Autocomplete functionality using elastic search - elasticsearch

Question

I have an elastic search index with following documents and I want to have an autocomplete functionality over the specified fields:
mapping: https://gist.github.com/anonymous/0609b1d110d91dceb9a90faa76d1d5d4
Usecase:
My query is of the form prefix type eg "sta", "star", "star w" .."start war" etc with an additional filter as tags = "science fiction". Also there queries could match other fields like description, actors(in cast field, not this is nested). I also want to know which field it matched to.
I investigated 2 ways for doing that but non of the methods seem to address the usecase above:
1) Suggester autocomplete:
https://www.elastic.co/guide/en/elasticsearch/reference/1.7/search-suggesters-completion.html
With this it seems I have to add another field called "suggest" replicating the data which is not desirable.
2) using a prefix filter/query:
https://www.elastic.co/guide/en/elasticsearch/reference/1.7/query-dsl-prefix-filter.html
this gives the whole document back not the exact matching terms.
Is there a clean way of achieving this, please advise.

Answer 1

Don't create mapping separately, insert data directly into index. It will create default mapping for that. Use below query for autocomplete.
GET /netflix/movie/_search
{
"query": {
"query_string": {
"query": "sta*"
}
}
}

Answer 2

I think completion suggester would be the cleanest way but if that is undesirable you could use aggregations on name field.
This is a sample index(I am assuming you are using ES 1.7 from your question
PUT netflix
{
"settings": {
"analysis": {
"analyzer": {
"prefix_analyzer": {
"tokenizer": "keyword",
"filter": [
"lowercase",
"trim",
"edge_filter"
]
},
"keyword_analyzer": {
"tokenizer": "keyword",
"filter": [
"lowercase",
"trim"
]
}
},
"filter": {
"edge_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
}
}
}
},
"mappings": {
"movie":{
"properties": {
"name":{
"type": "string",
"fields": {
"prefix":{
"type":"string",
"index_analyzer" : "prefix_analyzer",
"search_analyzer" : "keyword_analyzer"
},
"raw":{
"type": "string",
"analyzer": "keyword_analyzer"
}
}
},
"tags":{
"type": "string", "index": "not_analyzed"
}
}
}
}
}
Using multi-fields, name field is analyzed in different ways. name.prefix is using keyword tokenizer with edge ngram filter
so that string star wars can be broken into s, st, sta etc. but while searching, keyword_analyzer is used so that search query does not get broken into multiple small tokens. name.raw will be used for aggregation.
The following query will give top 10 suggestions.
GET netflix/movie/_search
{
"query": {
"filtered": {
"filter": {
"term": {
"tags": "sci-fi"
}
},
"query": {
"match": {
"name.prefix": "sta"
}
}
}
},
"size": 0,
"aggs": {
"unique_movie_name": {
"terms": {
"field": "name.raw",
"size": 10
}
}
}
}
Results will be something like
"aggregations": {
"unique_movie_name": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "star trek",
"doc_count": 1
},
{
"key": "star wars",
"doc_count": 1
}
]
}
}
UPDATE :
You could use highlighting for this purpose I think. Highlight section will get you the whole word and which field it matched. You can also use inner hits and highlighting inside it to get nested docs also.
{
"query": {
"query_string": {
"query": "sta*"
}
},
"_source": false,
"highlight": {
"fields": {
"*": {}
}
}
}