curl -XPUT "localhost:9200/products" -d '{
"settings": {
"index": {
"number_of_replicas" : 0,
"number_of_shards": 1
}
},
"mappings": {
"products": {
"properties": {
"location" : {
"type" : "geo_point"
}
}
}
}
}'
I currently have a bash script that creates my index. Code is above.
How do I add stemming to it?
The most generic way to do it is by replacing default analyzer with snowball analyzer. This will enable stemming for all dynamically-mapped string fields. This is how you can enable english stemmer:
curl -XPUT "localhost:9200/products" -d '{
"settings": {
"index": {
"number_of_replicas" : 0,
"number_of_shards": 1,
"analysis" :{
"analyzer": {
"default": {
"type" : "snowball",
"language" : "English"
}
}
}
}
},
"mappings": {
"products": {
"properties": {
"location" : {
"type" : "geo_point"
}
}
}
}
}'
Related
I have an indice named users
When I make a request on http://localhost:9200/users/_search?pretty=true with the following query:
curl -X GET "localhost:9200/users/_search?pretty=true" -H 'Content-Type: application/json' -d'
{
"query": {
"query_string": {
"query" : "firstName: Daulet"
}
}
}'
the query returns two users with the following names:
firstName: Daulet
firstName: Daulet Nurlanuly
How do I make the query string query return a the document with firstName: Daulet ?
I've looked up that Elasticsearch uses Apache Lucene's request syntax and that for the strict search I would need to do the following by enclosing request in quotes as followes:
firstName: "Daulet"
But it is already enclosed within quotes
How do I do that using only Query String Query?
** UPDATE **
The response I get when I make a GET request at http://localhost:9200/users:
{
"users": {
"aliases": {},
"mappings": {
"userentity": {
"properties": {
"firstName": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"id": {
"type": "long"
},
"language": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"lastName": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
}
},
"settings": {
"index": {
"refresh_interval": "1s",
"number_of_shards": "5",
"provided_name": "users",
"creation_date": "1530245236170",
"store": {
"type": "fs"
},
"number_of_replicas": "1",
"uuid": "IlE1Ynv2Q462LBttptVaTg",
"version": {
"created": "5060999"
}
}
}
}
}
You're correct that you need to surround the value with double quotes. You're on the right path and you simply need to escape the double quotes and use the firstName.keyword field instead of firstName, basically like this:
curl -X GET "localhost:9200/users/_search?pretty=true" -H 'Content-Type: application/json' -d'
{
"query": {
"query_string": {
"query" : "firstName.keyword:\"Daulet\""
}
}
}'
I'm new to elasticsearch and am trying to learn how to index using optimal mapping settings to achieve the following.
If I have a document like this
{"name":"Galapagos Islands"}
I want to get this a result for both the following queries
1) Partial matching
{
"query": {
"match": {
"name": "ga"
}
}
}
2) Exact matching
{
"query": {
"term": {
"name": "Galapagos Islands"
}
}
}
With the setting I have currently. I am able to achieve the partial matching part. But exact matching returns no results. Please find below the settings with which I indexed.
{
"mappings": {
"islands": {
"properties": {
"name":{
"type": "string",
"index_analyzer": "autocomplete",
"search_analyzer": "search_ngram"
}
}
}
},
"settings":{
"analysis":{
"analyzer":{
"autocomplete":{
"type":"custom",
"tokenizer":"standard",
"filter":[ "standard", "lowercase", "stop", "kstem", "ngram" ]
},
"search_ngram": {
"type": "custom",
"tokenizer": "keyword",
"filter": "lowercase"
}
},
"filter":{
"ngram":{
"type":"ngram",
"min_gram":2,
"max_gram":15
}
}
}
}
}
What is the correct way to do exact matching and partial matching on a field ?
UPDATE
After recreating the index with settings given below. My mappings look like this
curl -XGET 'localhost:9200/testing/_mappings?pretty'
{
"testing" : {
"mappings" : {
"islands" : {
"properties" : {
"name" : {
"type" : "string",
"index_analyzer" : "autocomplete",
"search_analyzer" : "search_ngram",
"fields" : {
"raw" : {
"type" : "string",
"analyzer" : "my_keyword_lowercase_analyzer"
}
}
}
}
}
}
}
}
My indexing settings are the below
{
"mappings": {
"islands": {
"properties": {
"name":{
"type": "string",
"index_analyzer": "autocomplete",
"search_analyzer": "search_ngram",
"fields": {
"raw": {
"type": "string",
"analyzer": "my_keyword_lowercase_analyzer"
}
}
}
}
}
},
"settings":{
"analysis":{
"analyzer":{
"autocomplete":{
"type":"custom",
"tokenizer":"standard",
"filter":[ "standard", "lowercase", "stop", "kstem", "ngram" ]
},
"search_ngram": {
"type": "custom",
"tokenizer": "keyword",
"filter": "lowercase"
},
"my_keyword_lowercase_analyzer": {
"type": "custom",
"filter": ["lowercase"],
"tokenizer": "keyword"
}
},
"filter":{
"ngram":{
"type":"ngram",
"min_gram":2,
"max_gram":15
}
}
}
}
}
And with all the above, when I query like this
curl -XGET 'localhost:9200/testing/islands/_search?pretty' -d '{"query": {"term": {"name.raw" : "Galapagos Islands"}}}'
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 0,
"max_score" : null,
"hits" : [ ]
}
}
And My document is this
curl -XGET 'localhost:9200/testing/islands/1?pretty'
{
"_index" : "testing",
"_type" : "islands",
"_id" : "1",
"_version" : 1,
"found" : true,
"_source":{"name":"Galapagos Islands"}
}
Add a subfield to your name property which should be not_analyzed. Or, if you care about lowercase/uppercase, a keyword tokenizer together with a lowercase filter.
This should index Galapagos as is, not modifications. Then you can do your term search.
For example, a keyword analyzer together with lowercase filter:
"my_keyword_lowercase_analyzer": {
"type": "custom",
"filter": [
"lowercase"
],
"tokenizer": "keyword"
}
And the mapping:
"properties": {
"name":{
"type": "string",
"index_analyzer": "autocomplete",
"search_analyzer": "search_ngram",
"fields": {
"raw": {
"type": "string",
"analyzer": "my_keyword_lowercase_analyzer"
}
}
}
}
The query to be used is:
{
"query": {
"term": {
"name.raw": "galapagos islands"
}
}
}
So, instead of using the same field - name - you should be using name.raw (the subfield).
I changed the elasticsearch mapping field type from:
"articles": {
"properties": {
"id": {
"type": "long"
}}}
to
"articles": {
"properties": {
"id": {
"type": "string",
"index": "not_analyzed"
}
After that I did the following steps:
Create the index with new mapping
Reindex the mapping to the new index
After the mapping update my previous query filter doesn't work anymore and I have no results:
GET /art/_search
{
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"bool": {
"must": [
{
"type": {
"value": "articles"
}
},
{
"term": {
"id": "123467679"
}
}
]
}
}
}
},
"size": 1,
"sort": [
{
"_score": "desc"
}
]
}
If I check with this query the result is what I expect:
GET /art/articles/_search
{
"query": {
"match_all": {}
}
}
I would appreciate if somebody have some idea why after the field type change the query is no longer working.
Thanks!
The problem in the query was with ID filter.
The query works correctly changing the filter from:
"term": {
"id": "123467679"
}
in:
"term": {
"_id": "123467679"
}
I'm still a beginner with elasticsearch to figure out why the mapping change broke the query although I did the reindex, but "_id" fixed my query.
You can find more informations in the :
elasticsearch mapping reference documentation.
I'm using elasticsearch and need to implement facet search for hierarchical object as follow:
category 1 (10)
subcategory 1 (4)
subcategory 2 (6)
category 2 (X)
...
So I need to get facets for two related objects. Documentation says that it's possible to get such kind of facets for numeric value, but I need it for strings http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-facets-terms-stats-facet.html
Here is another interesting topic, unfortunately it's old: http://elasticsearch-users.115913.n3.nabble.com/Pivot-facets-td2981519.html
Does it possible with elastic search?
If so, how can I do that?
The previous solution works really well until you have no more than a multi-level tag on a single-document. In this case a simple aggregation doesn't work, because the flat structure of the lucene fields mix the results on the internal aggregation.
See the example below:
DELETE /test_category
POST /test_category
# Insert a doc with 2 hierarchical tags
POST /test_category/test/1
{
"categories": [
{
"cat_1": "1",
"cat_2": "1.1"
},
{
"cat_1": "2",
"cat_2": "2.2"
}
]
}
# Simple two-levels aggregations query
GET /test_category/test/_search?search_type=count
{
"aggs": {
"main_category": {
"terms": {
"field": "categories.cat_1"
},
"aggs": {
"sub_category": {
"terms": {
"field": "categories.cat_2"
}
}
}
}
}
}
That's the WRONG response that I have got on ES 1.4, where the fields on the internal aggregation are mixed at a document level:
{
...
"aggregations": {
"main_category": {
"buckets": [
{
"key": "1",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "1.1",
"doc_count": 1
},
{
"key": "2.2", <= WRONG
"doc_count": 1
}
]
}
},
{
"key": "2",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "1.1", <= WRONG
"doc_count": 1
},
{
"key": "2.2",
"doc_count": 1
}
]
}
}
]
}
}
}
A Solution can be to use nested objects. These are the steps to do:
1) Define a new type in the schema with nested objects
POST /test_category/test2/_mapping
{
"test2": {
"properties": {
"categories": {
"type": "nested",
"properties": {
"cat_1": {
"type": "string"
},
"cat_2": {
"type": "string"
}
}
}
}
}
}
# Insert a single document
POST /test_category/test2/1
{"categories":[{"cat_1":"1","cat_2":"1.1"},{"cat_1":"2","cat_2":"2.2"}]}
2) Run a nested aggregation query:
GET /test_category/test2/_search?search_type=count
{
"aggs": {
"categories": {
"nested": {
"path": "categories"
},
"aggs": {
"main_category": {
"terms": {
"field": "categories.cat_1"
},
"aggs": {
"sub_category": {
"terms": {
"field": "categories.cat_2"
}
}
}
}
}
}
}
}
That's the response, now correct, that I have got:
{
...
"aggregations": {
"categories": {
"doc_count": 2,
"main_category": {
"buckets": [
{
"key": "1",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "1.1",
"doc_count": 1
}
]
}
},
{
"key": "2",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "2.2",
"doc_count": 1
}
]
}
}
]
}
}
}
}
The same solution can be extended to a more than two-levels hierarchy facet.
Currently, elasticsearch does not support hierarchical facetting out-of-the-box. But the upcoming 1.0 release features a new aggregations module, that can be used to get these kind of facets (which are more like pivot-facets rather than hierarchical facets). Version 1.0 is currently in beta, you can download the second beta and test out aggregatins by yourself. Your example might look like
curl -XPOST 'localhost:9200/_search?pretty' -d '
{
"aggregations": {
"main category": {
"terms": {
"field": "cat_1",
"order": {"_term": "asc"}
},
"aggregations": {
"sub category": {
"terms": {
"field": "cat_2",
"order": {"_term": "asc"}
}
}
}
}
}
}'
The idea is, to have a different field for each level of facetting and bucket your facets based on the terms of the first level (cat_1). These aggregations then would have sub-buckets, based on the terms of the second level (cat_2). The result may look like
{
"aggregations" : {
"main category" : {
"buckets" : [ {
"key" : "category 1",
"doc_count" : 10,
"sub category" : {
"buckets" : [ {
"key" : "subcategory 1",
"doc_count" : 4
}, {
"key" : "subcategory 2",
"doc_count" : 6
} ]
}
}, {
"key" : "category 2",
"doc_count" : 7,
"sub category" : {
"buckets" : [ {
"key" : "subcategory 1",
"doc_count" : 3
}, {
"key" : "subcategory 2",
"doc_count" : 4
} ]
}
} ]
}
}
}
Is there a way to filter ElasticSearch documents based on the length of a specific field?
For instance, I have a bunch of documents with the field "body", and I only want to return results where the number of characters in body is > 1000. Is there a way to do this in ES without having to add an extra column with the length in the index?
Use the script filter, like this:
"filtered" : {
"query" : {
...
},
"filter" : {
"script" : {
"script" : "doc['body'].length > 1000"
}
}
}
EDIT
Sorry, meant to reference the query DSL guide on script filters
You can also create a custom tokenizer and use it in a multifields property as in the following:
PUT test_index
{
"settings": {
"analysis": {
"analyzer": {
"character_analyzer": {
"type": "custom",
"tokenizer": "character_tokenizer"
}
},
"tokenizer": {
"character_tokenizer": {
"type": "nGram",
"min_gram": 1,
"max_gram": 1
}
}
}
},
"mappings": {
"person": {
"properties": {
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
},
"words_count": {
"type": "token_count",
"analyzer": "standard"
},
"length": {
"type": "token_count",
"analyzer": "character_analyzer"
}
}
}
}
}
}
}
PUT test_index/person/1
{
"name": "John Smith"
}
PUT test_index/person/2
{
"name": "Rachel Alice Williams"
}
GET test_index/person/_search
{
"query": {
"term": {
"name.length": 10
}
}
}