I'm looking for a way to analyze the string "abc123" as ["abc123", "321cba"]. I've looked at the reverse token filter, but that only gets me ["321cba"]. Documentation on this filter is pretty sparse, only stating that
"A token filter of type reverse ... simply reverses each token."
(see http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/analysis-reverse-tokenfilter.html).
I've also tinkered with using the keyword_repeat filter, which gets me two instances. I don't know if that's useful, but for now all it does it reverse both instances.
How can I use the reverse token filter but keep the original token as well?
My analyzer:
{ "settings" : { "analysis" : {
"analyzer" : {
"phone" : {
"type" : "custom"
,"char_filter" : ["strip_non_numeric"]
,"tokenizer" : "keyword"
,"filter" : ["standard", "keyword_repeat", "reverse"]
}
}
,"char_filter" : {
"strip_non_numeric" : {
"type" : "pattern_replace"
,"pattern" : "[^0-9]"
,"replacement" : ""
}
}
}}}
Make and put a analyzer to reverse a string (say reverse_analyzer).
PUT index_name
{
"settings": {
"analysis": {
"analyzer": {
"reverse_analyzer": {
"type": "custom",
"char_filter": [
"strip_non_numeric"
],
"tokenizer": "keyword",
"filter": [
"standard",
"keyword_repeat",
"reverse"
]
}
},
"char_filter": {
"strip_non_numeric": {
"type": "pattern_replace",
"pattern": "[^0-9]",
"replacement": ""
}
}
}
}
}
then, for a field, (say phoneno), use mapping as, (create a type and append mapping for phone as)
PUT index_name/type_name/_mapping
{
"type_name": {
"properties": {
"phone_no": {
"type": "string",
"fields": {
"reverse": {
"type": "string",
"analyzer": "reverse_analyzer"
}
}
}
}
}
}
So, phone_no is like multifield, which will store a string and its reverse as,
if you index
phone_no: 911220
then in elasticsearch, there will be fields as,
phone_no: 911220 and phone_no.reverse : 022119, so you can search, filter reverse or not-reversed field.
Hope this helps.
I don't believe you can do this directly, as I am unaware of any way to get the reverse token filter to also output the original.
However, you could use the fields parameter to index both the original and the reversed at the same time with no additional coding. You would then search both fields.
So let's say your field was called phone_number:
"phone_number": {
"type": "string",
"fields": {
"reverse": { "type": "string", "index": "phone" }
}
}
In this case we're indexing using the default analyzer (assume standard) plus also indexing into reverse with your customer analyzer phone which reverses. You then issue your queries against both fields.
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/_multi_fields.html
I'm not sure it's possible to do this using built-in set of token filters. I would recommend you to create your own plugin. There is ICU Analysis plugin supported by elastic search team, that you can use as example.
I wound up using the following two char_filter's in my analyzer. It's an ugly abuse of regex, but it seems to work. It is limited to the first 20 numeric characters, but in my use-case that is acceptable.
First it groups all numeric characters, then explicitly rebuilds the string with its own (numeric-only!) reverse. The space in the center of the replacement pattern then causes the tokenizer to split it into two tokens - the original and the reverse.
,"char_filter" : {
"strip_non_numeric" : {
"type" : "pattern_replace"
,"pattern" : "[^0-9]"
,"replacement" : ""
}
,"dupe_and_reverse" : {
"type" : "pattern_replace"
,"pattern" : "([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)([0-9]?)"
,"replacement" : "$1$2$3$4$5$6$7$8$9$10$11$12$13$14$15$16$17$18$19$20 $20$19$18$17$16$15$14$13$12$11$10$9$8$7$6$5$4$3$2$1"
}
}
Related
I am having a dilema on liferay portal 7.3.7 with case insensitive and diacritis free search through elasticsearch in JournalArticles with custom ddm fields. Liferay generated fieldmappings in Configuration->Search like this:
...
},
"localized_name_sk_SK_sortable" : {
"store" : true,
"type" : "keyword"
},
...
I would like to have these *_sortable fields usable for case insensitive and dia free searching, so I tried to add analyzer and normalizer to liferay search advanced configuration in System Settings->Search->Elasticsearch 7 like this:
{
"analysis":{
"analyzer":{
"ascii_analyzer":{
"tokenizer": "standard",
"filter":["asciifolding","lowercase"]
}
},
"normalizer": {
"ascii_normalizer": {
"type": "custom",
"char_filter": [],
"filter": ["lowercase", "asciifolding"]
}
}
}
}
After that, I overrided mapping for template_string_sortable:
{
"template_string_sortable" : {
"mapping" : {
"analyzer": "ascii_analyzer",
"normalizer": "ascii_normalizer",
"store" : true,
"type" : "keyword"
},
"match_mapping_type" : "string",
"match" : "*_sortable"
}
}
After reindexing, my sortable fields looks like this:
...
},
"localized_name_sk_SK_sortable" : {
"normalizer" : "ascii_normalizer",
"store" : true,
"type" : "keyword"
},
...
Next, I try to create new content for my ddm structure, but all my sortable fields looks same, like this:
"localized_title_sk_SK": "test diakrity časť 1 ľščťžýáíéôň title",
"localized_title_sk_SK_sortable": "test diakrity časť 1 ľščťžýáíéôň title",
but I need that sortable field without national characters, so i.e. I can find by "cast 1" through wildcardQuery in localized_title_sk_SK_sortable and so on... THX for any advice (maybe I just have wrong appearance to whole problem? And I am really new to ES)
First of all it would be better to apply original_ascii_folding and then lowercase filter, but keep in mind this filter are for search and your _source data wouldn't be changed because you applied analyzer on the field.
If you need to manipulate the data before ingesting it you can use Ingest pipeline feature in Elasticsearch for more information check here.
My use case requires to query for our elastic search domain with trailing wildcards. I wanted to get your opinion on the best practices of handling such wildcards in the queries.
Do you think adding the following clauses is a good practice for the queries:
"query" : {
"query_string" : {
"query" : "attribute:postfix*",
"analyze_wildcard" : true,
"allow_leading_wildcard" : false,
"use_dis_max" : false
}
}
I've disallowed leading wildcards since it is a heavy operation. However I wanted to how good is analyzing wildcard for every query request in the long run. My understanding is, analyze wildcard would have no impact if the query doesn't actually have any wildcards. Is that correct?
If you have the possibility of changing your mapping type and index settings, the right way to go is to create a custom analyzer with an edge-n-gram token filter that would index all prefixes of the attribute field.
curl -XPUT http://localhost:9200/your_index -d '{
"settings": {
"analysis": {
"filter": {
"edge_filter": {
"type": "edgeNGram",
"min_gram": 1,
"max_gram": 15
}
},
"analyzer": {
"attr_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": ["lowercase", "edge_filter"]
}
}
}
},
"mappings": {
"your_type": {
"properties": {
"attribute": {
"type": "string",
"analyzer": "attr_analyzer",
"search_analyzer": "standard"
}
}
}
}
}'
Then, when you index a document, the attribute field value (e.g.) postfixing will be indexed as the following tokens: p, po, pos, post, postf, postfi, postfix, postfixi, postfixin, postfixing.
Finally, you can then easily query the attribute field for the postfix value using a simple match query like this. No need to use an under-performing wildcard in a query string query.
{
"query": {
"match" : {
"attribute" : "postfix"
}
}
}
I'm using Nutch to crawl a site and index it into Elastic search. My site has meta-tags, some of them containing comma-separated list of IDs (that I intend to use for search). For example:
contentTypeIds="2,5,15". (note: no square brackets).
When ES indexes this, I can't search for contentTypeIds:5 and find documents whose contentTypeIds contain 5; this query returns only the documents whose contentTypeIds is exactly "5". However, I do want to find documents whose contentTypeIds contain 5.
In Solr, this is solved by setting the contentTypeIds field to multiValued="true" in the schema.xml. I can't find how to do something similar in ES.
I'm new to ES, so I probably missed something. Thanks for your help!
Create custom analyzer which will split indexed text into tokens by commas.
Then you can try to search. In case you don't care about relevance you can use filter to search through your documents. My example shows how you can attempt search with term filter.
Below you can find how to do this with sense plugin.
DELETE testindex
PUT testindex
{
"index" : {
"analysis" : {
"tokenizer" : {
"comma" : {
"type" : "pattern",
"pattern" : ","
}
},
"analyzer" : {
"comma" : {
"type" : "custom",
"tokenizer" : "comma"
}
}
}
}
}
PUT /testindex/_mapping/yourtype
{
"properties" : {
"contentType" : {
"type" : "string",
"analyzer" : "comma"
}
}
}
PUT /testindex/yourtype/1
{
"contentType" : "1,2,3"
}
PUT /testindex/yourtype/2
{
"contentType" : "3,4"
}
PUT /testindex/yourtype/3
{
"contentType" : "1,6"
}
GET /testindex/_search
{
"query": {"match_all": {}}
}
GET /testindex/_search
{
"filter": {
"term": {
"contentType": "6"
}
}
}
Hope it helps.
POST _analyze
{
"tokenizer": {
"type": "char_group",
"tokenize_on_chars": [
"whitespace",
"-",
"\n",
","
]
},
"text": "QUICK,brown, fox"
}
I have keyword analyzer as default analyzer, like so:
{
"settings": {
"index": {
"analysis": {
"analyzer": {
"default": {
"type": "keyword"
}}}}}}
```
But now I can't search anything. e.g:
{
"query": {
"query_string": {
"query": "cast"
}}}
Gives me 0 results all though "cast" is a common value i the indexed documents. (http://gist.github.com/baelter/b0720a52ee5a27e27d3a)
Search for "*" works fine btw.
I only have explicit defaults in my mapping:
{
"oceanography_point": {
"_all" : {
"enabled" : true
},
"properties" : {}
}
}
The index behaves as if no fields are included in _all, because field:value queries works fine.
Am I misusing the keyword analyzer?
Using keyword analyzer , you can only do an exact string match.
Lets assume that you have used keyword analyzer and no filters.
In that case for as string indexed as "Cast away in forest" , neither search for "cast" or "away" will work. You need to do an exact "Cast away in forest" string to match it. ( Assuming no lowercase filter used , you need to give the right case too)
A better approach would be to use multi fields to declare one copy as keyword analyzed and other one normal.
You can search on one of this field and aggregate on the other.
Okey, some 15h of trial and error I can conclude that this works for search:
{
"settings": {
"index": {
"analysis": {
"tokenizer": {
"default": {
"type": "keyword"
}}}}}}
How ever this breaks faceting so I ended up using a dynamic template instead:
"dynamic_templates" : [
{
"strings_not_analyzed" : {
"match" : "*",
"match_mapping_type" : "string",
"mapping" : {
"type" : "string",
"index" : "not_analyzed"
}
}
}
],
I am new to Elasticsearch. I have the following mapping for a string field:
"ipAddress": {
"type": "string",
"store": "no",
"index": "not_analyzed",
"omit_norms": "true",
"include_in_all": false
}
A document with value in the ipAddress field looks like:
"ipAddress": "123.3.4.12 134.4.5.6"
Notice that in the above there are two IP addresses, separated by a blank.
Now I need to filter documents based on this field. This is an example filter value
123.3.4.12
And the filter value is always a single IP address as shown above.
I look at the filters at
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-filters.html
and I cannot seem to be able to find right filter for this. I tried the term filter,
{
"query": {
"filtered" : {
"query" : {
"match_all" : {}
},
"filter": {
"term" : { "ipAddress" : "123.3.4.12" }
}
}
}
}
but it seems that it returns a document only when the filter value 100% matches the value of a document's field.
Can anyone help me out on this?
Update:
Based on John Petrone's suggestion, I got it working by defining a whitespace tokenizer based analyzer as follows:
{
"settings": {
"index": {
"analysis": {
"analyzer": {
"blank_sep_analyzer": {
"tokenizer": "whitespace"
}
}
}
}
},
"mappings": {
"ipAddress": {
"type": "string",
"store": "no",
"index": "analyzed",
"analyzer": "blank_sep_analyzer",
"omit_norms": "true",
"include_in_all": false
}
}
}
The problem is that the field is not analyzed, so if you have 2 IP addresses in it the term is actually the full field, e.g. "123.3.4.12 134.4.5.6".
I'd suggest a different approach - if you are always going to have lists of IP addresses separated by spaces consider using the whitespace tokenizer to create tokens as whitespaces - should create several tokens that the IP address will then match:
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/analysis-whitespace-tokenizer.html
Another approach could be storing the IP addresses as an array. And then the current mappings would work. You would just have to separate the IP addresses when indexing the document.