I have few json document with the following format :-
_source: {
userId: \"A1A1\",
customerId: \"C1\",
component: \"comp_
I think in this case the Bool query is the best shot.
Something like :
{
"bool" : {
"must" : { "term" : { "component" : "comp_1" } },
"should" : [
{ "term" : { "userId" : "A1A1" } },
{ "term" : { "customerId" : "C1" } },
{ "term" : { "currentRole" : "ADMIN" } }
],
"minimum_should_match" : 1
}
}
Which gives in Java:
QueryBuilder qb = QueryBuilders
.boolQuery()
.must(termQuery("component", currentComponent))
.should(termQuery("userId", currentUserId))
.should(termQuery("customerId", currentCustomerId))
.should(termQuery("currentRole", ADMIN))
.minimumNumberShouldMatch(1)
The must
parts are AND
s, the should
parts are more or less OR
s, except that you can specify a minimum number of should
s to match (using minimum_should_match
), this minimum being 1 by default I think (but you could set it to 0, meaning that a document matching no should
condition would be returned as well).
If you want to do more complex queries involving nested AND
s and OR
s, simply nest other bool queries inside must
or should
parts.
Also, as you're looking for exact values (ids and so on), maybe you can use term queries instead of match queries, which spare you the analysis phase (if those fields are analyzed at all, which doesn't necessarily make sense for ids). If they are analyzed, you still can do that, but only if you know exactly how your terms are stored (standard analyzer stores them lower cased for instance).
If you use a query_string query, your ANDs and ORs will be interpreted as such by the Lucene library.
This allows you to search for
(currentUserId OR currentCustomerId) AND currentComponent
for instance. By default, the values will be searched for in all fields.