Introduction to cassandra

Cassandra - A Decentralized
Structured Storage System
Nguyen Tuan Quang
Saltlux – Vietnam Development Center
2016.03.21

Agenda
• Database System Outlines
• Cassandra Overview
• Data Model & Architecture
• Key features
• Comparison

Relational DBMS
• Since 1970
• Use SQL to manipulate data
• Excellent for applications such as management
(accounting, reservations, staff management, etc)

Relational DBMS
• Schemas aren't designed for sparse data
• Databases are simply not designed to be distributed

CAP Theory
all nodes see the
same data at the
same time
the system
continues to operate
despite arbitrary
message loss
every request receives a response about
whether it was successful or failed

Consistency Level
• Strong (Sequential): After the update completes any
subsequent access will return the updated value
• Weak (weaker than Sequential): The system does not
guarantee that subsequent accesses will return the
updated value
• Eventual: All updates will propagate throughout all of the
replicas in a distributed system, but that this may take
some time. Eventually, all replicas will be consistent.

Cassandra
• Apache Cassandra was initially
developed at Facebook to power their
Inbox Search
• Originally designed at Facebook,
Cassandra came from Amazon’s highly
available Dynamo and Google’s BigTable
data model

Use-case: Facebook Inbox Search
• Cassandra developed to address this problem.
• 50+TB of user messages data in 150 node cluster on which
Cassandra is tested.
• Search user index of all messages in 2 ways.
– Term search : search by a key word
– Interactions search : search by a user id

Use-cases: Apple
• Cassandra is Apple's dominant NoSQL database
– MongoDB - 35 job listings (iTunes, Customer Systems Platform, and
others)
– Couchbase - 4 job listings (iTunes Social)
– Hbase - 33 job listings (Maps, Siri, iAd, iCloud, and more)
– Cassandra - 70 job listings (Maps, iAd, iCloud, iTunes, and more)
Replication and Multi Data Center Replication

Data Model
• Keyspace is the outermost container for data in Cassandra
• Columns are grouped into Column Families.
• Each Column has
– Name
– Value
– Timestamp

Keyspace: metasearch
Column Families: Metasearch_korean
Data Model for Tornado
Metasearch
TOPIC_URL
URL1
TOPIC_CONTENT
CONTENT 1
TOPIC_TITLE
TOPIC_TITLE1
Row 1 Key
TOPIC_URL
URL2
TOPIC_CONTENT
CONTENT 2
TOPIC_TITLE
TOPIC_TITLE2
Row 2 Key

• Partitioning
How data is partitioned across nodes
• Replication
How data is duplicated across nodes
• Cluster Membership
How nodes are added, deleted to the cluster
System Architecture

• Nodes are logically structured in Ring Topology.
• Hashed value of key associated with data partition is used
to assign it to a node in the ring.
• Hashing rounds off after certain value to support ring
structure.
• Lightly loaded nodes moves position to alleviate highly
loaded nodes.
Partitioning

Replication
• Each data item is replicated at N (replication factor) nodes.
• Different Replication Policies
– Rack Unaware – replicate data at N-1 successive nodes after its
coordinator
– Rack Aware – uses ‘Zookeeper’ to choose a leader which tells nodes
the range they are replicas for
– Datacenter Aware – similar to Rack Aware but leader is chosen at
Datacenter level instead of Rack level.

01
1/2
F
E
D
C
B
A N=3
h(key2)
h(key1)
24
Partitioning and Replication
* Figure taken from Avinash Lakshman and Prashant Malik (authors of the paper) slides.

25
Partitioning and Replication

Cassandra Key features
• Big Data Scalability
– Scalable to petabytes
– New nodes = linear performance increase
– Add new nodes online

• No Single Point of Failture
– All nodes are the same
– Read/write from any nodes
– Can replicate from different data centers

• Easy Replica/Data Distribution
– Transparently handled by Cassandra
– Multiple data centers are supported
– Exploit the benefits of cloud computing

• No need for caching software
– Peer-to-peer architectures removes needs for special caching layer
– Database cluster uses memory of its own nodes to cache data

• Tunable Data Consistency
– Choose between strong and eventually consistency
– Can be done on per-operation basis, and for both reads and writes

Comparison with MySQL
• MySQL > 50 GB Data
Writes Average : ~300 ms
Reads Average : ~350 ms
• Cassandra > 50 GB Data
Writes Average : 0.12 ms
Reads Average : 15 ms
• Stats provided by Authors using facebook data.

Key features Recaps
• Distributed and Decentralized
– Some nodes need to be set up as masters in order to organize other
nodes, which are set up as slaves
– That there is no single point of failure
• High Availability & Fault Tolerance
– You can replace failed nodes in the cluster with no downtime, and
you can replicate data to multiple data centers to offer improved
local performance and prevent downtime if one data center
experiences a catastrophe such as fire or flood.
• Tunable Consistency
– It allows you to easily decide the level of consistency you require, in
balance with the level of availability

Key features Recaps
• Elastic Scalability
– Elastic scalability refers to a special property of horizontal scalability.
It means that your cluster can seamlessly scale up and scale back
down.

References
• https://jaxenter.com/evaluating-nosql-performance-which-database-is-
right-for-your-data-107481.html
• http://www.slideshare.net/amcsquarelearning/learn-mongo-db-at-
amc-square-learning?next_slideshow=1
• https://en.wikipedia.org/wiki/Apache_Cassandra
• http://www.datastax.com/
• http://www.slideshare.net/asismohanty/cassandra-basics-20

Introduction to cassandra

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