Facebook 's Cassandra ( Column Oriented ) Essay

Relational Database Management Systems are probably the ones that we are most familiar with in 21st century computer science. Relational databases store

In order to overcome these limitations, a new database model known as Not Only SQL (NoSQL) database emerged with a set of new features. The main objective of NoSQL is not to discard SQL, but to be used as an alternative database data model for new features [1] [2] [3]. NoSQL database increases the performance of relational databases by a set of new characteristics and advantages. In contrast to relational databases, NoSQL databases introduced an additional feature that provides flexible and horizontal scalability and taking advantage of new clusters. The rise of NoSQL provides cost-effective management of data in modern web applications. With its new features, NoSQL can be used with applications that have a large transaction, and require low-latency access to huge datasets, service availability while

The wider insight about relational and non-relational database performance, particularly MySQL and Hadoop was gathered through the literature survey. By read textbooks, reviewing academic journals and research papers, I founded a gap in the performance of relational database compare to the non-relational.

Many social networking and/or big data companies like Facebook, Twitter, Yahoo, Google and Amazon are now known for using NoSQL databases. This is because NoSQL systems are non-relational and do not structure their data in tables or typically manipulate or process the data with SQL. Having less restrictions than a relational database, NoSQL has the ability to better handle huge quantities of data in a more efficient way (Moniruzzaman, “NoSQL Database…”). This paper will dig deeper in the several characteristics of NoSQL database systems that separate them from the relational ones. It will also introduce the different models that make up the system as well and a few examples that are currently being used and becoming popular today.

NoSQL databases are designed to expand transparently and horizontally to take advantage of new nodes, and designed with low-cost hardware. SQL have problems in Scalability.

Cassandra is a NoSQL column database which provides linear scalability and proven fault tolerance, this is done as Cassandra automatically replicated to multiple nodes, it also replaces failed nodes with no downtime. Cassandra is also decentralised, which means there are no points of failure or no network bottlenecks. (Cassandra, 2017)

NoSQL databases are a significant departure from the relational model that has dominated the business world for the past few decades. Standing for “Not Only SQL,” these products are all some variation of a non-relational, key-value pair database, and they are becoming very popular with companies that use Big Data and prioritize speed or availability over consistency of data.

NoSQL databases are databases designed to run on clusters of computers/servers, built for the ever-increasing data storage needs for websites. Devised as a way of scaling databases horizontally which is a challenge with traditional relational databases. Scaling horizontally is the ability to add more computers/servers as nodes to a database. These “clusters” work well with write-heavy systems and allow increase storage and processing power limited only by the number of connections you can have on the network. Defined as No-Schema, No-SQL data structures mean they are not limited to the original data structure. Objects and fields etc can be implemented at

In transactional workloads fault tolerant means that DBMS can recover from a failure without losing any data. In the distributed databases fault tolerances means that successfully commit transactions and make progress even in the worker node failures. For read-only queries in analytical workloads, query doesn’t have to be restarted if a case of one node’s query fails.In cloud there is a high failure rate. It can happen in single node failure during long query processing.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

SQL has dominated databases for a considerable length of time. The shared database show began to ascend in the 1970s and promptly grabbed balance. Its usage been in existence for forty years and sometime later, SQL is so far, the most used sort of database. As shown by db-engines.com, the four of the leading five most prominent databases are social; the main NoSQL database to get through the best five is MongoDB, which has overwhelmed PostgreSQL's fourth-place. A part of the best locales out there uses SQL to inquiry their information, including Facebook and Airbnb. NoSQL will be around in the future because it reflects the ability to give significant functionality, and performance benefits for a

There are many NoSQL databases and each either have some common functionalities or have some unique functionalities when compared to the relational model. The main thing to be considered is that none of the NoSQL results work for all scenarios. Each works better than the relational models and satisfies some subsets of the use cases. Apache Cassandra is one of the NoSQL databases which is most widely used in the industrial market. This article gives a detailed information about Cassandra, its functionalities, its advantages and disadvantages which seem to be deceptive for someone who look at Cassandra for the first time.

In this paper, we will review one of the graph database (Neo4j), which the graph database is part of the emerging technology that is called NoSQL and compared it with one of the traditional relational databases (MySQL). MySQL, it is being another name for Relational Databases and it has been used for a long time period until now. However, with the emergence of Big Data there was clearly a need for more flexible databases. Facebook 's Graph Search use Neo4j, a graph database, is an application which clearly displays how relationships need to be modeled in a more efficient and sophisticated manner than using conventional relational models. In this paper, we will make a comparison between MySQL and Neo4j based on the features like ACID, replication, availability and the language that is used in both of them.

With the appearance of Big Data, there was clearly a need for more flexible databases. In this paper, we will review one of the graph database (Neo4j), and compared it with one of the traditional relational databases (MySQL) based on the features like ACID, replication, and the language that is used for both of them. MySQL is being another name for Relational Databases and it has been used for a long time period until now. And Neo4j which is a graph database and it is a part of the emerging technology that is called NoSQL is now trying to prove that there is a need for NoSQL usage.

• Present a database that can accept large amounts of data and be able to provide database backup when needed.