I'm integrating elasticsearch into an asset tracking application. When I setup the mapping initially, I envisioned the 'brand' field being a single-term field like 'Hitachi', or 'Ford'. Instead, I'm finding that the brand field in the actual data contains multiple terms like: "MB 7 A/B", "B-7" or even "Brush Bull BB72X".
I have an autocomplete component setup now that I configured to do autocomplete against an edgeNGram field, and perform the actual search against an nGram field. It's completely useless the way I set it up because users expect the search results to be restricted to what the autocomplete matches.
Any suggestions on the best way to setup my mapping to support autocomplete and subsequent searches against a multiple term field like this? I'm considering a terms query against a keyword field, or possibly a match query with 'and' as the operator? I also have to deal with hyphens like "B-7".
you can use phrase suggest, the guide is here:
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-suggesters.html
the phrase suggest guide is here:
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-suggesters-phrase.html
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I would like to create SQL query on some text field (not keyword) for example "name" field and send that query to elastic server.
my problem is that I need to use the standard SQL language (not the MATCH and QUERY operators which are specials for elastic SQL) of text fields.
when I tried to use JDBC driver or when I tried to use high-level-java-client with LIKE operatorI got the following error
"No keyword/multi-field defined exact matches for [name]; define one or use MATCH/QUERY instead"
I also tried to use the translate API of elasticsearch- but even there I couldn't use the "LIKE" operator on text fields only on keyword fields.
does anyone have any solution for me? I want to use the LIKE operator on text fields instead of the full text operators which are unique to elastic sql.
Please check the this documentation. they have clearly mentioned in document that it is not possible.
One significant difference between LIKE/RLIKE and the full-text search
predicates is that the former act on exact fields while the latter
also work on analyzed fields. If the field used with LIKE/RLIKE
doesn’t have an exact not-normalized sub-field (of keyword type)
Elasticsearch SQL will not be able to run the query. If the field is
either exact or has an exact sub-field, it will use it as is, or it
will automatically use the exact sub-field even if it wasn’t
explicitly specified in the statement.
If you still want to used text field then you need to enabled multi-field as mentioned here. or you can try out to enable fielddata on text field but i am not sure that it will work SQL or not.
I am trying to set up autocomplete for my elasticsearch cluster. The field I want to use is a text field with journal titles. I tried to use the 'standard' completion suggester field type in elasticsearch, but it used too much memory so I had to disable it.
In the meantime I would like to get something basic working, such that someone typing "science" would get a list of suggestions like "science in religion", "science experiments". Then when they type "science in" they would get "science in religion".
I guess this is just a match_phrase query and I can limit it to the top 10 results? Or there is a way to do term frequency across the index?
You can experiment with match_phrase, match_phrase_prefix, prefix over a keyword, and edge n-grams as well. Each of these work well for different use cases.
I am new to Elastic Search. I would like to know if the following steps are how typically people use ES to build a search engine.
Use Elastic Search to get a list of qualified documents/results based on a user's input.
Build and use a search ranking model to sort this list.
Use this sorted list as the output of the search engine to the user.
I would probably add a few steps
Think about your information model.
What kinds of documents are you indexing?
What are the important fields and what field types are they?
What fields should be shown in the search result?
All this becomes part of your mapping
Index documents
Are the underlying data changing or can you index it just once?
How are you detecting new docuemtns/deletes/updates?
This will be included in your connetors, that can be set up in multiple ways, for example using the Documents API
A bit of trial and error to sort out your ranking model
Depending on your use case, the default ranking may be enough.
have a look at the Search API to try out different ranking.
Use the search result list to present the results to the end user
As I understand it, ElasticSearch searches on the magic _all field by default. The problem with this seems to be that if a field uses a different index analyzer, the analyzed data from this field is not searched.
I've had success with searching on the fields ['domain', '_all'] but I really need to avoid having to manually specify each field which was analyzed differently. I see fields supports wildcards but seemingly not '' on its own. I could do a, b*, c*, d* etc. but this seems a tad inefficient.
the special field "_all" is discontinued and copy_to function can be used instead as per the official documentation. This approach allows one to create a computed field (managed by elastic search) that one can specify to copy data from other fields to mimic _all search.
However there is an alternative approach through the use of multi_match providing wildcard field names as part of the query. This works just like the earlier mechanism searching "_all" field.
{"multi_match":{"query":"java","fields":["*"]}}]}}
What I'm trying to accomplish on a high level is an autocomplete input field which queries both customers and orders on multiple fields, with customers ranking higher for customer name searches.
It seems to me that there are various ways to approach this problem with the tools that elasticsearch provides.
The way that I have approached this is to use multi_match queries with prefix_phrase type in order to get partial queries to work across multiple fields.
For example, "bo" should return back matches for "Bob Smith" as well as "Adam Boss". I'm indexing fullname as a separate field from firstname and lastname, so that "adam boss" will return a valid prefix match as well.
In addition, I'd like to boost customer results - trying to do that with a boost param on the multi_match, but that doesn't seem to be working the way I'd expect it to.
What would be a straight forward way to tackle this problem?
One of the challenges I'm facing with the elasticsearch docs is that it's not always clear which properties and features apply to which others. For example, the multi_match documentation doesn't talk about using a custom boost, other than on a field-level.
I think the best way is using completion suggester of ES (v0.90.3+), please refer here for a real use case:
http://www.elasticsearch.org/blog/you-complete-me/
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-suggesters-completion.html