参考
一、测试
Must 里面的都得成立
should里面的成立一个即可，若should里面只有一个，那么就用这一个的条件
term 精确匹配，不会分词
match_phrase 精确匹配
会分词，一般是按照空格分目标文档包含，分词后的所有词文档彼此位于相同的位置
使用boost的场景，term、range、boost
Range gt、le 大于小于 y M w d h H m s
script脚本操作
//doc['price']
GET /mall/product/_search
{
"query": {
"script": {
"script": {
"inline": "doc['price'].value > params.num",
"params": {"num": 25}
}
}
}
}
//ctx._source
POST /mall/product/3/_update
{
"script": {
"inline": "ctx._source.price = params.num",
"params": {"num": 40}
}
}
PUT /lulog/doc/2
{
"log_size": 2
}
POST /lulog/doc/_search
{
"query": {
"match_all": {}
},
"script_fields": {
"total_size": {
"script": {
"inline": "int total=0;for(int i=0; i<doc['log_size'].length;i++){total += doc['log_size'][i];}return total"
}
}
}
}
POST /lulog/doc/_search
{
"query": {
"match_all": {}
},
"script_fields": {
"count": {
"script": {
"inline": "return doc['log_size'].value + 1"
}
}
}
}
聚合：
Metric统计，min、max、sum、avg、stats、extended_stats
Bucket分类，terms，range，date_range，histogram，date_histogram，filter过滤
Pipeline对bucket分类的进行统计，min_bucket，max_bucket，sum_bucket，avg_bucket，stats_bucket，extended_stats_bucket
Matrix计算两个数值型字段之间的关系
GET incall_daily-2020.04.24/doc/_search
{
"aggs": {
"asdjhasd": {
"matrix_stats": {
"fields": ["applause_count", "answer_count"]
}
}
}
}
{
"name": "applause_count", //字段名称
"count": 154, //字段样本数量
"mean": 44.31818181818182, //平均值
"variance": 276.04842543077837, //方差，偏离平均值的程度
"skewness": -1.60794152400977, //偏度，在平均值附近的非对称分布情况的量化
"kurtosis": 5.006387495325678, //峰度，分布的形状的量化
"covariance": { //协方差，描述一个字段数据随另一个字段数据变化程度的矩阵
"applause_count": 276.04842543077837,
"answer_count": 290.8722519310755
},
"correlation": { //相关性，描述两个字段数据之间的分布关系，其协方差矩阵取值为[-1, 1]
"applause_count": 1.0,
"answer_count": 0.9397723506362066
}
}, {
"name": "answer_count",
"count": 154,
"mean": 53.48051948051948,
"variance": 347.035565741448,
"skewness": -2.2011119165278625,
"kurtosis": 6.583666847748493,
"covariance": {
"applause_count": 290.8722519310755,
"answer_count": 347.035565741448
},
"correlation": {
"applause_count": 0.9397723506362066,
"answer_count": 1.0
}
}
如果是聚合，需要过滤数据，建议使用filter，不要使用query
为啥？filter 只会匹配文档是不是我要的，query匹配好之后，还多了一个相关性算分的过程，因此filter效率更高
GET incall_daily*/doc/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"parent_id": "17871"
}
}
]
}
},
"aggs": {
"a1": {
"date_histogram": {
"field": "create_time",
"interval": "2d"
},
"aggs": {
"a2": {
"stats": {
"field": "applause_count"
}
}
}
}
}
}
GET incall_daily-2020.04.24/doc/_search
{
"aggs": {
"f1": {
"filter": {
"term": {
"user_id": "17871"
}
},
"aggs": {
"a1": {
"date_histogram": {
"field": "create_time",
"interval": "2d"
},
"aggs": {
"a2": {
"terms": {
"field": "applause_count"
}
}
}
}
}
}
}
}
二、查询篇
1、高亮查询
GET callcenter/_search
{
"query": {
"match": {
"content": "天翼"
}
},
"size": 30,
"highlight": {
"pre_tags": "<span>",
"post_tags": "</span>",
"fields": {
"content": {
"fragment_size": 20, //从content内容的前20个字来找高亮字段
}
}
}
}
2、查询字符串查询
query_string，只有 AND OR，且必须是大写
simple_query_string，+与 |或 -非 () *
GET /callcenter/_search
{
"query": {
"query_string": {
"fields": ["title", "content"],
"query": "天翼 AND (骚扰 OR 商务)"
}
}
}
GET /callcenter/_search
{
"query": {
"query_string": {
"fields": ["title", "content"],
"query": "(商务 OR 直播) AND 商务"
}
}
}
GET callcenter/_search
{
"query": {
"simple_query_string": {
"query": "-商务 + -工号 + -外勤 + -网吧 + -天翼",
"fields": ["title"]
}
}
}
相关性
GET callcenter/_search
{
"query": {
"boosting": {
"positive": {
"match": {
"content": "天翼商务"
}
},
"negative": {
"match": {
"content": "商务"
}
},
"negative_boost": 0.2
}
},
"size": 50
}
3、复合查询
该查询实现和上面的查询一样的效果，看得出，代码写的贼麻烦
GET callcenter/_search
{
"query": {
"bool": {
"must": [
{
"bool": {
"must_not": [
{"match": {"title": "商务"}},
{"match": {"title": "工号"}},
{"match": {"title": "外勤"}},
{"match": {"title": "网吧"}},
{"match": {"title": "天翼"}}
]
}
},
{
"bool": {
"must_not": [
{"match": {"content": "商务"}},
{"match": {"content": "工号"}},
{"match": {"content": "外勤"}},
{"match": {"content": "网吧"}},
{"match": {"content": "天翼"}}
]
}
}
]
}
}
}
4、精确查找
GET callcenter/_search
{
"query": {
"term": {
"title": {
"value": "商务"
}
}
}
}
GET callcenter/_search
{
"query": {
"match_phrase": {
"title": "商务"
}
}
}
GET callcenter/_search
{
"query": {
"terms": {
"title": [
"电信",
"号码"
]
}
}
}
5、分页查询
GET article/_search
{
"query": {
"match_all": {}
},
"sort": [
{
"date": {
"order": "desc"
}
}
],
"from": 20,
"size": 20
}
滚动翻页
GET article/_search?scroll=1m
{
"query": {
"match_all": {}
},
"sort": [
{
"date": {
"order": "desc"
}
}
],
"size": 10
}
GET _search/scroll
{
"scroll": "1m",
"scroll_id": "DnF1ZXJ5VGhlbkZldGNoBQAAAAAABdUMFk5uSzVIbGRuU1FTb1RoT0x5dTVyM2cAAAAAAAXVDRZObks1SGxkblNRU29UaE9MeXU1cjNnAAAAAAAF1Q4WTm5LNUhsZG5TUVNvVGhPTHl1NXIzZwAAAAAABdUPFk5uSzVIbGRuU1FTb1RoT0x5dTVyM2cAAAAAAAXVEBZObks1SGxkblNRU29UaE9MeXU1cjNn"
}
6、聚合查询
GET incall_daily-2020.04.24/_search
{
"aggs": {
"f": {
"range": {
"field": "create_time",
"ranges": [
{
"from": "2020-04-23T00:00:00.000Z"
}
]
},
"aggs": {
"d1": {
"date_histogram": {
"field": "create_time",
"interval": "1d",
"order": {
"_key": "desc"
}
},
"aggs": {
"d2": {
"terms": {
"field": "order_type.keyword",
"order": {
"d3": "desc"
}
},
"aggs": {
"d3": {
"sum": {
"field": "answer_count"
}
}
}
}
}
}
}
}
}
}
7、建议查询
精准程度上(Precision)看： Completion > Phrase > term
速度上，Completion是最快的
自动补全设计：优先取Completion，依次...
POST /lu_article/_search
{
"_source": ["title", "descript", "create_time"],
"suggest": {
"s1": {
"prefix": "cento",
"completion": {
"field": "suggest",
"fuzzy": {
}
}
},
"s2": {
"text": "elasticseqrch mybatls mysal",
"phrase": {
"field": "descript",
"highlight": {
"pre_tag": "<em>",
"post_tag": "</em>"
}
}
},
"s3": {
"text": "elasticseqrch mybatls mysal",
"term": {
"field": "descript"
}
}
}
}
##completion创建字段，插入值，自动补全，suggest可以是字符串、对象、数组，weight选填
"suggest": {
"type": "completion",
"analyzer": "ik_max_word"
},
PUT /lu_article/_doc/1
{
"suggest": [
{
"input": "lucene solr",
"weight": 1
},
{
"input": "lucene so cool",
"weight": 4
},
{
"input": "lucene elasticsearch",
"weight": 3
}
]
}
POST /lu_article/_search
{
"_source": ["title", "descript", "create_time", "suggest"],
"suggest": {
"s1": {
"prefix": "luce",
"completion": {
"field": "suggest"
}
}
}
}
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