Venez en apprendre davantage sur notre nouvel opérateur de recherche en texte intégral pour MongoDB Atlas. Il s'agit d'une amélioration significative des fonctionnalités de recherches de MongoDB et c'est également la solution de recherche en texte intégral la plus simple et la plus puissante pour les bases de données MongoDB Atlas. Cette présentation est importante pour quiconque a mis en place ou en visage de mettre en place une fonctionnalité de recherche dans son application MongoDB. Vous assisterez à une démo de $searchBeta, apprendrez comment cela fonctionne, découvrirez des fonctionnalités spécifiques vous permettant d'obtenir des résultats de recherche pertinents et apprendrez comment vous pouvez commencer à utiliser la recherche en texte intégral dans votre application dès aujourd'hui.

1. #MDBlocal#MDBlocal
Still Haven’t Found What You’re Looking For?
Use Atlas Search
Karen Huaulmé, Developer Advocate
YOUOLDMAID
PARIS

2. #MDBLocal#MDBLocal
Safe Harbor Statement
This presentation contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of
1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. Such forward-looking
statements are subject to a number of risks, uncertainties, assumptions and other factors that could cause actual
results and the timing of certain events to differ materially from future results expressed or implied by the
forward-looking statements. Factors that could cause or contribute to such differences include, but are not limited
to, those identified our filings with the Securities and Exchange Commission. You should not rely upon forward-
looking statements as predictions of future events. Furthermore, such forward-looking statements speak only as
of the date of this presentation.
In particular, the development, release, and timing of any features or functionality described for MongoDB
products remains at MongoDB’s sole discretion. This information is merely intended to outline our general product
direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal
obligation to deliver any material, code, or functionality. Except as required by law, we undertake no obligation to
update any forward-looking statements to reflect events or circumstances after the date of such statements.

3. #MDBLocal#MDBLocal
{
“name”: “Karen Huaulmé”,
“MongoDB”: {
“positions”: [“Developer Advocate”, “Solutions Architect”],
“since” : new Date (“2018-01”),
},
“Pre-MongoDB”: {
“positions”: [“Solutions Architect”, “Integration Engineer”, “Dev Rel”],
“companies”: [ “Twitch”, “Amazon”, “Nvidia”]
},
“likes”: [“running”, “Gleitschirmfliegen”, “butchering languages” ]
}
About Me

4. #MDBLocal#MDBLocal
AGENDA
Why Atlas
Search?
Atlas Search
Features
Demos:
Queries + How
They Work
Indexes and
Analyzers
Architecture

5. Why Full-Text Search?
Search is a requirement
for most applications

6. #MDBLocal#MDBLocal
Why didn’t
[thing I want]
show up first?

7. #MDBLocal#MDBLocal
Developer: Matches most terms?
Marketing: Current promotion top
result?
Customer: Ou est l’objet que je
cherche?
Relevance is Complicated…

8. #MDBLocal#MDBLocal
RelevanceisCritical

9. #MDBLocal#MDBLocal
So, build the best search
for your application
Atlas Search
Full Text Search Indexes
$searchBeta

10. Searching with $text
db.movies.find(
{ $text : { $search : ‘football’ }},
{ score : { $meta : ‘textScore’ }},
{ title : 1, fullplot : 1 })
.sort({ score : {$meta : ‘textScore’ }})
.pretty()

11. #MDBLocal#MDBLocal
Sync

12. #MDBLocal#MDBLocal
DEMO:
Movie Search Engine
http://bit.ly/AtlasSearch_Movies
Atlas Sample Data:
- sample_mflix.movies

13. #MDBLocal#MDBLocal
Query
via
$searchBeta
How do I use it?
Create 4.2
Atlas
cluster
Database
&
Collection
Create
Full Text
Index

14. #MDBLocal#MDBLocal
Full Text Search Index
Creation
Dynamic Mapping
Static Mapping

15. #MDBLocal#MDBLocal
Sample_mflix movies
document model

16. #MDBLocal#MDBLocal
Query
via
$searchBeta
How do I use it?
Create 4.2
Atlas
cluster
Database
&
Collection
Create
Full Text
Index

17. #MDBLocal#MDBLocal
INPUT STAGE 2 RESULTSSTAGE 1
$searchBeta
Aggregation Pipeline Operator
STAGE 1
$searchBeta

18. $searchBeta aggregation pipeline stage
db.movies.aggregate([
{ $searchBeta: {
search: { query : ‘werewolves and vampires’,
path : ‘fullplot’ },
highlight: { path : ‘fullplot’ }}},
{ $project: {
fullplot: 1,
title: 1,
score: { $meta: 'searchScore' },
highlight: { $meta: 'searchHighlights’ }}},
{ $limit: 10 }
])

19. $searchBeta aggregation pipeline stage
{ $searchBeta : {
search : { query : ‘werewolves and vampires’ ,
path : 'fullplot’
},
highlight : { path:'fullplot’ }}},

20. $searchBeta aggregation pipeline stage
{ $project: {
fullplot: 1,
title: 1,
score: { $meta: 'searchScore' },
highlight: { $meta: 'searchHighlights’ }}},
{ $limit: 10 }
])
$meta
$limit

21. $searchBeta Results

22. $searchBeta Results
{ title : { “The Mortal Instruments: City of Bones”,
fullplot : “Set in contemporary New York City, a seemingly ..”
score : 6.8801455497,
highlight: [ ]
}

23. texts.value
texts.type
$searchBeta Results
{ title : { “The Mortal Instruments: City of Bones”,
fullplot : “Set in contemporary ]New York City, a seemingly …”
score : 6.8801455497,
highlight: [ {
path: ‘fullplot’,
texts: [ { value: “After the disappearance of her …”,
type: ‘text’ },
{ value: ‘vampires’,
type: ‘hit’ },
{ value: ‘werewolves’,
type: ‘hit’ },
… } ],
score: 3.556248
} ] }
]

24. Query-Time Scoring
db.movies.aggregate([
{ $searchBeta : {
"search": {
query : "The Godfather",
path: "title",
score: { boost: { value: 3 }
}
])

25. Query-Time Scoring
db.movies.aggregate([
{ $searchBeta : { compound : {
must : {
"search": { query : "The Godfather",
path : "title",
score : { boost : { value : 3 }}},
should : {
"search": { query : "The Godfather",
path : "fullplot" }
])

26. #MDBLocal#MDBLocal
DEMO:
Build a Champion
Football Team

27. #MDBLocal#MDBLocal

28. #MDBLocal#MDBLocal

29. #MDBLocal#MDBLocal
Placeholder –
photos from the Arsenal Match
Mbappé

30. #MDBLocal#MDBLocal
Messi

31. #MDBLocal#MDBLocal
Zidane

32. #MDBLocal#MDBLocal
Neymar Jr.

33. #MDBLocal#MDBLocal
Zlatan Ibrahimović

34. #MDBLocal#MDBLocal
DEMO:
Build a Champion
Football Team

35. Autocomplete / Typeahead
db.players2019.aggregate([
{ $searchBeta: {
search: { query : ’Mbap..',
path : ‘Last’ ,
phrase : {prefix : true},
} ,
{ $project: {
fullplot: 1,
title: 1,
score: { $meta: 'searchScore' },
highlight: { $meta: 'searchHighlights’. }}},
{ $limit: 10 }
])

36. Fuzzy Matching
db.players2019.aggregate([
{ $searchBeta: {
term: { query : ’Ibrahimovic',
path : ‘Last’ ,
fuzzy : {maxEdits : 2.0},
} ,
{ $project: {
fullplot: 1,
title: 1,
score: { $meta: 'searchScore' },
highlight: { $meta: 'searchHighlights’. }}},
{ $limit: 10 }
])

37. Wildcard Matching
db.players2019.aggregate([
{ $searchBeta: {
term: { query : ’schw*st*ger*',
path : ‘Last’ ,
wildcard : true
} ,
{ $project: {
fullplot: 1,
title: 1,
score: { $meta: 'searchScore' },
highlight: { $meta: 'searchHighlights’. }}},
{ $limit: 10 }
])
*
'schw*st*ger'

38. Indexes and Analyzers

39. #MDBLocal#MDBLocal
Inverted Index
{ _id: 1,
S: “The quick brown fox jumped over the lazy dog” }
{ _id: 2,
S: “Quick brown foxes leap over lazy dogs in summer” }
TERM DOC
The 1
Quick 2
brown 1, 2
fox 1
foxes 2
jumped 1
leap 2
TERM DOC
the 1
quick 1, 2
brown 1, 2
fox 1, 2
in 2
jump 1, 2
dog 1, 2
STEMMING, SYNONYMS
STANDARDANALYZER
SIMPLEANALYZER

40. #MDBLocal#MDBLocal
Analyzers used in Index Definitions
Specialized Text Processors
Language specific
Specified in Index Creation
Text -> Terms -> Lucene

41. $searchBeta aggregation pipeline stage
db.movies.aggregate([
{ $searchBeta: {
index: <indexname>, // optional, defaults to “default”
search: { query : ‘werewolves and vampires’,
path : ‘fullplot’ },
highlight: { path : 'fullplot’ }}},
{ $project: {
fullplot: 1,
title: 1,
score: { $meta: 'searchScore' },
highlight: { $meta: 'searchHighlights’. }}},
{ $limit: 10 }])

42. #MDBLocal#MDBLocal
Dynamic
{
name : "default",
mappings : {
dynamic : true
}
}
Index Definitions
Static
{
mappings: {
dynamic: false,
fields : {
category : {
type : "string",
analyzer: "lucene.keyword”
}}

43. #MDBLocal#MDBLocal
Static Index Definition
{ mappings: {
dynamic: false,
fields : { title : {
type : "string",
analyzer : "lucene.standard",
multi : { keywordAnalyzer : {
type : "string",
analyzer: "lucene.keyword” }}},
genres : { type: "string",
analyzer : "lucene.standard” },
plot : { type : "string",
analyzer : "lucene.standard” }}}}

44. Queries
Indexes
Analyzers
Indexes and Analyzers

45. #MDBLocal#MDBLocal
Advanced Search
Features

47. compound: {
"must" : {},
"mustNot": {},
"filter" : {},
"should": []
}
compound: operator – complex recursive queries
Must Match (scored)
Must not match(not scored)
Must match(not scored)
Should match(scored)
Operators can be other operator: term, span, search,
queryString, exists, or even another compound

48. #MDBLocal#MDBLocal
$searchBeta
compound
term
search
Index
Definitions
Highlights
Scores
Aggregation
Pipeline
In summary, what we used for queries …

49. Architecture
How FTS works on MongoDB Atlas

50. #MDBLocal#MDBLocal
MongoDB +
• Pre-existing functionality
• Highlights, fuzzy-matching, query-time scoring,
faceted searches and more
• Analyzers
• Language support
• Western languages: English, French, etc.
• Eastern Languages: CJK, bigram/unigram support
• Inverted index structure = fast searches

51. #MDBLocal#MDBLocal
MongoDB Atlas FTS components
mongod mongos mongot (NEW!)
● $searchBeta aggregation
pipeline stage
● Talks mongodb wire protocol to
mongot
● Shard aware implementation
● scatter-gather queries
● Based on Apache Lucene 8
● Integrated into MongoDB Atlas
● Separate java process from
mongod
● collocated with mongod

52. #MDBLocal#MDBLocal
FTS Indexing: Initial Sync
MongoDB Atlas
Query
Index Definition
Automation Agent
mongotmongod
Collection Scan
Complete!

53. #MDBLocal#MDBLocal
FTS Indexing: Steady State
Documents
mongotmongod
changestream
MongoDB Atlas
(per node)
mongot watches
the changestream
continuously and
updates the
search index

54. #MDBLocal#MDBLocal
Query Lifecycle
Wire Protocol
(over internet to
MongoDB Atlas) aggregate([ {$searchBeta: {
search: {
path: “name”,
query: “star wars”
}
}}]
mongod
Wire protocol
(localhost)
Lucene booleanQuery:
(should
(term(“name”, “star”),
term(“name”, wars”))
search: {
path: “name”,
query: “star wars”
}
[ { _id: “123”,score:
1.23,highlights: […] },
{…}]
{ “name” :
{ “star” : [123,124],
“wars” : [123,125,…]
}
}
Lookup([{_id: “123”],
{…}])
mongot
db.col.aggregate([
{$searchBeta: {
search: {
path: “name”,
query: “star wars”
}
}}
])
app
[ { _id: “123”,
title: “Star Wars”},
{ .… }
]
MongoDB Atlas Host

55. Key Takeaways
Ø Apache Lucene 8
Ø Uses MongoDB Query Language
Ø Wide variety of query operators – fuzzy, wildcard
Ø Flexible Scoring and Highlights
Ø Configurable Indexes

56. #MDBLocal#MDBLocal
Ø Free Tier
Ø Expanded Data Type Support
ü Dates
ü Numbers
Ø Synonyms
Ø Improved Operators / Syntax
Ø Performance Improvements
What’s Next? 2020 Roadmap
GA at MongoDB World, May 4-6

57. Atlas Code:
YouOldMaid100
YouOldMaid
MERCI BEAUCOUP