1、做一个实验，引出来为什么需要nested object
冗余数据方式的来建模，其实用的就是object类型，我们这里又要引入一种新的object类型，nested object类型
博客，评论，做的这种数据模型
PUT /website/blogs/6 { "title": "花无缺发表的一篇帖子", "content": "我是花无缺，大家要不要考虑一下投资房产和买股票的事情啊。。。", "tags": [ "投资", "理财" ], "comments": [ { "name": "小鱼儿", "comment": "什么股票啊？推荐一下呗", "age": 28, "stars": 4, "date": "2016-09-01" }, { "name": "黄药师", "comment": "我喜欢投资房产，风，险大收益也大", "age": 31, "stars": 5, "date": "2016-10-22" } ] }
被年龄是28岁的黄药师评论过的博客，搜索
GET /website/blogs/_search { "query": { "bool": { "must": [ { "match": { "comments.name": "黄药师" }}, { "match": { "comments.age": 28 }} ] } } } { "took": 102, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 1, "max_score": 1.8022683, "hits": [ { "_index": "website", "_type": "blogs", "_id": "6", "_score": 1.8022683, "_source": { "title": "花无缺发表的一篇帖子", "content": "我是花无缺，大家要不要考虑一下投资房产和买股票的事情啊。。。", "tags": [ "投资", "理财" ], "comments": [ { "name": "小鱼儿", "comment": "什么股票啊？推荐一下呗", "age": 28, "stars": 4, "date": "2016-09-01" }, { "name": "黄药师", "comment": "我喜欢投资房产，风，险大收益也大", "age": 31, "stars": 5, "date": "2016-10-22" } ] } } ] } }
结果是。。。好像不太对啊？？？
object类型数据结构的底层存储。。。
{ "title": [ "花无缺", "发表", "一篇", "帖子" ], "content": [ "我", "是", "花无缺", "大家", "要不要", "考虑", "一下", "投资", "房产", "买", "股票", "事情" ], "tags": [ "投资", "理财" ], "comments.name": [ "小鱼儿", "黄药师" ], "comments.comment": [ "什么", "股票", "推荐", "我", "喜欢", "投资", "房产", "风险", "收益", "大" ], "comments.age": [ 28, 31 ], "comments.stars": [ 4, 5 ], "comments.date": [ 2016-09-01, 2016-10-22 ] }
object类型底层数据结构，会将一个json数组中的数据，进行扁平化
所以，直接命中了这个document，name=黄药师，age=28，正好符合
2、引入nested object类型，来解决object类型底层数据结构导致的问题
修改mapping，将comments的类型从object设置为nested
PUT /website { "mappings": { "blogs": { "properties": { "comments": { "type": "nested", "properties": { "name": { "type": "string" }, "comment": { "type": "string" }, "age": { "type": "short" }, "stars": { "type": "short" }, "date": { "type": "date" } } } } } } } { "comments.name": [ "小鱼儿" ], "comments.comment": [ "什么", "股票", "推荐" ], "comments.age": [ 28 ], "comments.stars": [ 4 ], "comments.date": [ 2014-09-01 ] } { "comments.name": [ "黄药师" ], "comments.comment": [ "我", "喜欢", "投资", "房产", "风险", "收益", "大" ], "comments.age": [ 31 ], "comments.stars": [ 5 ], "comments.date": [ 2014-10-22 ] } { "title": [ "花无缺", "发表", "一篇", "帖子" ], "body": [ "我", "是", "花无缺", "大家", "要不要", "考虑", "一下", "投资", "房产", "买", "股票", "事情" ], "tags": [ "投资", "理财" ] }
再次搜索，成功了。。。
GET /website/blogs/_search { "query": { "bool": { "must": [ { "match": { "title": "花无缺" } }, { "nested": { "path": "comments", "query": { "bool": { "must": [ { "match": { "comments.name": "黄药师" } }, { "match": { "comments.age": 28 } } ] } } } } ] } } }
score_mode：max，min，avg，none，默认是avg
如果搜索命中了多个nested document，如何将个多个nested document的分数合并为一个分数
3、我们讲解一下基于nested object中的数据进行聚合分析
聚合数据分析的需求1：按照评论日期进行bucket划分，然后拿到每个月的评论的评分的平均值
GET /website/blogs/_search { "size": 0, "aggs": { "comments_path": { "nested": { "path": "comments" }, "aggs": { "group_by_comments_date": { "date_histogram": { "field": "comments.date", "interval": "month", "format": "yyyy-MM" }, "aggs": { "avg_stars": { "avg": { "field": "comments.stars" } } } } } } } } { "took": 52, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 2, "max_score": 0, "hits": [] }, "aggregations": { "comments_path": { "doc_count": 4, "group_by_comments_date": { "buckets": [ { "key_as_string": "2016-08", "key": 1470009600000, "doc_count": 1, "avg_stars": { "value": 3 } }, { "key_as_string": "2016-09", "key": 1472688000000, "doc_count": 2, "avg_stars": { "value": 4.5 } }, { "key_as_string": "2016-10", "key": 1475280000000, "doc_count": 1, "avg_stars": { "value": 5 } } ] } } } } GET /website/blogs/_search { "size": 0, "aggs": { "comments_path": { "nested": { "path": "comments" }, "aggs": { "group_by_comments_age": { "histogram": { "field": "comments.age", "interval": 10 }, "aggs": { "reverse_path": { "reverse_nested": {}, "aggs": { "group_by_tags": { "terms": { "field": "tags.keyword" } } } } } } } } } } { "took": 5, "timed_out": false, "_shards": { "total": 5, "successful": 5, "failed": 0 }, "hits": { "total": 2, "max_score": 0, "hits": [] }, "aggregations": { "comments_path": { "doc_count": 4, "group_by_comments_age": { "buckets": [ { "key": 20, "doc_count": 1, "reverse_path": { "doc_count": 1, "group_by_tags": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "投资", "doc_count": 1 }, { "key": "理财", "doc_count": 1 } ] } } }, { "key": 30, "doc_count": 3, "reverse_path": { "doc_count": 2, "group_by_tags": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 0, "buckets": [ { "key": "大侠", "doc_count": 1 }, { "key": "投资", "doc_count": 1 }, { "key": "理财", "doc_count": 1 }, { "key": "练功", "doc_count": 1 } ] } } } ] } } } }
