A Beginners Introduction to NoSQL

A beginners introduction to NoSQL

Jayesh Naithani
SEIS 605 Technical Communications
Graduate Programs in Software
University of St. Thomas, Saint Paul, Minnesota, USA

Abstract

This paper briefly describes what NoSQL systems are and the motivating factors for their recent
interest. Core concepts associated with the NoSQL data model are introduced, compared and
differentiated with the relational model. The four major types of NoSQL systems are then
discussed, along with brief descriptions of some well known data store implementations.
Included are short descriptions of how NoSQL systems are currently helping solve some of the
business problems of big data and search. Finally, the paper concludes by mentioning some
topics and issues that lie ahead for the NoSQL movement.

1. What is NoSQL?

NoSQL stands for NotonlySQL. It can be broadly defined as the following:

“NoSQL is a set of concepts that allows the rapid and efficient processing of data sets with a
focus on performance, reliability, and agility.” [1]1

NoSQL systems were born out of the need to handle larger data volumes and are characterized
by the following essential properties:

● They are schemaless and allow extraction and query of data using simple interfaces
without joins and which don’t require an entityrelational model.

_________________
1
The numbers in brackets correspond to those of the bibliography in the References section.

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● They allow storage of the database on clusters of processors and maintain highspeed
performance.
● They have the ability of trade off traditional relational system consistency for other useful
properties such as being more performant, offering greater scalability, and being easier
to program with.

The next section describes the main motivating factors for the use of NoSQL systems today.

2. Why NoSQL?

Two of the primary reasons why people consider using a NoSQL system are:

● Application development productivity
NoSQL systems provide a data model that better fits application needs, simplifying
application development effort which results in less application code to write, debug, and
maintain.  It greatly reduces the application development effort which is involved when
using relational database models with the mapping of inmemory data structures into the
database [2, 7].
● Large-scale data
NoSQL systems are designed to run on clusters of processors and machines, and are a
better fit for big data scenarios. Organizations are able to capture more data and process
it more quickly and economically than what is possible when using a relational database
[2, 7].

Before discussing NoSQL systems in more detail, a few common terms used to describe
NoSQL systems are provided in the next section.

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3. Terminology

Some of the terminology used in the paper is listed below.  They will be useful to understand and
refer back to when found in use within this paper.

● NoSQL systems: are those systems that use a variety of data store types (databases)
● Hashing: is the process of mapping data of arbitrary length to a fixed length and is used
to uniquely identity documents
● Caching: is the process of storing recently used information so that it can be quickly
accessed at a later time
● Sharding: is a process of dividing a data set and distributing it across multiple servers
● Horizontal scaling: or scale out, is the process of joining multiple computers together to
provide more processing power
● Cluster: is composed of a set of processors called nodes grouped together in racks
● Key/Value: is the term used when identifying data (value) by an arbitrary name (key)
● Indexing: is a method for sorting data by multiple fields
● Replication: is a term to describe the sharing of information between systems so as to
ensure it’s consistency and high availability
● JSON: or Javascript Object Notation, is a universal internet data exchange format
● XML:  or eXtensible Markup Language, is a universal internet data exchange format
● RDBMS: or Relational Database Management System, is a database management
system that stores data in the form of related tables
● CAP Theorem: or Consistency, Availability, and Partition tolerance theorem, is a
computing rule that states a system with the three properties of Consistency, Availability,
and Partition tolerance can only provide two of three services at any given time
● ACID: or Atomicity, Consistency, Isolation, Durability, are properties of transactional
control systems such as an RDBMS
● BASE: or Basic availability, Softstate, Eventual consistency, is the alternative to ACID,
and is used to describe systems, such as NoSQL, that focus on data availability more
than data consistency
● MapReduce: is a programming model for processing large amounts of unstructured
data in parallel on large clusters of commodity hardware

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● Hadoop: is an open source software project that uses the MapReduce framework to
enable the distributed processing of large data sets

Lets discuss some essential NoSQL concepts next.

4. No SQL Concepts

Important key concepts and architectural guidelines of NoSQL are:

● Simple Building Blocks
● Layered Architecture
● Hashing and Data Distribution
● Distributed Caching
● Sharding

Each of these concepts is described in more detail below.

4.1 Simple Building Blocks

NoSQL systems are created using modular and simple components that can be reassembled
to meet the needs of different applications [1]. For instance, a system could consist of several
simple functions: one that allows sharing of objects in memory, another that executes batch
jobs, and a third that is responsible for storing documents. The focus for each of the functions is
to provide efficient services that are frequently used to power a distributed service.

4.2 Layered Architecture

NoSQL systems make use of application tiers to simplify design, similar to many other systems
such as the Relational Database Management Systems (RDBMS).   However, NoSQL
applications are distributed differently than RDBMSs.  Both types of systems consist of the User

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Interface, Middle Tier, and the Database layer.  While the User Interface is on the top of both
systems, in NoSQL systems most of the application functionality is in the middle tier while in
RDBMs the application functionality is mostly in the database layer [1].  The database tier in
NoSQL systems is thus very simple, since all the functionality to move data into the database is
part of the middle tier. A featurebyfeature comparison between SQL and NoSQL systems is
provided in the following table.

SQL vs. NoSQL [14]
Feature SQL Systems NoSQL Systems
Types One type with minor
variations
Many different types
History Developed in 1970s Developed in 2000s
Examples MySQL, Postgres, DB2,
Oracle
CouchDB, Cassandra, Neo4j
Data Storage Model Individuals records stored in
tables
Varies according to database
types
Schemas Data type, structure fixed None (dynamic)
Scaling   Single Server, vertical Horizontal, distributed
Transaction support Always Sometimes
Query language SQL APIs
Consistency Strong Variable

4.3 Distributed Caching

NoSQL systems keep frequently used data in memory using a technique called consistent
hashing [1].  Consistent caching provides the ability to determine if a new query or document is
the same as the one already in cache. This prevents NoSQL systems from making unnecessary
calls to the database and keeps them running fast.

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4.4. Distributed data

NoSQL systems also use consistent hashing for synchronizing distributed databases [1].
Systems can assign documents to specific database nodes on distributed systems and quickly
compare remote databases to determine if they need to be synchronized.

4.5 Sharding

NoSQL systems make use of automatic database sharding to break the data into chunks called
shards and spread the chunks around across a number of distributed systems [1]. Sharding
also allows NoSQL systems to horizontally scale gracefully as the database grows.

We are now ready to discuss the four common categories of NoSQL systems.

5. The Four Categories of NoSQL system stores

The four most important categories of NoSQL system stores are:

● Keyvalue store
● Graph store
● Column store
● Document store

Each of these type of stores along with a well known product implementation for each, is briefly
described next.

5.1 Key-value Store

A keyvalue store is a simple database that which presented with a simple string, or the key,
returns an arbitrary binary large object (BLOB) of data, or the value. They are very similar to how

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a dictionary functions, where the dictionary has a list of words (keys) and each word has one or
more definitions (values).  These type of stores have no query language [1].

Implementation: Amazon DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service offered by Amazon as part of
it’s Amazon Web Services (AWS) portfolio.  It is designed to maintain predictably high
performance and to be highly cost efficient for workloads of any scale [3]. Data items are spread
over a sufficient number of servers, stored on solid state disks (SSD), and automatically
replicated across multiple servers to provide high availability and data durability.

5.2 Graph Stores

A graph data store is a system that contains a sequence of nodes and relationships that, when
combined, create a graph.  Graph data stores have three fields:  nodes, relationships, and
properties.  Graph stores allow simple queries to be performed that show nearest neighbouring
nodes as well as queries that look deep into networks and quickly discover patterns.  For
instance, a graph store can provide a list of your friends by last name, and who among them are
mostly likely to buy you a cool beverage [1].

Implementation: Neo4j

Neo4j is a highly scalable, robust (fully ACID) native graph database [4].  The use cases that
make Neo4j ideal for use are those where graph analytics is used to model relationships
between people/customer, object/product, or nodes/device in a network.  The other big
advantage that Neo4j offers is that it’s open source, with an active development team, and a
strong user community.  Among some well known large enterprises that are using Neo4j today
are eBay and Walmart.

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5.3 Column-family Stores

A columnfamily data store uses row and column identifiers as general purpose keys for data
lookup.  They are sometimes referred to as datastores instead of databases, since they lack
features that exist in traditional databases such as typed columns, secondary indexes, triggers,
and query languages.  An important feature of columnfamily stores is that they can scale very
well to manage large volumes of data [1].

Implementation: Cassandra

Apache Cassandra is a NoSQL column family store with a strong reputation for scalability and
high availability under intense write loads [5].  All nodes in a Cassandra cluster have identical
functionality, and clients can write to any node of the cluster at any time.  Since Cassandra does
not have a single master node, there is no single point of failure.  Because of this feature and it’s
strong peertopeer development model, Cassandra is a good fit for organizations that want both
scalability and availability in a columnfamily system.  Some well known enterprises that are
using Cassandra are Comcast, Hulu, and NetFlix.

5.4 Document Stores

A document store is keyvalue store with one major exception, instead of just storing a BLOB in
it, a document database requires that the data (document) be stored in a format that the
database can recognize.  The format can be XML, JSON, Binary JSON, or any other format the
database can understand.  A document store automatically indexes everything inside the
document when it is added into the store.  This means that everything inside the document is
searchable [1].

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Implementation: CouchDB

CouchDB is another NoSQL database management system developed by the Apache software
foundation, with powerful synchronization abilities.  It stores data in the form of a collection of
JSON style documents.  It uses a JavaScript like language to perform queries on the documents
[6].  CouchDB is used by applications that have a need to synchronize mobile phone data fast.
Although CouchDB is still an active Apache project, many of the original developers are now
working on another document store through a company called CouchBase.

After this short summary of the major types of datastores with corresponding example
implementations, the next section of the paper highlights some of the ways that NoSQL solutions
provide a positive return on investment for organizations.

6. NoSQL Solutions

This section discusses how NoSQL solution solve realworld business problems of big data,
search, high availability, and agility.

6.1 Managing Big Data

A big data class problem is any business problem that is so large that it cannot be easily
managed using a single processor [1]. NoSQL systems with their inherent support for horizontal
scaleout architectures are ideal for solving big data problems.  Here are the four ways that
NoSQL systems help handle big data problems:

● They make use of commodity processors that each hold a subset of the data on their
local drives. It becomes more efficient to query each node than to transfer large data sets
to a central processor.  By moving queries to the data and not data to the queries,
NoSQL systems keep big data queries fast.
● They make use of hash rings to evenly distribute data on a cluster.  This helps with the
balanced distribution of network load and helps improve the performance of queries.

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● They make use of replication to create backup copies of data in real time and deliver fast
read/write consistency.
● They let the database distribute queries evenly to data nodes, and then efficiently
combine the results together.

6.2 Search

Search involves finding an item of interest in a database when only partial information is available
about the item. NoSQL systems combine document store concepts with full text indexing to
deliver high quality search solutions [1].  Document stores keep data in single hierarchical tree
and don’t shred elements into rows within tables.  The retained structure then can be used to
exactly locate a matched keyword within the document.  NoSQL solutions when used together
with highly scalable processes such as MapReduce can be used to create reverse indexes for
enabling fast search.

6.3 High Availability

The features that allow NoSQL systems to scale out and handle big data problems can also be
used to increase the availability of database servers.  NoSQL architectures make effective use
of a couple of strategies to create high availability systems backed by clusters of multiple
systems [1]:

● By using a load balancer to direct traffic to the least busy node
● By using highavailability distributed file systems.  One such high availability system is
the Hadoop Distributed File System (HDFS).

Simplicity of design of NoSQL systems is another feature that promotes high availability.
Organizations benefit by the cost effectiveness provided by operating highavailability NoSQL
systems that run on multiple processors.

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6.4 Increasing Agility

Software agility is the ability of businesses to quickly adapt software systems to changing
business requirements.  Many organizations find a significant reduction in effort when using
NoSQL systems in use cases that involve data import and export [1]. When objects are used as
a middle layer between a web page and an RDBMS they undergo a four step translation process
versus when using a NoSQL solution which requires no translation in storing the object directly
to the database.  This lack of translation makes NoSQL systems simpler to use and allows both
subject matter experts and nonprogramming staff to participate directly in the application
development process.  Direct involvement by the technical members in building applications
allows corrections to be made early in the software development process, leading to savings in
time and money that would otherwise have been put towards rework.

The following table summarizes the most important types of datastores introduced by the the
NoSQL movement and their typical uses.

Significant NoSQL Data Stores and Typical Use [1]
Datastore Type Description   Typical Uses
Keyvalue store A simple way to associate
between a large data file and
text string
Dictionary, image store,
document/file store, lookup
table
Graph store A way to store nodes and
arcs of a graph
Social network queries,
friendoffriend queries,
pattern matching
Column Family store A way to store irregular type
data using row and a column
as the key
Webcrawling,
highlyadaptable systems
Document store A way to store hierarchical,
treestructured information in
a single unit
Office documents, invoices,
web pages, any data that has
a natural container structure

Next we take look at a few NoSQL adoption challenges and developments that lie ahead for the
NoSQL movement.

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7. Future and Beyond

NoSQL systems are relatively new.  There are a number of challenges that NoSQL systems
face: Some of these challenges and what is in the horizon for the further evolution of NoSQL
systems, along with some other viable persistence alternatives, are briefly described next.

7.1 Lack of trained Administrators and Developers

In today’s environment, most senior administrators and developers have extensive experience
writing code and managing relational databases.  NoSQL systems are still unchartered territory
for many of them.   There is a significant shortage of skilled professionals who can evaluate the
needs of enterprises to determine the type of NoSQL systems best suited for their business
purpose, and then also administer and develop the algorithms and programs to operate with their
choice of NoSQL system [8, 9].

7.2 Adoption Readiness

Many enterprises are reluctant to invest in commercial NoSQL technology due to the lack of
trained professionals to manage, optimize, and develop applications for them.  Also, there still
are a few enterprises that feel the technology is not quite ready for primetime. Finally, there is a
need for the increased development of more big data applications that are ideally suited for the
use of NoSQL systems [10].

7.3 Support for Real-Time Analytics

Realtime analytics is the use of all available enterprise data and resources whenever they are
needed.  It involves dynamic analysis and reporting, based on data entered into a system a
fraction of seconds before the actual moment of use. Relational databases are ideally suited for
complex query and analysis, however real time analysis of operational data is better suited for

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NoSQL systems.  Today, business intelligence and analytics support with NoSQL databases is
still very new, but has started to grow rapidly [2].

7.4 Global Transaction Support

The Consistency, Availability, and Partitiontolerance (CAP) theorem states that a distributed
system cannot simultaneously provide all three services, and at best can provide two of the
three services.  This is widely understood within the NoSQL community as justifying the need for
NoSQL systems to provide high availability over consistency.  Consistency of data is one of the
guarantees of transactional systems, and is must for certain types of enterprise applications,
such as financial applications.  The growing understanding that consistency of data is crucial for
many applications has many NoSQL system designers now favoring a return to support for
transactions with NoSQL [12]. And to provide the transaction support functionality without
sacrificing the NoSQL functional advantages in the area of scalability, faulttolerance,
concurrency, and performance.

7.5 Other Storage Technologies

NoSQL systems have done a great deal to open up the world of databases.  But they are still
only a part of the picture of choosing the right persistence option for the task at hand.  There are
several other persistence options besides relational and NoSQL systems, and here are just a
couple of them briefly described:

● File Systems.
File systems are ubiquitous, and are widely used for storing personal productivity
documents.  They are similar to keyvalue stories with a hierarchic key and provide little
control over concurrency.   They offer no support for queries on their own, and work best
for a relatively small number of large files that can be processed in big chunks [2].

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● XML Databases
XML databases are similar to document databases where the document is stored in a
data model compatible to XML, and then various XML technologies are used to
manipulate the document.  XML databases are capable of storing complex data, have an
expressive data format, provide superior search services, and tend to support web
standards better than other document stores.  They make a good choice to solve
business problems in specific areas that require the publishing and searching of data.
They have the additional advantage of ease of use which allows nondevelopment staff to
build and maintain enterprise applications, which results in the increase in application
development productivity [2].

8.0 Conclusion

NoSQL systems are not a replacement to relational systems (SQL) everywhere.  They have
specific advantages and use in situations where speed, flexibility, and scalability are required
when working with large amounts of nonuniform data.   And they offer programmer productivity
by greatly reducing the transformations and relationships required to persist data, and also by
providing relief from systems that support a strong schema in order to support ad hoc fields for
storing non uniform information [13].

Enterprises choosing a NoSQL system over SQL need to understand clearly the real
advantages that NoSQL systems offer for their business.  Furthermore, it is still the early days in
the production use of NoSQL systems, and there is always the possibility of running into the
rough edges typical of any new technology [2].  So while there are many cases where it
advantageous to use NoSQL systems today, it still may not  be a suitable option for most or all
situations.

References

[1]  D. McCreary and A. Kelly. Making Sense of NoSQL: A Guide for Managers and the Rest of
Us 2013.

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[2]  P. J. Sadalage and M. Fowler. NoSQL Distilled: A Brief Guide to the Emerging World of
Polyglot Persistence 2013.

[3]  G. DeCandia, D. Hastorun, M. Jampani, G. Kakulapati, A. Lakshman, A. Pilchin, S.
Sivasubramanian, P. Vosshall, and W. Vogels. “Dynamo: amazon's highly available keyvalue
store,”  In Proceedings of twentyfirst ACM SIGOPS symposium on Operating systems
principles (SOSP '07), 2007, pp. 205220. DOI=10.1145/1294261.1294281
http://doi.acm.org.ezproxy.stthomas.edu/10.1145/1294261.1294281

[4]  Neo4j. Get Neo4J, your graph database. [Online]. Available:  http://www.neo4j.org.

[5]  A. Lakshman and P. Malik. “Cassandra: a decentralized structured storage system.”
SIGOPS Oper. Syst. Rev. 44, 2, pp. 540, April 2010. DOI=10.1145/1773912.1773922
http://doi.acm.org.ezproxy.stthomas.edu/10.1145/1773912.1773922

[6]  CouchDB. A Database for the Web. [Online]. Available: http://couchdb.apache.org.

[7]  S. Uzayr, “A Beginner’s Guide to NoSQL.” Software Developer’s Journal, June 2013. [Online].
Available: http://www.codeproject.com/Articles/630103/ABeginnersGuidetoNoSQL

[8]  J. Kelly. “HadoopNoSQL Software and Services Market Forecast 20122017.” September
2013. [Online]. Available:
http://wikibon.org/wiki/v/HadoopNoSQL_Software_and_Services_Market_Forecast_20122017.

[9]  B. Woo. “Combating the Big Data skills shortage.” Forbes, January 2013. [Online]. Available:
http://www.forbes.com/sites/bwoo/2013/01/18/combatingthebigdataskillsshortage.

[10] T. J. Hampton. “NoSQL Market Snapshot: Past, Present, and Future,” siliconAngle, October
2013. [Online]. Available:
http://siliconangle.com/blog/2013/10/15/nosqlmarketsnapshotpastpresentandfuture.

[11]  FoundationDB. “The Future of NoSQL.” [Online]. Available:
https://foundationdb.com/whitepapers/futureofnosql.

[12]  S. Pimental. “The Return of ACID in the Design of NoSQL Databases.” Methods & Tools,
May 2013. [Online]. Available: http://www.methodsandtools.com/archive/acidnosqldatabase.php.

[13]  J. Dash. “RDBMS vs. NoSQL: How do you pick?” ZDNet, September 2014. [Online].
Available: http://www.zdnet.com/rdbmsvsnosqlhowdoyoupick7000020803.

[14]  MongoDB, NoSQL Databases Explained. [Online]. Available:
http://www.mongodb.com/nosqlexplained.

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