDEX SCAN
FETCH
Is your query using an index to provide the
projection?

41. Is your query using an index to provide the
projection?
//
With
index
//
{nFollowers:
1,
username:
1}.
db.users.find(
{nFollowers:
{$gte:
1000}},
{_id:
0,
nFollowers:
1,
username:
1}
)
✓
PROJECTI
INDEX SCAN
keyPattern: {
nFollowers: 1,
username: 1
}

42. Some Questions We Can Answer
• Is your query using the index you expect?
• Is your query using an index to provide the sort?
• Is your query using an index to provide the projection?
!

43. Some Questions We Can't Yet Answer
• How selective is your index?
• What is the most expensive part of your plan?
• Why was your winning plan chosen?
!

44. Query Explain Modes
• "queryPlanner" (default)
• "executionStats"
• "allPlansExecution"

45. Query Explain Modes
• "queryPlanner" (default)
>
db.collection.find(…).explain()
{"queryPlanner"
:
{
"winningPlan"
:
{
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
},
"rejectedPlans"
:
[
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
…
]
}}

46. • "queryPlanner" (default)
• "executionStats"
• "allPlansExecution"
Query Explain Modes
✓

47. • "executionStats"
Query Explain Modes
>
db.collection.find(…).explain()
{"queryPlanner"
:
{
"winningPlan"
:
{
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
},
"rejectedPlans"
:
[
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
…
]
}}

48. • "executionStats"
Query Explain Modes
created by Mike Ashley from Noun Project
created by Creative Stall from Noun Project

49. Explain Mode: "executionStats"
>
db.tweets.find(…).explain("executionStats")
{
"queryPlanner"
:
{
…
"winningPlan"
:
{…},
"rejectedPlans"
:
[…]
},
"executionStats":
{
//
New!
…,
"executionStages":
{…}
}
…
}

50. Explain Mode: "executionStats"
>
db.tweets.find(…).explain("executionStats")
{
"queryPlanner"
:
{
/*
Same
as
before.
*/
},
"executionStats":
{
//
Top-­‐level
stats.
"executionStages":
{
stage:
"SORT",
//
Sort
stats.
inputStage:
{
//
etc,
etc.
}
}
}
…
}
FETCH
SORT
INDEX SCAN
keyPattern:

51. Explain Mode: "executionStats"
>
db.tweets.find(…).explain("executionStats")
{
…,
"executionStats" : {
// Top-level stats.
"nReturned" : 390000,
"executionTimeMillis" : 4431,
"totalKeysExamined" : 390000,
"totalDocsExamined" : 390000,
"executionStages" : {…}
},
}
FETCH
SORT
INDEX SCAN
keyPattern:

52. Explain Mode: "executionStats"
db.tweets.find(…).explain("executionStats")
{
"executionStats" : {
// Top-level stats.
"executionStages" : {
"stage" : "SORT",
"nReturned" : 390000,
"executionTimeMillisEstimate" : 2030,
…
"sortPattern" : { "nFollowers" : 1 },
"memUsage" : 20280000,
"memLimit" : 33554432,
"inputStage" : {…}
}
}
}
FETCH
SORT
INDEX SCAN
keyPattern:

53. db.tweets.find(…).explain("executionStats")
{
"executionStats" : {
// Top-level stats.
"executionStages" : {
"stage" : "SORT",
"nReturned" : 390000,
"executionTimeMillisEstimate" : 2030,
"works" : 780003,
"advanced" : 390000,
"needTime" : 390002,
"isEOF" : 1,
"sortPattern" : { "b" : 1 },
…
"inputStage" : {…}
}
}
}
?
Explain Mode: "executionStats"
FETCH
SORT
INDEX SCAN
keyPattern:

54. Execution Stats: works, advanced, etc.
• These are all PlanStages
• SortStage
• FetchStage
• IndexScanStage FETCH
SORT
INDEX SCAN
keyPattern:

55. Execution Stats: works, advanced, etc.
• These are all PlanStages
• Each PlanStage implements
work()
FETCH
SORT
INDEX SCAN
keyPattern:

56. Execution Stats: works, advanced, etc.
• These are all PlanStages
• Each PlanStage implements
work()
• work() returns one of:
• ADVANCED
• NEED_TIME
• IS_EOF
FETCH
SORT
INDEX SCAN
keyPattern:

57. Execution Stats: works, advanced, etc.
FETCH
SORT
INDEX SCAN
keyPattern:
work()

58. Execution Stats: works, advanced, etc.
FETCH
SORT
INDEX SCAN
keyPattern:
work()
work()

59. Execution Stats: works, advanced, etc.
FETCH
SORT
INDEX SCAN
keyPattern:
work()
work()
work()

60. Execution Stats: works, advanced, etc.
FETCH
SORT
INDEX SCAN
keyPattern:
work()
work()
ADVANCED ID

61. Execution Stats: works, advanced, etc.
FETCH
SORT
INDEX SCAN
keyPattern:
work()
ADVANCED
ADVANCED {…}

62. Execution Stats: works, advanced, etc.
FETCH
SORT
INDEX SCAN
keyPattern:
work()
ADVANCED
ADVANCEDNEED_TIME

63. Explain Mode: "executionStats"
db.tweets.find(…).explain("executionStats")
{
"executionStats" : {
// Top-level stats.
"executionStages" : {
"stage" : "SORT",
"nReturned" : 390000,
"executionTimeMillisEstimate" : 2030,
"works" : 780003,
"advanced" : 390000,
"needTime" : 390002,
"isEOF" : 1,
"sortPattern" : { "b" : 1 },
…
"inputStage" : {…}
}
}
}
FETCH
SORT
INDEX SCAN
keyPattern:

64. Explain Mode: "executionStats"
>
db.tweets.find(…).explain("executionStats")
{"executionStats":
{
"executionStages":
{
stage:
"SORT",
//
Sort
stats,
includes
"works",
"advanced",
…
inputStage:
{
stage:
"FETCH",
//
Fetch
stats,
includes
"works",
"advanced",
…
inputStage:
{
//
etc,
etc.
}
}
}
}
…
}
FETCH
…
SORT
…
INDEX SCAN
…

65. • "queryPlanner" (default)
• "executionStats"
• "allPlansExecution"
Query Explain Modes
✓
✓

66. Applying This Information

67. How selective is your index?

68. How selective is your index?
db.tweets.explain("executionStats").find({
createdDate:
{$gte:
<today>},
favorites:
"@eliothorowitz"
})
{
"executionStats"
:
{
"nReturned" : 314,
"totalKeysExamined" : 2704, // < 8% matched
…
}
FETCH
INDEX SCAN
keyPattern: {createdDate:

69. How selective is your index?
db.tweets.explain("executionStats").find({
createdDate:
{$gte:
<today>},
favorites:
"@eliothorowitz"
})
12:02
12:03
12:04
12:06
…
INDEX SCAN
FETCH
INDEX SCAN
keyPattern: {createdDate:

70. How selective is your index?
db.tweets.explain("executionStats").find({
createdDate:
{$gte:
<today>},
favorites:
"@eliothorowitz"
})
12:02
{createdDate:
12:02,
favorites:
[
"@MongoDB",
"@taylorswift"
]}
12:03
12:04
12:06
…
FETCH
FETCH
filter: {
favorites:
"@eliothorowitz"
INDEX SCAN
keyPattern: {createdDate:

71. How selective is your index?
db.tweets.explain("executionStats").find({
createdDate:
{$gte:
<today>},
favorites:
"@eliothorowitz"
})
12:03
12:02
12:04
12:06
…
{createdDate:
12:03,
favorites:
[
"@eliothorowitz",
"@taylorswift"
]}
FETCH
FETCH
filter: {
favorites:
"@eliothorowitz"
INDEX SCAN
keyPattern: {createdDate:

72. What's the most expensive part of your plan?

73. What's the most expensive part of your plan?
db.tweets.explain("executionStats").find({
createdDate:
{$gte:
<today>},
favorites:
"@eliothorowitz"
})
FETCH
filter: {
favorites:
"@eliothorowitz"
INDEX SCAN
keyPattern: {createdDate:

74. What's the most expensive part of your plan?
db.tweets.explain("executionStats").find(…)
FETCH
executionTimeMillisEstimate: 431
INDEX SCAN
executionTimeMillisEstimate:

75. What's the most expensive part of your plan?
db.tweets.explain("executionStats").find(…)
FETCH
works: 2705
advanced: 314
needTime: 2391
//
314/2705
≈
8%
INDEX SCAN

76. Some Questions We Can Answer
• Is your query using the index you expect?
• Is your query using an index to provide the sort?
• Is your query using an index to provide the projection?
• How selective is your index?
• Which part of your plan is the most expensive?
!

77. Some Questions We Can't Yet Answer
• Why was your winning plan chosen?
!

78. Why was your winning plan chosen?
db.tweets.explain("executionStats").find({
createdDate:
{$gte:
<today>},
favorites:
"@eliothorowitz"
})
• We had an index on {favorites: 1}, would that have been faster?
!

79. • "queryPlanner" (default)
• "executionStats"
• "allPlansExecution"
Query Explain Modes
✓
✓ 😃

80. Query Explain Mode: "allPlansExecution"
>
db.collection.find(…).explain()
{"queryPlanner"
:
{
"winningPlan"
:
{
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
},
"rejectedPlans"
:
[
{"stage"
:
"SORT",
"inputStage"
:
{…}
}}
…
]
}}
❓

81. Query Explain Mode: "allPlansExecution"

82. • MultiPlanStage::pickBestPlan()
Query Planning
…
MultiPlanStage
work()
work()
work()

83. • MultiPlanStage::pickBestPlan()
Query Planning
…
MultiPlanStage
ADVANCED
NEED_TIME
ADVANCED

84. • MultiPlanStage::pickBestPlan()
Query Planning
…
MultiPlanStage
Advances: 78 22 50

85. • MultiPlanStage::pickBestPlan()
Query Planning
…
MultiPlanStage
Advances: 78 22 50

86. Explain Mode: "allPlansExecution"
>
db.tweets.find(…).explain("allPlansExecution")
{
"queryPlanner"
:
{
…
"winningPlan"
:
{…},
"rejectedPlans"
:
[…]
},
"executionStats":
{
…,
"executionStages":
{…},
"allPlansExecution":
[…]
//
New!
}
…
}

87. Explain Mode: "allPlansExecution"
>
db.collection.find(…).explain()
{"queryPlanner"
:
{
"winningPlan"
:
{…}
},
"rejectedPlans"
:
[
{…},
…
],
"executionStats":
{
"executionStages":
{…}
"allPlansExecution":
[
{…},
{…},
…
]
}
}}

88. Explain Mode: "allPlansExecution"
db.tweets.explain("allPlansExecution").find({
createdDate:
{$gte:
<today>},
favorites:
"@eliothorowitz"
})
{
"executionStats":
{
"allPlansExecution":
[
{nReturned:
34,
executionStages:
{
/*
Index
Scan
on
"favorites"
*/
}
},
{nReturned:
101,
executionStages:
{
/*
Index
Scan
on
"createdDate"
*/
}
}
]
}
…
}

89. • "queryPlanner" (default)
• "executionStats"
• "allPlansExecution"
Query Explain Modes
✓
✓
✓

90. One Final Warning
explain() output can get VERY
large

91. Compass Shoutout

92. Compass Shoutout

93. Compass Shoutout

94. Query Summary
• Query Plans
• Explain Format
• Different Verbosities

95. Different Verbosities
Diagnostics
Query
03
Overview
Make explain() exciting
Motivation
01 Why Do You Care?
What is explain()?
02
Similarities & Differences
Aggregation
04

96. Aggregation: Overview
• Aggregation Pipelines
• Explain Output
• Tips and Tricks

97. Aggregation Pipelines
• Like PlanStages, input of one stage feeds into the next
• db.tweets.aggregate([
{$match:
{hashtag:
"#MDBW16}},
{$group:
{
_id:
"$author",
nTweets:
{$sum:
1}
}}
])
MATCH GROUP{…}{…}{…} {…}

98. db.tweets.aggregate([
{$match:
{hashtag:
"#MDBW16}},
{$group:
{
_id:
"$author",
nTweets:
{$sum:
1}
}}
])
Collection Scan?!? Are you
kidding me??
Aggregation Pipelines
MATCH GROUP{…}{…}{…} {…}

99. db.tweets.aggregate([
{$match:
{hashtag:
"#MDBW16}},
{$group:
{
_id:
"$author",
nTweets:
{$sum:
1}
}}
])
Collection Scan?!? Are you
kidding me??
Aggregation Pipelines
MATCH GROUP{…}{…}{…} {…}

100. Aggregation Pipelines
• Aggregation delegates document selection to the query system
This is just a query!
MATCH GROUP{…}{…}{…} {…}

101. Aggregation Pipelines
• Aggregation delegates document selection to the query system
GROU {…}CURSOR

102. Aggregation Pipelines
• Aggregation delegates document selection to the query system
GROU {…}CURSOR

103. Aggregation: Overview
• Aggregation Pipelines
• Explain Output
• Tips and Tricks
✓

104. Explain Output
• db.tweets.explain().aggregate([{$match:
{…}},
{$group:
{…}}])
{
"stages"
:
[
{"$cursor"
:
{…}},
{"$group"
:
{…}}
],
}
GROCURSO

105. Explain Output
• db.tweets.explain().aggregate([{$match:
{…}},
{$group:
{…}}])
{
"stages"
:
[
{"$cursor"
:
{…}},
{"$group"
:
{…}}
],
}
GROCURSO

106. Explain Output
{
"$cursor"
:
{
"query"
:
{
/*
Same
as
$match
*/
},
"queryPlanner"
:
{…}
}
}
GROCURSO

107. Explain Output
{
"$cursor"
:
{
"query"
:
{…},
"queryPlanner"
:
{
"winningPlan":
{…},
"rejectedPlans":
[…]
}
}
}
GROCURSO

108. Explain Output
• db.tweets.explain().aggregate([{$match:
{…}},
{$group:
{…}}])
{
"stages"
:
[
{"$cursor"
:
{query:
{…},
queryPlanner:
{…}}},
{"$group"
:
{/*
Same
as
$group
specification
*/}}
],
}
GROCURSO

109. Explain Output
db.tweets.explain().aggregate([
{$match:
{…}},
{$group:
{…}},
{$sort:
{…}},
{$project:
{…}},
])
{
"stages"
:
[
{"$cursor"
:
{query:
{…},
queryPlanner:
{…}}},
{"$group"
:
{/*
Same
as
$group
specification
*/}}
{"$sort"
:
{/*
Same
as
$sort
specification
*/}}
{"$project"
:
{/*
Same
as
$project
specification
*/}}
],
}
GROCURSO PROJECSOR

110. Explain Output

111. Aggregation: Overview
• Aggregation Pipelines
• Explain Output
• Tips and Tricks
✓
✓

112. Tips & Tricks: Overview
1. Making the most of your indexes
2. Pipeline Optimizations

113. Tips & Tricks: Overview
1. Making the most of your indexes
2. Pipeline Optimizations
Disclaimer: All discussions assume MongoDB 3.2

114. Aggregation & Indexes
1. Index Provided Sorts
2. Index Provided Projections

115. Index Provided Sorts
//
Compute
the
most
favorited
#MDBW16
tweets.
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$sort:
{nFavorites:
-­‐1}},
])

116. Index Provided Sorts
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$sort:
{nFavorites:
-­‐1}},
])
{
"stages"
:
[
{"$cursor"
:
{…}},
],
}
CURSO

117. Index Provided Sorts
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$sort:
{nFavorites:
-­‐1}},
])
{
"stages"
:
[
{"$cursor"
:
{
"query"
:
{hashtag:
"#MDBW16"},
"sort"
:
{nFavorites:
-­‐1},
"queryPlanner"
:
{
"winningPlan":
{
/*
Index
Scan
on
nFavorites
*/
}
}
}
],
}
CURSO

118. Index Provided Sorts
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$sort:
{nFavorites:
-­‐1}},
])
{
"stages"
:
[
{"$cursor"
:
{
"query"
:
{hashtag:
"#MDBW16"},
//
No
"sort"
field.
"queryPlanner"
:
{
"winningPlan":
{
/*
No
index
on
nFavorites
*/
},
}
},
{$sort:
{nFavorites:
-­‐1}}
],
}
CURSO SORT

119. Index Provided Projections
//
Compute
the
users
who
tweeted
about
#MDBW16
most
often.
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$group:
{_id:
"$username",
count:
{$sum:
1}}},
{$sort:
{count:
-­‐1}},
])

120. Index Provided Projections
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$group:
{_id:
"$username",
count:
{$sum:
1}}},
{$sort:
{count:
-­‐1}},
])
{
"stages"
:
[
{"$cursor"
:
{…}},
{"$group"
:
{…}},
{"$sort"
:
{…}},
],
}
CURSO GROUP SORT

121. Index Provided Projections
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$group:
{_id:
"$username",
count:
{$sum:
1}}},
{$sort:
{count:
-­‐1}},
])
{
"stages"
:
[
"$cursor"
:
{
"query"
:
{hashtag:
"#MDBW16"},
"fields"
:
{_id:
0,
username:
1},
"queryPlanner"
:
{
"winningPlan":
{
/*
Index
Scan
on
{hashtag:
1,
username:
1}
*/
},
},
{$group:
{…}},
{$sort:
{…}}
],
}
CURS GROU SORT

122. Index Provided Projections
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$group:
{_id:
"$username",
count:
{$sum:
1}}},
{$sort:
{count:
-­‐1}},
])
{
"stages"
:
[
"$cursor"
:
{
"query"
:
{hashtag:
"#MDBW16"},
//
No
"fields"
entry
"queryPlanner"
:
{
"winningPlan":
{
/*
Index
Scan
on
{hashtag:
1}
*/
},
},
{$group:
{…}},
{$sort:
{…}}
],
}
CURS GROU SORT

123. Tips & Tricks: Overview
1. Making the most of your indexes
2. Pipeline Optimizations
✓

124. Other Pipeline Optimizations
//
Compute
the
top-­‐10
users
who
tweeted
about
#MDBW16
most
often.
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$group:
{_id:
"$username",
count:
{$sum:
1}}},
{$sort:
{count:
-­‐1}},
{$limit:
10}
])

125. Other Pipeline Optimizations
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$group:
{_id:
"$username",
count:
{$sum:
1}}},
{$sort:
{count:
-­‐1}},
{$limit:
10}
])
{
"stages"
:
[
{"$cursor"
:
{…}},
{"$group"
:
{…}},
{"$sort"
:
{sortKey:
{count:
-­‐1},
limit:
NumberLong(10)}},
],
}
CURS GROU SORT*

126. Other Pipeline Optimizations
db.tweets.explain().aggregate([
{$match:
{hashtag:
"#MDBW16"},
{$group:
{_id:
"$username",
count:
{$sum:
1}}},
{$sort:
{count:
-­‐1}},
{$project:
{…}},
{$limit:
10}
])
{
"stages"
:
[
…
{"$sort"
:
{sortKey:
{count:
-­‐1},
limit:
NumberLong(10)}},
{$project:
{…}}
],
}
CURS GROU SOR PROJE

127. Aggregation: Summary
• Explain output includes query section
• Sometimes the query is doing a lot of your work
• We don't always execute the stages in the order you specified

128. Summary
Understand how
MongoDB answers
queries
Knowledge
Figure out what's
going on
Debugging
Learn some tricks to
optimize your queries
& aggregations
Best Practices
created by Francielly Constantin Senra from Noun Project
created by Ramon Sandino from Noun Project