第一个分析需求：计算每个tag下的商品数量
例如：
GET /ecommerce/product/_search
{
"aggs": {
"group_by_tags": {
"terms": { "field": "tags" }
}
}
}
-------------------------------------------------------------------------------------------------
结果出现这种错误
解决方式：将fielddata设置为true
PUT /ecommerce/_mapping/product
{
"properties": {
"tags": {
"type": "text",
"fielddata": true
}
}
}
-------------------------------------------------------------------------------------------------
设置fielddata为true后查询结果：
{
"took": 9,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 4,
"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": "4",
"_score": 1,
"_source": {
"name": "special yagao",
"desc": "special meibai",
"price": 50,
"producer": "special yagao producer",
"tags": [
"meibai"
]
}
},
{
"_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"
]
}
}
]
},
"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
}
]
}
}
}
以上结果有其他数据，精简结果，设置size：0
GET /ecommerce/product/_search
{
"size": 0,
"aggs": {
"group_by_tags": {
"terms": {
"field": "tags"
}
}
}
}
查询结果
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
举例：
GET /ecommerce/product/_search
{
"size": 0,
"query": {
"match": {
"name": "yagao"
}
},
"aggs": {
"group_by_tags": {
"terms": {
"field": "tags"
}
}
}
}
对名称中包含yagao的商品，计算每个tag下的商品数量
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"
}
}
}
}
}
}
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
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"
}
}
}
}
}
}
-------------------------------------------------------------------------------------------------
结果：
{
"took": 20,
"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
}
}
]
}
}
}
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": 60
}
]
},
"aggs": {
"group_by_tags": {
"terms": {
"field": "tags"
}
, "aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}
-------------------------------------------------------------------------------------------------
结果：
{
"took": 6,
"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,
"avg_price": {
"value": 27.5
}
},
{
"key": "meibai",
"doc_count": 1,
"avg_price": {
"value": 30
}
}
]
}
},
{
"key": "40.0-60.0",
"from": 40,
"to": 60,
"doc_count": 2,
"group_by_tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "meibai",
"doc_count": 1,
"avg_price": {
"value": 50
}
},
{
"key": "qingxin",
"doc_count": 1,
"avg_price": {
"value": 40
}
}
]
}
}
]
}
}
}
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