If you want to create a robust web application then it is important that you think about database scalability, especially if you want to build something that can cope with a large number of uses or a huge amount of data. Scalable databases can be built in a number of ways, using in-memory computing in a lot of ways.
A lot of relational databases were originally developed to run on just a single server, and this can cause problems as the database grows. Some organizations might find that they struggle to cope with slow execution times or records being locked for so long that it renders the database unusable.
There are some high-performance RDBMS options, but these can be expensive to run. Another option for scalable databases is distributed solutions, with multiple servers in a cluster holding data. Sharding a database across multiple servers is an option but if you do not have the expertise to code this in the house it can often be a challenge to maintain this.
Alternatives to the RDBMS
NoSQL databases and options such as N1QL are a good option for companies that want to work with large datasets or with a high volume of users. Another popular approach is in-memory computing platforms which offer more rapid access to data. These two approaches to scalable database design both offer smoother and more reliable transactions.
Coping With the Workload on Your Database
The main objective of a transaction workload is to handle UPDATE, DELETE or INSERT queries. Databases that are designed to handle this are normalized in the 3NF. A lot of business databases broken up into small tables so that they can be indexed to improve responses to query, however, this will slow down the queries that actually need to change the database (such as update, insert, or delete).
NoSQL and other database formats offer better performance for large datasets because they handle searches in different ways.
Since they are more scalable, they are easier for developers to work within high volume applications. Some developers are nervous about having to learn a new way of working, but the good news is that with N1QL they won’t have to since the query language is SQL-like and should be familiar to them already. Even without learning about the differences between NoSQL and SQL/RDBMS, there can be performance improvements just from the new system.
Learning ACID, BASE and CAP theorem could help you to understand why some database types are more scalable than others, and why an RDBMS has so many scaling issues. It may be, however, that you would need to plan out your database and try it in the real world to see what it performs like before you come to understand what the limiting factors are. It’s easy to theorize about queries, locking, indexes, and search patterns, but sometimes flaws in your setup will not surface until the database is up and running for a while. The good news is it is usually possible to change a database over at a later date. It’s better to plan ahead, but if you are hitting limits now, change is an option.