三、elasticsearch 实际应用(碎片篇)

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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