When NoSQL makes better sense than MySQL

In large web applications where performance and scalability are key concerns a non –relational database like NoSQL is a better choice to the more traditional databases like MySQL, ORACLE, PostgreSQL etc. While the traditional databases are designed to preserve the ACID (atomic, consistent, isolated and durable) properties of data, these databases are capable of only small and frequent reads/writes.

However when there is a need to scale the application to be capable of handling millions of transactions the NoSQL model works better.  There are several examples of such databases –  the more reputed are Google’s BigTable, HBase, Amazon’s Dynamo, CouchDB  & MongoDB. These databases are based on a large number of regular commodity servers.  Accesses to the data are based on get(key) or set(key,value) type of APIs.

The database is itself distributed across several commodity servers. Accesses to the data are based on a consistent hashing scheme for example the Distributed Hash Table (DHT) method. In this method the key is hashed efficiently to one of the servers which can be visualized as lying on the circumference of the circle. The Chord System is one such example of the DHT algorithm. Once the destination server is identified the server does a local search in its data for the key value.  Hence the key benefit of the DHT is that it is able to spread the data across multiple servers rather than having a monolithic database with a hot standby present.

The ability to distribute data and the queries to one of several servers provides the key benefit of scalability. Clearly having a single database handling an enormous amount of transactions will result in performance degradation as the number of transaction increases.

However the design of distributing data across several commodity servers has its own challenges, besides the ability to have an appropriate function to distribute the queries to. For e.g. the NoSQL database has to be able handle the requirement of new servers joining the system. Similarly since the NoSQL database is based on general purpose commodity servers the DHT algorithm must be able to handle server crashes and failures.  In a distributed system this is usually done as follows. The servers in the system periodically convey messages to each other  in order to update and maintain their list of the active servers in the database system.  This is performed through a method known as “gossip protocol”

While databases like NoSQL, HBase, Dynamo etc do not have ACID properties they generally follow the CAP postulate. The CAP (Consistency, Availability and Partition Tolerance) theorem states that it is difficult to achieve all the 3 CAP features simultaneously. The NoSQL types of databases in order to provide for availability, typically also replicates data across servers in order to be able to handle server crashes. Since data is replicated across servers there is the issue of maintaining consistency across the servers. Amazon’s Dynamo system is based on a concept called “eventual consistency” where the data becomes consistent after a few seconds. What this signifies is that there is a small interval in which it is not consistent.

The NoSQL since it is non-relational does not provide for the entire spectrum of SQL queries. Since NoSQL is not based on the relational model queries that are based on JOINs must necessarily be iterated over in these applications. Hence the design of any application that needs to leverage the benefits of such non-relational databases must clearly separate the data management layer from the data storage layer. By separating the data management layer from how the data is stored we can easily accrue the benefits of databases like NoSQL.

While NoSQL kind of databases clearly have an excellent advantage over regular relational databases where high performance and scalability are key requirements the applications must be appropriately be tailored to take full advantage of the non-relation and distributed aspect of the database. You may also find the post “To Hadoop, or not to Hadoop” interesting.

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