MongoDB .local Paris 2020: Tout savoir sur le moteur de recherche Full Text Search MongoDB Atlas
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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.
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MongoDB .local Paris 2020: Tout savoir sur le moteur de recherche Full Text Search MongoDB Atlas
- 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…
- 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()
- 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
- 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
- 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" } ])
- 29. #MDBLocal#MDBLocal Placeholder – photos from the Arsenal Match Mbappé
- 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'
- 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” }}}}
- 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