Database Structure

Introduction

Report is based on the managing the movie details in MongoDB of Movie Maniac Site. They provide the movie details to their users on the facebook page like movie name, director, list of leading actors, release year, Oscar won by the movie and the country of the movie. Now they want users to give reviews for movies. So they need to store the ratings and the comment to be store in the database so can they provide the accurate ratings for the movie.

1. Collection movies has movieId of Integer type.

1. There are many award shows in the movie industry, Oscar is one of them very important one, if a movies wins Oscar then it has to store in the collection with Oscar field. This field Oscars is of integer type.

1. There can be multiple directors for the movie to store them into the collection there is field director which is of list of string type to store the names of all the directors.

1. There will be most of the time more than one actors in the movie, to store the names of those actors in the database there is a field actors which is of list of String type.
2. 5 Users of the movie maniac site can give review for the movie to store their review we have a field reviews which includes the name of the user and the rating he provide to movie and the comment by the user to store these reviews we keep a list of type object.

1. There is possibility that a movie can have future series so to store the series names of the movies we have a field name title to store the names of the movie series which is of list of string type.

1. To store the release year of the movie there is a field releasedate which is of integer type.
2. The origin country of the movie should be stored in the database, for this we have a field country to store the country details of the movie.

Structure of the movie document would look like this….

{

    “movieId” : 4,

    “title” : [

“XYZ”

    ],

    “director” : [

        “Director 1”

    ],

    “actors” : [

        “Actor 1”,

        “Actor 2”,

        “Actor 3”,

        “Actor 4”

    ],

    “releaseDate” : [2018],

    “oscars” : 4,

    “country” : “USA”

     “reviews” [

     {

     “name”: “Kimmy”,

     “rating”: 3,

     “comment”: “Poor.”

     },

     {

     “name”: “Jimmy”,

     “rating”: 5,

     “comment”: “Average.”

     }

    ]

}

Indexes are utilized in the database to get to information or data in simple way, Indexes are much the same as the book files, in the event that you need to question some data files are utilized to begin the seeking from the suitable record and not all records. In this database discharge date of the motion picture, a field is being set to list. It will assist us with finding the record effectively. In the event that we need to show just the motion pictures discharged before 1980 at that point file what will do is it won’t look for every one of the records and check them whether it has been discharged before 1980 or not but rather it will sort the motion pictures and begin hunting down some record and not all.

There are many alternatives are available in the market to the MongoDB, some are written below.

1. CouchDB:- Database consistency that depends on bi-directional replication, it is best for web applications that took care of the tremendous measure of information. CouchDB is a database that absolutely gets a handle on the web. Store your data with JSON reports. Access your reports with your web program, by methods for HTTP. Question, solidify, and change your records with JavaScript. CouchDB works honorably with present day web and flexible applications. You can circle your data, viably using CouchDB’s incremental replication. CouchDB supports expert setups with customized conflict recognizable proof.

1. Cassandra:- Apache is an open source, versatile, non-social database. Cassandra can versatile while as yet being solid, it is anything but difficult to convey over numerous servers. Cassandra uses a partitioner, or dividing, to choose how data is appropriated over the center points that make up a database gathering. A partitioner is a hashing segment that takes a table section’s basic key, figures a numerical token for it, and distributes it to one of the centers in a cluster. While Cassandra has various partitioners from which to pick, the default partitioner randomizes data over a gathering and ensures an even movement of most of the data. Besides, Cassandra thusly keeps up the evening out of data over a group despite while existing center points are removed or new centers are added to a structure.

1. MySQL:- The main database decision for electronic applications, streamlined for web applications, can scale to thousand questions for each second nut a few issues with solidness and bunching. MySQL works using client/server plan in which the server continues running on the machine containing the databases and clients connect with the server over a framework. The server working structures is commonly a Linux (like Redhat 9.0 et cetera.) or Windows 2000 working system. Ordinarily mySQL is maintained on Windows XP, Windows Server 2003, Red Hat Fedora Linux, and Debian Linux, and others. In like manner with some other client/server application, MySQL is a multi-customer database structure, which implies a couple of customers can get to the database in the meantime.

Relationship of one to one and one to many is handled by the embedded documents in this database design schema–

Reviews will have ratings and the comments for the movie and it is handled by the embedded documents like this-

review : [

{

name : “P 1”,

rating : 7,

comment : ”Good”

},

{

name : “P 2”,

rating : 2,

comment : ” Poor”

}

]

1. Movie poster to be stored in the datbase.
2. Store backstage videos shooting time of the movie.
3. Store facts and crispy information about the movies.
4. Add the genre to the movies.
5. They can add reviews of the leading actor actresses about the movies.
6. They can add the soundtracks of the movies
7. List of top 10 movies from past few months or week.
8. Total collection of the movies weekly.
9. Brief summary about the movie.

References

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Lawrence, R., 2014, March. Integration and virtualization of relational SQL and NoSQL systems including MySQL and MongoDB. In Computational Science and Computational Intelligence (CSCI), 2014 International Conference on (Vol. 1, pp. 285-290). IEEE.

Membrey, P., Plugge, E. and Hawkins, D., 2011. The definitive guide to MongoDB: the noSQL database for cloud and desktop computing. Apress.

Parker, Z., Poe, S. and Vrbsky, S.V., 2013, April. Comparing nosql mongodb to an sql db. In Proceedings of the 51st ACM Southeast Conference (p. 5). ACM.

Sattar, A., Lorenzen, T. and Nallamaddi, K., 2013. Incorporating NoSQL into a database course. acm Inroads, 4(2), pp.50-53.

Stanescu, L., Brezovan, M. and Burdescu, D.D., 2016, September. Automatic mapping of MySQL databases to NoSQL MongoDB. In Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on (pp. 837-840). IEEE.

Wei-Ping, Z., Ming-Xin, L.I. and Huan, C., 2011, May. Using MongoDB to implement textbook management system instead of MySQL. In Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on (pp. 303-305). IEEE.