N1QL is a system which combines the familiarity of SQL with the flexibility of JSON, allowing you to take advantage of ANSI joins for transforming, manipulating and querying JSON data. It lets you perform complex queries on large datasets, and provides the best performance even when you are dealing with huge volumes of data and a large number of users.
N1QL is a good option for NoSQL adoption, and it solves many common challenges. Most varieties of NoSQL database have limited or even no support for SQL. With N1QL you can migrate more easily because you don’t have to rewrite the data access layer from scratch or retrain your developers in a new system.
Smooth Performance With Large Datasets
Thanks to efficient data access and industry-standard joins you do not have to worry about database inconsistencies. You get to enjoy the smooth performance that you have come to expect from your other database systems with the scalability of NoSQL and the ease that comes with knowing that N1QL development is something that your SQL experts will be quite comfortable with.
N1QL is something that developers can adapt to quickly, and enjoy designing queries with ease using a visual Query Workbench. The N1QL development platform offers flexibility, scalability, easy full-text searching and increased robustness.
A Next Generation Language
N1QL, or ‘nickel’, as it is pronounced, is a true next-generation language. It is easy to use both with high-level abstraction via LINQ (although this is still being developed) or as an ad-hoc system. Since it is still relatively new, a lot of people are unfamiliar with it, but the good news is that it is so similar to SQL that it is easy for developers to just incorporate the N1QL interface and their existing SQL syntax.
Improving Database Performance
When it comes to eking out the best performance, you will find that a lot of the lessons that you learned in SQL still apply to N1QL. The ‘like’ query, for example is problematic, because it can increase your latency and cause issues with performance. Multi-match can hinder performance too, which is why options like B-TREE indexes exist.
N1QL gives you a number of options for fixing this. For example, you can index terms instead of words, and then search that index to find the information we need. Use this in addition to FTS search for fuzzy matches, ranges, conjuncts, etc and you get the prospect of some more sophisticated, useful and common sense matching.
Ordering of results can be done by relevance, too, which is more useful than simply sorting results in alphabetical order a lot of the time. You can even boost the score of certain terms so that you will see results that are more relevant to your preferences.
It takes a while to get used to this way of doing things if you learned more basic ways of handling your database, but it’s something that can massively cut execution times and make your database more robust and reliable as the data set grows.