DMDW Extra Lesson - NoSql and MongoDB

STAATLICHANERKANNTEFACHHOCHSCHULE

STUDIERENUND DURCHSTARTEN.

Author: Dip.-Inf. (FH) Johannes HoppeDate: 06.05.2011

STAATLICHANERKANNTEFACHHOCHSCHULE

NoSQL and MongoDB

Author: Dip.-Inf. (FH) Johannes HoppeDate: 06.05.2011

Not only SQL

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Trends

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2002 2004 2006 2008 2010 2012

Data

Trends

Data› Facebook had 60k servers in 2010› Google had 450k servers in 2006 (speculated)› Microsoft: between 100k and 500k servers (since Azure)› Amazon: likely has a similar numbers, too (S3)

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Facebook Server Footprint

Trends

Trend 1: increasing data sizes

Trend 2: more connectedness (“web 2.0”)

Trend 3: more individualization (fever structure)

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NoSQL

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NoSQL

Database paradigms

› Relational (RDBMS)› NoSQL

› Key-Value stores› Document databases› Wide column stores (BigTable and clones)› Graph databases

› Other

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NoSQL

Some NoSQL use cases

1. Massive data volumes› Massively distributed architecture required to store the data› Google, Amazon, Yahoo, Facebook…

2. Extreme query workload› Impossible to efficiently do joins at that scale with an RDBMS

3. Schema evolution› Schema flexibility (migration) is not trivial at large scale› Schema changes can be gradually introduced with NoSQ

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NoSQL - CAP theorem

Requirements for distributed systems:

› Consistency› Availability› Partition tolerance

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NoSQL - CAP theorem

Consistency› The system is in a consistent state after an operation› All clients see the same data› Strong consistency (ACID)

vs. eventual consistency (BASE)

ACID: Atomicity, Consistency, Isolation and Durability

BASE: Basically Available, Soft state, Eventually consistent

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NoSQL - CAP theorem

Availability› The system is “always on”, no downtime› Node failure tolerance

– all clients can find some available replica› Software/hardware upgrade tolerance

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NoSQL - CAP theorem

Partition tolerance› The system continues to function even when › Split into disconnected subsets (by a network disruption)› Not only for reads, but writes as well!

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NoSQL

CAP Theorem› E. Brewer, N. Lynch

› You can satisfy

at most 2 out of the 3 requirements

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NoSQL

CAP Theorem CA› Single site clusters

(easier to ensure all nodes are always in contact)› When a partition occurs, the system blocks› e.g. usable for two-phase commits (2PC) which already

require/use blocks

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NoSQL

CAP Theorem CA› Single site clusters

(easier to ensure all nodes are always in contact)› When a partition occurs, the system blocks› e.g. usable for two-phase commits (2PC) which already

require/use blocks

Obviously, any horizontal scaling strategy is based on data partitioning; therefore, designers are forced to decide between consistency and availability.

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NoSQL

CAP Theorem CP› Some data may be inaccessible (availability sacrificed),

but the rest is still consistent/accurate› e.g. sharded database

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NoSQL

CAP Theorem AP› System is still available under partitioning,

but some of the data returned my be inaccurate› Need some conflict resolution strategy› e.g. Master/Slave replication

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NoSQL

RDBMS

› Guaratnee ACID by CA (two-phase commits)

› SQL› Mature:

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NoSQL

NoSQL DBMS

› No relational tables› No fixed table schemas› No joins› No risk, no fun!

› CP and AP(and sometimes even AP and on top of CP MongoDB*)

* This is damn cool!

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NoSQL

Key-value

› One key one value, very fast› Key: Hash (no duplicates)› Value: binary object („BLOB“)

(DB does not understand your content)

› Players: Amazon Dynamo, Memcached…

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NoSQL

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customer_22

?=PQ)“§VN? =§(Q$U%V§W=(BN W§(=BU&W§$()= W§$(=%

GIVE ME A MEANING!

key value

NoSQL

Document databases

› Key-value store, too› Value is „understood“ by the DB› Querying the data is possible

(not just retrieving the key‘s content)

› Players: Amazon SimpleDB, CouchDB, MongoDB …

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NoSQL

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customer_22

{ Type: “Customer”, Name: "Norbert“, Invoiced: 2222 }

key value

NoSQL

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customer_22

{ Type: "Customer", Name: "Norbert", Invoiced: 2222 Messages: [ { Title: "Hello", Text: "World" }, { Title: "Second", Text: "message" } ] }

key value / documents

NoSQL

(Wide) column stores› Often referred as “BigTable clones”› Each key is associated with many attributes (columns)› NoSQL column stores are actually hybrid row/column

stores› Different from “pure” relational column stores!

› Players: Google BigTable, Cassandra (Facebook), HBase…

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NoSQL

Won‘t be stored as: It will be stored as:

22;Norbert;22222 22;23;2423;Hans;50000 Norbert;Hans;Franz24;Franz;44000 22222;50000;44000

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CustomerId Name Invoiced

22 Norbert 22222

23 Hans 50000

24 Franz 44000

NoSQL

Graph databases

› Multi-relational graphs› SPARQL query language (W3C Recommendation!)› Players: Neo4j, InfoGrid …

(note: graph DBs are special and somehow the “black sheep” in the NoSQL world –the following PROs/CONs don’t apply very well)

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NoSQL

PROs (& Promisses)

› Scheme-free / semi-structured data› Massive data stores› Scaling is easy› Very, very high availability› Often simpler to implement

(and OR Mappers aren’t required)

› „Web 2.0 ready“

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NoSQL

CONSs

› NoSQL implementations often „alpha“, no standards› Data consistency, no transactions,› Insufficient access control› SQL: strong for dynamic, cross-table queries (JOIN)› Relationships aren‘t enforced

(conventions over constrains – except for graph DBs (of course))

› Premature optimization: Scalability(Don’t build for scalability if you never need it!)

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MongoDB

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NoSQL

Lets rock!

MongoDB Quick Reference Cardshttp://www.10gen.com/reference

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Basic Deployment› Create the default data directory in c:\data\db› Start mongod.exe› Optionally: mongod.exe --dbpath c:\data\db --port 27017 --logpath c:\data\

mongodb.log

› Start the shell: mongo.exe

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Data Import› cd c:\dba-training-data\data› mongoimport -d twitter -c tweets twitter.json

› cd c:\dba-training-data\data\dump\training› mongorestore -d training -c scores scores.bson

› cd c:\dba-training-data\data\dump› mongorestore -d digg digg

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MongoDB Documents

(in the shell)› use digg› db.stories.findOne();

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JSON BSONAll JSON documents are stored in a binary format called BSON. BSON supports a richer set of types than JSON.http://bsonspec.org

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CRUD – Create

(in the shell)› db.people.save({name: 'Smith', age: 30});

See how the save command works:› db.foo.save

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CRUD – CreateHow training.scores was created:

for(i=0; i<1000; i++) { ['quiz', 'essay', 'exam'].forEach(function(name) { var score = Math.floor(Math.random() * 50) + 50; db.scores.save({student: i, name: name, score: score}); }); } db.scores.count();

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CRUD – ReadQueries are specified using a document-style syntax!

› use training› db.scores.find({score: 50});› db.scores.find({score: {"$gte": 70}});› db.scores.find({score: {"$gte": 70}});

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Cursor!

Exercises

1. Find all scores less than 65.

2. Find the lowest quiz score. Find the highest quiz score.

3. Write a query to find all digg stories where the view count is greater than 1000.

4. Query for all digg stories whose media type is either 'news' or 'images' and where the topic name is 'Comedy’.(For extra practice, construct two queries using different sets of operators to do this. )

5. Find all digg stories where the topic name is 'Television' or the media type is 'videos'. Skip the first 5 results, and limit the result set to 10.

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CRUD – Update

› use digg; › db.people.update({name: 'Smith'}, {'$set': {interests: []}});› db.people.update({name: 'Smith'},

{'$push': {interests: ['chess']}});

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Exercises

1. Set the proper 'grade' attribute for all scores. For example, users with scores greater than 90 get an 'A.' Set the grade to ‘B’ for scores falling between 80 and 90.

2. You're being nice, so you decide to add 10 points to every score on every “final” exam whose score is lower than 60. How do you do this update?

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CRUD – Delete

› db.dropDatabase();› db.foo.drop();› db.foo.remove();

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“Map Reduce is the Uzi of aggregation tools. Everything described with count, distinct and group can be done with MapReduce, and more.”

Kristina Chadorow, Michael Dirolf in MongoDB – The Definitive Guide

MapReduceTo use map-reduce, you first write a map function.

map = function() {emit(this.user.name, {diggs: this.diggs, posts: 0});

}

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MapReduceThe reduce functions then aggregation those docs by key.

reduce = function(key, values) { var diggs = 0; var posts = 0; values.forEach(function(doc) { diggs += doc.diggs; posts += 1; }); return {diggs: diggs, posts: posts};}

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MapReduceNow both are used to perform custom aggregation.

db.stories.mapReduce(map, reduce, {out: 'digg_users'});

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THANK YOUFOR YOUR ATTENTION

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