elasticsearch查询之keyword字段的查询相关度评分控制
一、数据情况
purchase记录每个用户的购买信息；
PUT purchase
{
"mappings":{
"properties":{
"id":{
"type":"keyword"
},
"name":{
"type":"text"
},
"goods":{
"properties":{
"id":{
"type":"keyword"
},
"name":{
"type":"text"
}
}
}
}
}
}
index 三个document
PUT purchase/_doc/1
{
"id":1,
"name":"sam",
"goods":[
{"id":"g1","name":"ipad"},
{"id":"g2","name":"iphone"}
]
}
PUT purchase/_doc/2
{
"id":2,
"name":"coco",
"goods":[
{"id":"g1","name":"ipad"},
{"id":"g2","name":"iphone"},
{"id":"g3","name":"ipod"}
]
}
PUT purchase/_doc/3
{
"id":3,
"name":"jim",
"goods":[
{"id":"g1","name":"ipad"},
{"id":"g2","name":"iphone"},
{"id":"g3","name":"ipod"},
{"id":"g4","name":"TV"}
]
}
查看索引数据情况
POST purchase/_search
{
"query": {
"match_all": {}
}
}
{
"took":331,
"timed_out":false,
"_shards":{
"total":1,
"successful":1,
"skipped":0,
"failed":0
},
"hits":{
"total":{
"value":3,
"relation":"eq"
},
"max_score":1,
"hits":[
{
"_index":"purchase",
"_id":"1",
"_score":1,
"_source":{
"id":1,
"name":"sam",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
}
]
}
},
{
"_index":"purchase",
"_id":"2",
"_score":1,
"_source":{
"id":2,
"name":"coco",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
}
]
}
},
{
"_index":"purchase",
"_id":"3",
"_score":1,
"_source":{
"id":3,
"name":"jim",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
},
{
"id":"g4",
"name":"TV"
}
]
}
}
]
}
}
二、查询需求
我们需要查询购买过某种商品的顾客，一般我们可以通过ui的业务逻辑得到需要筛选的一些商品的id，由于id字段是一个不需要分词的keyword字段，所以我们会直接使用term级别的查询；
POST purchase/_search
{
"query": {
"terms": {
"goods.id": [
"g2",
"g3",
"g4"
]
}
}
}
我们可以看到查询结果中的三条记录的权重打分都是1；正常情况下购买商品越多的客户，相对来说价值更大即命中的权重得分越大；
{
"took":0,
"timed_out":false,
"_shards":{
"total":1,
"successful":1,
"skipped":0,
"failed":0
},
"hits":{
"total":{
"value":3,
"relation":"eq"
},
"max_score":1,
"hits":[
{
"_index":"purchase",
"_id":"1",
"_score":1,
"_source":{
"id":1,
"name":"sam",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
}
]
}
},
{
"_index":"purchase",
"_id":"2",
"_score":1,
"_source":{
"id":2,
"name":"coco",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
}
]
}
},
{
"_index":"purchase",
"_id":"3",
"_score":1,
"_source":{
"id":3,
"name":"jim",
"goods":[
{
"id":"g1",
"name":"ipad"
},
{
"id":"g2",
"name":"iphone"
},
{
"id":"g3",
"name":"ipod"
},
{
"id":"g4",
"name":"TV"
}
]
}
}
]
}
}
三、terms查询分析
我们使用_explain分析一下terms查询怎么打分的；
POST purchase/_explain/3
{
"query": {
"terms": {
"goods.id": [
"g2",
"g3",
"g4"
]
}
}
}
我们可以看到elasticsearch最终使用ConstantScore查询重写的terms查询，此查询默认权重打分为1；
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 1.0,
"description" : "ConstantScore(goods.id:g2 goods.id:g3 goods.id:g4)",
"details" : [ ]
}
}
terms提供的查询参数十分有限，其中涉及权重的只有boost，但是这只是针对整个terms查询，而不是内部的子查询；
POST purchase/_explain/3
{
"query": {
"terms": {
"goods.id": [
"g2",
"g3",
"g4"
],
"boost":2
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 2.0,
"description" : "ConstantScore(goods.id:g2 goods.id:g3 goods.id:g4)^2.0",
"details" : [ ]
}
}
四、构建子查询打分
match是elasticsearch提供的一个跟terms类似的查询，由于goods.id的type是keyword，所以需要给match指定一个查询时的analyzer，才能保证输入的几个id分开作为不同的查询；
POST purchase/_search
{
"query": {
"match": {
"goods.id": {
"query": "g2 g3 g4",
"analyzer":"standard"
}
}
}
}
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 2.178501,
"hits" : [
{
"_index" : "purchase",
"_id" : "3",
"_score" : 2.178501,
"_source" : {
"id" : 3,
"name" : "jim",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
},
{
"id" : "g4",
"name" : "TV"
}
]
}
},
{
"_index" : "purchase",
"_id" : "2",
"_score" : 0.8298607,
"_source" : {
"id" : 2,
"name" : "coco",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
}
]
}
},
{
"_index" : "purchase",
"_id" : "1",
"_score" : 0.18360566,
"_source" : {
"id" : 1,
"name" : "sam",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
}
]
}
}
]
}
}
通过查看文档3的打分情况，我们可以看到elasticsearch先针对每个关键字计算打分，然后将三项打分的和作为最终的打分；在这里我们也可以看到elasticsearch内部会自动将match查询rewrite为三个子查询；
POST purchase/_explain/3
{
"query": {
"match": {
"goods.id": {
"query": "g2 g3 g4",
"analyzer":"standard"
}
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 2.178501,
"description" : "sum of:",
"details" : [
{
"value" : 0.18360566,
"description" : "weight(goods.id:g2 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 0.646255,
"description" : "weight(goods.id:g3 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 1.3486402,
"description" : "weight(goods.id:g4 in 2) [PerFieldSimilarity], result of:",
"details" : []
}
]
}
}
我们也可以通过bool查询，使用它的should在查询之前手动组建多个子查询；
POST purchase/_search
{
"query": {
"bool": {
"should": [
{"term": {"goods.id": "g2"}},
{"term": {"goods.id": "g3"}},
{"term": {"goods.id": "g4"}}
],
"minimum_should_match": 1
}
}
}
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 2.178501,
"hits" : [
{
"_index" : "purchase",
"_id" : "3",
"_score" : 2.178501,
"_source" : {
"id" : 3,
"name" : "jim",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
},
{
"id" : "g4",
"name" : "TV"
}
]
}
},
{
"_index" : "purchase",
"_id" : "2",
"_score" : 0.8298607,
"_source" : {
"id" : 2,
"name" : "coco",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
}
]
}
},
{
"_index" : "purchase",
"_id" : "1",
"_score" : 0.18360566,
"_source" : {
"id" : 1,
"name" : "sam",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
}
]
}
}
]
}
}
在bool查询中，通过查看文档3的打分情况，我们可以看到elasticsearch也是先针对每个关键字计算打分，然后将三项打分的和作为最终的打分；
POST purchase/_explain/3
{
"query": {
"bool": {
"should": [
{"term": {"goods.id": "g2"}},
{"term": {"goods.id": "g3"}},
{"term": {"goods.id": "g4"}}
],
"minimum_should_match": 1
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 2.178501,
"description" : "sum of:",
"details" : [
{
"value" : 0.18360566,
"description" : "weight(goods.id:g2 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 0.646255,
"description" : "weight(goods.id:g3 in 2) [PerFieldSimilarity], result of:",
"details" : []
},
{
"value" : 1.3486402,
"description" : "weight(goods.id:g4 in 2) [PerFieldSimilarity], result of:",
"details" : []
}
]
}
}
五、控制子查询的打分
不管是elasticsearch自动组建子查询，还是我们自己手动构建子查询，elasticsearch都会针对每个查询做相关性的打分计算，这对于一般的语义化关键字搜索是没有问题的；
我们这里的搜索条件goods.id一般是没有任何语义的，不同的值打分应该是一样的；这样我们只能使用bool+constant_score+term来手动构建查询语句；
POST purchase/_search
{
"query": {
"bool": {
"should": [
{"constant_score": {"filter": {"term": {"goods.id": "g2"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g3"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g4"}}}}
],
"minimum_should_match": 1
}
}
}
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 3.0,
"hits" : [
{
"_index" : "purchase",
"_id" : "3",
"_score" : 3.0,
"_source" : {
"id" : 3,
"name" : "jim",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
},
{
"id" : "g4",
"name" : "TV"
}
]
}
},
{
"_index" : "purchase",
"_id" : "2",
"_score" : 2.0,
"_source" : {
"id" : 2,
"name" : "coco",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
},
{
"id" : "g3",
"name" : "ipod"
}
]
}
},
{
"_index" : "purchase",
"_id" : "1",
"_score" : 1.0,
"_source" : {
"id" : 1,
"name" : "sam",
"goods" : [
{
"id" : "g1",
"name" : "ipad"
},
{
"id" : "g2",
"name" : "iphone"
}
]
}
}
]
}
}
我们看下文档3的打分情况，每一个命中项的打分都是固定的1，最终的打分命中项的和；
POST purchase/_explain/3
{
"query": {
"bool": {
"should": [
{"constant_score": {"filter": {"term": {"goods.id": "g2"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g3"}}}},
{"constant_score": {"filter": {"term": {"goods.id": "g4"}}}}
],
"minimum_should_match": 1
}
}
}
{
"_index" : "purchase",
"_id" : "3",
"matched" : true,
"explanation" : {
"value" : 3.0,
"description" : "sum of:",
"details" : [
{
"value" : 1.0,
"description" : "ConstantScore(goods.id:g2)",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "ConstantScore(goods.id:g3)",
"details" : [ ]
},
{
"value" : 1.0,
"description" : "ConstantScore(goods.id:g4)",
"details" : [ ]
}
]
}
}
