SlideShare a Scribd company logo
1 of 22
Download to read offline
Indexing, Query Optimization, the Query
                   Optimizer — MongoSV

                                 Richard M Kreuter
                                      10gen Inc.
                                 richard@10gen.com


                                  December 3, 2010




MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Indexing Basics




         Indexes are tree-structured sets of references to your
         documents.
         The query planner can employ indexes to efficiently enumerate
         and sort matching documents.




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
However, indexing strikes people as a gray art




         As is the case with relational systems, schema design and
         indexing go hand in hand...
         ... but you also need to know about your actual (not just
         predicted) query patterns.




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Some indexing generalities




         A collection may have at most 64 indexes.
         A query may only use 1 index (except that disjuncts in $or
         queries can each use separate indexes).
         Indexes entail additional work on inserts, updates, deletes.




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Creating Indexes
   The id attribute is always indexed. Additional indexes can be
   created with ensureIndex():

      // Create an index on the user attribute
      db.collection.ensureIndex({ user : 1 })
      // Create a compound index on
      // the user and email attributes
      db.collection.ensureIndex({ user : 1, email : 1 })
      // Create an index on the favorites
      // attribute, will index all values in list
      db.collection.ensureIndex({ favorites : 1 })
      // Create a unique index on the user attribte
      db.collection.ensureIndex({user:1}, {unique:true})
      // Create an index in the background.
      db.collection.ensureIndex({user:1}, {background:true})

   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Index maintenance




   // Drops an index on x
   db.collection.dropIndex({x:1})
   // drops all indexes
   db.collection.dropIndexes()
   // Rebuild indexes (need for this reduced in 1.6)
   db.collection.reIndex()




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Indexes are smart about data types and structures




         Indexes on attributes whose values are of different types in
         different documents can speed up queries by skipping
         documents where the relevant attribute isn’t of the
         appropriate type.
         Indexes on attributes whose values are lists will index each
         element, speeding up queries that look into these attributes.
         (You really want to do this for querying on tags.)




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
When can indexes be used?


   In short, if you can envision how the index might get used, it
   probably is. These will all use an index on x:
         db.collection.find( { x:                      1 } )
         db.collection.find( { x :{ $in :                          [1,2,3] } } )
         db.collection.find( { x :                       { $gt :       1 } } )
         db.collection.find( { x :                       /^a/ } )
         db.collection.count( { x :                       2 } )
         db.collection.distinct( { x :                         2 } )
         db.collection.find().sort( { x :                          1 } )




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Trickier cases where indexes can be used




         db.collection.find({ x : 1 }).sort({ y : 1 })
         will use an index on y for sorting, if there’s no index on x.
         (For this sort of case, use a compound index on both x and y
         in that order.)
         db.collection.update( { x : 2 } , { x : 3 } )
         will use an index on x (but older mongodb versions didn’t
         permit $inc and other modifiers on indexed fields.)




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Some array examples



   The following queries will use an index on x, and will match
   documents whose x attribute is the array [2,10]
         db.collection.find({ x :                      2 })
         db.collection.find({ x :                      10 })
         db.collection.find({ x :                      { $gt :     5 } })
         db.collection.find({ x :                      [2,10] })
         db.collection.find({ x :                      { $in :     [2,5] }})




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Geospatial indexes


   Geospatial indexes are a sort of special case; the operators that can
   take advantage of them can only be used if the relevant indexes
   have been created. Some examples:
         db.collection.find({ a : [50, 50]}) finds a
         document with this point for a.
         db.collection.find({a :                     {$near :   [50, 50]}})
         sorts results by distance.
         db.collection.find({
         a:{$within:{$box:[[40,40],[60,60]]}}}})
         db.collection.find({
         a:{$within:{$center:[[50,50],10]}}}})



   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
When indexes cannot be used

         Many sorts of negations, e.g., $ne, $not.
         Tricky arithmetic, e.g., $mod.
         Most regular expressions (e.g., /a/).
         Expressions in $where clauses don’t take advantage of
         indexes.
                Of course $where clauses are mostly for complex queries that
                often can’t be indexed anyway, e.g., ‘‘where a > b’’. (If
                these cases matter to you, it you can precompute the match
                and store that as an additional attribute, you can store that,
                index it, and skip the $where clause entirely.)
         JavaScript parts of map/reduce can’t take advantage of
         indexes (mapping function is opaque to the query optimizer).
   As a rule, if you can’t imagine how an index might be used, it
   probably can’t!
   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Never forget about compound indexes




         Whenever you’re querying on multiple attributes, whether as
         part of the selector document or in a sort(), compound
         indexes can be used.




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Schema/index relationships
   Sometimes, question isn’t “given the shape of these documents,
   how do I index them?”, but “how might I shape the data so I can
   take advantage of indexing?”

   // Consider a schema that uses a list of
   // attribute/value pairs:
   db.c.insert({ product : "SuperDooHickey",
                 manufacturer : "Foo Enterprises",
                 catalog : [ { stock : 50,
                               modtime: ’2010-09-02’ },
                             { price : 29.95,
                               modtime : ’2010-06-14’ } ] });
   db.c.ensureIndex({ catalog : 1 });
   // All attribute queries can use one index.
   db.c.find( { catalog : { stock : { $gt : 0 } } } )

   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Index sizes



   Of course, indexes take up space. For many interesting databases,
   real query performance will depend on index sizes; so it’s useful to
   see these numbers.
         db.collection.stats() shows indexSizes, the size of
         each index in the collection.
         db.collection.totalIndexSize() displays the size of all
         indexes in the collection.




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
explain()

   It’s useful to be able to ensure that your query is doing what you
   want it to do. For this, we have explain(). Query plans that use
   an index have cursor type BtreeCursor.

   db.collection.find({x:{$gt:5}}).explain()
   {
   "cursor" : "BtreeCursor x_1",
           ...
   "nscanned" : 12345,
           ...
   "n" : 100,
   "millis" : 4,
           ...
   }


   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
explain(), continued

   If the query plan doesn’t use the index, the cursor type will be
   BasicCursor.

   db.collection.find({x:{$gt:5}}).explain()
   {
   "cursor" : "BasicCursor",
          ...
   "nscanned" : 12345,
           ...
   "n" : 42,
   "millis" : 4,
           ...
   }


   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Really, compound indexes are important

   Try this at home:
      1   Create a collection with a few tens of thousands of documents
          having two attributes (let’s call them a and b).
      2   Create a compound index on {a :                     1, b :   1},
      3   Do a db.collection.find({a :                        constant}).sort({b :
          1}).explain().
      4   Note the explain result’s millis.
      5   Drop the compound index.
      6   Create another compound index with the attributes reversed.
          (This will be a suboptimal compound index.)
      7   Explain the above query again.
      8   The suboptimal index should produce a slower explain result.

   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
The DB Profiler
  MongoDB includes a database profiler that, when enabled, records
  the timing measurements and result counts in a collection within
  the database.
  // Enable the profiler on this database.
  > db.setProfilingLevel(1, 100)
  { "was" : 0, "slowms" : 100, "ok" : 1 }
  > db.foo.find({a: { $mod : [3, 0] } });
  ...
  // See the profiler info.
  > db.system.profile.find()
  { "ts" : "Thu Nov 18 2010 06:46:16 GMT-0500 (EST)",
     "info" : "query test.$cmd ntoreturn:1
         command: { count: "foo",
                               query: { a: { $mod: [ 3.0, 0.0 ] } },
         fields: {} } reslen:64 406ms",
     "millis" : 406 }
  MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Query Optimizer




         MongoDB’s query optimizer is empirical, not cost-based.
         To test query plans, it tries several in parallel, and records the
         plan that finishes fastest.
         If a plan’s performance changes over time (e.g., as data
         changes), the database will reoptimize (i.e., retry all possible
         plans).




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Hinting the query plan




   Sometimes, you might want to force the query plan. For this, we
   have hint().

   // Force the use of an                  index on attribute x:
   db.collection.find({x:                  1, ...}).hint({x:1})
   // Force indexes to be                  avoided!
   db.collection.find({x:                  1, ...}).hint({$natural:1})




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
Going forward



         www.mongodb.org — downloads, docs, community
         mongodb-user@googlegroups.com — mailing list
         #mongodb on irc.freenode.net
         try.mongodb.org — web-based shell
         10gen is hiring. Email jobs@10gen.com.
         10gen offers support, training, and advising services for
         mongodb




   MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV

More Related Content

What's hot

Indexing and Query Optimizer (Aaron Staple)
Indexing and Query Optimizer (Aaron Staple)Indexing and Query Optimizer (Aaron Staple)
Indexing and Query Optimizer (Aaron Staple)MongoSF
 
2011 Mongo FR - Indexing in MongoDB
2011 Mongo FR - Indexing in MongoDB2011 Mongo FR - Indexing in MongoDB
2011 Mongo FR - Indexing in MongoDBantoinegirbal
 
Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.
Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.
Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.GeeksLab Odessa
 
Indexing Strategies to Help You Scale
Indexing Strategies to Help You ScaleIndexing Strategies to Help You Scale
Indexing Strategies to Help You ScaleMongoDB
 
Indexing with MongoDB
Indexing with MongoDBIndexing with MongoDB
Indexing with MongoDBMongoDB
 
MongoDB and Indexes - MUG Denver - 20160329
MongoDB and Indexes - MUG Denver - 20160329MongoDB and Indexes - MUG Denver - 20160329
MongoDB and Indexes - MUG Denver - 20160329Douglas Duncan
 
Schema Design (Mongo Austin)
Schema Design (Mongo Austin)Schema Design (Mongo Austin)
Schema Design (Mongo Austin)MongoDB
 
엘라스틱서치 적합성 이해하기 20160630
엘라스틱서치 적합성 이해하기 20160630엘라스틱서치 적합성 이해하기 20160630
엘라스틱서치 적합성 이해하기 20160630Yong Joon Moon
 
Building a Scalable Inbox System with MongoDB and Java
Building a Scalable Inbox System with MongoDB and JavaBuilding a Scalable Inbox System with MongoDB and Java
Building a Scalable Inbox System with MongoDB and Javaantoinegirbal
 
Map/Confused? A practical approach to Map/Reduce with MongoDB
Map/Confused? A practical approach to Map/Reduce with MongoDBMap/Confused? A practical approach to Map/Reduce with MongoDB
Map/Confused? A practical approach to Map/Reduce with MongoDBUwe Printz
 
Mongo Nosql CRUD Operations
Mongo Nosql CRUD OperationsMongo Nosql CRUD Operations
Mongo Nosql CRUD Operationsanujaggarwal49
 
Spring data presentation
Spring data presentationSpring data presentation
Spring data presentationOleksii Usyk
 
Storing tree structures with MongoDB
Storing tree structures with MongoDBStoring tree structures with MongoDB
Storing tree structures with MongoDBVyacheslav
 

What's hot (18)

Indexing and Query Optimizer (Aaron Staple)
Indexing and Query Optimizer (Aaron Staple)Indexing and Query Optimizer (Aaron Staple)
Indexing and Query Optimizer (Aaron Staple)
 
2011 Mongo FR - Indexing in MongoDB
2011 Mongo FR - Indexing in MongoDB2011 Mongo FR - Indexing in MongoDB
2011 Mongo FR - Indexing in MongoDB
 
Mongo indexes
Mongo indexesMongo indexes
Mongo indexes
 
Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.
Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.
Java/Scala Lab: Борис Трофимов - Обжигающая Big Data.
 
Indexing Strategies to Help You Scale
Indexing Strategies to Help You ScaleIndexing Strategies to Help You Scale
Indexing Strategies to Help You Scale
 
Indexing with MongoDB
Indexing with MongoDBIndexing with MongoDB
Indexing with MongoDB
 
MongoDB and Indexes - MUG Denver - 20160329
MongoDB and Indexes - MUG Denver - 20160329MongoDB and Indexes - MUG Denver - 20160329
MongoDB and Indexes - MUG Denver - 20160329
 
Schema Design (Mongo Austin)
Schema Design (Mongo Austin)Schema Design (Mongo Austin)
Schema Design (Mongo Austin)
 
엘라스틱서치 적합성 이해하기 20160630
엘라스틱서치 적합성 이해하기 20160630엘라스틱서치 적합성 이해하기 20160630
엘라스틱서치 적합성 이해하기 20160630
 
Indexing In MongoDB
Indexing In MongoDBIndexing In MongoDB
Indexing In MongoDB
 
Building a Scalable Inbox System with MongoDB and Java
Building a Scalable Inbox System with MongoDB and JavaBuilding a Scalable Inbox System with MongoDB and Java
Building a Scalable Inbox System with MongoDB and Java
 
CouchDB-Lucene
CouchDB-LuceneCouchDB-Lucene
CouchDB-Lucene
 
Map/Confused? A practical approach to Map/Reduce with MongoDB
Map/Confused? A practical approach to Map/Reduce with MongoDBMap/Confused? A practical approach to Map/Reduce with MongoDB
Map/Confused? A practical approach to Map/Reduce with MongoDB
 
Mongo db
Mongo dbMongo db
Mongo db
 
Mongo Nosql CRUD Operations
Mongo Nosql CRUD OperationsMongo Nosql CRUD Operations
Mongo Nosql CRUD Operations
 
Indexing
IndexingIndexing
Indexing
 
Spring data presentation
Spring data presentationSpring data presentation
Spring data presentation
 
Storing tree structures with MongoDB
Storing tree structures with MongoDBStoring tree structures with MongoDB
Storing tree structures with MongoDB
 

Viewers also liked

Building a Mongo DSL in Scala at Hot Potato
Building a Mongo DSL in Scala at Hot PotatoBuilding a Mongo DSL in Scala at Hot Potato
Building a Mongo DSL in Scala at Hot PotatoMongoDB
 
Schema design short
Schema design shortSchema design short
Schema design shortMongoDB
 
Keeping data-safe-webinar-2010-11-01
Keeping data-safe-webinar-2010-11-01Keeping data-safe-webinar-2010-11-01
Keeping data-safe-webinar-2010-11-01MongoDB
 
Morning with MongoDB Paris 2012 - Making Big Data Small
Morning with MongoDB Paris 2012 - Making Big Data SmallMorning with MongoDB Paris 2012 - Making Big Data Small
Morning with MongoDB Paris 2012 - Making Big Data SmallMongoDB
 
2011 mongo sf-sharding
2011 mongo sf-sharding2011 mongo sf-sharding
2011 mongo sf-shardingMongoDB
 
Modeling for Performance
Modeling for PerformanceModeling for Performance
Modeling for PerformanceMongoDB
 

Viewers also liked (6)

Building a Mongo DSL in Scala at Hot Potato
Building a Mongo DSL in Scala at Hot PotatoBuilding a Mongo DSL in Scala at Hot Potato
Building a Mongo DSL in Scala at Hot Potato
 
Schema design short
Schema design shortSchema design short
Schema design short
 
Keeping data-safe-webinar-2010-11-01
Keeping data-safe-webinar-2010-11-01Keeping data-safe-webinar-2010-11-01
Keeping data-safe-webinar-2010-11-01
 
Morning with MongoDB Paris 2012 - Making Big Data Small
Morning with MongoDB Paris 2012 - Making Big Data SmallMorning with MongoDB Paris 2012 - Making Big Data Small
Morning with MongoDB Paris 2012 - Making Big Data Small
 
2011 mongo sf-sharding
2011 mongo sf-sharding2011 mongo sf-sharding
2011 mongo sf-sharding
 
Modeling for Performance
Modeling for PerformanceModeling for Performance
Modeling for Performance
 

Similar to Indexing and Query Optimizer

unit 4,Indexes in database.docx
unit 4,Indexes in database.docxunit 4,Indexes in database.docx
unit 4,Indexes in database.docxRaviRajput416403
 
Overview on NoSQL and MongoDB
Overview on NoSQL and MongoDBOverview on NoSQL and MongoDB
Overview on NoSQL and MongoDBharithakannan
 
Mongo db a deep dive of mongodb indexes
Mongo db  a deep dive of mongodb indexesMongo db  a deep dive of mongodb indexes
Mongo db a deep dive of mongodb indexesRajesh Kumar
 
Indexing documents
Indexing documentsIndexing documents
Indexing documentsMongoDB
 
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial Indexes
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial IndexesBack to Basics Webinar 4: Advanced Indexing, Text and Geospatial Indexes
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial IndexesMongoDB
 
MongoDB NoSQL database a deep dive -MyWhitePaper
MongoDB  NoSQL database a deep dive -MyWhitePaperMongoDB  NoSQL database a deep dive -MyWhitePaper
MongoDB NoSQL database a deep dive -MyWhitePaperRajesh Kumar
 
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & AggregationWebinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & AggregationMongoDB
 
2016 feb-23 pyugre-py_mongo
2016 feb-23 pyugre-py_mongo2016 feb-23 pyugre-py_mongo
2016 feb-23 pyugre-py_mongoMichael Bright
 
Using MongoDB and Python
Using MongoDB and PythonUsing MongoDB and Python
Using MongoDB and PythonMike Bright
 
MongoDB using Grails plugin by puneet behl
MongoDB using Grails plugin by puneet behlMongoDB using Grails plugin by puneet behl
MongoDB using Grails plugin by puneet behlTO THE NEW | Technology
 
1403 app dev series - session 5 - analytics
1403   app dev series - session 5 - analytics1403   app dev series - session 5 - analytics
1403 app dev series - session 5 - analyticsMongoDB
 
Introduction to MongoDB
Introduction to MongoDBIntroduction to MongoDB
Introduction to MongoDBRaghunath A
 
Indexing and Query Optimisation
Indexing and Query OptimisationIndexing and Query Optimisation
Indexing and Query OptimisationMongoDB
 
MongoDB .local London 2019: Fast Machine Learning Development with MongoDB
MongoDB .local London 2019: Fast Machine Learning Development with MongoDBMongoDB .local London 2019: Fast Machine Learning Development with MongoDB
MongoDB .local London 2019: Fast Machine Learning Development with MongoDBLisa Roth, PMP
 

Similar to Indexing and Query Optimizer (20)

unit 4,Indexes in database.docx
unit 4,Indexes in database.docxunit 4,Indexes in database.docx
unit 4,Indexes in database.docx
 
Query Optimization in MongoDB
Query Optimization in MongoDBQuery Optimization in MongoDB
Query Optimization in MongoDB
 
Nosql part 2
Nosql part 2Nosql part 2
Nosql part 2
 
Overview on NoSQL and MongoDB
Overview on NoSQL and MongoDBOverview on NoSQL and MongoDB
Overview on NoSQL and MongoDB
 
Introduction to MongoDB
Introduction to MongoDBIntroduction to MongoDB
Introduction to MongoDB
 
Mongo db a deep dive of mongodb indexes
Mongo db  a deep dive of mongodb indexesMongo db  a deep dive of mongodb indexes
Mongo db a deep dive of mongodb indexes
 
Indexing documents
Indexing documentsIndexing documents
Indexing documents
 
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial Indexes
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial IndexesBack to Basics Webinar 4: Advanced Indexing, Text and Geospatial Indexes
Back to Basics Webinar 4: Advanced Indexing, Text and Geospatial Indexes
 
MongoDB NoSQL database a deep dive -MyWhitePaper
MongoDB  NoSQL database a deep dive -MyWhitePaperMongoDB  NoSQL database a deep dive -MyWhitePaper
MongoDB NoSQL database a deep dive -MyWhitePaper
 
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & AggregationWebinar: Applikationsentwicklung mit MongoDB: Teil 5: Reporting & Aggregation
Webinar: Applikationsentwicklung mit MongoDB : Teil 5: Reporting & Aggregation
 
2016 feb-23 pyugre-py_mongo
2016 feb-23 pyugre-py_mongo2016 feb-23 pyugre-py_mongo
2016 feb-23 pyugre-py_mongo
 
Using MongoDB and Python
Using MongoDB and PythonUsing MongoDB and Python
Using MongoDB and Python
 
MongoDB_ppt.pptx
MongoDB_ppt.pptxMongoDB_ppt.pptx
MongoDB_ppt.pptx
 
MongoDB using Grails plugin by puneet behl
MongoDB using Grails plugin by puneet behlMongoDB using Grails plugin by puneet behl
MongoDB using Grails plugin by puneet behl
 
1403 app dev series - session 5 - analytics
1403   app dev series - session 5 - analytics1403   app dev series - session 5 - analytics
1403 app dev series - session 5 - analytics
 
Introduction to MongoDB
Introduction to MongoDBIntroduction to MongoDB
Introduction to MongoDB
 
Indexing and Query Optimisation
Indexing and Query OptimisationIndexing and Query Optimisation
Indexing and Query Optimisation
 
Mongodb Introduction
Mongodb IntroductionMongodb Introduction
Mongodb Introduction
 
Mongo db queries
Mongo db queriesMongo db queries
Mongo db queries
 
MongoDB .local London 2019: Fast Machine Learning Development with MongoDB
MongoDB .local London 2019: Fast Machine Learning Development with MongoDBMongoDB .local London 2019: Fast Machine Learning Development with MongoDB
MongoDB .local London 2019: Fast Machine Learning Development with MongoDB
 

More from MongoDB

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump StartMongoDB
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
 
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
 

More from MongoDB (20)

MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
MongoDB SoCal 2020: Migrate Anything* to MongoDB Atlas
 
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...
 
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDB
 
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...
 
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series Data
 
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 MongoDB SoCal 2020: MongoDB Atlas Jump Start MongoDB SoCal 2020: MongoDB Atlas Jump Start
MongoDB SoCal 2020: MongoDB Atlas Jump Start
 
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]
 
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2
 
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...
 
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!
 
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your Mindset
 
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
MongoDB .local San Francisco 2020: MongoDB Atlas Jumpstart
 
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...
 
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++
 
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...
 
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep Dive
 
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & Golang
 
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...
 
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...
 

Recently uploaded

Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxGDSC PJATK
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXTarek Kalaji
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioChristian Posta
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding TeamAdam Moalla
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfDaniel Santiago Silva Capera
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1DianaGray10
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
 
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesAI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesMd Hossain Ali
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
20230202 - Introduction to tis-py
20230202 - Introduction to tis-py20230202 - Introduction to tis-py
20230202 - Introduction to tis-pyJamie (Taka) Wang
 

Recently uploaded (20)

Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptx
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
VoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBXVoIP Service and Marketing using Odoo and Asterisk PBX
VoIP Service and Marketing using Odoo and Asterisk PBX
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
20230104 - machine vision
20230104 - machine vision20230104 - machine vision
20230104 - machine vision
 
Comparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and IstioComparing Sidecar-less Service Mesh from Cilium and Istio
Comparing Sidecar-less Service Mesh from Cilium and Istio
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team9 Steps For Building Winning Founding Team
9 Steps For Building Winning Founding Team
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 
201610817 - edge part1
201610817 - edge part1201610817 - edge part1
201610817 - edge part1
 
Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1Secure your environment with UiPath and CyberArk technologies - Session 1
Secure your environment with UiPath and CyberArk technologies - Session 1
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity Webinar
 
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just MinutesAI Fame Rush Review – Virtual Influencer Creation In Just Minutes
AI Fame Rush Review – Virtual Influencer Creation In Just Minutes
 
20150722 - AGV
20150722 - AGV20150722 - AGV
20150722 - AGV
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
20230202 - Introduction to tis-py
20230202 - Introduction to tis-py20230202 - Introduction to tis-py
20230202 - Introduction to tis-py
 

Indexing and Query Optimizer

  • 1. Indexing, Query Optimization, the Query Optimizer — MongoSV Richard M Kreuter 10gen Inc. richard@10gen.com December 3, 2010 MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 2. Indexing Basics Indexes are tree-structured sets of references to your documents. The query planner can employ indexes to efficiently enumerate and sort matching documents. MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 3. However, indexing strikes people as a gray art As is the case with relational systems, schema design and indexing go hand in hand... ... but you also need to know about your actual (not just predicted) query patterns. MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 4. Some indexing generalities A collection may have at most 64 indexes. A query may only use 1 index (except that disjuncts in $or queries can each use separate indexes). Indexes entail additional work on inserts, updates, deletes. MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 5. Creating Indexes The id attribute is always indexed. Additional indexes can be created with ensureIndex(): // Create an index on the user attribute db.collection.ensureIndex({ user : 1 }) // Create a compound index on // the user and email attributes db.collection.ensureIndex({ user : 1, email : 1 }) // Create an index on the favorites // attribute, will index all values in list db.collection.ensureIndex({ favorites : 1 }) // Create a unique index on the user attribte db.collection.ensureIndex({user:1}, {unique:true}) // Create an index in the background. db.collection.ensureIndex({user:1}, {background:true}) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 6. Index maintenance // Drops an index on x db.collection.dropIndex({x:1}) // drops all indexes db.collection.dropIndexes() // Rebuild indexes (need for this reduced in 1.6) db.collection.reIndex() MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 7. Indexes are smart about data types and structures Indexes on attributes whose values are of different types in different documents can speed up queries by skipping documents where the relevant attribute isn’t of the appropriate type. Indexes on attributes whose values are lists will index each element, speeding up queries that look into these attributes. (You really want to do this for querying on tags.) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 8. When can indexes be used? In short, if you can envision how the index might get used, it probably is. These will all use an index on x: db.collection.find( { x: 1 } ) db.collection.find( { x :{ $in : [1,2,3] } } ) db.collection.find( { x : { $gt : 1 } } ) db.collection.find( { x : /^a/ } ) db.collection.count( { x : 2 } ) db.collection.distinct( { x : 2 } ) db.collection.find().sort( { x : 1 } ) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 9. Trickier cases where indexes can be used db.collection.find({ x : 1 }).sort({ y : 1 }) will use an index on y for sorting, if there’s no index on x. (For this sort of case, use a compound index on both x and y in that order.) db.collection.update( { x : 2 } , { x : 3 } ) will use an index on x (but older mongodb versions didn’t permit $inc and other modifiers on indexed fields.) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 10. Some array examples The following queries will use an index on x, and will match documents whose x attribute is the array [2,10] db.collection.find({ x : 2 }) db.collection.find({ x : 10 }) db.collection.find({ x : { $gt : 5 } }) db.collection.find({ x : [2,10] }) db.collection.find({ x : { $in : [2,5] }}) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 11. Geospatial indexes Geospatial indexes are a sort of special case; the operators that can take advantage of them can only be used if the relevant indexes have been created. Some examples: db.collection.find({ a : [50, 50]}) finds a document with this point for a. db.collection.find({a : {$near : [50, 50]}}) sorts results by distance. db.collection.find({ a:{$within:{$box:[[40,40],[60,60]]}}}}) db.collection.find({ a:{$within:{$center:[[50,50],10]}}}}) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 12. When indexes cannot be used Many sorts of negations, e.g., $ne, $not. Tricky arithmetic, e.g., $mod. Most regular expressions (e.g., /a/). Expressions in $where clauses don’t take advantage of indexes. Of course $where clauses are mostly for complex queries that often can’t be indexed anyway, e.g., ‘‘where a > b’’. (If these cases matter to you, it you can precompute the match and store that as an additional attribute, you can store that, index it, and skip the $where clause entirely.) JavaScript parts of map/reduce can’t take advantage of indexes (mapping function is opaque to the query optimizer). As a rule, if you can’t imagine how an index might be used, it probably can’t! MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 13. Never forget about compound indexes Whenever you’re querying on multiple attributes, whether as part of the selector document or in a sort(), compound indexes can be used. MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 14. Schema/index relationships Sometimes, question isn’t “given the shape of these documents, how do I index them?”, but “how might I shape the data so I can take advantage of indexing?” // Consider a schema that uses a list of // attribute/value pairs: db.c.insert({ product : "SuperDooHickey", manufacturer : "Foo Enterprises", catalog : [ { stock : 50, modtime: ’2010-09-02’ }, { price : 29.95, modtime : ’2010-06-14’ } ] }); db.c.ensureIndex({ catalog : 1 }); // All attribute queries can use one index. db.c.find( { catalog : { stock : { $gt : 0 } } } ) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 15. Index sizes Of course, indexes take up space. For many interesting databases, real query performance will depend on index sizes; so it’s useful to see these numbers. db.collection.stats() shows indexSizes, the size of each index in the collection. db.collection.totalIndexSize() displays the size of all indexes in the collection. MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 16. explain() It’s useful to be able to ensure that your query is doing what you want it to do. For this, we have explain(). Query plans that use an index have cursor type BtreeCursor. db.collection.find({x:{$gt:5}}).explain() { "cursor" : "BtreeCursor x_1", ... "nscanned" : 12345, ... "n" : 100, "millis" : 4, ... } MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 17. explain(), continued If the query plan doesn’t use the index, the cursor type will be BasicCursor. db.collection.find({x:{$gt:5}}).explain() { "cursor" : "BasicCursor", ... "nscanned" : 12345, ... "n" : 42, "millis" : 4, ... } MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 18. Really, compound indexes are important Try this at home: 1 Create a collection with a few tens of thousands of documents having two attributes (let’s call them a and b). 2 Create a compound index on {a : 1, b : 1}, 3 Do a db.collection.find({a : constant}).sort({b : 1}).explain(). 4 Note the explain result’s millis. 5 Drop the compound index. 6 Create another compound index with the attributes reversed. (This will be a suboptimal compound index.) 7 Explain the above query again. 8 The suboptimal index should produce a slower explain result. MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 19. The DB Profiler MongoDB includes a database profiler that, when enabled, records the timing measurements and result counts in a collection within the database. // Enable the profiler on this database. > db.setProfilingLevel(1, 100) { "was" : 0, "slowms" : 100, "ok" : 1 } > db.foo.find({a: { $mod : [3, 0] } }); ... // See the profiler info. > db.system.profile.find() { "ts" : "Thu Nov 18 2010 06:46:16 GMT-0500 (EST)", "info" : "query test.$cmd ntoreturn:1 command: { count: "foo", query: { a: { $mod: [ 3.0, 0.0 ] } }, fields: {} } reslen:64 406ms", "millis" : 406 } MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 20. Query Optimizer MongoDB’s query optimizer is empirical, not cost-based. To test query plans, it tries several in parallel, and records the plan that finishes fastest. If a plan’s performance changes over time (e.g., as data changes), the database will reoptimize (i.e., retry all possible plans). MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 21. Hinting the query plan Sometimes, you might want to force the query plan. For this, we have hint(). // Force the use of an index on attribute x: db.collection.find({x: 1, ...}).hint({x:1}) // Force indexes to be avoided! db.collection.find({x: 1, ...}).hint({$natural:1}) MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV
  • 22. Going forward www.mongodb.org — downloads, docs, community mongodb-user@googlegroups.com — mailing list #mongodb on irc.freenode.net try.mongodb.org — web-based shell 10gen is hiring. Email jobs@10gen.com. 10gen offers support, training, and advising services for mongodb MongoDB – Indexing and Query Optimiz(ation—er) — MongoSV