文章目錄

• 概述
• 案例
• • 需求
  • 解決
  • • Step1.對每種顏色進行bucket分組
    • Step2.對每種顏色進行bucket分組 ， 然后對每個分組再次計算平均價格
    • Step3.對每種顏色進行bucket分組 ， 然后對每個分組再次計算平均價格 ， 緊接再對每種顏色按照brand分組，直接寫到和 color_avg_price 并列的地方就可以了
    • Step4.對每種顏色進行bucket分組 ， 然后對每個分組再次計算平均價格 ， 緊接再對每種顏色按照brand分組，直接寫到和 color_avg_price 并列的地方就可以了。 最后對品牌進行metrics操作，即求每種品牌的平均價格，所以要在 brand 這個bucket中，再次aggs

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概述

繼續跟中華石杉老師學習ES，第34篇

課程地址： https://www.roncoo.com/view/55

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案例

原始數據：

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需求

在白話Elasticsearch33-深入聚合數據分析之案例實戰Terms Aggregation + Avg Aggregation （ bucket + metrics ） 中，我們演示了 對顏色進行bucket操作以后，再計算每種顏色的平均價格的metrics操作。

假設 又來了個新需求： 從顏色到品牌進行下鉆分析，每種顏色的平均價格，以及找到每種顏色每個品牌的平均價格

那就需要進行多層次的下鉆分析了

舉個例子：比如說，我們現在的索引中紅色的電視有4臺，同時這4臺電視中，有3臺是屬于長虹的，1臺是屬于小米的

那如何計算出 ： 紅色電視中的3臺長虹的平均價格是多少？ 紅色電視中的1臺小米的平均價格是多少？

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解決

Step1.對每種顏色進行bucket分組

GET /tvs/sales/_search {"size": 0 ,"aggs": {"group_by_color": {"terms": {"field": "color"}}} }

返回

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Step2.對每種顏色進行bucket分組 ， 然后對每個分組再次計算平均價格

GET /tvs/sales/_search {"size": 0,"aggs": {"group_by_color": {"terms": {"field": "color"},"aggs": {"color_avg_price": {"avg": {"field": "price"}}}}} }

返回：

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Step3.對每種顏色進行bucket分組 ， 然后對每個分組再次計算平均價格 ， 緊接再對每種顏色按照brand分組，直接寫到和 color_avg_price 并列的地方就可以了

GET /tvs/sales/_search {"size": 0,"aggs": {"group_by_color": {"terms": {"field": "color"},"aggs": {"color_avg_price": {"avg": {"field": "price"}},"group_by_brand": {"terms": {"field": "brand"}}}}} }

返回

{"took": 1,"timed_out": false,"_shards": {"total": 5,"successful": 5,"skipped": 0,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"group_by_color": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "紅色","doc_count": 4,"color_avg_price": {"value": 3250},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "長虹","doc_count": 3},{"key": "三星","doc_count": 1}]}},{"key": "綠色","doc_count": 2,"color_avg_price": {"value": 2100},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "TCL","doc_count": 1},{"key": "小米","doc_count": 1}]}},{"key": "藍色","doc_count": 2,"color_avg_price": {"value": 2000},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "TCL","doc_count": 1},{"key": "小米","doc_count": 1}]}}]}} }

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Step4.對每種顏色進行bucket分組 ， 然后對每個分組再次計算平均價格 ， 緊接再對每種顏色按照brand分組，直接寫到和 color_avg_price 并列的地方就可以了。 最后對品牌進行metrics操作，即求每種品牌的平均價格，所以要在 brand 這個bucket中，再次aggs

GET /tvs/sales/_search {"size": 0 ,"aggs": {"group_by_color": {"terms": {"field": "color"},"aggs": {"color_avg_price": {"avg": {"field": "price"}},"group_by_brand":{"terms": {"field": "brand"},"aggs": {"brand_avg_price": {"avg": {"field": "price"}}}}}}} }

到這里，就一步步完成了該需求,來看下返回結果吧

返回：

{"took": 4,"timed_out": false,"_shards": {"total": 5,"successful": 5,"skipped": 0,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"group_by_color": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "紅色","doc_count": 4,"color_avg_price": {"value": 3250},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "長虹","doc_count": 3,"brand_avg_price": {"value": 1666.6666666666667}},{"key": "三星","doc_count": 1,"brand_avg_price": {"value": 8000}}]}},{"key": "綠色","doc_count": 2,"color_avg_price": {"value": 2100},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "TCL","doc_count": 1,"brand_avg_price": {"value": 1200}},{"key": "小米","doc_count": 1,"brand_avg_price": {"value": 3000}}]}},{"key": "藍色","doc_count": 2,"color_avg_price": {"value": 2000},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "TCL","doc_count": 1,"brand_avg_price": {"value": 1500}},{"key": "小米","doc_count": 1,"brand_avg_price": {"value": 2500}}]}}]}} }

校驗下

原始數據：

我們通過ES算出來的數據：

對比下原始數據，符合預期，至此實現了該需求的DSL 。

總結

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