Elasticsearch超强聚合函数(四) buckets的嵌套使用

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• Elasticsearch超强聚合函数(四) buckets的嵌套使用
  • • 案例:构建聚合以便按季度展示所有汽车品牌总销售额。同时按季度、按每个汽车品牌计算销售总额，以便可以找出哪种品牌最赚钱：
      • http代码
      • java-api
      • 返回结果

 

原始数据还是引用第一篇中的数据:ElasticSearch超强聚合查询(一)

案例:构建聚合以便按季度展示所有汽车品牌总销售额。同时按季度、按每个汽车品牌计算销售总额，以便可以找出哪种品牌最赚钱：

http代码

GET /cars/transactions/_search
{"size" : 0,"aggs": {"sales": {"date_histogram": {"field": "sold","interval": "quarter", "format": "yyyy-MM-dd","min_doc_count" : 0,"extended_bounds" : {"min" : "2014-01-01","max" : "2014-12-31"}},"aggs": {"per_make_sum": {"terms": {"field": "make"},"aggs": {"sum_price": {"sum": { "field": "price" } }}},"total_sum": {"sum": { "field": "price" } }}}}
}

• java-api

@Testpublic void bucketsInsideBuckets(){SearchResponse response = transportClient.prepareSearch("cars").setTypes("transactions").addAggregation(AggregationBuilders.dateHistogram("sales").field("sold").dateHistogramInterval(DateHistogramInterval.QUARTER).format("yyyy-MM-dd").minDocCount(0l).extendedBounds(new ExtendedBounds("2014-01-01","2014-12-31")).subAggregation(//按照季度划分,每个季度所有品牌的的销售额AggregationBuilders.sum("total_sum").field("price"))//添加一个集合的嵌套,在每个季度中,再根据品牌进行划分.subAggregation(AggregationBuilders.terms("per_make_sum").field("make").subAggregation(//计算各个品牌在每个解读中的销售额AggregationBuilders.sum("sum_price").field("price")))).setSize(0).get();Aggregation sales = response.getAggregations().get("sales");System.out.println(sales);

• 返回结果

{"took": 1,"timed_out": false,"_shards": {"total": 5,"successful": 5,"skipped": 0,"failed": 0},"hits": {"total": 7,"max_score": 0.0,"hits": []},"aggregations": {"sales": {"buckets": [{"key_as_string": "2014-01-01",//第一个季度"key": 1388534400000,"doc_count": 1,"per_make_sum": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "bmw","doc_count": 1,"sum_price": {//各个品牌的销售总额"value": 80000.0}}]},"total_sum": {//所有品牌销售总额"value": 80000.0}},{"key_as_string": "2014-04-01","key": 1396310400000,"doc_count": 1,"per_make_sum": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "ford","doc_count": 1,"sum_price": {"value": 30000.0}}]},"total_sum": {"value": 30000.0}},{"key_as_string": "2014-07-01","key": 1404172800000,"doc_count": 2,"per_make_sum": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "toyota","doc_count": 2,"sum_price": {"value": 27000.0}}]},"total_sum": {"value": 27000.0}},{"key_as_string": "2014-10-01","key": 1412121600000,"doc_count": 3,"per_make_sum": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "honda","doc_count": 3,"sum_price": {"value": 50000.0}}]},"total_sum": {"value": 50000.0}}]}}
}

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