Large-Scale and Multi-Structured

Databases
Document Databases
Introduction
Prof. Pietro Ducange
 Main Features
Document databases are non-relational databases that store
data as structured documents, usually in XML or JSON formats.

They ensure a high flexibility level and allow also to handle

complex data structures (despite key-value databases).

Document databases are schema-less.

They allow complex operations, such as queries and filtering.

Some document databases allows also ACID transactions.

 XML Documents
• XML stands for eXtensible Markup Language.

• A markup language specifies the structure and content of a

document.

• Tags are added to the document to provide the extra

information.

• XML tags give a reader some idea what some of the data means.

• XML is capable of representing almost any form of information.

 XML Documents: Use cases
1. XML and Cascading Style Sheets (CSS) allowed second-
generation websites to separate data and format.

2. XML is also the basis for many data interchange protocols

and, in particular, was a foundation for web service
specifications such as SOAP (Simple Object Access
Protocol).

3. XML is a standard format for many document types,

including word processing documents and spreadsheets
(docx, xlsx and pptx formats are based on XML).
 Advantages of XML
• XML is text (Unicode) based.
– Can be transmitted efficiently.

• One XML document can be displayed differently in

different media and software platforms.

• XML documents can be modularized. Parts can be

reused.
An Example of XML Document
 XML Ecosystem
XPath: useful to navigate through elements and attributes in an XML
document.

XQuery: is the language for querying XML data and is built on XPath
expressions.

XML schema: A special type of XML document that describes the elements
that may be present in a specified class of XML documents.

XSLT (Extensible Stylesheet Language Transformations): A language for

transforming XML documents into alternative formats, including non-XML
formats such as HTML.

DOM (Document Object Model): a platform- and language-neutral interface

for dynamically managing the content, structure and style of documents such
as XML and XHTML. A document is handled as tree.
 Example of XML Scheme Usage

Image extracted from: https://doc.qt.io/qt-5/qtxmlpatterns-schema-example.html

DOM Example
 XML Databases
XML databases: platforms that
implement the various XML standards
such as XQuery and XSLT,

They provide services for the storage,

indexing, security, and concurrent
access of XML files.

XML databases did not represent an

alternative for RDBMSs.

On the other hand, some RDBMSs

introduced XML , allowing the storage
Image extracted from “Guy Harrison, Next of XML documents within A BLOB
Generation Databases, Apress, 2015” (binary large object) columns.
 XML : Main Drawbacks
• XML tags are verbose and repetitious, thus the amount of storage
required increases.

• XML documents are wasteful of space and are also computationally

expensive to parse.

• In general, XML databases are used as content-management

systems: collections of text files (such as academic papers and
business documents) are organized and maintained in XML format.

• On the other hand, JSON-based document databases are more

suitable to support web-based operational workloads, such as
storing and modifying dynamic contents.
 JSON Documents
• JSON acronym of JavaScript Object Notation.

• Used to format data.

• Thanks to its integration with JavaScript, a JSON

document has been often preferred to an XML
document for data interchanging on the
Internet.
 JSON example
• “JSON” stands for “JavaScript Object Notation”
– Despite the name, JSON is a (mostly) language-independent way of
specifying objects as name-value pairs

Image extracted from

http://secretgeek.net/json_3mins
 JSON syntax
• An object is an unordered set of name/value
pairs
– The pairs are enclosed within braces, { }
– There is a colon between the name and the value
– Pairs are separated by commas
– Example: { "name": "html", "years": 5 }
• An array is an ordered collection of values
– The values are enclosed within brackets, [ ]
– Values are separated by commas
– Example: [ "html", ”xml", "css" ]
 JSON syntax
• A value can be: A string, a number, true, false,
null, an object, or an array
– Values can be nested
• Strings are enclosed in double quotes, and can
contain the usual assortment of escaped
characters
• Numbers have the usual C/C++/Java syntax,
including exponential (E) notation
– All numbers are decimal--no octal or hexadecimal
Example of Nested Objects

Image extracted from:

https://www.digitalocean.com/co
mmunity/tutorials/an-
introduction-to-json
 Example of Nested Arrays

Image extracted from: https://www.digitalocean.com/community/tutorials/an-introduction-

to-json
 Comparison of JSON and XML
• Similarities:
– Both are human readable
– Both have very simple syntax
– Both are hierarchical
– Both are language independent
– Both supported in APIs of many programming languages
• Differences:
– Syntax is different
– JSON is less verbose
– JSON includes arrays
– Names in JSON must not be JavaScript reserved words
 JSON vs XML

Image extracted from: https://www.digitalocean.com/community/tutorials/an-introduction-

to-json
 Main Feature of JSON Databases
• Data is stored in JSON format.

• A document is the basic unit of storage. It includes one or more more

key-value pairs and may also contain nested documents and arrays.

• Arrays may also contain documents allowing for a complex

hierarchical structure.

• A collection or data bucket is a set of documents sharing some

common purpose.

• Schema less: predefined document elements must not be defined.

• Polymorphic Scheme: the documents in a collection may be different.

Schema-less vs Schema Definition
A schema is a specification that
describes the structure of an object,
such as a table.

Data modelers have to define tables in

a relational database before developers
can execute code to add, remove, or
update rows in the table.

Document databases do not require

this formal definition step.

Developers can create collections and

documents in collections by simply
inserting them into the database
 Schema-less pros and cons
Pros: High flexibility in handling the structure of the objects to store

Cons: the DBMS may be not allowed to enforce rules based on the structure of
the data.
 Some considerations
A document database could theoretically implement a third
normal form schema.

Tables, as in relational databases, may be “simulated”

considering collections with JSON documents with an identical
pre-defined structure.

Image extracted from “Guy Harrison, Next Generation Databases, Apress, 2015”
 JSON Databases: An example
Document databases usually
adopts a reduced number of
collections for modeling data.

Nested documents are used for

representing relationships among
the different entities.

Document databases do not

generally provide join operations.

Programmers like to have the

JSON structure map closely to the
object structure of their code!!!
Image extracted from “Guy Harrison, Next Generation Databases, Apress, 2015”
 Suggested Readings
Chapter 4 of the book “Guy Harrison, Next
Generation Databases, Apress, 2015”.

Chapters 6 of the book “Dan Sullivan, NoSQL For

Mere Mortals, Addison-Wesley, 2015”