Structured Query Language (SQL) databases have successfully been there as a primary data storage machinery for over four decades. Its usage seemed to have boomed during the late 1990s with the introduction of open-source options and web applications such as PostgreSQL, MySQL, and SQLite. We all are aware that NoSQL databases have been around since the 1960s. However, it is really gaining traction of late with popular choices such as Redis, Couch DB, MongoDB, and Apache Cassandra. Though SQL is quite readily available these days on practically all platforms, there could be some other alternatives that would be including NoSQL and even NewSQL which have come up for addressing big data and even less-structured content. These three databases actually are known to cover a broad spectrum of implementations and architectures. So you need to know the underlying differences between them.
SQL is supposed to be an RDBMS (Relational Database Management System) that is based on tuple relational calculus and relational algebra. The relational model that was presented by Edgar F. Codd in his paper in the year 1970 had left a great impact on SQL. The paper was titled as “A Relational Model of Data for Large Shared Data Banks”. Most important database server applications, especially from vendors such as Microsoft, Oracle, and IBM, are based on this relational model. Some embedded versions are also available from some other software vendors. You now have open-source implementations such as MariaDB and PostgreSQL. MariaDB is a sequel or an off-shoot of MySQL. MariaDB is presently under the management of MariaDB Foundation while MySQL is currently owned by Oracle.
These are databases which have not been created on SQL and have been designed for handling huge data stores known for variable contents like documents. They are usually designed for supporting clusters of storage and compute nodes which are pretty common in huge cloud environments.
Some Striking Differences between SQL & NoSQL
- SQL databases are actually structured into tables. However, NoSQL databases seem to be based on graphs, documents, key-value pairs or wide column stores. In NoSQL, you would find no standard schemata definition which should have been worked out while structuring the data.
- In SQL, the databases are supposed to be based on the predefined schema which is actually supposed to be based precisely on structured data, whereas in NoSQL we find that the schema is actually pretty dynamic for managing unstructured data.
- SQL databases actually scale vertically. However, NoSQL databases would be scaling horizontally.
- SQL are created and managed exclusively using SQL language whereas, NoSQL databases are known to use Unstructured Query Language. The syntax of Unstructured Query Language UnQL would be varying between databases.
- SQL is the best choice for complex queries as NoSQL is lacking in the conventional interfaces required for carrying out complicated queries. SQL queries are more robust and powerful as opposed to NoSQL.
- NoSQL is known to tackle hierarchical data much better than SQL.
NewSQL acts as a conduit, bringing some scalability and features of NoSQL to traditional SQL. The read-write workloads related to online transaction processing require SQL’s ACID guarantees, but as the requirements grow, it is only logical that you move up to using clusters for parallel transactions. NewSQL systems can modify or even forgo concurrency and recovery controls to gain the needed scalability. At the core, NewSQL systems are built on the familiar semantics and syntax of SQL. There are several implementations for transparent sharding and modified SQL engines. There are a number of new architectures like MemSQL, Trafodion, Google Spanner and NuoDB, optimized for flow and concurrency control that use clusters of nodes, each node being a subset of the entire database. There are several modified SQL engines too like Infobright, MySQL Cluster, and TokuDB.
The concept of sharding is an implementation of horizontal data partitioning into portions called shards. Each shard has unique or shared data and is kept in a node. A table can hence span across several nodes. While they are fairly complex and can be hard to understand and implement, if you can pull it off, you see significant improvements in scalability and performance.
As a developer, you will always have a vast pool of tools to choose from for each function, especially data handling, storage, and querying. No one approach is superior to the rest, as they are all optimized for particular tasks. Some applications may even use multiple database systems. Make an informed decision based on the task at hand, and you should be fine.