Jared Rosoff MongoDB Schema Design

[email protected] @forjared

Topics Introduction • Working with documents • Evolving a schema • Queries and indexes • Rich documents

Topics Introduction • Working with documents • Evolving a schema • Queries and indexes • Rich documents Common patterns • Single table inheritance • One-to-Many & Many-to-Many • Trees • Queues

Ways to model data: http://www.flickr.com/photos/42304632@N00/493639870/

Normalized

Denormalized

Terminology RDBMS MongoDB Table Collection Row(s) JSON  Document Index Index Join Embedding  &  Linking

Schema-design Criteria How can we manipulate this data? • Dynamic Queries • Secondary Indexes • Atomic Updates • Map Reduce

Schema-design Criteria How can we manipulate this data? • Dynamic Queries • Secondary Indexes • Atomic Updates • Map Reduce Access Patterns? • Read / Write Ratio • Types of updates • Types of queries • Data life-cycle

Schema-design Criteria How can we manipulate this data? • Dynamic Queries • Secondary Indexes • Atomic Updates • Map Reduce Considerations • No Joins • Document writes are atomic Access Patterns? • Read / Write Ratio • Types of updates • Types of queries • Data life-cycle

Here’s an example with a book:

A simple start: Map the documents to your application. book  =  {author:  “Hergé”,                date:  new  Date(),                text:  “Destination  Moon”,                tags:  [“comic”,  “adventure”]} >  db.books.save(book)

>  db.books.find()    {  _id:  ObjectId("4c4ba5c0672c685e5e8aabf3"),        author:  "Hergé",          date:  "Sat  Jul  24  2010  19:47:11  GMT-­‐0700  (PDT)",          text:  "Destination  Moon",          tags:  [  "comic",  "adventure"  ]    }     Notes: • _id must be unique, but can be anything you’d like • Default BSON ObjectId if one is not supplied Find the document

Secondary index on “author”  >  db.books.ensureIndex({author:  1})  >  db.books.find({author:  'Hergé'})          {  _id:  ObjectId("4c4ba5c0672c685e5e8aabf3"),          date:  "Sat  Jul  24  2010  19:47:11  GMT-­‐0700  (PDT)",          author:  "Hergé",            ...  } Add an index, find via index

Examine the query plan >  db.books.find({author:  'Hergé'}).explain() {   "cursor"  :  "BtreeCursor  author_1",   "nscanned"  :  1,   "nscannedObjects"  :  1,   "n"  :  1,   "millis"  :  5,   "indexBounds"  :  {     "author"  :  [       [         "Hergé",         "Hergé"       ]     ]   } }

Multi-key indexes //  Build  an  index  on  the  ‘tags’  array >  db.books.ensureIndex({tags:  1})

Multi-key indexes //  Build  an  index  on  the  ‘tags’  array >  db.books.ensureIndex({tags:  1}) //  find  posts  with  a  specific  tag //  (This  will  use  an  index!) >  db.books.find({tags:  ‘comic’})

Query operators Conditional operators: $ne, $in, $nin, $mod, $all, $size, $exists, $type, .. $lt, $lte, $gt, $gte, $ne Update operators: $set, $inc, $push, $pop, $pull, $pushAll, $pullAll

Extending the Schema        new_comment  =  {author:  “Kyle”,                                  date:  new  Date(),                                text:  “great  book”,                                votes:  5}  >  db.books.update(                      {text:  “Destination  Moon”  },                        {  ‘$push’:  {comments:  new_comment},                          ‘$inc’:    {comments_count:  1}})

     {  _id  :  ObjectId("4c4ba5c0672c685e5e8aabf3"),          author  :  "Hergé",        date  :  "Sat  Jul  24  2010  19:47:11  GMT-­‐0700  (PDT)",          text  :  "Destination  Moon",        tags  :  [  "comic",  "adventure"  ],                comments  :  [   {     author  :  "Kyle",     date  :  "Sat  Jul  24  2010  20:51:03  GMT-­‐0700  (PDT)",     text  :  "great  book",                  votes  :  5   }        ],        comments_count:  1    } Extending the Schema

//  create  index  on  nested  documents: >  db.books.ensureIndex({"comments.author":  1}) >  db.books.find({comments.author:”Kyle”}) The ‘dot’ operator

//  create  index  on  nested  documents: >  db.books.ensureIndex({"comments.author":  1}) >  db.books.find({comments.author:”Kyle”}) //  create  index  comment  votes: >  db.books.ensureIndex({comments.votes:  1}) //  find  all  books  with  any  comments  with  more  than //  50  votes >  db.books.ensureIndex({comments.votes:  {$gt:  50}}) The ‘dot’ operator

Rich Documents

Rich Documents

No content

Rich Documents

Rich Documents {      _id  :  ObjectId("4c4ba5c0672c685e5e8aabf3"),                  line_items  :  [  {  sku:  ‘tt-­‐123’,                                          name:  ‘Tintin  a  la  Lune’  },                                      {  sku:  ‘tt-­‐457’,                                          name:  ‘Coke  en  stock’  }  ],                address  :  {  name:  ‘Banker’,                                street:  ‘111  Main’,                                zip:  10010  },                  payment:  {  cc:  4567,                              exp:  Date(2011,  7,  7)  },        subtotal:  2355 }

Inheritance

Single Table Inheritance - RDBMS shapes table id type area radius d length width 1 circle 3.14 1 2 square 4 2 3 rect 10 5 2

Single Table Inheritance - MongoDB >  db.shapes.find()  {  _id:  "1",  type:  "circle",area:  3.14,  radius:  1}  {  _id:  "2",  type:  "square",area:  4,  d:  2}  {  _id:  "3",  type:  "rect",    area:  10,  length:  5,  width:  2}

Single Table Inheritance - MongoDB >  db.shapes.find()  {  _id:  "1",  type:  "circle",area:  3.14,  radius:  1}  {  _id:  "2",  type:  "square",area:  4,  d:  2}  {  _id:  "3",  type:  "rect",    area:  10,  length:  5,  width:  2} //  find  shapes  where  radius  >  0   >  db.shapes.find({radius:  {$gt:  0}})

Single Table Inheritance - MongoDB >  db.shapes.find()  {  _id:  "1",  type:  "circle",area:  3.14,  radius:  1}  {  _id:  "2",  type:  "square",area:  4,  d:  2}  {  _id:  "3",  type:  "rect",    area:  10,  length:  5,  width:  2} //  find  shapes  where  radius  >  0   >  db.shapes.find({radius:  {$gt:  0}}) //  create  sparse  index >  db.shapes.ensureIndex({radius:  1},  {sparse:  true})

One to Many One to Many relationships can specify

One to Many - Embedded Array / Array Keys - $slice operator to return subset of array - some queries hard e.g find latest comments across all documents

One to Many - Embedded Array / Array Keys - $slice operator to return subset of array - some queries hard e.g find latest comments across all documents - Embedded tree - Single document - Natural - Hard to query

One to Many - Embedded Array / Array Keys - $slice operator to return subset of array - some queries hard e.g find latest comments across all documents - Embedded tree - Single document - Natural - Hard to query - Normalized (2 collections) - most flexible - more queries

One to Many - patterns - Embedded Array / Array Keys - Embedded Array / Array Keys - Embedded tree - Normalized

     {  _id  :  ObjectId("4c4ba5c0672c685e5e8aabf3"),          author  :  "Hergé",        date  :  "Sat  Jul  24  2010  19:47:11  GMT-­‐0700  (PDT)",          text  :  "Destination  Moon",        tags  :  [  "comic",  "adventure"  ],                comments  :  [      {     author  :  "Kyle",     date  :  "Sat  Jul  24  2010  20:51:03  GMT-­‐0700  (PDT)",     text  :  "great  book",                  votes  :  5      }        ],        comments_count:  1    }

     {  _id  :  ObjectId("4c4ba5c0672c685e5e8aabf3"),          author  :  "Hergé",        date  :  "Sat  Jul  24  2010  19:47:11  GMT-­‐0700  (PDT)",          text  :  "Destination  Moon",        tags  :  [  "comic",  "adventure"  ]    }        {   book_id:  ObjectId("4c4ba5c0672c685e5e8aabf3"),        author  :  "Kyle",   date  :  "Sat  Jul  24  2010  20:51:03  GMT-­‐0700  (PDT)",   text  :  "great  book",          votes  :  5    }    

Referencing vs. Embedding - Embed when the ‘many’ objects always appear with their parent. - Reference when you need more flexibility.

Many - Many Example: - Product can be in many categories - Category can have many products

products:      {  _id:  ObjectId("4c4ca23933fb5941681b912e"),          name:  "Destination  Moon",          category_ids:  [  ObjectId("4c4ca25433fb5941681b912f"),                                          ObjectId("4c4ca25433fb5941681b92af”]}     Many - Many

products:      {  _id:  ObjectId("4c4ca23933fb5941681b912e"),          name:  "Destination  Moon",          category_ids:  [  ObjectId("4c4ca25433fb5941681b912f"),                                          ObjectId("4c4ca25433fb5941681b92af”]}     categories:      {  _id:  ObjectId("4c4ca25433fb5941681b912f"),            name:  "adventure",            product_ids:  [  ObjectId("4c4ca23933fb5941681b912e"),                                        ObjectId("4c4ca30433fb5941681b9130"),                                        ObjectId("4c4ca30433fb5941681b913a"]} Many - Many

products:      {  _id:  ObjectId("4c4ca23933fb5941681b912e"),          name:  "Destination  Moon",          category_ids:  [  ObjectId("4c4ca25433fb5941681b912f"),                                          ObjectId("4c4ca25433fb5941681b92af”]}     categories:      {  _id:  ObjectId("4c4ca25433fb5941681b912f"),            name:  "adventure",            product_ids:  [  ObjectId("4c4ca23933fb5941681b912e"),                                        ObjectId("4c4ca30433fb5941681b9130"),                                        ObjectId("4c4ca30433fb5941681b913a"]} //  Multi-­‐key  index  on  array  fields >  db.products.ensureIndex({category_ids:  1}); >  db.categories.ensureIndex({product_ids:  1}); Many - Many

products:      {  _id:  ObjectId("4c4ca23933fb5941681b912e"),          name:  "Destination  Moon",          category_ids:  [  ObjectId("4c4ca25433fb5941681b912f"),                                          ObjectId("4c4ca25433fb5941681b92af”]}     categories:      {  _id:  ObjectId("4c4ca25433fb5941681b912f"),            name:  "adventure"} Better alternative

products:      {  _id:  ObjectId("4c4ca23933fb5941681b912e"),          name:  "Destination  Moon",          category_ids:  [  ObjectId("4c4ca25433fb5941681b912f"),                                          ObjectId("4c4ca25433fb5941681b92af”]}     categories:      {  _id:  ObjectId("4c4ca25433fb5941681b912f"),            name:  "adventure"} //  All  products  for  a  given  category >  db.products.find({category_ids:  ObjectId ("4c4ca25433fb5941681b912f")})     Alternative

products:      {  _id:  ObjectId("4c4ca23933fb5941681b912e"),          name:  "Destination  Moon",          category_ids:  [  ObjectId("4c4ca25433fb5941681b912f"),                                          ObjectId("4c4ca25433fb5941681b92af”]}     categories:      {  _id:  ObjectId("4c4ca25433fb5941681b912f"),            name:  "adventure"} //  All  products  for  a  given  category >  db.products.find({category_ids:          ObjectId("4c4ca25433fb5941681b912f")})   //  All  categories  for  a  given  product product    =  db.products.find(_id  :  some_id) >  db.categories.find({_id  :  {$in  :  product.category_ids}})   Alternative

Trees Full Tree in Document {  comments:  [          {  author:  “Kyle”,  text:  “...”,                replies:  [                                            {author:  “Fred”,  text:  “...”,                                              replies:  []}                ]}    ] } Pros: Single Document, Performance, Intuitive Cons: Hard to search, Partial Results, 16MB limit    

Trees Parent Links - Each node is stored as a document - Contains the id of the parent Child Links - Each node contains the id’s of the children - Can support graphs (multiple parents / child)

Array of Ancestors - Store all Ancestors of a node    {  _id:  "a"  }    {  _id:  "b",  ancestors:  [  "a"  ],  parent:  "a"  }    {  _id:  "c",  ancestors:  [  "a",  "b"  ],  parent:  "b"  }    {  _id:  "d",  ancestors:  [  "a",  "b"  ],  parent:  "b"  }    {  _id:  "e",  ancestors:  [  "a"  ],  parent:  "a"  }    {  _id:  "f",  ancestors:  [  "a",  "e"  ],  parent:  "e"  }

Array of Ancestors - Store all Ancestors of a node    {  _id:  "a"  }    {  _id:  "b",  ancestors:  [  "a"  ],  parent:  "a"  }    {  _id:  "c",  ancestors:  [  "a",  "b"  ],  parent:  "b"  }    {  _id:  "d",  ancestors:  [  "a",  "b"  ],  parent:  "b"  }    {  _id:  "e",  ancestors:  [  "a"  ],  parent:  "a"  }    {  _id:  "f",  ancestors:  [  "a",  "e"  ],  parent:  "e"  } //find  all  descendants  of  b: >  db.tree2.find({ancestors:  ‘b’}) //find  all  direct  descendants  of  b: >  db.tree2.find({parent:  ‘b’})

Array of Ancestors - Store all Ancestors of a node    {  _id:  "a"  }    {  _id:  "b",  ancestors:  [  "a"  ],  parent:  "a"  }    {  _id:  "c",  ancestors:  [  "a",  "b"  ],  parent:  "b"  }    {  _id:  "d",  ancestors:  [  "a",  "b"  ],  parent:  "b"  }    {  _id:  "e",  ancestors:  [  "a"  ],  parent:  "a"  }    {  _id:  "f",  ancestors:  [  "a",  "e"  ],  parent:  "e"  } //find  all  descendants  of  b: >  db.tree2.find({ancestors:  ‘b’}) //find  all  direct  descendants  of  b: >  db.tree2.find({parent:  ‘b’}) //find  all  ancestors  of  f: >  ancestors  =  db.tree2.findOne({_id:’f’}).ancestors >  db.tree2.find({_id:  {  $in  :  ancestors})

Queue Requirements • See jobs waiting, jobs in progress • Ensure that each job is started once and only once      {  inprogress:  false,          priority:  1,          message:  “Rich  documents  FTW!”      ...      }

Queue Requirements • See jobs waiting, jobs in progress • Ensure that each job is started once and only once      {  inprogress:  false,          priority:  1,          message:  “Rich  documents  FTW!”      ...      } //  find  highest  priority  job  and  mark  as  in-­‐progress job  =  db.jobs.findAndModify({                              query:    {inprogress:  false},                              sort:      {priority:  -­‐1),                                update:  {$set:  {inprogress:  true,                                                                started:  new  Date()}}})    

Summary Schema design is different in MongoDB Basic data design principals stay the same Focus on how the apps manipulates data Rapidly evolve schema to meet your requirements Enjoy your new freedom, use it wisely :-)

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