ICT704 Non Relational Database Systems

ICT704 Non Relational Database Systems

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ICT704 Non Relational Database Systems

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ICT704 Non Relational Database Systems

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Course Code: ICT704
University: University Of The Sunshine Coast

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Country: Australia

For the report you are required to explain the structure of the database you created. This includes justifying the indexes you created. You need to describe how the relationships were handled in the database. In your report, discuss potential alternatives to how the relationships could have been modeled and implemented in MongoDB and the benefits/issues of each. Provide recommendations to Movie Maniacs for any additional functionality for the database

Structure of the database:
Our database comprises a single collection movie. Movies contain a number of Movie documents which have various attributes like MovieId, MovieName etc. Each Movie document may or may not have ratings associated with it. We are using the embedded relationship approach in our database. So all the rating documents associated with a Movie are embedded inside it. There is a one to many relationships between movies and ratings. Each movie in the collection can have zero or more ratings associated with it. That means that there can be some movies which do not have any rating associated with it. Each rating is associated with only one movie at a time. Also, ratings and movies have a strong relationship between them. No rating can exist without a movie whereas movies can exist without ratings also.
Example Movie Document:-
“MovieID”: “11”,
“MovieName”: “King Kong”,
“Director”: “Ernest B Schoedsack, Merian C Cooper”,
“Leading actors”: “Bruce Cabot, Ernest B Schoedsack, Fay Wray, Frank Reicher, James Flavin, John Armstrong, Noble Jhonson, Robert Armstrong”,
“ReleaseDate”: “1933”,
“Country”: “USA”,
        “ReviewedBy”: “Mitch”,
        “Date”: “6/9/18”,
        “Rating”: 5,
        “Comments”: “Was ok, could have been better”
        “ReviewedBy”: “Matt”,
        “Date”: “6/7/18”,
        “Rating”: 9,
        “Comments”: “Brilliant”
This is an example movie document having two ratings associated with it. These rating documents are stored like an array having rating document.
Indexes Created: 
We have created the following index:
This index is a combination of two columns MovieId and MovieName. “MovieId”:1 means that the index is created as ascending order for r the column MovieId and as the Descending order for MovieName. Id and name of an element is the most common and important attribute associated with it. So I have chosen these two columns for creating the index. When our database will become bulky and the number of documents in it will rise to a large extent, then this index will help in fetching the results from the database faster. The index helps in improving the performance of the database by making the search results faster.
Handling of relationships in database:-
We have used the embedded relationship approach in our database design. In the embedded approach various child documents are directly embedded inside the parent documents. There is an attribute named ratings that only exist for the documents having any rating. If no rating exists for any movie, then the rating attribute is not present for that Movie. We have a one to many relationships between movie and Rating. Each movie can have zero or many ratings associated with it but one rating can have only one movie associated with it. Also, a rating cannot even exist without a movie. If a movie is deleted, then all the ratings associated with it are automatically deleted.
Alternatives to how the relationships could have been modelled and implemented in MongoDB:-
There are only two ways in which the relationships can be handled in Mongo Db.

Embedded Approach: –

This is the most common and easiest approach. In this approach, one type of documents is embedded into other types of documents. We have used this approach in our database design. In such an approach, the child entities which are embedded cannot exist without the parent entities. If we delete the parent entity, then all the child entities associated with it are automatically deleted. Only the parent document in the embedded approach has the unique identifier (_id) provided by Mongo DB. All child documents do not have this unique identifier.
Benefits of using Embedded Approach:-

All the data can be retrieved by just one query. When we retrieve the parent document, all the child documents embedded inside this parent document are also automatically fetched inside it. So queries in embedded approach are faster and simpler.
If we remove the parent data which has various embedded documents in it. Then all those embedded documents are also automatically deleted.

Issues of using Embedded Approach:- 

Size is a constraint in the embedded approach, as all the embedded documents are also a part of the main parent document. So the size of the parent document goes on increasing as the number of child documents is increasing. MongoDB limits the size of a document to 16MB. 

Duplication of documents is there as the documents are embedded. So, if one document needs to be used by more than one parent, then its copying is required. 

Document References Approach: –

In this approach, rather than embedding documents inside one another, we can give each document an id and then the document ids are stored as references rather than storing the actual documents.
In this approach, the child documents also exist as a separate entity. So, deletion of the main entity which has references to the child entity do not means deleting the main entity. In such a case only the parent entity is deleted, but all the referenced entities are not deleted with it. All the referenced entities are also separate documents and they have their own unique identifier (_id).  
Benefits of using Document References Approach:-

Consistency is there among various referenced documents on updates as all the parent documents have references to the same chills document.
Size constraint is not there as the parent document is just storing the references and not the whole documents.
No duplication of data is there as the same document can be referenced as the child in more than one document 

Issues of using Document References Approach:-

Queries are slower in this approach as one query just retrieves the references and then the second query retrieves the referenced documents associated with these references. 

Recommendations to Movie Maniacs for any additional functionality for the database. :-

We can try to store various links to various banners/posters that are associated with a particular movie.
We can store a short story of the movie as the description in the movies database. So this will give an idea to the users of what this movie is all about.
We can store the Genre of the movie and its type like is it A romantic movie, action Movie etc. 

References: –
Alam, M. (2014) Oracle NoSQL database. New York: McGraw-Hill Education/Oracle Press.
Copeland, R. (2013) MongoDB applied design patterns. Sebastopol, CA: O’Reilly Media, Inc.
Dayley, B. (2015) Sams Teach Yourself NoSQL with MongoDB in 24 hours. Indianapolis, Ind.: Sams.
Eliot, G. (2016) The mill on the Floss. New York: Open Road Integrated Media.
Francia, S. (2012) MongoDB and PHP. Beijing: O’Reilly.
McCreary, D. and Kelly, A. (2014) Making sense of NoSQL. Shelter Island: Manning.
Mehrabani, A. (2014) MongoDB high availability. Birmingham: Packt Publishing.
Perkins, L., Redmond, E. and Wilson, J. (2018) Seven databases in seven weeks. Raleigh, N. C.: The Pragmatic Bookshelf.
Vaish, G. (2013) Getting started with NoSQL. Birmingham: Packt Publishing.

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