elasticsearch 之聚合分析 aggregate analysis_aggregation analysis-优快云博客

本文介绍如何使用Elasticsearch对商品数据进行聚合分析，包括按标签分组计数、计算特定名称商品的标签分布、求各标签下商品平均价格、按平均价格排序以及按价格区间分组后再按标签聚合分析。

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0、构造数据
1、第一个聚合分析的需求：计算每个tag下的商品数量
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
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
4、第四个聚合分析的需求：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
5、第五个聚合分析的需求：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，按照平均价格升序

0、构造数据

PUT /ecommerce/product/1
{
"_index": "ecommerce",
"_type": "product",
"_id": "1",
"_score": 1,
"_source": {
"name": "zhonghua yagao",
"desc": "caoben zhiwu",
"price": 30,
"producer": "zhonghua producer",
"tags": [
"qingxin"
]
}

1、第一个聚合分析的需求：计算每个tag下的商品数量

GET /ecommerce/product/_search
{
"size": 0,
"aggs": {
"group_by_tags": {
"terms": {
"field": "tags"
}
}
}
}

反馈信息

{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 6,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "fangzhu",
"doc_count": 3
},
{
"key": "qingxin",
"doc_count": 3
}
]
}
}
}

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": {
"group_by_tags": {
"terms": {
"field": "tags"
}
}
}
}

反馈信息：

{
"took": 8,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 6,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "fangzhu",
"doc_count": 3
},
{
"key": "qingxin",
"doc_count": 3
}
]
}
}
}

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_salary": {
"avg": {
"field": "price"
}
}
}
}
}
}

反馈信息

{
"took": 7,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 6,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "fangzhu",
"doc_count": 3,
"avg_salary": {
"value": 35
}
},
{
"key": "qingxin",
"doc_count": 3,
"avg_salary": {
"value": 34.333333333333336
}
}
]
}
}
}

4、第四个聚合分析的需求：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": {
"group_by_tags": {
"terms": {
"field": "tags",
"order": {
"avg_salary": "desc"
}
},
"aggs": {
"avg_salary": {
"avg": {
"field": "price"
}
}
}
}
}
}

5、第五个聚合分析的需求：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，按照平均价格升序

GET ecommerce/product/_search
{
"size": 0,
"aggs": {
"group_by_price": {
"range": {
"field": "price",
"ranges": [
{
"from": 20,
"to": 40
},
{
"from": 40,
"to": 60
}
]
},
"aggs": {
"group_by_tags": {
"terms": {
"field": "tags",
"order": {
"avg_price": "asc"
}
},
"aggs": {
"avg_price": {
"avg": {
"field": "price"
}
}
}
}
}
}
}
}

反馈信息

{
"took": 6,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 6,
"max_score": 0,
"hits": []
},
"aggregations": {
"group_by_price": {
"buckets": [
{
"key": "20.0-40.0",
"from": 20,
"to": 40,
"doc_count": 4,
"group_by_tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "fangzhu",
"doc_count": 2,
"avg_price": {
"value": 30
}
},
{
"key": "qingxin",
"doc_count": 2,
"avg_price": {
"value": 31.5
}
}
]
}
},
{
"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": "qingxin",
"doc_count": 1,
"avg_price": {
"value": 40
}
},
{
"key": "fangzhu",
"doc_count": 1,
"avg_price": {
"value": 45
}
}
]
}
}
]
}
}
}