NoSQL Database- cassandra column Base DB

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NoSQL – Part 2CAP Theorem & Column Oriented

Mohammad Sadegh Salehi

Dr.Baraani

Winter2015 Sheikh Bahaie

University

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NoSQL (part 2) - CAP Theorem & Column Oriented

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Agenda

— Review NoSQL

— Dynamo and BigTable

—NoSQL Classification

— Key-value Stores

— Column Oriented

—Casandra

— Why Casandra

— Question

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What is NoSQLreview

Stands for Not Only SQL

Class of non-relational data storage systems

Usually do not require a fixed table schema nor do

they use the concept of joins

All NoSQL offerings relax one or more of the ACID

properties (will talk about the CAP theorem)

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Dynamo and BigTable

Three major papers were the seeds of the NoSQL

movement

• BigTable (Google)

• Dynamo (Amazon)

—Gossip protocol (discovery and error detection)

— Distributed key-value data store

— Eventual consistency

• CAP Theorem (discuss in a sec ..)

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What kinds of NoSQLReview

NoSQL solutions fall into two major areas:

• Key/Value or ‘the big hash table’.

— Amazon S3 (Dynamo)

— Voldemort

—Scalaris

• Schema-less which comes in multiple flavors, column-

based, document-based or graph-based.

—Cassandra (column-based)

— CouchDB (document-based)

— Neo4J (graph-based)

— HBase (column-based)

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Key-Value Stores

Extremely simple interface• Data model: (key, value) pairs• Operations:

— Insert(key,value), —Fetch(key),—Update(key), —Delete(key).

Implementation: efficiency, scalability, fault-tolerance• Records distributed to nodes based on key• Replication• Single-record transactions, “eventual consistency”

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Key-Value Data Stores

Storing Session Information User Profiles, Preferences: Almost every user has

a unique userID as well as preferences such as language, color, timezone, which products the user has access to , and so on.

Suitable Use Cases

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As we want the shopping carts to be available all the time, across browsers, machines, and sessions, all the shopping information can be put into value where the key is the userID

Shopping Cart Data

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Relationships among data

Multi-operation Transactions

Query by Data

Operations by Sets

Not to Use

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Column-oriented

Store data in column order Allow key-value pairs to be stored (and retrieved

on key) in a massively parallel system,

• Data model: families of attributes defined in a schema, new attributes can be added,

• Storing principle: big hashed distributed tables,

• Properties: partitioning (horizontally and/or vertically), high availability etc. completely transparent to application,

Intro

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Cassandra

Apache Cassandra™ is a free Distributed… High performance… Extremely scalable… Fault tolerant (i.e. no single point of failure)…

Post-relational database solution.

Cassandra can serve as both real-time datastore and as a read-intensive database.

Compiles to: C++, Java, PHP, Ruby, Erlang, Perl, ...

Thrift

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CassandraInfographic

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Cassandra

Originally developed at Facebook Follows the BigTable data model: column-oriented Uses the Dynamo Eventual Consistency model Written in Java Open-sourced and exists within the Apache family Uses Apache Thrift as it’s API Some of its myriad users:

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Cassandra

keyspace: Usually the name of the application; e.g., 'Twitter', 'Wordpress‘.

column family: structure containing an unlimited number of rows• Simple• Super (nested Column Families)

column: a tuple with name, value and time stamp• Each Column has

— Name— Value— Timestamp

key: name of record super column: contains more columns

Data Model

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Cassandra – Data Model

keyspace

settings

column family

settings

column

name value timestamp

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CassandraColumn Family & Super Column Family

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Cassandra

Cassandra was designed with the understanding that system/hardware failures can and do occur

Peer-to-peer, distributed system All nodes the same Data partitioned among all nodes

in the cluster Custom data replication to ensure

fault tolerance Read/Write-anywhere design

Architecture Overview

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Cassandra

Each node communicates with each other through the Gossip protocol, which exchanges information across the cluster every second,

A commit log is used on each node to capture write activity. Data durability is assured,

Data also written to an in-memorystructure (memtable) and then to disk once the memory structure is full (an SStable).

Architecture Overview

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Why Cassandra?

Gigabyte to Petabyte scalability Linear performance gains through adding nodes No single point of failure Easy replication / data distribution Multi-data center and Cloud capable No need for separate caching layer Tunable data consistency Flexible schema design Data Compression CQL language (like SQL) Support for key languages and platforms No need for special hardware or software

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Why Cassandra?

Capable of comfortably scaling to petabytes New nodes = Linear performance increases Add new nodes online

Big Data Scalability

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Double Throughput

Capabilities

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Why Cassandra?

All nodes the same Customized replication affords tunable data redundancy Read/write from any node Can replicate data among different physical data center

racks

No Single Point of Failure

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Why Cassandra?

Peer-to-peer architecture removes need for special caching layer and the programming that goes with it

The database cluster uses the memory from all participating nodes to cache the data assigned to each node

No irregularities between a memory cache and database are encountered

No Need for Caching Software

Database Server

Memcached Servers

Application ServersW

rite

s

Re

ad

s

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Why Cassandra?

Uses Google’s Snappy data compression algorithm Compresses data on a per column family level Internal tests at DataStax show up to 80%+ compression

of raw data No performance penalty (and some increases in overall

performance due to less physical I/O)!

Data Compression

Portfolio Keyspace

Customer Column Family

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Why Cassandra?

Very similar to RDBMS SQL syntax Create objects via DDL (e.g. CREATE…) Core DML commands supported: INSERT, UPDATE,

DELETE Query data with SELECT

CQL Language

Portfolio Keyspace1

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SELECT *

FROM USERS

WHERE STATE = ‘TX’;

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Comparison with MySQL

MySQL > 50 GB Data Writes Average : ~300 msReads Average : ~350 ms

Stats provided by Authors using facebook data.

Cassandra > 50 GB DataWrites Average : 0.12 msReads Average : 15 ms

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Cassandra Tools

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../../../../Desktop/noSqlCassandra-sadegh/noSqlCassandra-sadegh.mp4

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Where to get Cassandra?

Go to www.datastax.com DataStax makes free smart start installers available for

Cassandra that include: • The most up-to-date Cassandra version that is production quality• A version of DataStax OpsCenter, which is a visual, browser-

based management tool for managing and monitoring Cassandra

• Drivers and connectors for popular development languages • Same database and application• Automatic configuration assistance for ensuring optimal

performance and setup for either stand-alone or cluster implementations

• Getting Started Guide

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Where Can I Learn More?

www.datastax.com

Free Online Documentation User/Customer Cas Studies Technical White Papers Software downloads Technical Articles

User Forums Videos Tutorials FAQ’s Blogs

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ResourcesSites

Cassandra• http://cassandra.apache.org

NoSQL News websites• http://nosql.mypopescu.com• http://www.nosqldatabases.com

“a practical guide to noSQL”, Posted by Denise Miura on March 17, 2011 at • http://blogs.marklogic.com/2011/03/17/a-practical-

guide-to-nosql/

http://cassandra.apache.org/

http://www.nosqldatabases.com/

http://blogs.marklogic.com/2011/03/17/a-practical-guide-to-nosql/

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ResourcesBooks

“Cassandra The Definition Guide”, O'Reilly Media, nov2013

“Cassandra Essential Toturial”, DataStax 2014

“Professional NoSQL”, Wrox, 2011

“NoSQL Distilled”, Martin Fowler, 2013

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Questions

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Mohammad Sadegh [email protected]

Thank You

NoSQL Database- cassandra column Base DB

Engineering

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