How to build scalable search engine systems

Building Scalable Search Systems

Building a search system?  So how do you go about it? Rather how would you wish it to perform? Would it be good to have better response time or should you try for getting in more relevant results per query? Lots of questions right. Few we ignore some left unanswered.

Let’s talk about them

We all know that search is the doorway for data in a search engine. You could be cornering real estate or job portal or even any hotel booking domain, the system has to be quick in response time while it should also get the most close and optimum response.  You could use the best of the technology available but what wraps up is that you need to make the search experience worthwhile and get the complete utilization of the time spent by the user with satisfaction.

What is search system all about?

Well technically search engine works in the picking up the few tit bit questions fired by the user and query the database and come out with most nearest solutions and display to the user. We are living in the age of Big Data where you have really lot of information, but most of it is unstructured.  So the system actually has to dig out from the complete muck and find the actual gems. The trick is actually how you are going to stop the hiccup when man communicates with the machine.  Difficult task.. Isn’t it? Well we could try making it easier.

One step at a time

The biggest blunder that usually is seen is that they try reading user’s mind. Even really good people fall back when they assume that doing the simplest things in the sophisticated way will actually spoil it. You should be analyzing the route where user’s intention is towards. Now if you are wishing to create a search engine say for a real estate you could not simply guess what the user will be thinking while he types 7A Park Street. But if you have done a proper homework then probably you would display the apt results that he seeks for.

This brings us to the next important point that one should keep track of query history.  We need to realize that all queries are not of the same level, but there is some inter link between the ways two queries are being followed. So when you keep track of the past queries you could easily cache in the previous solution and all you need to do is refine it. This will make the search to be a type ahead experience which is appreciated more.

Time to dust off again

We have been gathering knowledge statistics and various functionality vector since long. All we now need to do is use it to in such a manner that it creates a search that actually works on the faceted and human curation technique. This way we will be aware of the way our crawler should go about accessing the data and to what extent it should also with parts that logically require to be excluded. So you are recycling the past data by re searching for already searched solutions.

In all this there is a plausibility of duplication of data that needs to be filtered to save storage and there has to be made precise calculating prowess that would make the best efficient use of the search engine that would be of proficient information of the best quality. One way could be use of proper keywords which are descriptive in nature providing better insight. The other way is if we try and structure the queries or curtail their length by segmenting it only in case if they do not hamper the overall nature of them.

Towards finale

All need scalability in their business, but firstly we actually need to understand the true meaning of scalability. And when working towards search engine which actual makes the passage towards the information would be need even more precision. You want the user to be satisfied in the very first place so that his probability to visit back increase in various manifolds. After all we are in the consumer world right?