1、第一个分析需求：计算每个tag下的商品数量

GET /ecommerce/product/_search
{
  @H_301_5@"aggs": {
    @H_301_5@"group_by_tags": {
      @H_301_5@"terms": { @H_301_5@"field": @H_301_5@"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.@H_301_5@" } ],"type@H_301_5@": "search_phase_execution_exception@H_301_5@","reason@H_301_5@": "all shards Failed@H_301_5@","phase@H_301_5@": "query@H_301_5@","grouped@H_301_5@": true,"Failed_shards@H_301_5@": [ { "shard@H_301_5@": 0,"index@H_301_5@": "ecommerce@H_301_5@","node@H_301_5@": "urqovJ9yQPCO6fNM70Lc8w@H_301_5@","reason@H_301_5@": { "type@H_301_5@": "illegal_argument_exception@H_301_5@","reason@H_301_5@": "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.@H_301_5@" } } ],"caused_by@H_301_5@": { "type@H_301_5@": "illegal_argument_exception@H_301_5@","reason@H_301_5@": "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.@H_301_5@" } },"status@H_301_5@": 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
{
  @H_301_5@"properties": {
    @H_301_5@"tags": {
      @H_301_5@"type": @H_301_5@"text",@H_301_5@"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
{
  @H_301_5@"aggs": {
    @H_301_5@"group_by_tags": {
      @H_301_5@"terms": {@H_301_5@"field": @H_301_5@"tags"}
    }
  }
}

执行，然后看最后面的结果：

@H_301_5@"aggregations": {
    @H_301_5@"group_by_tags": {
      @H_301_5@"doc_count_error_upper_bound": 0,@H_301_5@"sum_other_doc_count": 0,@H_301_5@"buckets": [
        {
          @H_301_5@"key": @H_301_5@"fangzhu",@H_301_5@"doc_count": 2
        },{
          @H_301_5@"key": @H_301_5@"meibai",{
          @H_301_5@"key": @H_301_5@"qingxin",@H_301_5@"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
{
  @H_301_5@"size": 0,@H_301_5@"aggs": {
    @H_301_5@"all_tags": {
      @H_301_5@"terms": { @H_301_5@"field": @H_301_5@"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": @H_301_5@"fangzhu","doc_count": 2 },{ "key": @H_301_5@"meibai",{ "key": @H_301_5@"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
{
  @H_301_5@"size": 0,@H_301_5@"query": {
    @H_301_5@"match": {
      @H_301_5@"name": @H_301_5@"yagao"
    }
  },@H_301_5@"aggs": {
    @H_301_5@"all_tags": {
      @H_301_5@"terms": {
        @H_301_5@"field": @H_301_5@"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
{
    @H_301_5@"size": 0,@H_301_5@"aggs" : {
        @H_301_5@"group_by_tags" : {
            @H_301_5@"terms" : { @H_301_5@"field" : @H_301_5@"tags" },@H_301_5@"aggs" : {
                @H_301_5@"avg_price" : {
                    @H_301_5@"avg" : { @H_301_5@"field" : @H_301_5@"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
{
    @H_301_5@"size": 0,@H_301_5@"aggs" : {
        @H_301_5@"all_tags" : {
            @H_301_5@"terms" : { @H_301_5@"field" : @H_301_5@"tags",@H_301_5@"order": { @H_301_5@"avg_price": @H_301_5@"desc" } },@H_301_5@"aggs" : {
                @H_301_5@"avg_price" : {
                    @H_301_5@"avg" : { @H_301_5@"field" : @H_301_5@"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

@H_301_5@"terms" : { @H_301_5@"field" : @H_301_5@"tags",@H_301_5@"order": { @H_301_5@"avg_price": @H_301_5@"desc" } }

上面的运行结果是：

{
  "took": 3,"buckets": [ { "key": @H_301_5@"meibai",{ "key": @H_301_5@"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
{
  @H_301_5@"size": 0,@H_301_5@"aggs": {
    @H_301_5@"group_by_price": {
      @H_301_5@"range": {
        @H_301_5@"field": @H_301_5@"price",@H_301_5@"ranges": [
          {
            @H_301_5@"from": 0,@H_301_5@"to": 20
          },{
            @H_301_5@"from": 20,@H_301_5@"to": 40
          },{
            @H_301_5@"from": 40,@H_301_5@"to": 50
          }
        ]
      },@H_301_5@"aggs": {
        @H_301_5@"group_by_tags": {
          @H_301_5@"terms": {
            @H_301_5@"field": @H_301_5@"tags"
          },@H_301_5@"aggs": {
            @H_301_5@"average_price": {
              @H_301_5@"avg": {
                @H_301_5@"field": @H_301_5@"price"
              }
            }
          }
        }
      }
    }
  }
}

最终的结果：

{
  "took": 61,"aggregations": { "group_by_price": { "buckets": [ { "key": @H_301_5@"0.0-20.0","from": 0,"to": 20,"doc_count": 0,"group_by_tags": { "doc_count_error_upper_bound": 0,"buckets": [] } },{ "key": @H_301_5@"20.0-40.0","from": 20,"to": 40,"average_price": { "value": 27.5 } },"average_price": { "value": 30 } } ] } },{ "key": @H_301_5@"40.0-50.0","from": 40,"to": 50,"buckets": [ { "key": @H_301_5@"qingxin","average_price": { "value": 40 } } ] } } ] } } }