Apache CassandraC* (Not Only) for C# developers

Grégory Ruiz (@gregoryruiz)

.NET Tech Lead at

ruiz.g@sfeir.com

NoSQL - Season01 Episode02(March 19, 2015)

§1: Relational → NoSQL§2: Firebase§3: Cassandra

1. What is Cassandra anyway?

2. How does Cassandra work?

3. Which tools to play with Cassandra?

Agenda

1. Cassandra?what is Apache Cassandra about

● open source NoSQL distributed DBMS

● kind of a hybrid betweenkey-value & column-oriented database

● very high availability● painless scalability● predictable performances● fault tolerant resistant

1. Cassandra? / Brief Definition

datastaxhttp://www.datastax.com/documentation/cassandra/2.0/cassandra/gettingStartedCassandraIntro.html

Cassandra -- affectionately “C*” -- is an open source, distributed store for structured data that scales-out on cheap, commodity hardware and stays up even when things get really bad.

-- DataStax

1. Cassandra? / Academic Background

the scalable & efficient storage model from[2006]

the fault resistant & distributed design from[2007] Dynamo

Rick Branson - How do I Cassandra ?http://fr.slideshare.net/rbranson/how-do-i-cassandra

● early 2008developed @ Facebook

● july 2008open source project

● march 2009Apache Incubator

● february 2010top-level project

1. Cassandra? / Early Timeline

wikipediahttp://en.wikipedia.org/wiki/Apache_Cassandra

● on march 2015○ 8th most popular DBMS○ 2nd most popular NoSQL

○ 1st most popular wide-column store

● Prominent users

1. Cassandra? / Popularity Rank

db engineshttp://db-engines.com/en/system/Cassandra

2. Architecturehow does Cassandra work

2. Architecture / Node, Cluster, Gossip

● node

2. Architecture / Node, Cluster, Gossip

● node● data center

2. Architecture / Node, Cluster, Gossip

● node● data center● cluster

2. Architecture / Node, Cluster, Gossip

● node● data center● cluster● gossip: p2p protocol

2. Architecture / Node, Cluster, Gossip

⇒ no SPOF

⇒ masterless architecture

● partition key● consistent hashing● nodes own hash ranges

2. Architecture / Partitionner, Hash Ranges

2. Architecture / ⇒ Predictable Perfs

2. Architecture / Painless Scalability

Netflix Tech Bloghttp://techblog.netflix.com/2011/11/benchmarking-cassandra-scalability-on.html

2. Architecture / Painless Scalability

2. Architecture / Fault Tolerance Resistance

2. Architecture / Fault Tolerance Resistance

2. Architecture / Fault Tolerance Resistance

RDBMS (comparison)Key-Value~Column oriented / TabularDocument oriented

MS-SQL

Vertica

Dynamo

Cassandra

CouchDB

Redis

HyperTable

MongoDB

C* = _AP system Eventually Consistent

2. Architecture / Fault Tolerance Resistance

Data Replication

Replication Strategy :

the nodes where replicas are placed

Replication Factor (RF) :

the total number of replicas across the cluster

2. Architecture / Fault Tolerance Resistance

Data Replication :

Simple Strategy

CREATE KEYSPACE “Test” WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 3 };

2. Architecture / Fault Tolerance Resistance

Data Replication :

Net. Topology Strategy

CREATE KEYSPACE “Test” WITH REPLICATION = { 'class' : 'NetworkTopologyStrategy', 'DCEurope' : 3, 'DCAsia' : 4 };

2. Architecture / Fault Tolerance Resistance

● ALL++ consistency, -- availability

● QUORUM / EACH_QUORUM / LOCAL_QUORUM+ consistency, - availability

● ONE, TWO, THREE / LOCAL_ONE- consistency, + availability

● ANY-- consistency, ++ availability

2. Architecture / Fault Tolerance Resistance

Quorum = floor(RF/2)+1

2. Architecture / Fault Tolerance Resistance

● Clients write local● Datas sync over WAN● RF per datacenter

2. Architecture / Fully Replicated

2. Data Modelinga major paradigm shift

2. Data Modeling / shift your vocabulary

RDBMS C*

Structured Data,Fixed Schema

Unstructured Data,Flexible Schema

Array of Arrays ⇒ “2D”(row X column)

Nested K-V pairs ⇒ “3D”(rowKey X colKey X colValue)

Schema / Database KeySpace

Table Column Family Table

Row Partition (aka Row)⇒ unit of replication

Column Cluster (aka Column)⇒ unit of storage

Foreign Keys, Joins … ACID no Ref Integrity, Collections … CAP

2. Data Modeling / Column Family Table

Geo age:30 car:camaro gender:M

Line age:20 car:clio gender:F

Céd age:15 gender:M

Greg age:60 gender:M

2. Data Modeling / Column Family Table

Geo age:30 car:camaro gender:M

Line age:20 car:clio gender:F

Céd age:15 gender:M

Greg age:60 gender:M

Row Key

2. Data Modeling / Column Family Table

Geo age:30 car:camaro gender:M

Line age:20 car:clio gender:F

Céd age:15 gender:M

Greg age:60 gender:M

Columns

2. Data Modeling / Column Family Table

Geo age:30 car:camaro gender:M

Line age:20 car:clio gender:F

Céd age:15 gender:M

Greg age:60 gender:M

Columns

2. Data Modeling / Column Family Table

Line1 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

Line2 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

Line3 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

INSERT INTO coworker(id,car) VALUES (’Line’,’merco’);

2. Data Modeling / Column Family Table

Line1 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

Line2 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

Line3 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

INSERT INTO coworker(id,car) VALUES (’Line’,’merco’);

2015/03/19 18:30 > 2015/03/19 12:15

2. Data Modeling / Column Family Table

Line1 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

Line2 age:202015/03/19 12:15

car:merco2015/03/19 18:30

gender:F2015/03/19 12:19

Line3 age:202015/03/19 12:15

car:clio2015/03/19 12:15

gender:F2015/03/19 12:19

INSERT INTO coworker(id,car) VALUES (’Line’,’merco’);

2015/03/19 18:30 > 2015/03/19 12:15

Line1 age:202015/03/19 12:15

car:merco2015/03/19 18:30

gender:F2015/03/19 12:19

Line2 age:202015/03/19 12:15

car:merco2015/03/19 18:30

gender:F2015/03/19 12:19

Line3 age:202015/03/19 12:15

car:merco2015/03/19 18:30

gender:F2015/03/19 12:19

2. Data Modeling / Column Family Table

CREATE TABLE temperature (

weatherstation_id text,

event_time timestamp,

temperature text,

PRIMARY KEY (weatherstation_id,event_time));

/******/

INSERT INTO temperature(weatherstation_id,event_time,temperature)

VALUES (’1234ABCD’,’2013-04-03 07:01:00′,’72F’);

/******/

SELECT event_time,temperature FROM temperature

WHERE weatherstation_id=’1234ABCD’;

/******/

SELECT temperature FROM temperature

WHERE weatherstation_id=’1234ABCD’

AND event_time > ’2013-04-03 07:01:00′ AND event_time < ’2013-04-03 07:04:00′;

2. Data Modeling / ex: Time Series, Single Device/row

CREATE TABLE temperature_by_day (

weatherstation_id text,

date text,

event_time timestamp,

temperature text,

PRIMARY KEY ((weatherstation_id,date),event_time));

/******/

INSERT INTO temperature_by_day(weatherstation_id,date,event_time,temperature)

VALUES (’1234ABCD’,’2013-04-03′,’2013-04-03 07:01:00′,’72F’);

/******/

SELECT *

FROM temperature_by_day

WHERE weatherstation_id=’1234ABCD’

AND date=’2013-04-03′;

2. Data Modeling / ex: Time Series, Partitioning

CREATE TABLE latest_temperatures (

weatherstation_id text,

event_time timestamp,

temperature text,

PRIMARY KEY (weatherstation_id,event_time),

) WITH CLUSTERING ORDER BY (event_time DESC);

/******/

INSERT INTO latest_temperatures(weatherstation_id,event_time,temperature)

VALUES (’1234ABCD’,’2013-04-03 07:03:00′,’72F’) USING TTL 20;

2. Data Modeling / ex: Time Series, DESC & TTL

3. Toolswhat do i need to start playing with

3. Tools / Apache Cassandra binaries

3. Tools / Datastax Client Driver

Demo Timeusing the datastax C# driver

References

● wikipedia○ http://en.wikipedia.org/wiki/Apache_Cassandra

● db rank○ http://db-engines.com/en/ranking

● netflix○ http://techblog.netflix.com/2011/11/benchmarking-

cassandra-scalability-on.html

● C* wiki○ https://wiki.apache.org/cassandra○ http://www.datastax.com/docs○ http://planetcassandra.org

5. References

Questions?

Questions?

Special thanks

● Geoffrey Garnotel● Aline Paponaud● Cédric Tesnière

● my sweet little bae

● you● my coffee maker