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Raoul Meyer on February 09, 2019

People are bound to make typo's. You probably know how Google handles these situations: The search engine suggests a corrected search term, or ev... [Read Full]
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Every single time I used this, I kinda regretted. It was hard to configure for bigger datasets and I often was getting funny suggestions.

For instance beer would be suggested as bear 😂

 

Yeah agreed, it can give interesting suggestions. That's why I would mostly use it in cases where any alternative is better than showing no results.

Out of curiosity, what was your use case? Did you try limiting your suggestions with a collate query?

 

To solve the problem in this example ("bear" vs "beer") - we need to provide user's query history alongside the current request. So search can determine if user hunts often, or uses Bear framework, or cooks wheat beer

 

It was classic search. Search data consisted of categories, countries, companies, brands, other attributes. Millions of possible combinations of these.

I did use collate query but it was very likely that suggestion with bear also would have brought back results. But there's a chance I did something wrong. 🤔

 

With these things I always wonder how much of this is pure ElasticSearch or "just" some icing on Lucene.

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