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Conférence NoSQL et scalabilité

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Information about Conférence NoSQL et scalabilité
Technology

Published on March 10, 2014

Author: samuelberthe

Source: slideshare.net

Description

Conférence proposée à Epitech Nantes le 10/03/2014.

Target : tek1-3 (3 first year in Epitech)
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:: Conférence :: NoSQL / Scalabilite Etat de l’art Samuel BERTHE 10 Mars 2014Epitech Nantes

Késako ?? Not Only SQL

Késako ?? WHATSAPP : * 5 years * 450 millions users ouch !

Scalability Scale UP Scale OUT

Scalability Scale UP Scale OUT - Hard to maintain - Single Point Of Failure (SPOF) - A fat application - 1To of RAM on a server doesn’t exist… - A server is broken ? Doesn’t matter ! - Easy to grow - Easy to maintain - More flexible (cloud)

Scalability Capacity Cost

Scalability – File system Don’t try to scale your FS : you can’t ! - Hard to maintain - SPOF Riak CS or AWS S3 is a good choice

Scalability - Stateless Your memory isn’t a database. - Don’t use global variables -> Use a datastore - Consistent request -> 1 variable = 1 request

Technologies actuelles

Technologies actuelles

Use case – Web Agency One server with : - httpd (sometimes with load balancing) - RDBMS (sometimes separated) SQL …only

Use case – Web Agency How to scale up ? How to protect you data ? What is you faults tolerance ?

Use case – Web Agency WORDPRESS / SQL == Single Point Of Failure + Fu****g backup management + I’m poor, so I can’t use cloud to scale up

Use case – Worldwide chain store So many DATA

Use case – Worldwide chain store Operational database

Use case – Worldwide chain store Data Wharehouse

SQL – Transactions Customer 1 Customer 2 GET nbr of Samsung Galaxy S5 -> answer = 42 GET nbr of Samsung Galaxy S5 -> answer = 42 Customer buy 2 phones : -> nbr -= 2 (== 40) Customer buy 1 phone : -> nbr -= 1 (== 41) UPDATE value in DB : 40 UPDATE value in DB : 41 Example : stock management system at Amazon.com -> two customers buy at the same time a Samsung Galaxy S5

SQL – ACID Transaction Atomicity Consistency Isolation Durability

SQL …but that was before !

NoSQL – Théorème CAP Availability Consistency Partition Tolerance Pick two

NoSQL – Key/Value-oriented DB Use case : - Session storage - Cache

NoSQL – Document-oriented DB Use case : - Natural data modeling - Fast to develop - Polyvalent

NoSQL – Column-oriented DB Use case : - Large datasets - Logs - Write flooding - BigData

NoSQL – Graph-oriented DB Use case : - Social relations - Graph architecture

NoSQL – Théorème CAP Availability Consistency Partition Tolerance Pick two Mysql, PostgreSQL Couchdb, Cassandra, Riak Couchbase, Mongodb, HBase

NoSQL – Replication Partitioning A - H I - P Q - Z

NoSQL – Replication Sharding A-Z A-Z A-Z

NoSQL – Replication Partitioning + Sharding A – H + I - P I – P + Q - Z Q – Z + A - H

NoSQL – Replication Cross Datacenter Replication (XDCR)

NoSQL – Replication Tunable consistency

More about MongoDB Document oriented database Collections Big community Big documentation Shell client Supported in several languages “Transactional” operators Aggregation

More about MongoDB Easy to index Easy to request Fast to learn Replica set Master-Slave replication Fucking shard key Hard to maintain

More about Couchbase Document oriented database Buckets TTL Shell client Browser Interface Statistics Asynchronous write Master-Master replication Auto-rebalancing

More about Couchbase Memcached integration Index replication Map/Reduce - Views - Stale Supported in less languages than MongoDB Harder to request Small community A lot of Memory (at least 4Go)

ElasticSearch Scalable indexing engine

ElasticSearch Rivers JSON Request Real time GET Segments + shards With Leader Election CP but can be AP

NoSQL is used for Big Data Big new challenges : * capture data * storage * data exploration Usages : * Marketing * Customer relation * Research * Merchandising * Spying (NSA) ;-) 3V : Variété, Volume, Vélocité

BigData – Map/Reduce

BigData – Map/Reduce function map(doc) { if (doc.video_id) emit(doc.video_id, 1); } Lets try to make a youtube video view counter function reduce(id, docs) { var res = 0; for (var i = 0; I < docs.length; ++i) { res++; } emit(id, res); } OUT : - “a”, 1 - “b”, 1 - “a”, 1 - “a”, 1 - “b”, 1 OUT : - “a”, 3 - “b”, 2

BigData – Hadoop Framework HBase HDFS Map/Reduce JobTracker Hive

Learn more Advises : - Use many different databases, for each usage, in a same project… - …but one database to begin

Learn more - Training MOOCs : - 10gen educations (Mongodb learning) - DataStax Academy (Cassandra learning) Online testing db Mongodb and Couchdb : pretty easy Node.JS / Python You mean BigData ? I tell you Java ! Download datasets, consume API or make a crawler

Enjoy ! Samuel BERTHE --- contact@samuel-berthe.fr @SamuelBerthe www.samuel-berthe.fr

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