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Outline

Title
Abstract
Figures
Introduction
-OVERVIEW of Big Data Concepts
-CHARACTERISTICS of SQL Databases
-CHARACTERISTICS of Nosql Databases
-ADVANTAGES and Disadvantage of Nosql Databases
-CATEGORIES of Nosql Databases
-COMPARISON of Nosql Databases Categories
-DISCUSSION
-CONCLUSION
References
All Topics
Computer Science

A COMPARATIVE STUDY AMONG THE MAIN CATEGORIES OF NoSQL DATABASES

Profile image of AbdAllah A . AlHabshyAbdAllah A . AlHabshyProfile image of Gaber AbutalebGaber Abutaleb

2020, Al-Azhar Bulletin of Science

https://doi.org/10.21608/ABSB.2020.210374
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10 pages

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1 file

Abstract

Relational databases are usually used for data storage and retrieval. They are suitable for limited data volume. But when it comes to Bigdata, we need to use more flexible databases that satisfy the need to handle semi-structured and unstructured data. These databases are called NoSQL (Not only SQL) databases. This type of database was developed to interact with data of large volumes. NoSQL databases provide many features such as scalability, availability, replication models, file sharing, and schema-free. This paper's main purpose is to present a comparative study of the five main categories of NoSQL databases; key-value stores, document stores, column family stores, graph stores databases, and object store NoSQL systems. Also, it discusses the famous database management systems for each one of these five categories. The comparison criteria used are performance, scalability, flexibility, complexity, and functionality. Moreover, this paper presents an overview of big data concepts. It briefly discusses the SQL databases versus NoSQL databases in terms of their high-level characteristics. Furthermore, this paper emphasizes the advantages and disadvantages of NoSQL databases. It illustrates the query languages in both SQL and NoSQL databases and represents the most common uses for each category to help users choose the most convenient DBMS for their organization.

Figures (4)
Key-value NoSQL stores are schema-less. That is, the data values can be inserted in any format at any time and, without the need for predefined metadata. Being schema-less and the no need for referential integrity make the key- value stores efficient for queries and partitioning and flexible regarding the data types of the data to be stored. Key-value stores are suitable for efficient reading and writing of massive amounts of data. Buffering the key- value pairs in the main memory of the database can enhance the performance even further. This setup is called in-memory database and is enabled by employing technologies that allow caching the value in the main memory [14]. A An effective simple method of data storage to resolve concurrent queries is. The Database ensures that each query receives the document version with the most updated number of changes. This is called eventual consistency as it doesn’t assure full transactional security. The consistency takes some time to be achieved, which remarkably accelerates the data processing in a tradeoff for the transactional security. In the document-based _ stores, parallelizing and speeding up the queries can be achieved by the map-reduce technique. This kind of processing occurs in two phases; the Map phase which is corresponding to grouping and the Reduce phase which is corresponding to aggregation in SQL [14]. They also fit for storing semi-structured data that would require using extensive Null values for data completion purpose [13]. Examples of Document-Based NoSQL DBMSs include - but are not limited to — CouchDB (JSON), Mongo DB (BSON)[12, 25], Perservere, Thru DB, Orient DB, Terrastore, RavenDB, and Jackrabbit[26]. Data objects can be retrieved with a simple query using the key:
Key-value NoSQL stores are schema-less. That is, the data values can be inserted in any format at any time and, without the need for predefined metadata. Being schema-less and the no need for referential integrity make the key- value stores efficient for queries and partitioning and flexible regarding the data types of the data to be stored. Key-value stores are suitable for efficient reading and writing of massive amounts of data. Buffering the key- value pairs in the main memory of the database can enhance the performance even further. This setup is called in-memory database and is enabled by employing technologies that allow caching the value in the main memory [14]. A An effective simple method of data storage to resolve concurrent queries is. The Database ensures that each query receives the document version with the most updated number of changes. This is called eventual consistency as it doesn’t assure full transactional security. The consistency takes some time to be achieved, which remarkably accelerates the data processing in a tradeoff for the transactional security. In the document-based _ stores, parallelizing and speeding up the queries can be achieved by the map-reduce technique. This kind of processing occurs in two phases; the Map phase which is corresponding to grouping and the Reduce phase which is corresponding to aggregation in SQL [14]. They also fit for storing semi-structured data that would require using extensive Null values for data completion purpose [13]. Examples of Document-Based NoSQL DBMSs include - but are not limited to — CouchDB (JSON), Mongo DB (BSON)[12, 25], Perservere, Thru DB, Orient DB, Terrastore, RavenDB, and Jackrabbit[26]. Data objects can be retrieved with a simple query using the key:
Fig. 3 Graph store example Column-based databases optimize the key- value stores by providing further structure. It has been proven, in certain uses, that to optimize the read operations it is better to store the data into the relational datasets per column, not per row. This is due to the occasional need for a thorough retrieval of all the columns in one row; however, there are groups of columns that are usually read together[31 ,10]. A useful way The Graph database model has a structuring schema, which makes it significantly different from the other NoSQL data models. A graph database is a collection of nodes (entities or businesses), the relationships between nodes(i.e. edges), and key-value pairs attached to the nodes and relationships [1,29]. This database stores the data in graphs that use the Index-Free adjacency technique in which each node holds a pointer pointing to the adjacent node. Graph databases are schema-free and come up with efficient storage of semi-structured and unstructured data [15,29]. In Graph databases, the queries are expressed as traversals which make them much faster than the relational databases[2]. The below figure 3 demonstrates an example of the graph databases called the G_USERS. It illustrates information of a
Fig. 3 Graph store example Column-based databases optimize the key- value stores by providing further structure. It has been proven, in certain uses, that to optimize the read operations it is better to store the data into the relational datasets per column, not per row. This is due to the occasional need for a thorough retrieval of all the columns in one row; however, there are groups of columns that are usually read together[31 ,10]. A useful way The Graph database model has a structuring schema, which makes it significantly different from the other NoSQL data models. A graph database is a collection of nodes (entities or businesses), the relationships between nodes(i.e. edges), and key-value pairs attached to the nodes and relationships [1,29]. This database stores the data in graphs that use the Index-Free adjacency technique in which each node holds a pointer pointing to the adjacent node. Graph databases are schema-free and come up with efficient storage of semi-structured and unstructured data [15,29]. In Graph databases, the queries are expressed as traversals which make them much faster than the relational databases[2]. The below figure 3 demonstrates an example of the graph databases called the G_USERS. It illustrates information of a
to optimize the data access is to structure the data in column families—groups of columns — as storage units. In 2008, Google released its column-based Bigtable DBMS. This remarkably influenced the development of the wide-column databases. The row key is an addressing of a database object in column-based stores within which there is another structure divides the row into multiple columns, all addressed by the same key. Timestamp versions are associated with the new entries in the table. Each storage unit is identified by a combination of column key, row key, and time stamp and is referred to as acell. To control the access to the dataset, its column families are used for granting privileges of reading and writing to users and applications [10]. Column families are the only schemas that are defined when creating the table. The mechanism used to minimize the data retrieval time is by storing the data of the same type in one column family in one row of the dataset and reding it together [2]. Figure 4demonstrates how data is stored in the column family stores[13]. - The column-family NoSQL Database Management System is ideal for data distributed storage, especially versioned data due the time-stamping functions of wide- column and column-family; massive scale batch-oriented data processing conversion; storage parsing; predictive and exploratory analytics performed by expert statisticians and programmers [13] and data mining and analytic applications, where the storage of the column family stores is ideal. Common examples for column-oriented DBMSsare HBase and Hypertable; both are derivatives of Big Table [6, 24].
to optimize the data access is to structure the data in column families—groups of columns — as storage units. In 2008, Google released its column-based Bigtable DBMS. This remarkably influenced the development of the wide-column databases. The row key is an addressing of a database object in column-based stores within which there is another structure divides the row into multiple columns, all addressed by the same key. Timestamp versions are associated with the new entries in the table. Each storage unit is identified by a combination of column key, row key, and time stamp and is referred to as acell. To control the access to the dataset, its column families are used for granting privileges of reading and writing to users and applications [10]. Column families are the only schemas that are defined when creating the table. The mechanism used to minimize the data retrieval time is by storing the data of the same type in one column family in one row of the dataset and reding it together [2]. Figure 4demonstrates how data is stored in the column family stores[13]. - The column-family NoSQL Database Management System is ideal for data distributed storage, especially versioned data due the time-stamping functions of wide- column and column-family; massive scale batch-oriented data processing conversion; storage parsing; predictive and exploratory analytics performed by expert statisticians and programmers [13] and data mining and analytic applications, where the storage of the column family stores is ideal. Common examples for column-oriented DBMSsare HBase and Hypertable; both are derivatives of Big Table [6, 24].
Table 1. Comparison of NoSQL databases categories[17]
Table 1. Comparison of NoSQL databases categories[17]

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