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",{
          "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",{ "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,"aggregations": { "all_tags": { "doc_count_error_upper_bound": 0,"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,"doc_count": 2,"avg_price": { "value": 27.5 } },"avg_price": { "value": 40 } },"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,"buckets": [ { "key": "meibai",{ "key": "fangzhu","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,"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,"buckets": [] } },{ "key": "20.0-40.0","from": 20,"to": 40,"average_price": { "value": 27.5 } },"average_price": { "value": 30 } } ] } },{ "key": "40.0-50.0","from": 40,"to": 50,"buckets": [ { "key": "qingxin","average_price": { "value": 40 } } ] } } ] } } }