Presentation of Apache Cassandra

1. Introduction to NoSQL systems, Extensible Record Stores and Amazon’s Dynamo
+ Google Bigtable
2. What Cassandra is and how it is compared with other similar systems
3. What applications are better supported - examples, case studies
4. Technical Description, architecture, internals
5. How is it used and installed, requirements and in what platforms does it run on
6. Demo
7. References
Contents

Background
NoSQL, Extensible Record Stores, Cassandra’s Parents
1.

NoSQL or Not-Only-SQL systems: Next Generation Databases. The initial movement started in 2009
with the goal of creating modern, web-scale DBs. Currently, they exist more than 225 NoSQL
systems.
In general, they share the following features:
• Schema-free databases
• Easy replication support
• Simple API
• Distributed
• Open Source
NoSQL Systems
• BASE (instead of ACID)
• Huge amount of data
• Horizontally scalable

• Motivated by Google’s Big Table.
• Basic Data Model: Rows and Columns
• Basic Scalability Model: Rows and Columns are splitted into nodes.
• Rows: split across nodes through sharding on the primary key.
• Columns: distributed over multiple nodes by using ‘column groups’.
• Other systems that use this technology: Hypertable, HBase.
Extensible Record Stores (or Wide Column Stores)

What is it?
A highly-available and scalable storage system used by Amazon to store and retrieve user shopping
charts and other core services. It pioneered the idea of eventual consistency. Key-Value Store.
How it works?
Allows read and write operations to continue even during network partitions and resolves update
conflicts using different conflict resolution mechanisms.
Sacrifices consistency for availability.
Allows customization to meet desired preference.
Consistent Hashing, Vector Clocks (not in Cassandra), Gossip Protocol, Hinted Handoff, Read Repair
Cassandra’s Parents - Amazon Dynamo

Cassandra’s Parents - Google Bigtable
What is it?
A high performance data storage system built on Google File System and other Google
technologies.
How it works?
Provides both structure and data distribution but relies on a distributed file system for
durability.
Richer data model from Dynamo. One key, many values. Fast sequential access.
Columnar, SSTable Storage, Append-only, Memtable, Compaction

What features does Cassandra use from Google’s BigTable?
1. Column Families
2. Memtables
3. SSTables
What features does Cassandra use from Amazon Dynamo?
1. Consistent hashing
2. Partitioning
3. Replication
Cassandra’s Parents

Description and Comparisons
What Cassandra is and how it is compared with other similar systems
2.

Avinash Lakshman
• Inventor, Apache Cassandra
• Co-inventor, Amazon Dynamo

Prashant Malik
• Inventor, Apache Cassandra
• Technical Leader, Facebook

Definition
• A distributed NoSQL database system for managing large
amounts of structured data across many commodity servers,
while providing highly available service and no single point of
failure.

Timeline with activities
• July 2008
Facebook released Cassandra as an open-source project
• March 2009
Cassandra became an Apache Incubator project
• 17th February 2010
Cassandra graduated to a top-level project
• 2012
University of Toronto researchers studying NoSQL systems concluded that “In
terms of scalability, there is a clear winner throughout our experiments”
• 2010-2015
New releases of Cassandra

Strengths
• Linear scale performance
The ability to add nodes without failures leads to predictable increases in
performance
• Supports multiple languages
Python, C#/.NET, C++, Ruby, Java, Go, and many more…
• Operational and developmental simplicity
There are no complex software tiers to be managed, so administration duties are
greatly simplified.
• Ability to deploy across data centres
Cassandra can be deployed across multiple, geographically dispersed data centres

• Cloud availability
Installations in cloud environments
• Peer to peer architecture
Cassandra follows a peer-to-peer architecture, instead of master-slave architecture
• Flexible data model
Supports modern data types with fast writes and reads
• Fault tolerance
Nodes that fail can easily be restored or replaced
• High Performance
Cassandra has demonstrated brilliant performance under large sets of data
Strengths (1)

• ColumnFamily Store
Cassandra stores columns based on the column names, leading to very quick slicing
• Tunable consistency
Support for strong or eventual data consistency across a widely distributed cluster
• Schema-free/Schema-less
In Cassandra, columns can be created at your will within the rows. Cassandra data
model is also famously known as a schema-optional data model
• AP-CAP
Cassandra is typically classified as an AP system, meaning that availability and
partition tolerance are generally considered to be more important than consistency in
Cassandra
Strengths (2)

Variable number of columns per row

Weaknesses
Use Cases where is better to avoid using Cassandra
• If there are too many joins required to retrieve the data
• To store configuration data
• During compaction, things slow down and throughput degrades
• Basic things like aggregation operators are not supported
• Range queries on partition key are not supported
• If there are transactional data which require 100% consistency
• Cassandra can update and delete data but it is not designed to do so

Business Insider
“The basic problem Cassandra solved is that when you have a lot of
data sitting on a lot of servers, as Facebook does, you end up with a
house of cards. A single server going down can collapse the whole
stack.”

Cassandra compared to other
NoSQL Systems

Read & Write latency for workload Read/Write

Throughput for workload Read/Write &
Read/Scan/Write

Mixed Operational & Analytical Workload

Differences between Cassandra and RDBMS
RDBMS Cassandra
relational database keyspace
b-trees log-structured merge-trees
rows which do not include a particular
column value → NULL (in that position)
for each row, only the columns with a value
are stored
support ACID transactions only supports AID

Supported Applications -
Customers - Case Studies
3.

What kind of applications are supported by Cassandra
>80% of the clients fit into one of the next categories:
I. Product Catalog/Playlist
II. Recommendation/Personalization Engine
III. Sensor Data/Internet of Things
IV. Messaging (and generally time-series data)
V. Fraud Detection

In other words, applications that need to...
• store and handle time-series data (most common use case)
• store and handle large volumes of data
• scale predictably
• be continuously available
• protect their data

Datastax
• A software company that develops and provides support for a commercial
edition of Cassandra.
• Massively scalable NoSQL platform able to run online applications for
innovative and data-intensive companies (e.g. Netflix).
• Faster to deploy and less expensive to maintain than other database
platforms.
• Powered by Cassandra and contains only selected releases of it, chosen
by its expert staff.

Datastax (1)
• Supports businesses that need a progressive data management.
• Can serve as a real-time datastore for online production.
• Delivers a unique, smart data platform, suitable for the cloud.

Customers
• Over 3.000 companies around the world use (or have used)
Apache Cassandra in production.
• Most famous:

Cassandra Summit
• Organized by DataStax for 7 consecutive years (in both US and Europe).
• New product releases are announced.
• Customers describe their usage of Cassandra

Key Terms
• Cluster
• Distributed Location
• Node

Facebook Inbox Search - Requirements
“The system was required to handle a very high write throughput,
billions of writes per day, and also scale with the number of users”
“Since users are served from data centres that are geographically
distributed, being able to replicate data across data centres was key
to keep search latencies down”
• Lakshman, Malik

Facebook Inbox Search
The reason why Cassandra was initially built.
Facebook maintains a per user index of all messages that have
been exchanged between the senders and the recipients of the
message.
Two kinds of search features enabled at 2008:
I. term search
II. interactions - given a person’s name, returns all the messages
have been sent/received from that person

Facebook Inbox Search (1)
How did they do that?
The schema consists of two column families. Exploits the “time sorting”
feature of Cassandra.
For the term search:
• UserID → key
• Words that make up the message → super columns
• Columns within the super column → individual message identifiers
(MessageID) of the messages that contain the word.

For the interactions:
• UserID → key
• RecipientsID’s → super columns
• Columns within the super columns → MessageID’s
• Cassandra provides certain hooks for intelligent caching of data

In 2008:
• system was storing 50+ TB of data
• on a 150 node cluster
• spread out between east and west coast data centres
Performance:

Facebook abandoned Cassandra for the Inbox at late 2010
Cassandra has been deployed as the backend storage system for
multiple services within Facebook.

Categories: Fraud Detection and Time-series data

Instagram Fraud Detection
Initially was using Redis for auditing information related to security
and site integrity purposes (e.g. fighting spam, finding abusive
users).
But…
• data size was growing too quickly
• high write and low read rate
• keeping the data in memory was too costly
So… Cassandra

Instagram Fraud Detection (1)
• Started with 3 nodes and very soon they had grown to a 12 node
cluster.
• No need to store very large instances in memory → put everything on
disks.
“Implementing Cassandra cut our costs to the point where we were paying
around a quarter of what we were paying before. Not only that, but it also
freed us to just throw data at the cluster because it was much more scalable
and we could add nodes whenever needed.”
- Brick Branson, Software Engineer at Instagram

Instagram “Inbox”
Newsfeed or inbox part of Instagram: a feed of all the activity that would be
associated with a given user’s account.
Previously in Redis, with the same (memory) limitations as in the Fraud Detection
case.
Instagram’s Cassandra Cluster:
• 12 nodes on EC2 (AWS)
• 1.2 TB of data stored
• 20.000 writes/sec.
• 15.000 reads/sec.

i2O Water
Description: i2O Water helps utility companies operate more
efficiently through the use of IoT aiming at solving the water crisis.
Challenges:
• Massive volumes of time-series data (>1.5 TB and growing)
• Need for search and analysis of high velocity, streaming data in
near real-time
• SQL Server (previously used) has limitations on scalability and
performance

i2O Water (1)
Solution: After evaluating many common NoSQL technologies, they chose Cassandra.
Why?
I. performance (50-60.000 writes and 20-40.000 reads/sec instead of 0.5 writes/sec and 5
reads/sec with SQL Server)
II. easy to maintain
III. easy to upgrade
IV. ability to handle structured and unstructured real-time streaming data
V. continuous availability and reliability
VI. operationally simple to manage

i2O Water (2)
Results:
I. 235 m. lt. of water saved per day
II. successfully handling massive volumes of data from 15.000
devices without latency or downtime
III. fault tolerance even during upgrades (99.9% availability)

Category: Product Catalogs and Playlists

Spotify
Description: Spotify delivers streaming music in real time to over 40
million active users (the number is growing), without interruption.
Challenges:
• postgreSQL (previously used) and generally RDBMSs cannot
deliver 100% availability
• limited scalability across data centers
• difficult to analyze massive volumes of data

Spotify (1)
Solution: Cassandra.
Why?
I. high availability (due to masterclass architecture)
II. stores data for the entire product catalog and key customer
experience capabilities
III. multi data centre application and no single point of failure
IV. integration with Apache Spark for real time processing and
analytics

Spotify (2)
Results:
I. 40.000 requests/sec. handled successfully and on-time
II. >500 nodes across 4.000 servers in 4 data centres
III. >1.5 bn playlists created from 40m active users and managed in
real time

Data
Centre Data
Centre
Data
Centre
Data
Centre
Spotify - Data Centres (2 in the US - 2 in Europe)

Category: Recommendation/Personalization Engine

Netflix
Description: Netflix is the world’s leading internet television network with
more than 48 million users in 40 countries.
Challenges:
• Oracle database (was used until 2010) was approaching its limits
on traffic and capacity
• single centre → single point of failure
• system downtime every two weeks for schema changes
• need for reliability and flexibility for international expansion

Netflix (1)
Solution: Cassandra (on the Cloud, AWS) was the clear winner of
the extensive evaluation of NoSQL DB options. (Later on, Netflix
migrated to DataStax Enterprise for security and production.)
Why?
I. persistent datastore, 100% uptime and cost-effective scalability
II. ability to create a cluster in any region in 10’
III. expert support

Netflix (2)
Results:
I. throughput of >10 m. transactions/sec.
II. process of >2.1 bn. reads and 4.3 bn. writes/day
III. delivers >76.000 genre types and captures every detail of
customers’ habits for tailoring the customer experience

Coursera
Description: Coursera is an education platform which partners with
top universities and organizations worldwide, to offer courses online
for anyone to take, for free.
Challenges:
• MySQL (previously used for class interaction) was insufficient:
• unstable performance,
• unexpected downtime,
• limitation in introducing new features

Coursera (1)
Solution: After evaluating emerging database technologies, it chose
Cassandra (DataStax).
Why?
• 100% application uptime needed (customers from all over the
world)
• Scalability (enabling storage of growing user data)

Coursera (2)
Results:
I. 3 nodes on AWS in the US East region and plans to expand to multiple data
centers across different regions
II. 24x7 availability to the users
III. Helps innovation
IV. Reduced time to market on new features
“High availability with reliable performance is a big win for us. With Datastax Enterprise,
our customers around the world are able to take any course, anytime through our on-
demand model.”
• Daniel Chia, Software Engineer at Coursera

The Weather Channel
Description: The Weather Channel delivers breaking news to
countless viewers and users from web, desktop and mobile
applications.
Challenges:
• Customer experience in the center of attention (continuous
availability, global and diverse users)
• New capabilities including statistics from unstructured data,
CGS for customer engagement etc.

The Weather Channel (1)
Solution: Cassandra
Why?
I. linear scalability
II. 100% uptime
III. supports almost all possible types of content (e.g. observations,
forecasts, marine data, ads)

The Weather Channel (2)
Results:
I. billions requests/month are processed - no fear of downtime
II. node count was grown from 3 to 36 in AWS in 1 year across 3
data centers (US East and West and Western Europe).
III. capability for new offerings (e.g. social weather)

Technical Description,
Architecture, Internals
4.

Key Terms – Data Structures
• Commit Log
• Memtable
• Sorted String Table (SST)
• Bloom Filter
• Index File

Key Terms
• Gossip protocol: helps each node learn about the topology of the cluster
(communication and detection of faulty nodes).
• Snitch: indicates which node is closest to the current location.

Log-Structured Merge-Tree (LSM-Tree)
What is it?
• A disk-based data structure designed to provide low-cost indexing for a file
experiencing a high rate of record inserts.
• A simple LSM-Tree comprises of two tree-like structures: Co (in memory)
and C1 (disk).
• Maintains key-value pairs.
• In Cassandra, each value represents a row.
Used in:
• BigTable, HBase, MongoDB, SQLite, RocksDB, InfluxDB

Data Model
• Each Row → Identified by a Unique Key (Primary Key)
• Keyspace → Outermost container for data (one or more column families)
• Column Family → Contains Supercolumns or Columns (but not both)
• Column → Basic data structures with: key, value, timestamp
• Supercolumn → Special column, stores a map of sub-columns. Columns that
you are likely to query together should be placed in the same column family.
• Columns could be of variable number per key. For instance, key K1 could
have 1024 columns/supercolumns while K2 could have 64
columns/supercolumns

Data Model (1)
• Partition key: The first column declared in the primary key. Determines which node stores the
data.
• Clustering Columns: The remaining fields of the primary key, which determine the ordering of the
data in the disk.
• Any column within a column family is accessed using the convention: column_family: column
• For Supercolumns: column_family: super_column: column
• Values → Addressed by the triple (row-key, column-key, timestamp)
• The system allows columns to be sorted either by time or by name.
• Time sorting: exploited by applications such as FB Inbox Search where the results are always
displayed in time sorted order.

Relational Schema vs Cassandra

Introduction
The architecture of a storage system that needs to operate in a production setting is complex.
We will focus on the core distributed systems techniques used in Cassandra:
I. Partitioning
II. Replication
III. Membership
IV. Failure Handling
V. Scaling
All these modules work in synchrony to handle read/write requests.

Partitioning
Offers the ability to scale incrementally.
How?
• Dynamically partition the data over the set of nodes in the cluster.
• Consistent hashing (order preserving hash function).
• Output range: a ring.
• Each node: is assigned a random value which determines its place
on the ring.

Partitioning (1)
• Each data item: is assigned to a node by hashing its key to yield
its position on the ring and then walking the ring clockwise to
find the first node with a position larger than the item’s position.
• Each node becomes responsible for the region in the ring
between it and its predecessor node on the ring.
• Departure or arrival of a node only affects the immediate
neighbours.

Partitioning (2)
Challenges:
I. Random positioning of each node leads to non-uniform data and load
distribution.
II. The basic algorithm is oblivious to the heterogeneity in the performance
of nodes.
Addressed by:
Analysing load information on the ring and having lightly loaded nodes move
on the ring to alleviate heavily loaded ones.

Node: Storage layer within a server
Before:
● 1 server/machine (machine: physical server or EC2 instance-AWS)
● 1 node/server (server: an installation of Cassandra)
Now:
● 256 vnodes/server (virtual nodes)
Vnodes or Virtual Nodes or Tokens:
Define the section of the ring (token ranges) the node will become responsible for
Why?
Much easier and faster in case of a node failure

Replication
Used to achieve high availability and durability.
How?
• Replication factor: determines how many copies of your data exist.
• Each data item: is replicated at N hosts (N=replication factor).
• Coordinator node: in charge of the replication of the data items that fall within its range.
• Consistency level: refers to how much up-to-date and synchronized a row of Cassandra
is in all of its replicas e.g. quorum → replication_factor/2 + 1.
• Various replication policies: Rack Unaware, Rack Aware and Datacentre Aware.
• Each row is replicated across multiple datacentres which are connected through high
speed network links.

Replication - Zookeeper
• Cassandra elects a leader amongst its nodes using Zookeeper.
• All nodes on joining the cluster contact the leader who tells them for
what ranges they are replicas for.
• Leader tries to maintain the invariant that no node is responsible for
more than N-1 ranges in the ring.

Replication - Zookeeper
• Cassandra elects a leader amongst its nodes using Zookeeper.
• All nodes on joining the cluster contact the leader who tells them for what ranges
they are replicas for.
• Leader tries to maintain the invariant that no node is responsible for more than N-1
ranges in the ring.
• Metadata about the ranges a node is responsible is 1) cached locally at each node
and 2) in a fault-tolerant manner inside Zookeeper.
• This way, a node that crashes and comes back knows what ranges it was
responsible for.

Membership
Based on Scuttle-butt, a very efficient anti-entropy Gossip based
mechanism.
Benefits:
I. Efficient CPU utilization.
II. Efficient utilization of the Gossip Channel.
Gossip: a P2P communication protocol to discover and share location
and state information about the other nodes in a Cassandra cluster.
Gossip information is also persisted locally by each node to use
immediately when a node restarts.

Membership - Failure Detection
Every node can locally determine if any other node in the system is up or down.
Used to avoid attempts to communicate with unreachable nodes.
How?
• Make use of Φ Accrual Failure Detector (emits a value which represent a suspicion level
for each of monitored nodes)
• With Φ=1, likelihood of mistake: 10%
• With Φ=2, likelihood of mistake: 1%
• and so on…

Bootstrapping (adding a new node in the cluster)
Process of getting data from other nodes in the ring for a new node that starts
for the first time.
How?
• When the new node enters the cluster, it chooses a random token for its
position in the ring.
• It also reads its configuration file which contains the seeds (initial contact
points) of the cluster.
• Token information is then gossiped around the cluster enabling any node
to route a request for a key to the correct node.

Bootstrapping (adding a new node in the cluster) (1)
In Facebook’s environment…
• Node outages are often transient but may last for extended intervals.
• Failures can be of various forms such as disk failures, bad CPU, etc.
• A node failure rarely signifies a permanent departure and therefore should not result in re-
balancing of the partition assignment.
• Manual error could result in the unintentional startup of new nodes.
• To that effect, every message contains the cluster name of each Cassandra instance.
• An admin uses a cmd tool or a browser to connect to a Cassandra node and issue a
membership change to join or leave a cluster.

Scaling the Cluster
Adding a new node on the system in order to alleviate another heavily
loaded node.
How?
• Gets assigned a token.
• Splits the responsibility range of the other node.
• Data are streamed between the nodes using kernel-kernel copy
techniques.
• Data are transferred at approximately the rate of 40 MB/sec.

Local Persistence
• Cassandra relies on the local file system for data persistence.
• The data is represented on disk using a format that lends itself to
efficient data retrieval.

Implementation Details
The Cassandra process on a single machine primarily consists of:
I. A partitioning module,
II. The cluster membership and failure detection module,
III. The storage engine module.
Each of these modules has been implemented from the ground up using Java.
The II) is built on top of a network layer which uses non-blocking I/O.
Application relate messages for replication and request routing relies on TCP.

Implementation Details (1)
The request routing modules are implemented using a certain state machine.
When a read/write request arrives at any node in the cluster the state
machine…
I. Identifies the node(s) that own the data for the key
II. Routes the requests to the nodes and wait for the responses to arrive
III. If the replies do not arrive within a configured timeout value fail the
request
IV. Figures out the latest response based on a timestamp
V. Schedules a repair of the data at any replica if they do not have the latest
piece of data.

No coordination at all?
“We have learnt that having some amount of coordination is essential
to making the implementation of some distributed features tractable”
-Lakshman & Malik
• Integration with Zookeeper → can be used for various tasks in
large scale distributed systems.

In case of a Node Failure...
I. A locally stored hint with a specified time to live
II. When the nodes are available again, the write operation is sent

How is a Memtable flushed on the disk?
• A background thread keeps checking the size of all the
Memtables while the clients keep writing on the cluster
• If one of the above conditions is met, a new Memtable is created
and the previous one is marked for flushing.
i. node’s global memory thresholds have been reached,
ii. commit log is full,
iii. a table level interval has been reached

How is a Memtable flushed on the disk? (1)
• Another thread (or multiple threads) flushes all the marked Memtables on
the disc.
• The commit log segments corresponding to the entries of the flushed
Memtable are marked for recycling.
• A bloom filter and an index file are created.

Compaction
When the number of SStables has been increased, Cassandra
automatically merges multiple SStables, based on an algorithm,
specified in the compaction strategy.
• Optimizes read requests.

Installation, Usage, Requirements,
Platforms
5.

Client Interfaces and Language Support
• CQL (Cassandra Query Language) and Thrift
• Internal API: StorageProxy API available to JVM-based clients
(internal use, highly specialized use-cases)
• Spark
• Hadoop (Map/Reduce jobs)
• Client Libraries for: Python, Java, .Net, Ruby, PHP, Perl, C++ etc.

APIs
The Cassandra API consists of the following three simple methods:
• insert(table, key, rowMutation)
• get(table, key, columnName)
• delete(table, key, columnName)

Thrift to CQL - An ongoing transition from 2012
Where the legacy Thrift API exposes the internal storage structure
of Cassandra pretty much directly, CQL provides a thin abstraction
layer over this internal structure.

CQL
• The primary language for communicating with the Cassandra
database.
• Most basic way to interact with Cassandra is using the CQL shell,
cqlsh.
• Very similar syntax with SQL.
• Does not support creation of supercolumns.

Cassandra as a Cloud Database
Meets all the requirements of a Cloud
Database:
• Transparent elasticity
• Transparent scalability
• High availability
• Security
• Easy data distribution
• Data redundancy
• Support all data formats
• Low cost
• Simple manageability

Integration with other tools
BI Tools:
• MS Excel
• Pentaho
• Tableau
• Jaspersoft
• Talend

Monitoring Cassandra
• Integration with Ganglia (distributed performance tool).
• Several system level metrics have been exposed to Gaglia.
• Helps in understanding the system’s behavior in production
conditions.

Installation on Windows
Requirements:
• The latest version of Java 8
• The latest version of Python 2.7
• Download the Software (DataStax Community Edition for
Apache Cassandra™ v3.0.9 installer)

Creating a Virtual Machine
Requirements:
• Install Ubuntu Server 12.04 LTS 64 bit OS (any linux system
with linux kernel -2.6.x or later)
• Update the OS
• sudo apt-get update (Internet Connection is mandatory)

Installing Virtual Machine
• Download and install Virtual Box
• Steps:

Installation on Mac
Requirements:
• Download the software(DataStax Community Edition for Apache
Cassandra™ v3.0.9 Tarball)

Steps
• Open a terminal window
• Navigate to the Cassandra folder (via cd command) and then to bin directory e.g.
robinsmac:dev robin$ cd dsc-cassandra-1.2.2/bin
• Start Cassandra on terminal e.g. robinsmac:bin robin$ ./cqlsh .
• Your terminal window should look like this

Installation on Ubuntu
Requirements:
• Download the software(DataStax Community Edition for Apache
Cassandra™ v3.0.9 Tarball)

Steps
• Start Cassandra in foreground mode
• cd home/virtualmachine_name/cassandra/apache-
cassandra-2.0.14- bin
• bin/cassandra –f
• Test Cassandra
• bin/cqlsh

Steps (1)
• Untar Cassandra
• cd Cassandra
• tar –xvf apache-cassandra-2.0.14-bin.tar
• Create necessary directories and change ownership
• Sudo mkdir /var/lib/Cassandra
• Sudo mkdir /var/log/Cassandra
• Sudo chown –R $USER:$GROUP /var/lib/Cassandra
• Sudo chown –R$USER:$GROUP /var/log/Cassandra

Ubuntu commands
Terminal commands
Result

References
1. A. Lakshman and P. Malik. Cassandra: a decentralized structured storage system. SIGOPS Oper. Syst. Rev., 44(2):
35-40, 2010
2. Cassandra.apache.org. (2016). Apache Cassandra. [online] Available at: http://cassandra.apache.org/
3. Cattell, R. (2011). Scalable SQL and NoSQL data stores. ACM SIGMOD Record, 39(4), p.12.
4. Cockcroft, A. (2011). Benchmarking Cassandra Scalability on AWS - Over a million writes per second. [online]
Techblog.netflix.com. Available at: http://techblog.netflix.com/2011/11/benchmarking-cassandra-scalability-
on.html
5. Cs.uwaterloo.ca. (2016). [online] Available at:
https://cs.uwaterloo.ca/~tozsu/courses/CS848/W15/presentations/Cassandra.pdf
6. Chang, F., Dean, J., Ghemawat, S., Hsieh, W., Wallach, D., Burrows, M., Chandra, T., Fikes, A. and Gruber,
R. (2008). Bigtable. ACM Transactions on Computer Systems, 26(2), pp.1-26.
7. DataStax. (2016). Case Studies. [online] Available at: http://www.datastax.com/resources/casestudies

References (1)
8. Docs.datastax.com. (2016). About hinted handoff writes. [online] Available at:
https://docs.datastax.com/en/cassandra/2.0/cassandra/dml/dml_about_hh_c.html
9. DataStax. (2016). Customers. [online] Available at: http://www.datastax.com/customers
10. Docs.datastax.com. (2016). Introduction to Cassandra Query Language. [online] Available at:
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