For over a decade, the de facto standard for database design was relational models. Relational databases use primary and foreign keys and have strict constraints when you manipulate the tables’ data. These databases are good for smaller data storage requirements, but you need “big data” capabilities to manage large queries. This is the goal of NoSQL. NoSQL databases work entirely different than relational databases, so you need to learn how to work with NoSQL to properly manage big data queries. If you don’t implement NoSQL properly, you can actually slow down your website or applications that use the NoSQL database system.
Types of NoSQL Databases
There are several types of NoSQL database systems although a couple of them are more popular than the others.
The first type is the most common. Document type NoSQL databases use structures called documents to store data. The document is given an ID and you can also link these documents together using IDs just like relational databases. This type of structure is used with MongoDB, which is a common open source database system. You can think of the document system in the same way you think of a regular document. You “file” the document in the database. The document can contain any amount of data and any data types. The next document can also contain any data and any data types. There are no constraints for the data these documents contain.
The next popular data system is the key-value scheme. If you are used to programming languages, the key-value system will be familiar to you. The key-value stores a key, which is the identifier for the value. You can look up data using the key or the value. Again, there are no constraints with the data, because you simply have key-value pairs for each element.
The other two types of NoSQL are graph and wide column stores. A graph store keeps data about network systems, which can grow to several terabytes of data. Wide column stores are used for big data that stored across several data sets. These systems are mainly used for reporting.
One common graph store NoSQL database is XML databases. Most developers are familiar with XML, because it’s an older schema that supports data queries and storage. You can more easily store XML in NoSQL databases, but XML is also a bit out of style. Most developers are working more towards JSON as a data model schema, so it might be more difficult to fit an XML database into your current systems.
Benefits of NoSQL
There are several reasons to use big data databases. The main and most popular reason is the amount of data you can handle with your queries and reports. Relational databases can store and handle tables with millions of records. This was a large data amount a decade ago. However, these database systems are not so good when your tables grow to billions or trillions of rows. With a relational database, your only option is to store data using the same setup as your tables, and any manipulated data must adhere to any strict constraints. This doesn’t allow much room for dynamic information. NoSQL databases are typically able to retrieve large data sets more efficiently than relational databases.
NoSQL databases are also able to allow for quicker code releases and work better with object oriented programming. For instance, when you create an object oriented program in a language such as C#, you have more freedom with your database creation. NoSQL lets you create database tables using your OO classes. When these classes store data, they represent a document and store in the database dynamically. If you change the class models, NoSQL databases will let you store the new data without needing to change your entire database model. Older programs know that to change your code, you needed to change your tables, any constraints and then refresh your database and your code. With NoSQL databases, the tables update automatically.
With older relational databases, developers remember first changing the database then refactoring code to work with the new changes. Large changes were sometimes not scalable with old relational databases. NoSQL is dynamic, so changes to the code are reflected in the database. You don’t need to change the database several times during the development process.
NoSQL databases also offer a concept called “sharding.” With relational databases, you need to scale vertically. This means that when you host a database, you typically host it on one server. When you need more resources, you add another database server or more resources to the current server. With NoSQL, you can “shard” your database files. This basically means that you can share database files across multiple servers. Sharding is using done on very fast storage hardware such as a SAN and a NAS. Using several database servers at once speeds up your queries especially when you have millions of rows in your data sets.
You can implement cloud computing along with sharding. This makes your queries especially fast when you need reports available for several different locations. Using old techniques, your users would log in to the network and pull reports from miles away, sometimes in other countries. The result was that the user sometimes needed to wait several minutes for a report to render. With cloud computing, reporting data is served up from the closest data center to your users. The result is faster data processing and rendering.
Not only does NoSQL support manual sharding, but NoSQL servers will also do automatic sharding. The database server will automatically spread a dynamic amount of data across several servers, so the load on each server individually is reduced.
In addition to using NoSQL in a cloud computing environment for performance, it’s also beneficial when it comes to cost. Cloud computing hosts only charge you for the resources you use, so the costs scale with the growth of your business.
Replication with NoSQL is an option. When you gather data, you probably need to replicate that data to other servers. For instance, you probably want to replicate data to a reporting server for performance reasons on your main production server. NoSQL supports automatic replication, so you can send data to several servers as your database collects data real-time.
You can also use NoSQL with more current languages. For instance, NoSQL works with Node.js for your real-time network communication web applications. Just give the table name and schema name and you can pull data directly from your NoSQL database. With the weak typing in the newer languages and the dynamic way NoSQL stores and implements data, you can create very powerful dynamic apps with your web applications.
NoSQL still supports common CRUD queries. CRUD is the name giving to “create, read, update and delete” query procedures. These procedures are the four major ways you work with data in any database. The create statement creates a new record, the read (select) statement retrieves the data for your application, the update statement changes and edits the data already stored in your database and the delete statement removes a record from the database.
NoSQL statements are dependent on the type of database structure you incorporate. In most cases, you’ll use a programming language to create apps that interface with your NoSQL server. Statements are much different than the statements you’re used to with relational databases. Even if you had different SQL servers, the statements were still similar between the platforms (Oracle, MySQL or SQL Server). Because NoSQL is dynamic and works more with documents or entities completely different than relational tables, you have a different structure for your queries. However, when you use different programming languages, there are plugins that you can use to retrieve data from your databases. These plugins help minimize the need to learn raw query information.
If you believe you need to gather large sets of data, it’s probably worth looking into a NoSQL database solution. You’ll also be up on the latest database languages and technology. To be a good programmer, you need to know the latest technology and languages. NoSQL is a trending type of application that is taking over from relational database systems.