Elasticsearch 之（5）kibana多种搜索方式
query string search
搜索全部商品：GET /ecommerce/product/_search
took：耗费了几毫秒
timed_out：是否超时，这里是没有
_shards：数据拆成了5个分片，所以对于搜索请求，会打到所有的primary shard（或者是它的某个replica shard也可以）
hits.total：查询结果的数量，3个document
hits.max_score：score的含义，就是document对于一个search的相关度的匹配分数，越相关，就越匹配，分数也高
hits.hits：包含了匹配搜索的document的详细数据
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 1,
"hits": [
{
"_index": "ecommerce",
"_type": "product",
"_id": "2",
"_score": 1,
"_source": {
"name": "jiajieshi yagao",
"desc": "youxiao fangzhu",
"price": 25,
"producer": "jiajieshi producer",
"tags": [
"fangzhu"
]
}
},
{
"_index": "ecommerce",
"_type": "product",
"_id": "1",
"_score": 1,
"_source": {
"name": "gaolujie yagao",
"desc": "gaoxiao meibai",
"price": 30,
"producer": "gaolujie producer",
"tags": [
"meibai",
"fangzhu"
]
}
},
{
"_index": "ecommerce",
"_type": "product",
"_id": "3",
"_score": 1,
"_source": {
"name": "zhonghua yagao",
"desc": "caoben zhiwu",
"price": 40,
"producer": "zhonghua producer",
"tags": [
"qingxin"
]
}
}
]
}
}query string search的由来，因为search参数都是以http请求的query string来附带的
搜索商品名称中包含yagao的商品，而且按照售价降序排序：GET /ecommerce/product/_search?q=name:yagao&sort=price:desc
适用于临时的在命令行使用一些工具，比如curl，快速的发出请求，来检索想要的信息；但是如果查询请求很复杂，是很难去构建的
在生产环境中，几乎很少使用query string search
query DSL
DSL：Domain Specified Language，特定领域的语言
http request body：请求体，可以用json的格式来构建查询语法，比较方便，可以构建各种复杂的语法，比query string search肯定强大多了
查询所有的商品
GET /ecommerce/product/_search
{
"query": { "match_all": {} }
}查询名称包含yagao的商品，同时按照价格降序排序
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"name" : "yagao"
}
},
"sort": [
{ "price": "desc" }
]
}分页查询商品，总共3条商品，假设每页就显示1条商品，现在显示第2页，所以就查出来第2个商品
GET /ecommerce/product/_search
{
"query": { "match_all": {} },
"from": 1,
"size": 1
}指定要查询出来商品的名称和价格就可以
GET /ecommerce/product/_search
{
"query": { "match_all": {} },
"_source": ["name", "price"]
}更加适合生产环境的使用，可以构建复杂的查询
multi match
查询test_field 或 test_field1列中包含test
GET /test_index/test_type/_search
{
"query": {
"multi_match": {
"query": "test",
"fields": ["test_field", "test_field1"]
}
}
}bool
用bool组合多个搜索条件，来搜索name
GET /ecommerce/product/_search
{
"query": {
"bool": {
"must": { "match": { "name": "gaolujie" }},
"must_not": { "match": { "name": "jiajieshi" }},
"should": [
{ "match": { "title": "gaolujie" }},
{ "match": { "title": "lengsuanling" }}
]
}
}
}控制搜索结果的精准度的第二步：指定一些关键字中，必须至少匹配其中50%的关键字，才能作为结果返回
GET /ecommerce/product/_search
{
"query": {
"match": {
"title": {
"query": "gaolujie zhonghua yagao",
"minimum_should_match": "50%"
}
}
}
}query filter
搜索商品名称包含yagao，而且售价大于25元的商品
GET /ecommerce/product/_search
{
"query" : {
"bool" : {
"must" : {
"match" : {
"name" : "yagao"
}
},
"filter" : {
"range" : {
"price" : { "gt" : 25 }
}
}
}
}
}full-text search（全文检索）
GET /ecommerce/product/_search
{
"query" : {
"match" : {
"producer" : "yagao producer"
}
}
}producer这个字段，会先被拆解，建立倒排索引
special 4
yagao 4
producer 1,2,3,4
gaolujie 1
zhognhua 3
jiajieshi 2
yagao producer ---> yagao 和 producer
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 4,
"max_score": 0.70293105,
"hits": [
{
"_index": "ecommerce",
"_type": "product",
"_id": "4",
"_score": 0.70293105,
"_source": {
"name": "special yagao",
"desc": "special meibai",
"price": 50,
"producer": "special yagao producer",
"tags": [
"meibai"
]
}
},
{
"_index": "ecommerce",
"_type": "product",
"_id": "1",
"_score": 0.25811607,
"_source": {
"name": "gaolujie yagao",
"desc": "gaoxiao meibai",
"price": 30,
"producer": "gaolujie producer",
"tags": [
"meibai",
"fangzhu"
]
}
},
{
"_index": "ecommerce",
"_type": "product",
"_id": "3",
"_score": 0.25811607,
"_source": {
"name": "zhonghua yagao",
"desc": "caoben zhiwu",
"price": 40,
"producer": "zhonghua producer",
"tags": [
"qingxin"
]
}
},
{
"_index": "ecommerce",
"_type": "product",
"_id": "2",
"_score": 0.1805489,
"_source": {
"name": "jiajieshi yagao",
"desc": "youxiao fangzhu",
"price": 25,
"producer": "jiajieshi producer",
"tags": [
"fangzhu"
]
}
}
]
}
}搜索结果精准控制的第一步：灵活使用and关键字，如果你是希望所有的搜索关键字都要匹配的，那么就用and，可以实现单纯match query无法实现的效果
通常解决方案是，将一个string field建立两次索引，一个分词，用来进行搜索；一个不分词，用来进行排序（后续篇章讲解）
GET /ecommerce/product/_search
{
"query": {
"match": {
"title": {
"query": "java elasticsearch",
"operator": "and"
}
}
}
}如果对一个string field进行排序，结果往往不准确，因为分词后是多个单词，再排序就不是我们想要的结果了通常解决方案是，将一个string field建立两次索引，一个分词，用来进行搜索；一个不分词，用来进行排序（后续篇章讲解）
相当于
{
"bool": {
"must": [
{ "term": { "title": "java" }},
{ "term": { "title": "elasticsearch" }}
]
}
}phrase search (短语搜索)
跟全文检索相对应，相反，全文检索会将输入的搜索串拆解开来，去倒排索引里面去一一匹配，只要能匹配上任意一个拆解后的单词，就可以作为结果返回
phrase search，要求输入的搜索串，必须在指定的字段文本中，完全包含一模一样的，才可以算匹配，才能作为结果返回
GET /ecommerce/product/_search
{
"query" : {
"match_phrase" : {
"producer" : "yagao producer"
}
}
}
{
"took": 11,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0.70293105,
"hits": [
{
"_index": "ecommerce",
"_type": "product",
"_id": "4",
"_score": 0.70293105,
"_source": {
"name": "special yagao",
"desc": "special meibai",
"price": 50,
"producer": "special yagao producer",
"tags": [
"meibai"
]
}
}
]
}
}proximity match (近似匹配)
query string，搜索文本，中的几个term，要经过几次移动才能与一个document匹配，这个移动的次数，就是slop
hello world, java is very good, spark is also very good.
java spark，match phrase，搜不到
如果我们指定了slop，那么就允许java spark进行移动，来尝试与doc进行匹配
java is very good spark is
java spark
java --> spark
java --> spark
java --> spark
这里的slop，就是3，因为java spark这个短语，spark移动了3次，就可以跟一个doc匹配上了
slop的含义，不仅仅是说一个query string terms移动几次，跟一个doc匹配上。一个query string terms，最多可以移动几次去尝试跟一个doc匹配上
slop，设置的是3，那么就ok
GET /forum/article/_search
{
"query": {
"match_phrase": {
"title": {
"query": "java spark",
"slop": 3
}
}
}
}其实，加了slop的phrase match，就是proximity match，近似匹配
1、java spark，短语，doc，phrase match
2、java spark，可以有一定的距离，但是靠的越近，越先搜索出来，proximity match
mget 批量查询
1、批量查询的好处
就是一条一条的查询，比如说要查询100条数据，那么就要发送100次网络请求，这个开销还是很大的
如果进行批量查询的话，查询100条数据，就只要发送1次网络请求，网络请求的性能开销缩减100倍
2、mget的语法
（1）一条一条的查询
GET /test_index/test_type/1
GET /test_index/test_type/2
（2）mget批量查询
GET /_mget
{
"docs" : [
{
"_index" : "test_index",
"_type" : "test_type",
"_id" : 1
},
{
"_index" : "test_index",
"_type" : "test_type",
"_id" : 2
}
]
}
{
"docs": [
{
"_index": "test_index",
"_type": "test_type",
"_id": "1",
"_version": 2,
"found": true,
"_source": {
"test_field1": "test field1",
"test_field2": "test field2"
}
},
{
"_index": "test_index",
"_type": "test_type",
"_id": "2",
"_version": 1,
"found": true,
"_source": {
"test_content": "my test"
}
}
]
}
（3）如果查询的document是一个index下的不同type种的话
GET /test_index/_mget
{
"docs" : [
{
"_type" : "test_type",
"_id" : 1
},
{
"_type" : "test_type",
"_id" : 2
}
]
}（4）如果查询的数据都在同一个index下的同一个type下，最简单了
GET /test_index/test_type/_mget
{
"ids": [1, 2]
}3、mget的重要性
可以说mget是很重要的，一般来说，在进行查询的时候，如果一次性要查询多条数据的话，那么一定要用batch批量操作的api
尽可能减少网络开销次数，可能可以将性能提升数倍，甚至数十倍，非常非常之重要
bulk语法
POST /_bulk
{ "delete": { "_index": "test_index", "_type": "test_type", "_id": "3" }}
{ "create": { "_index": "test_index", "_type": "test_type", "_id": "12" }}
{ "test_field": "test12" }
{ "index": { "_index": "test_index", "_type": "test_type", "_id": "2" }}
{ "test_field": "replaced test2" }
{ "update": { "_index": "test_index", "_type": "test_type", "_id": "1", "_retry_on_conflict" : 3} }
{ "doc" : {"test_field2" : "bulk test1"} }每一个操作要两个json串，语法如下：
{"action": {"metadata"}}
{"data"}
举例，比如你现在要创建一个文档，放bulk里面，看起来会是这样子的：
{"index": {"_index": "test_index", "_type", "test_type", "_id": "1"}}
{"test_field1": "test1", "test_field2": "test2"}
有哪些类型的操作可以执行呢？
（1）delete：删除一个文档，只要1个json串就可以了
（2）create：PUT /index/type/id/_create，强制创建
（3）index：普通的put操作，可以是创建文档，也可以是全量替换文档
（4）update：执行的partial update操作
bulk api对json的语法，有严格的要求，每个json串不能换行，只能放一行，同时一个json串和一个json串之间，必须有一个换行
{
"error": {
"root_cause": [
{
"type": "json_e_o_f_exception",
"reason": "Unexpected end-of-input: expected close marker for Object (start marker at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@5a5932cd; line: 1, column: 1])\n at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@5a5932cd; line: 1, column: 3]"
}
],
"type": "json_e_o_f_exception",
"reason": "Unexpected end-of-input: expected close marker for Object (start marker at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@5a5932cd; line: 1, column: 1])\n at [Source: org.elasticsearch.transport.netty4.ByteBufStreamInput@5a5932cd; line: 1, column: 3]"
},
"status": 500
}
{
"took": 41,
"errors": true,
"items": [
{
"delete": {
"found": true,
"_index": "test_index",
"_type": "test_type",
"_id": "10",
"_version": 3,
"result": "deleted",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"create": {
"_index": "test_index",
"_type": "test_type",
"_id": "3",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true,
"status": 201
}
},
{
"create": {
"_index": "test_index",
"_type": "test_type",
"_id": "2",
"status": 409,
"error": {
"type": "version_conflict_engine_exception",
"reason": "[test_type][2]: version conflict, document already exists (current version [1])",
"index_uuid": "6m0G7yx7R1KECWWGnfH1sw",
"shard": "2",
"index": "test_index"
}
}
},
{
"index": {
"_index": "test_index",
"_type": "test_type",
"_id": "4",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": true,
"status": 201
}
},
{
"index": {
"_index": "test_index",
"_type": "test_type",
"_id": "2",
"_version": 2,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"created": false,
"status": 200
}
},
{
"update": {
"_index": "test_index",
"_type": "test_type",
"_id": "1",
"_version": 3,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
}
]
}bulk操作中，任意一个操作失败，是不会影响其他的操作的，但是在返回结果里，会告诉你异常日志
POST /test_index/_bulk
{ "delete": { "_type": "test_type", "_id": "3" }}
{ "create": { "_type": "test_type", "_id": "12" }}
{ "test_field": "test12" }
{ "index": { "_type": "test_type" }}
{ "test_field": "auto-generate id test" }
{ "index": { "_type": "test_type", "_id": "2" }}
{ "test_field": "replaced test2" }
{ "update": { "_type": "test_type", "_id": "1", "_retry_on_conflict" : 3} }
{ "doc" : {"test_field2" : "bulk test1"} }
POST /test_index/test_type/_bulk
{ "delete": { "_id": "3" }}
{ "create": { "_id": "12" }}
{ "test_field": "test12" }
{ "index": { }}
{ "test_field": "auto-generate id test" }
{ "index": { "_id": "2" }}
{ "test_field": "replaced test2" }
{ "update": { "_id": "1", "_retry_on_conflict" : 3} }
{ "doc" : {"test_field2" : "bulk test1"} }
2、bulk size最佳大小
bulk request会加载到内存里，如果太大的话，性能反而会下降，因此需要反复尝试一个最佳的bulk size。一般从1000~5000条数据开始，尝试逐渐增加。另外，如果看大小的话，最好是在5~15MB之间。
scoll搜索会在第一次搜索的时候，保存一个当时的视图快照，之后只会基于该旧的视图快照提供数据搜索，如果这个期间数据变更，是不会让用户看到的
采用基于_doc进行排序的方式，性能较高
每次发送scroll请求，我们还需要指定一个scoll参数，指定一个时间窗口，每次搜索请求只要在这个时间窗口内能完成就可以了
scoll
如果一次性要查出来比如10万条数据，那么性能会很差，此时一般会采取用scoll滚动查询，一批一批的查，直到所有数据都查询完处理完。使用scoll滚动搜索，可以先搜索一批数据，然后下次再搜索一批数据，以此类推，直到搜索出全部的数据来scoll搜索会在第一次搜索的时候，保存一个当时的视图快照，之后只会基于该旧的视图快照提供数据搜索，如果这个期间数据变更，是不会让用户看到的
采用基于_doc进行排序的方式，性能较高
每次发送scroll请求，我们还需要指定一个scoll参数，指定一个时间窗口，每次搜索请求只要在这个时间窗口内能完成就可以了
GET /test_index/test_type/_search?scroll=1m
{
"query": {
"match_all": {}
},
"sort": [ "_doc" ],
"size": 3
}
{
"_scroll_id": "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAACxeFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAAsYBY0b25zVFlWWlRqR3ZJajlfc3BXejJ3AAAAAAAALF8WNG9uc1RZVlpUakd2SWo5X3NwV3oydwAAAAAAACxhFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAAsYhY0b25zVFlWWlRqR3ZJajlfc3BXejJ3",
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 10,
"max_score": null,
"hits": [
{
"_index": "test_index",
"_type": "test_type",
"_id": "8",
"_score": null,
"_source": {
"test_field": "test client 2"
},
"sort": [
0
]
},
{
"_index": "test_index",
"_type": "test_type",
"_id": "6",
"_score": null,
"_source": {
"test_field": "tes test"
},
"sort": [
0
]
},
{
"_index": "test_index",
"_type": "test_type",
"_id": "AVp4RN0bhjxldOOnBxaE",
"_score": null,
"_source": {
"test_content": "my test"
},
"sort": [
0
]
}
]
}
}获得的结果会有一个scoll_id，下一次再发送scoll请求的时候，必须带上这个scoll_idGET /_search/scroll
{
"scroll": "1m",
"scroll_id" : "DnF1ZXJ5VGhlbkZldGNoBQAAAAAAACxeFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAAsYBY0b25zVFlWWlRqR3ZJajlfc3BXejJ3AAAAAAAALF8WNG9uc1RZVlpUakd2SWo5X3NwV3oydwAAAAAAACxhFjRvbnNUWVZaVGpHdklqOV9zcFd6MncAAAAAAAAsYhY0b25zVFlWWlRqR3ZJajlfc3BXejJ3"
}scoll，看起来挺像分页的，但是其实使用场景不一样。分页主要是用来一页一页搜索，给用户看的；scoll主要是用来一批一批检索数据，让系统进行处理的
