上篇提过query模式除对记录的筛选之外还对符合条件的记录进行了评分，即与条件的相似匹配程度。我们把评分放在后面的博文中讨论，这篇我们只介绍query查询。
查询可以分为绝对值查询和全文查询：绝对值查询是指非text类型字段的查询，全文查询一般指对于text字段的查询。如果需要对text字段进行绝对值查询的话可以用fields在text字段下定义一个keyword字段。text类型字段在建索引时会经分词器处理分解成许多单词，然后在查询时查询目标也会经历分词处理后才逐个单词进行匹配。所以要注意录入的查询条件不一定是最终的查询内容，因为会首先进行分词处理。
我们先看几个绝对值查询例子：
POST /bank/_search
{
"query" : {
"term" : {
"state.keyword": "IL"
}
}
}
POST /bank/_search
{
"query" : {
"terms" : {
"state.keyword": ["IL","WA"]
}
}
}
POST /bank/_search
{
"query" : {
"range" : {
"age": {
"gte" : 20,
"lte" : 40
}
}
}
}
POST /bank/_search
{
"query" : {
"prefix" : {
"address.keyword": "880"
}
}
}
POST /bank/_search
{
"query" : {
"wildcard": {
"address.keyword": "*Holmes*"
}
}
}
POST /bank/_search
{
"query" : {
"regexp": {
"address.keyword": ".*Holmes.*"
}
}
}
elastic4s的表达形式如下：
val qTerm = search("bank").query(termQuery("state.keyword","IL"))
val qTerms = search("bank").query(termsQuery("state.keyword","IL","WA"))
val qRange = search("bank").query(rangeQuery("age").gte(20).lte(40))
val qPrefix = search("bank").query(prefixQuery("address.keyword","880"))
val qWildcard = search("bank").query(wildcardQuery("address.keyword","*Holmes*"))
val qRegex = search("bank").query(regexQuery("address.keyword",".*Holmes.*"))
全文查询最简单的例子就是match query 了：
POST /bank/_search
{
"query" : {
"match" : { "address" : "holmes"}
}
}
val qMatch = search("bank").query(matchQuery("address","holmes"))
以上是个单字查询示范。多字全文查询如下：
POST /bank/_search
{
"query" : {
"match" : { "address" : "holmes lane"}
}
}
val qMMatch = search("bank").query(matchQuery("address","holmes lane"))
问题出现了：查询结果不但有"880 Holmes Lane"还包括了"685 School Lane"，这是因为分词器把"holmes lane" 分解成了"holmes","lane"两个单字，而多字查询默认关系是or，只要包含holmes,lane任何一项都符合条件。我们可以用and关联：
POST /bank/_search
{
"query" : {
"match" : {
"address" : {
"query": "holmes lane",
"operator": "and"
}
}
}
}
val qMMatchAnd = search("bank").query(matchQuery("address","holmes lane").operator("and"))
现在结果只剩下"880 Holmes Lane"一条了。下面这个query与之有同效：
POST /bank/_search
{
"query" : {
"match" : {
"address" : {
"query": "holmes lane",
"minimum_should_match": "100%"
}
}
}
}
val qMMatchMin = search("bank").query(matchQuery("address","holmes lane").minimumShouldMatch("100%"))
以上例子都是简单类型的查询，即单语句查询。现实中我们普遍需要用and,or来结合多种条件形成复合式查询。最具代表性的也就是boolQuery了。boolQuery的格式如下：
GET /bank/_search
{
"query": {
"bool": {
"must": [ // lastname=duke and gender.keyword = M
{ "match": { "lastname": "duke" }},
{ "term": { "gender.keyword": "M" }}
],
"must_not": [ // and firstname.keyword != Jackson and city.keyword != Jackson
{ "term": { "firstname.keyword": "Jackson"}},
{ "term": { "city.keyword": "Brogan" }}
],
"should": [ // or email.keyword = *.cn or age >= 80
{ "wildcard": { "email.keyword": "*.cn" }},
{ "range": { "age": {"gte" : 80}}}
],
"filter": [ // filter state.keyword in (IL,WA,TA) and balance >= 100000
{ "terms": { "state.keyword": ["IL","WA","TA"] }},
{ "range": { "balance": { "gte": 100000 }}}
]
}
}
}
在elastic4s里这样表示：
val qBool = search("bank").query(
boolQuery().must(
matchQuery("lastname","duke"),
termQuery("gender.keyword","M")
).not(
termQuery("fistname.keyword","Jackson"),
termQuery("city.keyword","Brogan")
).should(
termsQuery("state.keyword",Seq("IL","WA","TA")),
rangeQuery("balance").gte(100000)
)
)
上面例子里的must,must_no,should,filter各段落可以单独或联合形式任意出现在boolQuery里。在任何段落里还可以嵌入boolQuery, 如下：
GET /bank/_search { "query": { "bool": { "must": [ { "match": { "lastname": "duke" }}, { "term": { "gender.keyword": "M" }} ], "must_not": [ { "term": { "firstname.keyword": "Jackson"}}, { "term": { "city.keyword": "Brogan" }} ], "should": [ { "wildcard": { "email.keyword": "*.cn" }}, { "range": { "age": {"gte" : 80}}} ], "filter": [ { "terms": { "state.keyword": ["IL","WA","TA"] }}, { "range": { "balance": { "gte": 100000 }}}, { "bool" : { "should" : [ {"range" : {"balance" :{"gte" : 1000}}} ] } } ] } } }
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文章来源: 博客园
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