以bank account 数据为例，认识elasticsearch query 和 filter
Elasticsearch 查询语言（Query DSL）认识(一)
一、基本认识
查询子句的行为取决于
- query context
- filter context
也就是执行的是查询（query）还是过滤(filter)
query context 描述的是：被搜索的文档和查询子句的匹配程度
filter context 描述的是： 被搜索的文档和查询子句是否匹配
一个是匹配程度问题，一个是是否匹配的问题
二、实例
- 导入数据 bank account data download
将数据导入到elasticsearch
curl -XPOST 'localhost:9200/bank/account/_bulk?pretty' --data-binary "@accounts.json" curl 'localhost:9200/_cat/indices?v'这里有两个地方需要注意，1.host要改成符合自己的。2.早期版本中下载的数据可以能是'accounts.json?raw=true'
大概如下 curl -XPOST 'wbelk:9200/bank/account/_bulk?pretty' --data-binary "@accounts.json?raw=true"参数认识
为了便捷操作，可以安装一个kiabna sense
$./bin/kibana plugin --install elastic/sense
$./bin/kibana
sudo -i service restart kibana(或者用这个启动kibana)
match_all 搜索，直接返回所有文档
GET /bank/_search
{
"query": {
"match_all": {}
}
}返回大致如下：
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1000,
"max_score": 1,
"hits": [
{
"_index": "bank",
"_type": "account",
"_id": "25",
"_score": 1,
"_source": {
"account_number": 25,
"balance": 40540,
"firstname": "Virginia",
"lastname": "Ayala",
"age": 39,
"gender": "F",
"address": "171 Putnam Avenue",
"employer": "Filodyne",
"email": "[email protected]",
"city": "Nicholson",
"state": "PA"
}
},参数大致解释：
- took: 执行搜索耗时，毫秒为单位，也就是本文我1ms
- time_out: 搜索是否超时
- _shards: 多少分片被搜索，成功多少，失败多少
- hits： 搜索结果展示
- hits.total: 匹配条件的文档总数
- hits.hits: 返回结果展示，默认返回十个
- hits.max_score：最大匹配得分
- hits._score: 返回文档的匹配得分（得分越高，匹配程度越高，越靠前）
- _index _type _id 作为剥层定位到特定的文档
- _source 文档源
- 查询语言之 执行查询
只显示account_number 和 balance
POST /bank/_search { "query": { "match_all": {} }, "_source": ["account_number", "balance"] }{ "took": 2, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1000, "max_score": 1, "hits": [ { "_index": "bank", "_type": "account", "_id": "25", "_score": 1, "_source": { "account_number": 25, "balance": 40540 } }, { "_index": "bank", "_type": "account", "_id": "44", "_score": 1, "_source": { "account_number": 44, "balance": 34487 } }, { "_index": "bank", "_type": "account", "_id": "99", "_score": 1, "_source": { "account_number": 99, "balance": 47159 } },返回accountu_number 为20的document
POST /bank/_search { "query": { "match": { "account_number": 20 } } }{ "took": 4, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1, "max_score": 5.6587105, "hits": [ { "_index": "bank", "_type": "account", "_id": "20", "_score": 5.6587105, "_source": { "account_number": 20, "balance": 16418, "firstname": "Elinor", "lastname": "Ratliff", "age": 36, "gender": "M", "address": "282 Kings Place", "employer": "Scentric", "email": "[email protected]", "city": "Ribera", "state": "WA" } } ] } }返回地址中包含(term)mill的所有账户
POST /bank/_search { "query": { "match": { "address": "mill" } } }返回地址中包含term 'mill'或者 'lane'的所有账户
POST /bank/_search { "query": { "match": { "address": "mill lane" } } }匹配phrase 'mill lane'
POST /bank/_search { "query": { "match_phrase": { "address": "mill lane" } } }返回address包含'mill'和'lane'的所有账户 (AND)
POST /bank/_search { "query": { "bool": { "must": [ { "match": { "address": "mill" } }, { "match": { "address": "lane" } } ] } } }返回address包含'mill'或'lane'的所有账户 (OR)
POST /bank/_search { "query": { "bool": { "should": [ { "match": { "address": "mill" } }, { "match": { "address": "lane" } } ] } } }返回address既不包含'mill'也不包含'lane'的所有账户 (NO)
POST /bank/_search { "query": { "bool": { "must_not": [ { "match": { "address": "mill" } }, { "match": { "address": "lane" } } ] } } }返回age为40，并且state不是ID的所有账户 （组合）
POST /bank/_search { "query": { "bool": { "must": [ { "match": { "age": "40" } } ], "must_not": [ { "match": { "state": "ID" } } ] } } }
- 查询语言之 执行过滤
过滤不会进行相关度得分的计算
- 在所有账户中寻找balance 在29900到30000之间（闭区间）的所有账户
（先查询到所有的账户，然后进行过滤）
POST /bank/_search
{
"query": {
"filtered": {
"query": { "match_all": {} },
"filter": {
"range": {
"balance": {
"gte": 29900,
"lte": 30000
}
}
}
}
}
}{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 5,
"max_score": 1,
"hits": [
{
"_index": "bank",
"_type": "account",
"_id": "243",
"_score": 1,
"_source": {
"account_number": 243,
"balance": 29902,
"firstname": "Evangelina",
"lastname": "Perez",
"age": 20,
"gender": "M",
"address": "787 Joval Court",
"employer": "Keengen",
"email": "[email protected]",
"city": "Mulberry",
"state": "SD"
}
},
{
"_index": "bank",
"_type": "account",
"_id": "781",
"_score": 1,
"_source": {
"account_number": 781,
"balance": 29961,
"firstname": "Sanford",
"lastname": "Mullen",
"age": 26,
"gender": "F",
"address": "879 Dover Street",
"employer": "Zanity",
"email": "[email protected]",
"city": "Martinez",
"state": "TX"
}
},
...根据返回结果我们可以看到filter得到的_score为1.不存在程度上的问题。是0和1的问题
三、query和filter效率
一般认为filter的速度快于query的速度
- filter不会计算相关度得分，效率高
- filter的结果可以缓存到内存中，方便再用