13.12.2011 Dipl.-Inf. (FH) Johannes Hoppe M.Sc. Johannes Hofmeister

aus der Praxis

Johannes Hoppe .NET Webentwickler

www.johanneshoppe.de

Johannes Hofmeister .NET Domänenexperte

www.twitter.com/pro_cessor

Not only SQL

01

2002 2004 2006 2008 2010 2012

Daten

Daten › Facebook hat 60k Server (2010)

› Google hat 450k Server (2006)

› Microsoft: hat 100k - 500k Server (seit Azure)

Facebook Server Footprint

Trend 1: increasing data sizes

Trend 2: more connectedness (“web 2.0”)

Trend 3: more individualization (fever structure)

verteilte und horizontale Skalierbarkeit

kein relationales Datenmodell (kein SQL)

schemafrei / schwache Schemarestriktionen

anderes Konsistenzmodell

Scale-up Vertikale Skalierung

Server auf mehr Leistungsfähigkeit trimmen

Scale-out horizontale Skalierung

Einfügen von Nodes (Rechnerknoten)

Schemafrei kein ALTER TABLE

kein Wartungsfenster *

Datenversionierung im Code!

* morgens ausschlafen

Anforderungen

an ein verteiltes System

Consistency Konsistenz

AvailabilityVerfügbarkeit

Partition

Tolerance Ausfalltoleranz

CAP Theorem › 2000: E. Brewer, N. Lynch

› You can satisfy

at most 2 out of the 3 requirements

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

Availability › System is “always on”, no downtime

› Node failure tolerance

– all clients can find some available replica

› Software/hardware upgrade tolerance

Partition tolerance › System continues to function even when

split into disconnected subsets (network disruption)

› Not only for reads, but writes as well

“Drum prüfe,

wer sich ewig bindet.” Friedrich Schiller

Kategorisierung › Key-Value stores

› Document stores

› Wide Column stores

› Graphdatenbanken

› weitere

MongoDB

02

NoSQL

MongoDB Quick Reference Cards

http://www.10gen.com/reference

“Deployment” › Standardverzeichnis erstellen: c:\data\db

› Server-Start: mongod.exe

› Shell: mongo.exe

BSON Master/Slave

JavaScript C# Driver

Sharding GNU AGPL

JSON BSON

All JSON documents are stored in a binary

format called BSON. BSON supports a

richer set of types than JSON. http://bsonspec.org

CRUD – Create

(in the shell)

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

See how the save command works:

› db.foo.save

CRUD – Create …with a bit JavaScript

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();

CRUD – Read Queries are specified using a document-style syntax!

› db.scores.find();

› db.scores.find({score: 50});

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

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

CRUD – Update

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

› db.people.update({name: 'Smith'},

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

CRUD – Delete

› db.dropDatabase();

› db.foo.drop();

› db.foo.remove();

“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

https://webnote.codeplex.com

RavenDB

03

NoSQL

Raven Db Documentation – All you need to know

http://beta.ravendb.net/docs

JSON Transactional

LINQ Lucene

100% .NET AGPL / dual

RavenDb › Written by Oren Eini aka Ayende Rahien

› Hibernating Rhinos

› Rhino Mocks & Rhino.ServiceBus

› Written in C#

Deployment › Get it via NuGet

› Change defaults in Raven.Server.exe.config

› It’s safe by default

› Just run the Raven.Server.exe in the /server/ folder

http://localhost:8080/

Safe by default › Useful defaults

› E.g. Limited page size – No Accidental SELECT *

› ACID

› Client / Server

› Cross shard

Units › Documents

› Collections

› Indexes

› Attachments

Designed to “just work” › Schema Free

› Hardly any mapping required

› dynamic (C# 4) yields great power

Designed to “just work” › Sleek Client API

› Fluent API

› Unit of Work Pattern

› Extensible – Plugin Support

Makes developers happy › Testable

› Interfaces all over

› In-Memory Database

› Extensible – Plugin Support

APIs › Native .NET Client API

› HTTP API (Pseudo REST)

Indexes › Written as Linq Queries

› Indexed with Lucene .NET

› Lucene Syntax for querying

“While being RESTful is a goal of the

HTTP API, it is secondary to the goal of

exposing easy to use and powerful

functionality” Ayende Rahien on the HTTP API - http://ravendb.net/documentation/docs-http-api-restful

HTTP API › Caching

› E-Tags

› Lucene Queries possible

C:\>curl -X GET http://localhost:8080/docs/Categories/1 -i HTTP/1.1 200 OK Content-Type: application/json; charset=utf-8 ETag: 00000000-0000-0200-0000-000000000004 { "Name" : "Normal Importance", "Color" : "green" }

Lucene Style Queries

Lucene Style var notes = session.Advanced .LuceneQuery<Note>() .Where(“Category:Important") .ToArray();

Classic Linq Style var notes = session .Query<Note>() .Where(n => n.Category == “Important") .ToArray();

Tabellen Dokumente

04

Key and Index Strategies

05

FRAGEN?

https://webnote.codeplex.com/

Vielen Dank! Johannes Hoppe

Johannes Hofmeister

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