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Back to Basics Webinar 2: Your First MongoDB Application

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The document provides instructions for installing and using MongoDB to build a simple blogging application. It demonstrates how to install MongoDB, connect to it using the mongo shell, insert and query sample data like users and blog posts, update documents to add comments, and more. The goal is to illustrate how to model and interact with data in MongoDB for a basic blogging use case.

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Back to Basics Webinar 2: Your First MongoDB Application
Code JoeD gets you a 25% discount off the list price
Back to Basics 2016 : Webinar 2 Your First MongoDB Application Joe Drumgoole Director of Developer Advocacy, EMEA MongoDB @jdrumgoole #back2basics V1.0
4 Summary of Part 1 • Why NoSQL exists • The types of NoSQL database • The key features of MongoDB • Data durability in MongoDB – Replica Sets • Scalability in MongoDB - Sharding
5 Agenda • Database concepts • Installing MongoDB • Building a basic blogging application • Adding an index • Query optimization with explain
6 Concepts Relational MongoDB Database Database Table Collection Row Document Index Index Join Lookup Foreign Key Reference Multi-table transaction Single document transaction
7 Installing MongoDB $ curl -O https://fastdl.mongodb.org/osx/mongodb-osx-x86_64-3.2.6.tgz % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 60.9M 100 60.9M 0 0 2730k 0 0:00:22 0:00:22 --:--:-- 1589k $ tar xzvf mongodb-osx-x86_64-3.2.6.tgz x mongodb-osx-x86_64-3.2.6/README x mongodb-osx-x86_64-3.2.6/THIRD-PARTY-NOTICES x mongodb-osx-x86_64-3.2.6/MPL-2 x mongodb-osx-x86_64-3.2.6/GNU-AGPL-3.0 x mongodb-osx-x86_64-3.2.6/bin/mongodump x mongodb-osx-x86_64-3.2.6/bin/mongorestore x mongodb-osx-x86_64-3.2.6/bin/mongoexport x mongodb-osx-x86_64-3.2.6/bin/mongoimport x mongodb-osx-x86_64-3.2.6/bin/mongostat x mongodb-osx-x86_64-3.2.6/bin/mongotop x mongodb-osx-x86_64-3.2.6/bin/bsondump x mongodb-osx-x86_64-3.2.6/bin/mongofiles x mongodb-osx-x86_64-3.2.6/bin/mongooplog x mongodb-osx-x86_64-3.2.6/bin/mongoperf x mongodb-osx-x86_64-3.2.6/bin/mongosniff x mongodb-osx-x86_64-3.2.6/bin/mongod x mongodb-osx-x86_64-3.2.6/bin/mongos x mongodb-osx-x86_64-3.2.6/bin/mongo $ ln -s mongodb-osx-x86_64-3.2.6 mongodb
8 Running Mongod JD10Gen:mongodb jdrumgoole$ ./bin/mongod --dbpath /data/b2b 2016-05-23T19:21:07.767+0100 I CONTROL [initandlisten] MongoDB starting : pid=49209 port=27017 dbpath=/data/b2b 64- bit host=JD10Gen.local 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] db version v3.2.6 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] git version: 05552b562c7a0b3143a729aaa0838e558dc49b25 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] allocator: system 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] modules: none 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] build environment: 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] distarch: x86_64 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] target_arch: x86_64 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] options: { storage: { dbPath: "/data/b2b" } } 2016-05-23T19:21:07.769+0100 I - [initandlisten] Detected data files in /data/b2b created by the 'wiredTiger' storage engine, so setting the active storage engine to 'wiredTiger'. 2016-05-23T19:21:07.769+0100 I STORAGE [initandlisten] wiredtiger_open config: create,cache_size=4G,session_max=20000,eviction=(threads_max=4),config_base=false,statistics=(fast),log=(enabled=true ,archive=true,path=journal,compressor=snappy),file_manager=(close_idle_time=100000),checkpoint=(wait=60,log_size=2GB) ,statistics_log=(wait=0), 2016-05-23T19:21:08.837+0100 I CONTROL [initandlisten] 2016-05-23T19:21:08.838+0100 I CONTROL [initandlisten] ** WARNING: soft rlimits too low. Number of files is 256, should be at least 1000 2016-05-23T19:21:08.840+0100 I NETWORK [HostnameCanonicalizationWorker] Starting hostname canonicalization worker 2016-05-23T19:21:08.840+0100 I FTDC [initandlisten] Initializing full-time diagnostic data capture with directory '/data/b2b/diagnostic.data' 2016-05-23T19:21:08.841+0100 I NETWORK [initandlisten] waiting for connections on port 27017 2016-05-23T19:21:09.148+0100 I NETWORK [initandlisten] connection accepted from 127.0.0.1:59213 #1 (1 connection now open)
9 Connecting Via The Shell $ ./bin/mongo MongoDB shell version: 3.2.6 connecting to: test Server has startup warnings: 2016-05-17T11:46:03.516+0100 I CONTROL [initandlisten] 2016-05-17T11:46:03.516+0100 I CONTROL [initandlisten] ** WARNING: soft rlimits too low. Number of files is 256, should be at least 1000 >
10 Inserting your first record > show databases local 0.000GB > use test switched to db test > show databases local 0.000GB > db.demo.insert( { "key" : "value" } ) WriteResult({ "nInserted" : 1 }) > show databases local 0.000GB test 0.000GB > show collections demo > db.demo.findOne() { "_id" : ObjectId("573af7085ee4be80385332a6"), "key" : "value" } >
11 Object ID 573af7085ee4be80385332a6 TS------ID----PID-Count-
12 A Simple Blog Application • Lets create a blogging application with: – Articles – Users – Comments
13 Typical Entity Relation Diagram
14 In MongoDB we can build organically > use blog switched to db blog > db.users.insert( { "username" : "jdrumgoole", "password" : "top secret", "lang" : "EN" } ) WriteResult({ "nInserted" : 1 }) > db.users.findOne() { "_id" : ObjectId("573afff65ee4be80385332a7"), "username" : "jdrumgoole", "password" : "top secret", "lang" : "EN" }
15 How do we do this in a program? ''' Created on 17 May 2016 @author: jdrumgoole ''' import pymongo # # client defaults to localhost and port 27017. eg MongoClient('localhost', 27017) client = pymongo.MongoClient() blogDatabase = client[ "blog" ] usersCollection = blogDatabase[ "users" ] usersCollection.insert_one( { "username" : "jdrumgoole", "password" : "top secret", "lang" : "EN" }) user = usersCollection.find_one() print( user )
16 Next up Articles … articlesCollection = blogDatabase[ "articles" ] author = "jdrumgoole" article = { "title" : "This is my first post", "body" : "The is the longer body text for my blog post. We can add lots of text here.", "author" : author, "tags" : [ "joe", "general", "Ireland", "admin" ] } # # Lets check if our author exists # if usersCollection.find_one( { "username" : author }) : articlesCollection.insert_one( article ) else: raise ValueError( "Author %s does not exist" % author )
17 Create a new type of article # # Lets add a new type of article with a posting date and a section # author = "jdrumgoole" title = "This is a post on MongoDB" newPost = { "title" : title, "body" : "MongoDB is the worlds most popular NoSQL database. It is a document database", "author" : author, "tags" : [ "joe", "mongodb", "Ireland" ], "section" : "technology", "postDate" : datetime.datetime.now(), } # # Lets check if our author exists # if usersCollection.find_one( { "username" : author }) : articlesCollection.insert_one( newPost )
18 Make a lot of articles 1 import pymongo import string import datetime import random def randomString( size, letters = string.letters ): return "".join( [random.choice( letters ) for _ in xrange( size )] ) client = pymongo.MongoClient() def makeArticle( count, author, timestamp ): return { "_id" : count, "title" : randomString( 20 ), "body" : randomString( 80 ), "author" : author, "postdate" : timestamp } def makeUser( username ): return { "username" : username, "password" : randomString( 10 ) , "karma" : random.randint( 0, 500 ), "lang" : "EN" }
19 Make a lot of articles 2 blogDatabase = client[ "blog" ] usersCollection = blogDatabase[ "users" ] articlesCollection = blogDatabase[ "articles" ] bulkUsers = usersCollection.initialize_ordered_bulk_op() bulkArticles = articlesCollection.initialize_ordered_bulk_op() ts = datetime.datetime.now() for i in range( 1000000 ) : #username = randomString( 10, string.ascii_uppercase ) + "_" + str( i ) username = "USER_" + str( i ) bulkUsers.insert( makeUser( username ) ) ts = ts + datetime.timedelta( seconds = 1 ) bulkArticles.insert( makeArticle( i, username, ts )) if ( i % 500 == 0 ) : bulkUsers.execute() bulkArticles.execute() bulkUsers = usersCollection.initialize_ordered_bulk_op() bulkArticles = articlesCollection.initialize_ordered_bulk_op() bulkUsers.execute() bulkArticles.execute()
20 Find a User > db.users.findOne() { "_id" : ObjectId("5742da5bb26a88bc00e941ac"), "username" : "FLFZQLSRWZ_0", "lang" : "EN", "password" : "vTlILbGWLt", "karma" : 448 } > db.users.find( { "username" : "VHXDAUUFJW_45" } ).pretty() { "_id" : ObjectId("5742da5bb26a88bc00e94206"), "username" : "VHXDAUUFJW_45", "lang" : "EN", "password" : "GmRLnCeKVp", "karma" : 284 }
21 Find Users with high Karma > db.users.find( { "karma" : { $gte : 450 }} ).pretty() { "_id" : ObjectId("5742da5bb26a88bc00e941ae"), "username" : "JALLFRKBWD_1", "lang" : "EN", "password" : "bCSKSKvUeb", "karma" : 487 } { "_id" : ObjectId("5742da5bb26a88bc00e941e4"), "username" : "OTKWJJBNBU_28", "lang" : "EN", "password" : "HAWpiATCBN", "karma" : 473 } { …
22 Using projection > db.users.find( { "karma" : { $gte : 450 }}, { "_id" : 0, username : 1, karma : 1 } ) { "username" : "JALLFRKBWD_1", "karma" : 487 } { "username" : "OTKWJJBNBU_28", "karma" : 473 } { "username" : "RVVHLKTWHU_31", "karma" : 493 } { "username" : "JBNESEOOEP_48", "karma" : 464 } { "username" : "VSTBDZLKQQ_51", "karma" : 487 } { "username" : "UKYDTQJCLO_61", "karma" : 493 } { "username" : "HZFZZMZHYB_106", "karma" : 493 } { "username" : "AAYLPJJNHO_113", "karma" : 455 } { "username" : "CXZZMHLBXE_128", "karma" : 460 } { "username" : "KKJXBACBVN_134", "karma" : 460 } { "username" : "PTNTIBGAJV_165", "karma" : 461 } { "username" : "PVLCQJIGDY_169", "karma" : 463 }
23 Update an Article to Add Comments 1 > db.articles.find( { "_id" : 19 } ).pretty() { "_id" : 19, "body" : "nTzOofOcnHKkJxpjKAyqTTnKZMFzzkWFeXtBRuEKsctuGBgWIrEBrYdvFIVHJWaXLUTVUXblOZZgUq Wu", "postdate" : ISODate("2016-05-23T12:02:46.830Z"), "author" : "ASWTOMMABN_19", "title" : "CPMaqHtAdRwLXhlUvsej" } > db.articles.update( { _id : 18 }, { $set : { comments : [] }} ) WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })
24 Update an article to add Comments 2 > db.articles.find( { _id :18 } ).pretty() { "_id" : 18, "body" : "KmwFSIMQGcIsRNTDBFPuclwcVJkoMcrIPwTiSZDYyatoKzeQiKvJkiVSrndXqrALVIYZxGpaMjucgX UV", "postdate" : ISODate("2016-05-23T16:04:39.497Z"), "author" : "USER_18", "title" : "wTLreIEyPfovEkBhJZZe", "comments" : [ ] } >
25 Update an Article to Add Comments 3 > db.articles.update( { _id : 18 }, { $push : { comments : { username : "joe", comment : "hey first post" }}} ) WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 }) > db.articles.find( { _id :18 } ).pretty() { "_id" : 18, "body" : "KmwFSIMQGcIsRNTDBFPuclwcVJkoMcrIPwTiSZDYyatoKzeQiKvJkiVSrndXqrALVIYZxGpaMjucgXUV" , "postdate" : ISODate("2016-05-23T16:04:39.497Z"), "author" : "USER_18", "title" : "wTLreIEyPfovEkBhJZZe", "comments" : [ { "username" : "joe", "comment" : "hey first post" } ] } >
26 Delete an Article > db.articles.remove( { "_id" : 25 } ) WriteResult({ "nRemoved" : 1 }) > db.articles.remove( { "_id" : 25 } ) WriteResult({ "nRemoved" : 0 }) > db.articles.remove( { "_id" : { $lte : 5 }} ) WriteResult({ "nRemoved" : 6 }) • Deletion leaves holes • Dropping a collection is cheaper than deleting a large collection element by element
27 A Quick Look at Users and Articles Again > db.users.findOne() { "_id" : ObjectId("57431c07b26a88bf060e10cb"), "username" : "USER_0", "lang" : "EN", "password" : "kGIxPxqKGJ", "karma" : 266 } > db.articles.findOne() { "_id" : 0, "body" : "hvJLnrrfZQurmtjPfUWbMhaQWbNjXLzjpuGLZjsxHXbUycmJVZTeOZesTnZtojThrebRcUoiYwivjpwG" , "postdate" : ISODate("2016-05-23T16:04:39.246Z"), "author" : "USER_0", "title" : "gpNIoPxpfTAxWjzAVoTJ" } >
28 Find a User > db.users.find( { "username" : "ABOXHWKBYS_199" } ).explain() { "queryPlanner" : { "plannerVersion" : 1, "namespace" : "blog.users", "indexFilterSet" : false, "parsedQuery" : { "username" : { "$eq" : "ABOXHWKBYS_199" } }, "winningPlan" : { "stage" : "COLLSCAN", "filter" : { "username" : { "$eq" : "ABOXHWKBYS_199" } }, "direction" : "forward" }, "rejectedPlans" : [ ] }, "serverInfo" : { "host" : "JD10Gen.local", "port" : 27017, "version" : "3.2.6", "gitVersion" : "05552b562c7a0b3143a729aaa0838e558dc49b25" }, "ok" : 1 }
29 Find a User – Execution Stats > db.users.find( {"username" : "USER_999999" } ).explain( "executionStats" ).executionStats { "executionSuccess" : true, "nReturned" : 1, "executionTimeMillis" : 433, "totalKeysExamined" : 0, "totalDocsExamined" : 1000000, "executionStages" : { "stage" : "COLLSCAN", "filter" : { "username" : { "$eq" : "USER_999999" } }, "nReturned" : 1, "executionTimeMillisEstimate" : 330, "works" : 1000002, "advanced" : 1, "needTime" : 1000000, "needYield" : 0, "saveState" : 7812, "restoreState" : 7812, "isEOF" : 1, "invalidates" : 0, "direction" : "forward", "docsExamined" : 1000000
30 We need an index > db.users.createIndex( { username : 1 } ) { "createdCollectionAutomatically" : false, "numIndexesBefore" : 1, "numIndexesAfter" : 2, "ok" : 1 } >
31 Indexes Overview • Parameters – Background : Create an index in the background as opposed to locking the database – Unique : All keys in the collection must be unique. Duplicate key insertions will be rejected with an error. – Name : explicitly name an index. Otherwise the index name is autogenerated from the index field. • Deleting an Index – db.users.dropIndex({ “username” : 1 }) • Get All the Indexes on a collection – db.users.getIndexes()
32 Query Plan Execution Stages • COLLSCAN : for a collection scan • IXSCAN : for scanning index keys • FETCH : for retrieving documents • SHARD_MERGE : for merging results from shards
33 Add an Index > db.users.find( {"username" : "USER_999999”} ).explain("executionStats”).executionStats { "executionSuccess" : true, "nReturned" : 1, "executionTimeMillis" : 0, "totalKeysExamined" : 1, "totalDocsExamined" : 1, …
34 Execution Stage "executionStages" : { "stage" : "FETCH", "nReturned" : 1, "executionTimeMillisEstimate" : 0, "docsExamined" : 1,, "inputStage" : { "stage" : "IXSCAN", "nReturned" : 1, "executionTimeMillisEstimate" : 0, "keyPattern" : { "username" : 1 }, "indexName" : "username_1", "isMultiKey" : false, "isUnique" : false, "isSparse" : false, "isPartial" : false, "indexVersion" : 1, "direction" : "forward", "indexBounds" : { "username" : [ "["USER_999999", "USER_999999"]" ] }, "keysExamined" : 1, "seenInvalidated" : 0 } } }
35 Drivers and Frameworks Morphia MEAN Stack
36 What we have learned • How to create a database and a collection • How to insert content into that collection • How to query the collection • How to update a document in place • How to delete a document • How to check the efficiency of an operation • How to add an index • How to check an index is being used in an operation
37 Next Webinar : Thinking in Documents • Rather than normalisation we look at a hybrid approach to schema that provides a coherent mapping between objects in your application and objects in your database. • Then we will optimise that schema for query purposes based on the pattern of queries we expect to see. • Finally we will show how dynamic schema and schema validation allow you to extend your schema in a controlled fashion 14-June-2016, 14:00 GMT.
Q&A

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Back to Basics Webinar 2: Your First MongoDB Application

  • 2. Code JoeD gets you a 25% discount off the list price
  • 3. Back to Basics 2016 : Webinar 2 Your First MongoDB Application Joe Drumgoole Director of Developer Advocacy, EMEA MongoDB @jdrumgoole #back2basics V1.0
  • 4. 4 Summary of Part 1 • Why NoSQL exists • The types of NoSQL database • The key features of MongoDB • Data durability in MongoDB – Replica Sets • Scalability in MongoDB - Sharding
  • 5. 5 Agenda • Database concepts • Installing MongoDB • Building a basic blogging application • Adding an index • Query optimization with explain
  • 6. 6 Concepts Relational MongoDB Database Database Table Collection Row Document Index Index Join Lookup Foreign Key Reference Multi-table transaction Single document transaction
  • 7. 7 Installing MongoDB $ curl -O https://fastdl.mongodb.org/osx/mongodb-osx-x86_64-3.2.6.tgz % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 60.9M 100 60.9M 0 0 2730k 0 0:00:22 0:00:22 --:--:-- 1589k $ tar xzvf mongodb-osx-x86_64-3.2.6.tgz x mongodb-osx-x86_64-3.2.6/README x mongodb-osx-x86_64-3.2.6/THIRD-PARTY-NOTICES x mongodb-osx-x86_64-3.2.6/MPL-2 x mongodb-osx-x86_64-3.2.6/GNU-AGPL-3.0 x mongodb-osx-x86_64-3.2.6/bin/mongodump x mongodb-osx-x86_64-3.2.6/bin/mongorestore x mongodb-osx-x86_64-3.2.6/bin/mongoexport x mongodb-osx-x86_64-3.2.6/bin/mongoimport x mongodb-osx-x86_64-3.2.6/bin/mongostat x mongodb-osx-x86_64-3.2.6/bin/mongotop x mongodb-osx-x86_64-3.2.6/bin/bsondump x mongodb-osx-x86_64-3.2.6/bin/mongofiles x mongodb-osx-x86_64-3.2.6/bin/mongooplog x mongodb-osx-x86_64-3.2.6/bin/mongoperf x mongodb-osx-x86_64-3.2.6/bin/mongosniff x mongodb-osx-x86_64-3.2.6/bin/mongod x mongodb-osx-x86_64-3.2.6/bin/mongos x mongodb-osx-x86_64-3.2.6/bin/mongo $ ln -s mongodb-osx-x86_64-3.2.6 mongodb
  • 8. 8 Running Mongod JD10Gen:mongodb jdrumgoole$ ./bin/mongod --dbpath /data/b2b 2016-05-23T19:21:07.767+0100 I CONTROL [initandlisten] MongoDB starting : pid=49209 port=27017 dbpath=/data/b2b 64- bit host=JD10Gen.local 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] db version v3.2.6 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] git version: 05552b562c7a0b3143a729aaa0838e558dc49b25 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] allocator: system 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] modules: none 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] build environment: 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] distarch: x86_64 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] target_arch: x86_64 2016-05-23T19:21:07.768+0100 I CONTROL [initandlisten] options: { storage: { dbPath: "/data/b2b" } } 2016-05-23T19:21:07.769+0100 I - [initandlisten] Detected data files in /data/b2b created by the 'wiredTiger' storage engine, so setting the active storage engine to 'wiredTiger'. 2016-05-23T19:21:07.769+0100 I STORAGE [initandlisten] wiredtiger_open config: create,cache_size=4G,session_max=20000,eviction=(threads_max=4),config_base=false,statistics=(fast),log=(enabled=true ,archive=true,path=journal,compressor=snappy),file_manager=(close_idle_time=100000),checkpoint=(wait=60,log_size=2GB) ,statistics_log=(wait=0), 2016-05-23T19:21:08.837+0100 I CONTROL [initandlisten] 2016-05-23T19:21:08.838+0100 I CONTROL [initandlisten] ** WARNING: soft rlimits too low. Number of files is 256, should be at least 1000 2016-05-23T19:21:08.840+0100 I NETWORK [HostnameCanonicalizationWorker] Starting hostname canonicalization worker 2016-05-23T19:21:08.840+0100 I FTDC [initandlisten] Initializing full-time diagnostic data capture with directory '/data/b2b/diagnostic.data' 2016-05-23T19:21:08.841+0100 I NETWORK [initandlisten] waiting for connections on port 27017 2016-05-23T19:21:09.148+0100 I NETWORK [initandlisten] connection accepted from 127.0.0.1:59213 #1 (1 connection now open)
  • 9. 9 Connecting Via The Shell $ ./bin/mongo MongoDB shell version: 3.2.6 connecting to: test Server has startup warnings: 2016-05-17T11:46:03.516+0100 I CONTROL [initandlisten] 2016-05-17T11:46:03.516+0100 I CONTROL [initandlisten] ** WARNING: soft rlimits too low. Number of files is 256, should be at least 1000 >
  • 10. 10 Inserting your first record > show databases local 0.000GB > use test switched to db test > show databases local 0.000GB > db.demo.insert( { "key" : "value" } ) WriteResult({ "nInserted" : 1 }) > show databases local 0.000GB test 0.000GB > show collections demo > db.demo.findOne() { "_id" : ObjectId("573af7085ee4be80385332a6"), "key" : "value" } >
  • 12. 12 A Simple Blog Application • Lets create a blogging application with: – Articles – Users – Comments
  • 14. 14 In MongoDB we can build organically > use blog switched to db blog > db.users.insert( { "username" : "jdrumgoole", "password" : "top secret", "lang" : "EN" } ) WriteResult({ "nInserted" : 1 }) > db.users.findOne() { "_id" : ObjectId("573afff65ee4be80385332a7"), "username" : "jdrumgoole", "password" : "top secret", "lang" : "EN" }
  • 15. 15 How do we do this in a program? ''' Created on 17 May 2016 @author: jdrumgoole ''' import pymongo # # client defaults to localhost and port 27017. eg MongoClient('localhost', 27017) client = pymongo.MongoClient() blogDatabase = client[ "blog" ] usersCollection = blogDatabase[ "users" ] usersCollection.insert_one( { "username" : "jdrumgoole", "password" : "top secret", "lang" : "EN" }) user = usersCollection.find_one() print( user )
  • 16. 16 Next up Articles … articlesCollection = blogDatabase[ "articles" ] author = "jdrumgoole" article = { "title" : "This is my first post", "body" : "The is the longer body text for my blog post. We can add lots of text here.", "author" : author, "tags" : [ "joe", "general", "Ireland", "admin" ] } # # Lets check if our author exists # if usersCollection.find_one( { "username" : author }) : articlesCollection.insert_one( article ) else: raise ValueError( "Author %s does not exist" % author )
  • 17. 17 Create a new type of article # # Lets add a new type of article with a posting date and a section # author = "jdrumgoole" title = "This is a post on MongoDB" newPost = { "title" : title, "body" : "MongoDB is the worlds most popular NoSQL database. It is a document database", "author" : author, "tags" : [ "joe", "mongodb", "Ireland" ], "section" : "technology", "postDate" : datetime.datetime.now(), } # # Lets check if our author exists # if usersCollection.find_one( { "username" : author }) : articlesCollection.insert_one( newPost )
  • 18. 18 Make a lot of articles 1 import pymongo import string import datetime import random def randomString( size, letters = string.letters ): return "".join( [random.choice( letters ) for _ in xrange( size )] ) client = pymongo.MongoClient() def makeArticle( count, author, timestamp ): return { "_id" : count, "title" : randomString( 20 ), "body" : randomString( 80 ), "author" : author, "postdate" : timestamp } def makeUser( username ): return { "username" : username, "password" : randomString( 10 ) , "karma" : random.randint( 0, 500 ), "lang" : "EN" }
  • 19. 19 Make a lot of articles 2 blogDatabase = client[ "blog" ] usersCollection = blogDatabase[ "users" ] articlesCollection = blogDatabase[ "articles" ] bulkUsers = usersCollection.initialize_ordered_bulk_op() bulkArticles = articlesCollection.initialize_ordered_bulk_op() ts = datetime.datetime.now() for i in range( 1000000 ) : #username = randomString( 10, string.ascii_uppercase ) + "_" + str( i ) username = "USER_" + str( i ) bulkUsers.insert( makeUser( username ) ) ts = ts + datetime.timedelta( seconds = 1 ) bulkArticles.insert( makeArticle( i, username, ts )) if ( i % 500 == 0 ) : bulkUsers.execute() bulkArticles.execute() bulkUsers = usersCollection.initialize_ordered_bulk_op() bulkArticles = articlesCollection.initialize_ordered_bulk_op() bulkUsers.execute() bulkArticles.execute()
  • 20. 20 Find a User > db.users.findOne() { "_id" : ObjectId("5742da5bb26a88bc00e941ac"), "username" : "FLFZQLSRWZ_0", "lang" : "EN", "password" : "vTlILbGWLt", "karma" : 448 } > db.users.find( { "username" : "VHXDAUUFJW_45" } ).pretty() { "_id" : ObjectId("5742da5bb26a88bc00e94206"), "username" : "VHXDAUUFJW_45", "lang" : "EN", "password" : "GmRLnCeKVp", "karma" : 284 }
  • 21. 21 Find Users with high Karma > db.users.find( { "karma" : { $gte : 450 }} ).pretty() { "_id" : ObjectId("5742da5bb26a88bc00e941ae"), "username" : "JALLFRKBWD_1", "lang" : "EN", "password" : "bCSKSKvUeb", "karma" : 487 } { "_id" : ObjectId("5742da5bb26a88bc00e941e4"), "username" : "OTKWJJBNBU_28", "lang" : "EN", "password" : "HAWpiATCBN", "karma" : 473 } { …
  • 22. 22 Using projection > db.users.find( { "karma" : { $gte : 450 }}, { "_id" : 0, username : 1, karma : 1 } ) { "username" : "JALLFRKBWD_1", "karma" : 487 } { "username" : "OTKWJJBNBU_28", "karma" : 473 } { "username" : "RVVHLKTWHU_31", "karma" : 493 } { "username" : "JBNESEOOEP_48", "karma" : 464 } { "username" : "VSTBDZLKQQ_51", "karma" : 487 } { "username" : "UKYDTQJCLO_61", "karma" : 493 } { "username" : "HZFZZMZHYB_106", "karma" : 493 } { "username" : "AAYLPJJNHO_113", "karma" : 455 } { "username" : "CXZZMHLBXE_128", "karma" : 460 } { "username" : "KKJXBACBVN_134", "karma" : 460 } { "username" : "PTNTIBGAJV_165", "karma" : 461 } { "username" : "PVLCQJIGDY_169", "karma" : 463 }
  • 23. 23 Update an Article to Add Comments 1 > db.articles.find( { "_id" : 19 } ).pretty() { "_id" : 19, "body" : "nTzOofOcnHKkJxpjKAyqTTnKZMFzzkWFeXtBRuEKsctuGBgWIrEBrYdvFIVHJWaXLUTVUXblOZZgUq Wu", "postdate" : ISODate("2016-05-23T12:02:46.830Z"), "author" : "ASWTOMMABN_19", "title" : "CPMaqHtAdRwLXhlUvsej" } > db.articles.update( { _id : 18 }, { $set : { comments : [] }} ) WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })
  • 24. 24 Update an article to add Comments 2 > db.articles.find( { _id :18 } ).pretty() { "_id" : 18, "body" : "KmwFSIMQGcIsRNTDBFPuclwcVJkoMcrIPwTiSZDYyatoKzeQiKvJkiVSrndXqrALVIYZxGpaMjucgX UV", "postdate" : ISODate("2016-05-23T16:04:39.497Z"), "author" : "USER_18", "title" : "wTLreIEyPfovEkBhJZZe", "comments" : [ ] } >
  • 25. 25 Update an Article to Add Comments 3 > db.articles.update( { _id : 18 }, { $push : { comments : { username : "joe", comment : "hey first post" }}} ) WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 }) > db.articles.find( { _id :18 } ).pretty() { "_id" : 18, "body" : "KmwFSIMQGcIsRNTDBFPuclwcVJkoMcrIPwTiSZDYyatoKzeQiKvJkiVSrndXqrALVIYZxGpaMjucgXUV" , "postdate" : ISODate("2016-05-23T16:04:39.497Z"), "author" : "USER_18", "title" : "wTLreIEyPfovEkBhJZZe", "comments" : [ { "username" : "joe", "comment" : "hey first post" } ] } >
  • 26. 26 Delete an Article > db.articles.remove( { "_id" : 25 } ) WriteResult({ "nRemoved" : 1 }) > db.articles.remove( { "_id" : 25 } ) WriteResult({ "nRemoved" : 0 }) > db.articles.remove( { "_id" : { $lte : 5 }} ) WriteResult({ "nRemoved" : 6 }) • Deletion leaves holes • Dropping a collection is cheaper than deleting a large collection element by element
  • 27. 27 A Quick Look at Users and Articles Again > db.users.findOne() { "_id" : ObjectId("57431c07b26a88bf060e10cb"), "username" : "USER_0", "lang" : "EN", "password" : "kGIxPxqKGJ", "karma" : 266 } > db.articles.findOne() { "_id" : 0, "body" : "hvJLnrrfZQurmtjPfUWbMhaQWbNjXLzjpuGLZjsxHXbUycmJVZTeOZesTnZtojThrebRcUoiYwivjpwG" , "postdate" : ISODate("2016-05-23T16:04:39.246Z"), "author" : "USER_0", "title" : "gpNIoPxpfTAxWjzAVoTJ" } >
  • 28. 28 Find a User > db.users.find( { "username" : "ABOXHWKBYS_199" } ).explain() { "queryPlanner" : { "plannerVersion" : 1, "namespace" : "blog.users", "indexFilterSet" : false, "parsedQuery" : { "username" : { "$eq" : "ABOXHWKBYS_199" } }, "winningPlan" : { "stage" : "COLLSCAN", "filter" : { "username" : { "$eq" : "ABOXHWKBYS_199" } }, "direction" : "forward" }, "rejectedPlans" : [ ] }, "serverInfo" : { "host" : "JD10Gen.local", "port" : 27017, "version" : "3.2.6", "gitVersion" : "05552b562c7a0b3143a729aaa0838e558dc49b25" }, "ok" : 1 }
  • 29. 29 Find a User – Execution Stats > db.users.find( {"username" : "USER_999999" } ).explain( "executionStats" ).executionStats { "executionSuccess" : true, "nReturned" : 1, "executionTimeMillis" : 433, "totalKeysExamined" : 0, "totalDocsExamined" : 1000000, "executionStages" : { "stage" : "COLLSCAN", "filter" : { "username" : { "$eq" : "USER_999999" } }, "nReturned" : 1, "executionTimeMillisEstimate" : 330, "works" : 1000002, "advanced" : 1, "needTime" : 1000000, "needYield" : 0, "saveState" : 7812, "restoreState" : 7812, "isEOF" : 1, "invalidates" : 0, "direction" : "forward", "docsExamined" : 1000000
  • 30. 30 We need an index > db.users.createIndex( { username : 1 } ) { "createdCollectionAutomatically" : false, "numIndexesBefore" : 1, "numIndexesAfter" : 2, "ok" : 1 } >
  • 31. 31 Indexes Overview • Parameters – Background : Create an index in the background as opposed to locking the database – Unique : All keys in the collection must be unique. Duplicate key insertions will be rejected with an error. – Name : explicitly name an index. Otherwise the index name is autogenerated from the index field. • Deleting an Index – db.users.dropIndex({ “username” : 1 }) • Get All the Indexes on a collection – db.users.getIndexes()
  • 32. 32 Query Plan Execution Stages • COLLSCAN : for a collection scan • IXSCAN : for scanning index keys • FETCH : for retrieving documents • SHARD_MERGE : for merging results from shards
  • 33. 33 Add an Index > db.users.find( {"username" : "USER_999999”} ).explain("executionStats”).executionStats { "executionSuccess" : true, "nReturned" : 1, "executionTimeMillis" : 0, "totalKeysExamined" : 1, "totalDocsExamined" : 1, …
  • 34. 34 Execution Stage "executionStages" : { "stage" : "FETCH", "nReturned" : 1, "executionTimeMillisEstimate" : 0, "docsExamined" : 1,, "inputStage" : { "stage" : "IXSCAN", "nReturned" : 1, "executionTimeMillisEstimate" : 0, "keyPattern" : { "username" : 1 }, "indexName" : "username_1", "isMultiKey" : false, "isUnique" : false, "isSparse" : false, "isPartial" : false, "indexVersion" : 1, "direction" : "forward", "indexBounds" : { "username" : [ "["USER_999999", "USER_999999"]" ] }, "keysExamined" : 1, "seenInvalidated" : 0 } } }
  • 36. 36 What we have learned • How to create a database and a collection • How to insert content into that collection • How to query the collection • How to update a document in place • How to delete a document • How to check the efficiency of an operation • How to add an index • How to check an index is being used in an operation
  • 37. 37 Next Webinar : Thinking in Documents • Rather than normalisation we look at a hybrid approach to schema that provides a coherent mapping between objects in your application and objects in your database. • Then we will optimise that schema for query purposes based on the pattern of queries we expect to see. • Finally we will show how dynamic schema and schema validation allow you to extend your schema in a controlled fashion 14-June-2016, 14:00 GMT.
  • 38. Q&A

Editor's Notes

  • #4: Who I am, how long have I been at MongoDB.
  • #11: This is javascript. Lazy evaluation. Databases and collections spring to life as needed.
  • #12: 12 byte value.