This could be in the form of words relevant to a specific topic (such as COVID-19, or The Matrix), and the search engine uses these words to find the relevant information from a database. It usually consists of an interface that lets you enter a search query where you specify criteria about an item of interest.
ELASTICSEARCH SUGGESTER WEIGHT SOFTWARE
A search engine is a software component that allows you to find information in a computer. If you're reading this on your phone or laptop right now, you've likely heard of the term search engine. It probably took less than a second for Google to return documents from the web about recursion neatly ordered by relevance. But jokes aside, let's look at the top 5 results: Well I'm a programmer, so I'll just go ahead and type in the word recursion. You saw a search box inviting you to type something. What do you see? Screenshot of Google's home page Decrease relevance for certain search termsĮnhancing search experiences on mobile devicesĪ day in the life of an Elasticsearch Architect.Boosting relevance of individual fields.Search and filter for products by ratings.Search for products within a certain category.Search for a particular category of products.In such a case, increasing tie_breaker parameter increases the degree by which all fields influence on resulting score.Īnd finally, once in doubt about why query results don’t match your expectations, you may resort to explain":true query parameter. When combining autocomplete on multiple fields, you may use dis_max query type. So it might be sufficient to resort solely to query-time improvements such as bool_prefix query type if you want to get more lenient results or match_phrase_prefix query type if you want your results to be more strict. Leveraging search_as_you_type mapping at index-time might not be a silver bullet as well as the main aim of it is to combat search queries with out-of-order words by creating n-gram fields for you. Explore rich Elasticsearch query language first. When implementing autocomplete functionality with Elasticsearch, don’t jump straight away to the naive query_string approach. Success! This was exactly what we were looking for! Conclusion So as we see increasing it leads us in the wrong direction. It doesn’t seem that obvious what tie_breaker parameter does. Since the output is huge, I’ll focus only on important parts.
Huh? What happened? Let’s run the same query with explain":true to understand. " title": " 3 completely irrelevant to real name words", In order to boost exact match relevance, I’ve switched to match phrase prefix query.
Since typing out-of-order words wasn’t my use case, I’ve decided not to mess with it and improve my relevance query-time instead of index-time.
And fancy search_as_you_type mapping just means that n-gram fields are created automatically. The downside of this is that Elasticsearch cluster consumes extra memory to store n-grams which may affect cluster state. After reading for a while, I’ve learned that n-grams are basically sequences of words extracted from the text mixed in a random order which allows searching words out of order in my autocomplete query. Since out of the box solution didn’t help, I’ve decided to read on about search_as_you_type mapping and n-gram fields. " _id": " 6f1588bbe1a440028af1de4337bf8fac",īut still, it was not good enough since the exact match was only second.