Einführung in MongoDB

1. .NET
User
Group
Bern
Roger Rudin
bbv Software Services AG
roger.rudin@bbv.ch

2. Agenda
– What is NoSQL
– Understanding the Motivation behind NoSQL
– MongoDB: A Document Oriented Database
– NoSQL Use Cases

3. What is NoSQL?
NoSQL = Not only SQL

4. NoSQL Definition http://nosql-database.org/
NoSQL DEFINITION: Next Generation Databases mostly
addressing some of the points: being non-relational,
distributed, open-source and horizontal scalable. The original
intention has been modern web-scale databases. The
movement began early 2009 and is growing rapidly. Often
more characteristics apply as: schema-free, easy replication
support, simple API, eventually consistent /BASE (not ACID),
a huge data amount, and more. So the misleading
term "nosql" (the community now translates it mostly with
"not only sql") should be seen as an alias to something like
the definition above.

5. Who Uses NoSQL?
• Twitter uses DBFlock/MySQL and Cassandra
• Cassandra is an open source project from Facebook
• Digg, Reddit use Cassandra
• bit.ly, foursquare, sourceforge, and New York Times use
MongoDB
• Adobe, Alibaba, Ebay, use Hadoop

6. UNDERSTANDING THE
MOTIVATION BEHIND NOSQL

7. Why SQL sucks..
• O/R mapping (also known as Impedance Mismatch)
• Data-Model changes are hard and expensive
• SQL database are designed for high throughput, not
low latency
• SQL Databases do no scale out well
• Microsoft, Oracle, and IBM charge big bucks for
databases
– And then you need to hire a database admin
• Take it from the context of Google, Twitter, Facebook
and Amazon.
– Your databases are among the biggest in the world and
nobody pays you for that feature
– Wasting profit!!!

8. What has NoSQL done?
• Implemented the most common use cases
as a piece of software
• Designed for scalability and performance

9. Visual Guide To NoSQL
http://blog.nahurst.com/visual-guide-to-nosql-systems

10. NoSQL Data Models
• Key-Value
• Document-Oriented
• Column Oriented/Tabular

11. MONGODB: A DOCUMENT
ORIENTED DATABASE

12. NoSQL Data Model: Document
Oriented
• Data is stored as “documents”
• We are not talking about Word documents
• Comparable to Aggregates in DDD
• It means mostly schema free structured data
• Can be queried
• Is easily mapped to OO systems (Domain
Model, DDD)
• No join need to implement via programming

13. Network Communications
• REST/JSON
• TCP/BSON (ClientDriver)
BSON [bee · sahn], short for Binary JSON, is a binary-en-
coded serialization of JSON-like documents. Like JSON,
BSON supports the embedding of documents and arrays
within other documents and arrays. BSON also contains
extensions that allow representation of data types that
are not part of the JSON spec. For example, BSON has a
Date type and a BinData type.

14. Client Drivers (Apache License)
• MongoDB currently has client support for the following
programming languages:
• C
• C++
• Erlang
• Haskell
• Java
• Javascript
• .NET (C# F#, PowerShell, etc)
• Perl
• PHP
• Python
• Ruby
• Scala

15. Collections vs. Capped Collection
(Table in SQL)
• Collections
• blog.posts
• blog.comments
• forum.users
• etc.
• Capped collections (ring buffer)
• Logging
• Caching
• Archiving
db.createCollection("log", {capped: true, size:
<bytes>, max: <docs>});

16. Indexes
• Every field in the document can be indexed
• Simple Indexes:
db.cities.ensureIndex({city: 1});
• Compound indexes:
db.cities.ensureIndex({city: 1, zip: 1});
• Unique indexes:
db.cities.ensureIndex({city: 1, zip: 1},
{unique: true});
• Sort order: 1 = descending, -1 = ascending

18. Relations
• ObjectId
db.users.insert(
{name: "Umbert", car_id: ObjectId("<GUID>")});
• DBRef
db.users.insert(
{name: "Umbert", car: new DBRef("cars“,
ObjectId("<GUID>")});
db.users.findOne(
{name: "Umbert"}).car.fetch().name;

19. Queries (1)

20. Queries (Regular Expressions)
{field: /regular.*expression/i}
// get all cities that start with “atl”
and end on “a” (e.g. atlanta)
db.cities.count({city: /atl.*a/i});

21. Queries (2) : LINQ
https://github.com/craiggwilson/fluent-mongo
Equals
x => x.Age == 21 will translate to {"Age": 21}
Greater Than, $gt:
x => x.Age > 18 will translate to {"Age": {$gt: 18}}
Greater Than Or Equal, $gte:
x => x.Age >= 18 will translate to {"Age": {$gte: 18}}
Less Than, $lt:
x => x.Age < 18 will translate to {"Age": {$lt: 18}}
Less Than Or Equal, $lte:
x => x.Age <= 18 will translate to {"Age": {$lte: 18}}
Not Equal, $ne:
x => x.Age != 18 will translate to {"Age": {$ne: 18}}

22. Atomic Operations (Optimistic
Locking)
• Update if current:
• Fetch the object.
• Modify the object locally.
• Send an update request that says "update the object
to this new value if it still matches its old value".

23. Atomic Operations: Sample
> t=db.inventory
> s = t.findOne({sku:'abc'})
{"_id" : "49df4d3c9664d32c73ea865a" , "sku" : "abc" , "qty" : 1}
> t.update({sku:"abc",qty:{$gt:0}}, { $inc : { qty : -1 } } ) ;
> db.$cmd.findOne({getlasterror:1})
{"err" : , "updatedExisting" : true , "n" : 1 , "ok" : 1} // it has worked
> t.update({sku:"abcz",qty:{$gt:0}}, { $inc : { qty : -1 } } ) ;
>db.$cmd.findOne({getlasterror:1})
{"err" : , "updatedExisting" : false , "n" : 0 , "ok" : 1} // did not work

24. Atomic Operations: multiple
items
db.products.update(
{cat: “boots”, $atomic: 1},
{$inc: {price: 10.0}},
false, //no upsert
true //update multiple
);

25. Replica set (1)
• Automatic failover
• Automatic recovery of servers that were
offline
• Distribution over more than one
Datacenter
• Automatic nomination of a new Master
Server in case of a failure
• Up to 7 server in one replica set

26. ReplicaSet
RECOVERING
Replica set (2)
PRIMARY
DOWN
PRIMARY

27. Mongo Sharding
• Partitioning data across multiple physical servers to
provide application scale-out
• Can distribute databases, collections or objects in a
collection
• Choose how you partition data (shardkey)
• Balancing, migrations, management all automatic
• Range based
• Can convert from single master to sharded system with
0 downtime
• Often works in conjunction with object replication
(failover)

28. Sharding-Cluster

29. Map Reduce
http://www.joelonsoftware.com/items/2006/08/01.html
• It is a two step calculation where one
step is used to simplify the data, and the
second step is used to summarize the
data

30. Map Reduce Sample

31. Map Reduce using LINQ
https://github.com/craiggwilson/fluent-mongo/wiki/Map-Reduce
• LINQ is by far an easier way to compose map-reduce functions.
// Compose a map reduce to get the sum everyone's ages.
var sum = collection.AsQueryable().Sum(x => x.Age);
// Compose a map reduce to get the age range of everyone
grouped by the first letter of their last name.
var ageRanges =
from p in collection.AsQueryable()
group p by p.LastName[0] into g
select new
{
FirstLetter = g.Key,
AverageAge = g.Average(x => x.Age),
MinAge = g.Min(x => x.Age),
MaxAge = g.Max(x => x.Age)
};

32. Store large Files: GridFS
• The database supports native storage of
binary data within BSON objects (limited in
size 4 – 16 MB).
• GridFS is a specification for storing large
files in MongoDB
• Comparable to Amazon S3 online storage
service when using it in combination with
replication and sharding

33. Performance
On MySql, SourceForge was reaching its limits of
performance at its current user load. Using some of
the easy scale-out options in MongoDB, they fully
replaced MySQL and found MongoDB could handle
the current user load easily. In fact, after some
testing, they found their site can now handle 100
times the number of users it currently supports.
It means you can charge a lot less per user of
your application and get the same revenue. Think
about it.

34. Performance
http://www.michaelckennedy.net/blog/2010/04/29/MongoDBVsSQLServer2008PerformanceShowdown.aspx
• It’s the inserts where the differences are most
obvious between MongoDB and SQL Server
(about 30x-50x faster than SQL Server)

35. Administration: MongoVUE
(Windows)

36. Administration: Monitoring
• MongoDB
Monitoring Service

37. NOSQL USE CASES

38. Use Cases: Well suited
• Archiving and event logging
• Document and Content Management Systems
• E-Commerce
• Gaming. High performance small read/writes,
geospatial indexes
• High volume problems
• Mobile. Specifically, the server-side
infrastructure of mobile systems
• Projects using iterative/agile development
methodologies
• Real-time stats/analytics

39. Use Cases: Less Well Suited
• Systems with a heavy emphasis on
complex transactions such as banking
systems and accounting (multi-object
transactions)
• Traditional Non-Realtime Data
Warehousing
• Problems requiring SQL

40. Questions?
roger.rudin@bbv.ch