#Elasticsearch suggester weight movie
#Elasticsearch suggester weight series

#Elasticsearch suggester weight series

I hope you enjoyed this series and it added something to your knowledge. And there is overhead of managing another index too. But yes, it is a separate index after all, you will have to keep data in sync between your main index and new index. This will minimize the chances of bloating up nodes and also can provide faster suggestions. In that index, only fields needed for auto-complete functionality will be stored as unique documents, instead of saving them with other data in the same index. Do we need to have a separate index for this field? If all of the three approaches mentioned here don't satisfy your requirements, then you will need to create another index.What can be the maximum size of the text that will be supplied as value to our field? Can it create problems if it's saved in-memory? Completion suggesters save data in memory, n-gram based approaches create additional tokens after basic tokenization for faster matches.Does it need to support infix matches? Is the order of words in text is fixed? Is the order well known to users? Completion suggesters don't support infix matches and are not suitable for fields having highly known order.In what ways can this field be queried? Does it make sense to store it in more than one way?.Is the data already indexed? In what format? Can we re-index it to make it more suitable for auto-complete functionality? If the data is already indexed as text field and we can't re-index it, we will need to go with query time approach - i.e.So to conclude this series, we can say that following factors should be considered while choosing an approach for implementing auto-complete functionality in Elasticsearch: Advanced filtering for suggestions by other fields in the document is not supported.matching by middle-name, is not supported. Have to be mindful of the size of the index as suggestions are stored in-memory.So how does completion suggester compare with other approaches seen so far? It is definitely the fastest one as data to be searched is available in memory, but there are some things we need to keep in mind if we decide to implement auto-complete using it: That was all about our third approach for implementing auto-complete in ES. Both the queries in example below return 'Goblet of Fire' as suggestions -Īs it can be observed in the response above, only 'Harry Potter and the Prisoner of Azkaban' from the 'crime' genre is returned even though the passed prefix matches both the documents indexed above. You can also specify prefix text as regex query.

You can manually tokenize your input string and pass the tokens to Elasticsearch in the input option, like how we have done in the examples above by providing 'Goblet of Fire' as additional input.Ĭompletion suggester supports fuzzy queries that allow us to consider typos while searching documents. You can, however, use the input option to provide multiple matches.

#Elasticsearch suggester weight movie

if you have a movie named 'Harry Potter and the Goblet of Fire' and you type in 'goblet of fire', it won't return the document as a match. So, it won't be able to match in the middle of the input like n-gram based approaches. As discussed above, it preserves the order of input in FST.

In case of completion suggester, ES matches the documents one character at a time starting from the first character, moving ahead one position as a new character is typed in.

This can be avoided by using skip_duplicates option. 'Goblet of Fire' is returned twice in suggestions as we had provided this text as input in both the documents. Enter fullscreen mode Exit fullscreen mode