In elastic search is it possible to search aliases of search tokens, for example, if users are searching for ‘USD’ then they should also get results matching ‘dollar’.
Yes, what you are referring is the synonym search which can be achieved by creating the additional tokens in your index for USD, which is dollar in your case, Please read more about here. This way now as you have both these tokens present in your inverted index, hence searching for any of this would give the search result.
Related
Let's say I have two texts:
Text 1 - "The fox has been living in the wood cabin for days."
Text 2 - "The wooden hammer is a dangerous weapon."
And I would like to search for the word "wood", without it matching me "wooden hammer". How would I do that in Elastic Search or nest?
Term query is used for exact matches search. However it's not recommended to use it against text fields, the following quote from term query documentation:
To better search text fields, the match query also analyzes your
provided search term before performing a search. This means the match
query can search text fields for analyzed tokens rather than an exact
term.
The term query does not analyze the search term. The term query only
searches for the exact term you provide. This means the term query may
return poor or no results when searching text fields.
The problem with text exact matches, as described in the Term query documentation:
By default, Elasticsearch changes the values of text fields as part of
analysis. This can make finding exact matches for text field values
difficult.
So, the documents data is modified (i.e., analyzed) before indexing. This depends on the index mapping definition for each field, defaults to the default index analyzer, or the standard analyzer.
But the default standard analyzer will not change the token "Wooden" to "Wood", this might happen if you used stemming for this field.
This means, if you don't use a different analyzer or stemming, querying with "Wood" shouldn't match "Wooden" token.
To summarize: Indexed data is modified/analyzed before indexing (based on the field mapping definition). Match query analyze the search query, while Term query doesn't analyze the search query. So you have to properly chose the field mapping and the search query to better suit your use case
For some use cases, like storing email addressed, phone numbers or keyword fields that always have the same value, consider using the Keyword type, which is suitable for exact matches in these use cases. However, ES recommends:
Avoid using keyword fields for full-text search. Use the text field
type instead.
So for better visibility and practical solution for your use case, it's better to elaborate more the field mapping you use and what you want to achieve.
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
I'm trying to find out how to properly write my query in order to do a LIKE query with ElasticSearch.
Let's say I have a record of firstname and I want to find every one where there is ma in it.
So I've tried multiple things but none are working. Here is a list :
{"match": {"text": ".*ma.*"}}
{"match": {"text": "*ma*"}}
{"match":{"text"{"query":"ma","fuzziness":"AUTO","prefix_length":1}}}
Do you have an idea of how to do that or where am I missing something?
You might look into using the N-Gram tokenizer to split your documents' tokens up into their substrings.
This will allow you to search against the index with the "partial" matches you're describing.
Bear in mind that this will affect how your documents are tokenized for search so, if you are using other types of analysis for other parts of your application, you may want to create additional fields for your N-Gram tokenized values (or even create a separate index for them).
As a rule of thumb, always try to optimize your index for the queries you want to perform, rather than trying to solve your search problems at query time.
I apparently misunderstood how nGram works with Elasticsearch. I wanted to be able to efficiently search for a substring. That way I could type 'loud' and still find words like 'clouds'. I have my nGram tokenizer set up to have min=2 and max=10.
Apparently, nGram splits up the search term ('loud') into 'lo', 'ou', 'ud', 'lou', 'oud' and 'loud'. In some cases this is nice because it will find 'louder' if I search for 'cloud'. However, I think generally it just confuses my users.
Is there a way to prevent Elasticsearch from splitting up the search term? I tried using quotes in the querystring but that doesn't seem to work.
You should specify 2 separate analyzers for index and for search in your mapping, called index_analyzer and search_analyzer. Index analyzer is the same, as search analyzer, but with nGram filter added.
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