This article on SQL and NoSQL comparison will throw light on the debate on the advantages and limitations of each. Ever since the buzz of NoSQL database evolved in storing data in the NoSQL databases, I thought of exploring both concepts to reach out to its depth. And it took me some time to figure out things that led to the evolution of the NoSQL database.
Difference between SQL and NoSQL
Well, it all comes down to providing the best possible experience to the end-users in a quick, real and connected way. Database developers are trying to optimize things to yield better performance as the technology in the storage department is changing drastically.
Basics of SQL and NoSQL database:
What is SQL database
Talking about SQL database, the basic concept is that; it is a Relational database. Yes! SQL database is a relational database. So what exactly is a relational database? A relational database strictly uses relations (frequently called tables) to store data. A relational database matches data by using common characteristics found in the dataset. And the resulting group is termed Schema.
A relation (table) in a relational database is divided into rows and columns. A Tuple is a row in a database table that is retrieved using a query.
So how does SQL help?
SQL (Structured Query Language) is a programming language used to manage data in relational databases. Microsoft SQL Server is the best example. Microsoft SQL Server is a relational database that is used to store and retrieve data by applications either on the same computers or over the network.
Basic features of SQL server
- A relational database is a set of tables containing data fitted into predefined categories.
- Each table contains one or more data categories in columns.
- Each row contains a unique instance of data for the categories defined by the columns.
- The user can access data from the database without knowing the structure of the database table.
Limitations for SQL database
Scalability: Users have to scale relational databases on powerful servers that are expensive and difficult to handle. To scale a relational database it has to be distributed on multiple servers. Handling tables across different servers is chaos.
Complexity: In SQL server’s data has to fit into tables anyhow. If your data doesn’t fit into tables, you need to design your database structure that will be complex and, again, difficult to handle.
What is NoSQL database?
In the past few years, the ”one size fits all“ – thinking concerning data stores has been questioned by both Science and web companies, which has led to the emergence of a great variety of alternative databases. The movement and the new datastores are commonly subsumed under the term NoSQL.
The basic quality of NoSQL is that it may not require fixed table schemas, usually avoid join operations, and typically scale horizontally. Academic researchers typically refer to these databases as structured storage, which includes classic relational databases as a subset.
NoSQL database also trades off “ACID” (atomicity, consistency, isolation, and durability). NoSQL databases, to varying degrees, even allow for the schema of data to differ from record to record. If there doesn’t exist a schema or a table in NoSQL, then how do you visualize the database structure? Well, here is the answer
No schema required: Data can be inserted into a NoSQL database without first defining a rigid database schema. As a corollary, the format of the data being inserted can be changed at any time, without application disruption. This provides immense application flexibility, which ultimately delivers substantial business flexibility.
Auto elasticity: NoSQL automatically spreads your data onto multiple servers without requiring application assistance. Servers can be added or removed from the data layer without application downtime.
Integrated caching: In order to increase data through and increase performance, advanced NoSQL techniques cache data in system memory. This is in contrast to SQL database, which has to be done using a separate infrastructure.
Describing the architecture of data storage in NoSQL, there are three types of popular NoSQL databases.
- Key-value stores. As the name implies, a key-value store is a system that stores values indexed for retrieval by keys. These systems can hold structured or unstructured data.
- Column-oriented databases. Rather than store sets of information in a heavily structured table of columns and rows with uniform-sized fields for each record, as is the case with relational databases, column-oriented databases contain one extendable column of closely related data.
- Document-based stores. These databases store and organize data as collections of documents rather than as structured tables with uniform-sized fields for each record. With these databases, users can add any number of fields of any length to a document.
The image shows the difference between the three of them.
Advantages of NoSQL database
- NoSQL databases generally process data faster than relational databases.
- NoSQL databases are also often faster because their data models are simpler.
- Major NoSQL systems are flexible enough to better enable developers to use the applications in ways that meet their needs.
SQL NoSQL Comparision and Conclusion:
SQL and NoSQL have been great inventions over time in order to keep data storage and retrieval optimized and smooth. Criticizing any one of them will not help the cause. If there is a buzz about NoSQL these days, it doesn’t mean it is a silver bullet to all your needs. Both technologies are best in what they do. It is up to a developer to make better use of them depending on the situation and needs.
Go here if you want to learn about the difference between MySQL and SQL Server.
It is true that SQL databases have historically scaled-up rather than scaled-out, and that is a very expensive and inelastic proposition. However it is not true to say that SQL does not scale. NuoDB (www.nuodb.com) scales out/in elastically as you assign or remove machines.
Sorry but there are many errors in the statements you make. Here are just some of them.
1) There are various ways to scale SQL servers, both SQL Server and MySql provide server clustering. It is not difficult to do this although some thought would need to be applied. It is certainly not chaos.
2) In many applications, having to fit your data into schemas is not difficult, it just means you have to know what your data is. In other applications, loosely related data might be common but I have not encountered any of these in my experience, SQL servers with their schemas work just fine.
3) The idea that just because NoSql has no schema means changes are easy is false. Changing the data you put into the database has an impact on the data layer of the application as well as business logic and display layer. In most cases you cannot change data, even with NoSql, without doing other work, it just isn’t in the database itself.
4) Again you say that changes to data do not cause application disruption but this depends entirely on the application and I would suggest 90% of the time, any data changes you implement will definitely require application modifications.
5) No Sql does NOT automatically scale your data across servers. As with most systems, there is management involved in this and understanding how the data is spread out is essential to avoid performance problems. Simply spreading data out does not make the system faster automatically.
6) There is most definitely caching available in most SQL servers, although even for those servers that do not, using something like memcache is hardly difficult. I would also question how many NoSql databases actually include caching by default.
7) Saying that NoSql processes data faster than SQL is nonsense. At the end of the data, the physics involved is the same. The question is how much processing is being carried out in your SQL server vs the same processing that needs to be applied in the application to perform the same types of querying. The famous SQL servers have had much longer to optimise their performance and I would suggest for most operations are faster.
8) Saying that simple data models equals faster means you are comparing apples with oranges. Saying that I can retrieve 1000 records of unstructured data from NoSql faster than 1000 records of already structured data from SQL is not comparing the same thing. Also, if I chose to have no relationships in my SQL and simply pulled table data directly, it would be exactly as fast or faster than NoSql.
9) Flexibility is a double-edged sword. Flexibility does NOT always mean better, just like the argument between loose and strongly typed programming languages, you trade resilience against development time. I can build PHP apps really quickly but it is really easy to inject errors that only occur at runtime. .Net takes me longer but the compiler helps me out.
Your conclusion however is correct. By all means evaluate both but don’t believe the hype!
That is nicely put. However, I do think that the buzz around NoSQL has lead to a lot of people making uninformed decisions in the wrong direction. I like Mark Madsen’s slightly cynical “history of databases in no-tation” from the NoSQL-vs-SQL page
Like in the most cases the answer is: It depends.
Hey guys, please find detailed level of key difference and best fit for on the following link http://viratgaywala.blogspot.in/2014/05/difference-between-sql-and-nosql.html