Elasticsearch arrays query/filter - arrays

Question

I'm looking at Elasticsearch for the first time and spent around a day looking at it. We already use Lucene extensively and want to start using ES instead. I'm looking at alternative data structures to what we currently have.
If I run *match_all* query this is what I get at the moment. I am happy with this structure.
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 22,
"max_score": 1,
"hits": [
{
"_index": "integration-test-static",
"_type": "sport",
"_id": "4d38e07b-f3d3-4af2-9221-60450b18264a",
"_score": 1,
"_source": {
"Descriptions": [
{
"FeedSource": "dde58b3b-145b-4864-9f7c-43c64c2fe815",
"Value": "Football"
},
{
"FeedSource": "e4b9ad44-00d7-4216-adf5-3a37eafc4c93",
"Value": "Football"
}
],
"Synonyms": [
"Football"
]
}
}
]
}
}
What I can't figure out is how a query is written to pull back this document by searching for the synonym "Football". Looks like it should be easy!
I got this approach after reading this: http://gibrown.wordpress.com/2013/01/24/elasticsearch-five-things-i-was-doing-wrong/
He mentions storing multiple fields in arrays. I realise my example does not have multiple fields, but we will certainly be looking for a solution which can cater for them.
Tried various different queries with filters, bool things, term this and terms that, none return.

Answer 1

What does your search and mappings look like?
If you let Elasticsearch generate the mapping, it'll use the standard analyzer which lowercases the text (and removes stopwords).
So Football will actually be indexed as football. The term-family of queries/filters do not do text analysis, so term:Football will be looking for Football, which is not indexed. The match-family of queries do.
This is a very common problem, and is covered quite extensively in my article on Troubleshooting Elasticsearch searches, for Beginners, which can be worth skimming through. Text analysis is a very important part of working with search, so there's some more articles about it as well.

Answer 2

A simple match query would work in this scenario.
POST integration-test-static/_search
{
"query": {
"match": {
"Synonyms": "Football"
}
}
}
Which returns:
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.30685282,
"hits": [
{
"_index": "integration-test-static",
"_type": "sport",
"_id": "4d38e07b-f3d3-4af2-9221-60450b18264a",
"_score": 0.30685282,
"_source": {
"Descriptions": [
{
"FeedSource": "dde58b3b-145b-4864-9f7c-43c64c2fe815",
"Value": "Football"
},
{
"FeedSource": "e4b9ad44-00d7-4216-adf5-3a37eafc4c93",
"Value": "Football"
}
],
"Synonyms": [
"Football"
]
}
}
]
}
}