fuzzy search in elasticsearch different than fuzziness match boolean - solr

Question

i'm trying to figure out why the following queries produce vastly different results. i'm told a fuzzy query is almost never a good idea per this document Found-fuzzy so i'm trying to use a match query with a fuzziness parameter. they produce extremely different results. i'm not sure what's the best way of doing this.
my example is a movie title containing 'batman'. the user, however, types 'bat man' (with a space). this would make sense that a fuzzy query should find batman. it should also find other variations like spider man, but for now that's ok i guess. (not really, but...)
so the fuzzy search is actually returning more relevant results than the match one below. any ideas?
--fuzzy:
{
"query":{
"bool":{
"should": [
{
"fuzzy": {
"title": {
"value": "bat man",
"boost": 4
}
}
}
], "minimum_number_should_match": 1
}
}
}
--match:
{
"query":{
"bool":{
"should": [
{
"match": {
"title": {
"query": "bat man",
"boost": 4
}
}
}
], "minimum_number_should_match": 1
}
}
}
EDIT
i'm adding examples of what gets returned.
first, nothing gets returned using the match query, even with a high fuzziness value added (fuzziness: 5)
but i do get several 'batman' related titles using the fuzzy query such as 'batman' or 'batman returns'.
this gets even stranger when i do multiple fuzzy searches on 'bat man' using the fuzzy search... if i search my 'starring' field, in addition to the title field, (starring contains lists of actors), i get 'jason bateman' as well as the title 'batman'.
{
"_index": "store24",
"_type": "searchdata",
"_id": "081227987909",
"_score": 4.600759,
"fields": {
"title": [
"Batman"
]
}
},
{
"_index": "store24",
"_type": "searchdata",
"_id": "883929053353",
"_score": 4.1418676,
"fields": {
"title": [
"Batman Forever"
]
}
},
{
"_index": "store24",
"_type": "searchdata",
"_id": "883929331789",
"_score": 3.5298011,
"fields": {
"title": [
"Batman Returns"
]
}
}
BEST SO FAR (STILL NOT GREAT)
what i've found that works best so far is to combine both queries. this seems redundant, but i can't as yet make one work like the other. so, this seems to be better:
"should": [
{
"fuzzy": {
"title": {
"boost": 6.0,
"min_similarity": 1.0,
"value": "batman"
}
}
},
{
"match": {
"title": {
"query": "batman",
"boost": 6.0
,"fuzziness": 1
}
}
}
]

Answer 1

Elastic Search analyzes docs and converts them into terms, which are what is actually searched (not the docs themselves). The key difference between the two query types is that the match query does not analyze the query text before sending the query. So consider the example below:
The search of 'bat man' in a fuzzy search would first tokenize the term, then search. So what it really looks for is 'btmn,' which might not turn up the same matches. A good example of this is how Jason Bateman showed up because the last name was tokenized to btmn or a similar form.
More detailed information on the Analyzing of text fields when searching can be read http://exploringelasticsearch.com/searching_data.html#sec-searching-analysis
When a search is performed on an analyzed field, the query itself is
analyzed, matching it up to the documents which are analyzed when
added to the database. Reducing words to these short tokens normalizes
the text allowing for fast efficient lookups. Whether you’re searching
for "rollerblading" in any form, internally we’re just looking for
"rollerblad".