Whenever you need to find something, Google is the first thing that comes to mind. Google is the place where you can find almost anything and everything without any hurdle. But have you ever thought that whenever you type something in the Google Bar, several suggestions appear even though you don't finish your sentence or even a word?
You may have noticed that these suggestions become more accurate as you continue to type your sentence or a word. But have you ever wondered how this happens? What is the story behind this auto-completion? In this article, we will cover the subjects of how to build a Google Search Autocomplete and what does it require?
Requirements of the System
There are many requirements required by the systems but depending upon Google scale, the top most requirements are:
- Latency
- Consistency
- Availability
The most important requirement to look out here is the usage of desirable latency which must be kept low. This latency is also utilized to provide changing or giving suggestions to the words or statements typed by the users. For the provision of suitable suggestions or recommendations, the system must remain present at all times.
The next thing to keep in mind is that sometimes the consistency of the system may get compromised. This happens because whenever you type a new word or statement, a slight disruption in the frequency of the previously stored inquiry is undermined. This can have a little effect on the recommendations provided by Google and it may sometimes alter the solutions you want to find.
At the start of establishing a Google search autocomplete, this delay will be considered as okay because as the systems adjust to all the Google searches, the consistency of such systems will also be established.
Start Your 7-Day FREE TRIAL with Cloud Institute.
How to Build Google Search Autocomplete
The concept of building a Google search autocomplete is called a Trie which is referred to as a tree that is utilized to store the digital information and all the inclusive set of arrays needed to build a specific structure. Keeping in view of this concept, we have come up with the three approaches to build an effective Google search autocomplete.
- Understanding Nodes
A Trie tree consists of multiple nodes which are represented as nodes and the first node is called as the parent node and the next node is referred to as the child node. A single node has about 26 child nodes which are used to store the data and all the related information. Google utilizes these nodes to store every word or statement that you searched in terms of their frequency. For example, the parent node H saves the frequency of letter H whereas the child note which is given the letter a saves the frequency of the letter as Ha. There can be millions of recommendations for this but whenever you type H in the search bar you will receive the suggestion of Harry Styles or Harry Potter.
- The Frequency of the Nodes
Whenever one needs to update the frequency of a given node or want to transverse it to some other node, we need to make sure that it gets updated from the roots. We have to start from the parent node and then go down to all the sub-nodes in the Trie and transform the given frequency to the ones that we want. To attain maximum consistency, you need to make sure that once you update the entire system, you must lock the changes that you made in the nodes.
- Storage of Data Offline
Once you have updated the frequencies of the given nodes, you can store as much data as you want and you can even do it offline. Once every query is mapped out in the Trie and threshold of the frequencies has been reached, it is time to move onto the next servers. You can add the same approach to the next servers to provide enhanced consistency and availability of the recommendations.
Start Your 7-Day FREE TRIAL with Cloud Institute.
Conclusion
It can be difficult to mark everything on the same server, therefore, it is more convenient to use several servers and save the information in the form of prefixes. These prefixes can be a, b, ab, aab, abb, and it can be saved on server number 1. This is called scaling of the stored data that helps in the latency to build Google search autocomplete.
Learning and understanding the creation of Google search autocomplete is the prime responsibility of the Google Cloud user. This is considered to be a very effective skill and every organization seeks such expertise in their candidates. Therefore, apart from having Google Cloud Certification, you must master the art of building this autocomplete to give a tough time to your competitors in the IT industry.