I want to treat the field of one of the indexed items as one big string even though it might have whitespace. I know how to do this by setting a non-custom field to be 'not-analyzed', but what tokenizer can you use via a custom analyzer?
The only tokenizer items I see on elasticsearch.org are:
Edge
NGram
Keyword
Letter
Lowercase
NGram
Standard
Whitespace
Pattern
UAX URL Email
Path
Hierarchy
None of these do what I want.
The Keyword tokenizer is what you are looking for.
The Keyword tokenizer doesn't really do:
When searching, it'll tokenize the entire query string into a single token, making text queries behave like a term query.
The issue I run into is that I want to add filters and then search indexed keywords in a long text (Keyword assignment). I would say there's no tokenizer that could do this, and that the normalizer can't accept necessary filters. The workaround for me is to prepare the text before feeding it to elasticsearch.
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.
Actually, as we know for keyword data type we have to use normalizer, but I am gettting an error to use stopword in normalizer. Is there any other way to add stopwords in normalizer for keyword data type?
It is working for lowercase but not for stop word.
Can any one help me out?
I am just want track or any sample code...
Or I have to convert keyword data type into text?
With a text field, if I do a doc['my_text_field'] it shows analyzed tokens. As a result, the regex and wildcard queries can not be formulated in a resonable way...
Although, the text is available when doing adding a scripted field via params._source.my_text_field in its original form.
How do I get the unanalyed text like params._source.my_text_field for a normal elasticsearch query?
Can't understand the difference between setting a Search-as-you-type datatype to a field, setting an Edge NGram Tokenizer in analyzer, and adding an index_prefixes parameter. It seems to me that they do the same job after all.
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-as-you-type.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-edgengram-tokenizer.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/index-prefixes.html
edge_ngram is a tokenizer, which means it kicks in at indexing time to tokenize your input data. There is also a edge_ngram token filter. Both are similar but work at different levels.
search_as_you_type is a field type which contains a few sub-fields, one of which is called _index_prefix and which leverages the edge_ngram tokenizer.
So basically, what you see in the edge_ngram tokenizer documentation has actually been leveraged when they decided to add the new search_as_you_type field type.
Rafiqul is correct that search_as_you_go is built using edge_ngram, but it also incorporates the concept of shingles. Shingles are sets of words, which allows search_as_you_go to better handle multi-word queries.
Note that search_as_you_go requires the words to be in the order entered, which is especially ideal for known entities like movie titles than free form documents.
What is the scenario when you would need to use a mapping of Keyword-Analyzer compared to marking it as not_analyzed with Doc values turned on for the field.
From the elasticsearch documentation, it seems that if a field is not analyzed , then it is better to turn doc_Values on for this field.
The elasticsearch documentation also states specifically that [sic] Note, when using mapping definitions, it might make more sense to simply mark the field as not_analyzed
I am a bit confused as to why the Keyword Analyzer will ever be used ?
According to a core committer, both are equivalent.
That wouldn't be the case of the keyword tokenizer, though, which can be combined with other filters (lowercase, etc) and thus participate in many different ways of tokenizing your input.
I had the exact usecase for keyword analyzer, analyzers allow you to add character filters in addition to common filters and that's where they are used.
I had a field containing numeric values including whitespaces. Numbers may be persian , for ex ۱۲۳۴۵۶۷۸۹ or English, so i needed a char filter to normalize them to english ones with a char filter without any other tokenizing process, when I marked the field not_analyzed I was unable to use my char filter.