ES分组聚合：计算每个tag下的商品数量且某个filed包含指定关键字，分组，平均，每个tags下的平均价格，排序，指定范围区间
2024-06-02 20:27:47
1、第一个分析需求：计算每个tag下的商品数量
GET /ecommerce/product/_search
{"aggs": {"group_by_tags": {"terms": { "field": "tags" }}}
}执行之后的结果是：
{"error": {"root_cause": [{"type": "illegal_argument_exception","reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [tags] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory."}],"type": "search_phase_execution_exception","reason": "all shards failed","phase": "query","grouped": true,"failed_shards": [{"shard": 0,"index": "ecommerce","node": "urqovJ9yQPCO6fNM70Lc8w","reason": {"type": "illegal_argument_exception","reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [tags] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory."}}],"caused_by": {"type": "illegal_argument_exception","reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [tags] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory."}},"status": 400
}上面的报错的意思是要将文本field的fielddata属性设置为true
PUT /ecommerce/_mapping/product
{"properties": {"tags": {"type": "text","fielddata": true}}
}设置完成之后的效果是：
{"acknowledged": true
}然后再执行下面的操作：
GET /ecommerce/product/_search
{"aggs": {"group_by_tags": {"terms": {"field": "tags"}}}
}执行，然后看最后面的结果：
"aggregations": {"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "fangzhu","doc_count": 2},{"key": "meibai","doc_count": 2},{"key": "qingxin","doc_count": 1}]}
}说明按照tags里面的内容进行了buckets分组统计，可以看到每个tags出现的次数。
GET /ecommerce/product/_search
{"size": 0,"aggs": {"all_tags": {"terms": { "field": "tags" }}}
}{"took": 20,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 4,"max_score": 0,"hits": []},"aggregations": {"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "fangzhu","doc_count": 2},{"key": "meibai","doc_count": 2},{"key": "qingxin","doc_count": 1}]}}
}2、第二个聚合分析的需求：对名称中包含yagao的商品，计算每个tag下的商品数量
GET /ecommerce/product/_search
{"size": 0,"query": {"match": {"name": "yagao"}},"aggs": {"all_tags": {"terms": {"field": "tags"}}}
}运行结果是：
{"took": 6,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 4,"max_score": 0,"hits": []},"aggregations": {"all_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "fangzhu","doc_count": 2},{"key": "meibai","doc_count": 2},{"key": "qingxin","doc_count": 1}]}}
}3、第三个聚合分析的需求：先分组，再算每组的平均值，计算每个tag下的商品的平均价格
GET /ecommerce/product/_search
{"size": 0,"aggs" : {"group_by_tags" : {"terms" : { "field" : "tags" },"aggs" : {"avg_price" : {"avg" : { "field" : "price" }}}}}
}{"took": 8,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 4,"max_score": 0,"hits": []},"aggregations": {"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "fangzhu","doc_count": 2,"avg_price": {"value": 27.5 }},{"key": "meibai","doc_count": 2,"avg_price": {"value": 40 }},{"key": "qingxin","doc_count": 1,"avg_price": {"value": 40 }}]}}
}4、第四个数据分析需求：计算每个tag下的商品的平均价格，并且按照平均价格降序排序
GET /ecommerce/product/_search
{"size": 0,"aggs" : {"all_tags" : {"terms" : { "field" : "tags", "order": { "avg_price": "desc" } },"aggs" : {"avg_price" : {"avg" : { "field" : "price" }}}}}
}下面的语句的意思是：按照tags进行分组，并按照它里面的平均值进行降序排列
"terms" : { "field" : "tags", "order": { "avg_price": "desc" } } 上面的运行结果是：
{"took": 3,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 4,"max_score": 0,"hits": []},"aggregations": {"all_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "meibai","doc_count": 2,"avg_price": {"value": 40 }},{"key": "qingxin","doc_count": 1,"avg_price": {"value": 40 }},{"key": "fangzhu","doc_count": 2,"avg_price": {"value": 27.5 }}]}}
}5、第五个数据分析需求：按照指定的价格范围区间进行分组，然后在每组内再按照tag进行分组，最后再计算每组的平均价格
GET /ecommerce/product/_search
{"size": 0,"aggs": {"group_by_price": {"range": {"field": "price","ranges": [{"from": 0,"to": 20},{"from": 20,"to": 40},{"from": 40,"to": 50}]},"aggs": {"group_by_tags": {"terms": {"field": "tags"},"aggs": {"average_price": {"avg": {"field": "price"}}}}}}}
}最终的结果：
{"took": 61,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 4,"max_score": 0,"hits": []},"aggregations": {"group_by_price": {"buckets": [{"key": "0.0-20.0","from": 0,"to": 20,"doc_count": 0,"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [] }},{"key": "20.0-40.0","from": 20,"to": 40,"doc_count": 2,"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [ { "key": "fangzhu", "doc_count": 2, "average_price": { "value": 27.5 } }, { "key": "meibai", "doc_count": 1, "average_price": { "value": 30 } } ] }},{"key": "40.0-50.0","from": 40,"to": 50,"doc_count": 1,"group_by_tags": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [ { "key": "qingxin", "doc_count": 1, "average_price": { "value": 40 } } ] }}]}}
}ES分组聚合：计算每个tag下的商品数量且某个filed包含指定关键字，分组，平均，每个tags下的平均价格，排序，指定范围区间相关推荐
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