search（13）- elastic4s-histograms：聚合直方图

在聚合的分组统计中咱们会面临两种分组元素类型：连续型如时间，天然数等、离散型如地点、产品等。离散型数据自己就表明不一样的组别，但连续型数据则须要手工按等长间隔进行切分了。下面是一个按价钱段聚合的例子：

POST /cartxns/_search { "size" : 1, "aggs": { "sales_per_pricerange": { "histogram": { "field": "price", "interval": 20000 }, "aggs": { "total sales": { "sum": { "field": "price" } } } } } } }

在上面这个例子中咱们把价钱按20000进行分段。得出0-19999,20000-39999,40000-59999 ... 价格段的度量：

  "aggregations" : { "sales_per_pricerange" : { "buckets" : [ { "key" : 0.0, "doc_count" : 3, "total sales" : { "value" : 37000.0 } }, { "key" : 20000.0, "doc_count" : 4, "total sales" : { "value" : 95000.0 } }, { "key" : 40000.0, "doc_count" : 0, "total sales" : { "value" : 0.0 } }, { "key" : 60000.0, "doc_count" : 0, "total sales" : { "value" : 0.0 } }, { "key" : 80000.0, "doc_count" : 1, "total sales" : { "value" : 80000.0 } } ] } }

在elastic4s中是这样表达的：

  val aggHist = search("cartxns").aggregations( histogramAggregation("sales_per_price") .field("price") .interval(20000).subAggregations( sumAggregation("total_sales").field("price") ) ) println(aggHist.show) val histResult = client.execute(aggHist).await

  if (histResult.isSuccess) histResult.result.aggregations.histogram("sales_per_price").buckets .foreach(hb => println(s"${hb.key},${hb.docCount}:${hb.sum("total_sales").value}")) else println(s"error: ${histResult.error.reason}") .... POST:/cartxns/_search? StringEntity({"aggs":{"sales_per_price":{"histogram":{"interval":20000.0,"field":"price"},"aggs":{"total_sales":{"sum":{"field":"price"}}}}}},Some(application/json)) 0.0,3:37000.0
20000.0,4:95000.0
40000.0,0:0.0
60000.0,0:0.0
80000.0,1:80000.0

下面这个按车款分组统计的就是一个离散元素的聚合统计了：

POST /cartxns/_search { "size" : 1, "aggs": { "avage price per model" : { "terms": {"field" : "make.keyword"}, "aggs": { "average price": { "avg": {"field": "price"} }, "max price" : { "max": { "field": "price" } }, "min price" : { "min": { "field": "price" } } } } } }

咱们能够获得每一款车的平均售价、最低最高售价：

  "aggregations" : { "avage price per model" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "honda", "doc_count" : 3, "max price" : { "value" : 20000.0 }, "average price" : { "value" : 16666.666666666668 }, "min price" : { "value" : 10000.0 } }, { "key" : "ford", "doc_count" : 2, "max price" : { "value" : 30000.0 }, "average price" : { "value" : 27500.0 }, "min price" : { "value" : 25000.0 } }, { "key" : "toyota", "doc_count" : 2, "max price" : { "value" : 15000.0 }, "average price" : { "value" : 13500.0 }, "min price" : { "value" : 12000.0 } }, { "key" : "bmw", "doc_count" : 1, "max price" : { "value" : 80000.0 }, "average price" : { "value" : 80000.0 }, "min price" : { "value" : 80000.0 } } ] } }

elastic4s示范以下：

  val aggDisc = search("cartxns").aggregations( termsAgg("prices_per_model","make.keyword").subAggregations( avgAgg("average_price","price"), minAgg("min_price","price"), maxAgg("max_price","price") ) ) println(aggDisc.show) val discResult = client.execute(aggDisc).await

  if (discResult.isSuccess) discResult.result.aggregations.terms("prices_per_model").buckets .foreach(mb => println(s"${mb.key},${mb.docCount}:${mb.avg("average_price").value}," + s"${mb.min("min_price").value.getOrElse(0)}," + s"${mb.max("max_price").value.getOrElse(0)}")) else println(s"error: ${discResult.error.causedBy.getOrElse("unknown")}") ... POST:/cartxns/_search? StringEntity({"aggs":{"prices_per_model":{"terms":{"field":"make.keyword"},"aggs":{"average_price":{"avg":{"field":"price"}},"min_price":{"min":{"field":"price"}},"max_price":{"max":{"field":"price"}}}}}},Some(application/json)) honda,3:16666.666666666668,10000.0,20000.0 ford,2:27500.0,25000.0,30000.0 toyota,2:13500.0,12000.0,15000.0 bmw,1:80000.0,80000.0,80000.0

date_histogram是一种按时间间隔聚合的统计方法。对于按时间趋势变化的数据分析十分有用：

POST /cartxns/_search { "aggs": { "sales_per_month": { "date_histogram": { "field": "sold", "calendar_interval":"1M", "format": "yyyy-MM-dd" } } } } ... "aggregations" : { "sales_per_month" : { "buckets" : [ { "key_as_string" : "2014-01-01", "key" : 1388534400000, "doc_count" : 1 }, { "key_as_string" : "2014-02-01", "key" : 1391212800000, "doc_count" : 1 }, { "key_as_string" : "2014-03-01", "key" : 1393632000000, "doc_count" : 0 }, { "key_as_string" : "2014-04-01", "key" : 1396310400000, "doc_count" : 0 }, { "key_as_string" : "2014-05-01", "key" : 1398902400000, "doc_count" : 1 }, { "key_as_string" : "2014-06-01", "key" : 1401580800000, "doc_count" : 0 }, { "key_as_string" : "2014-07-01", "key" : 1404172800000, "doc_count" : 1 }, { "key_as_string" : "2014-08-01", "key" : 1406851200000, "doc_count" : 1 }, { "key_as_string" : "2014-09-01", "key" : 1409529600000, "doc_count" : 0 }, { "key_as_string" : "2014-10-01", "key" : 1412121600000, "doc_count" : 1 }, { "key_as_string" : "2014-11-01", "key" : 1414800000000, "doc_count" : 2 } ] } }

上面这个例子产生以月为单元的bucket。elastic4s示范：

  val aggDateHist = search("cartxns").aggregations( dateHistogramAggregation("sales_per_month") .field("sold") .calendarInterval(DateHistogramInterval.Month) .format("yyyy-MM-dd") .minDocCount(1) ) println(aggDateHist.show) val dtHistResult = client.execute(aggDateHist).await

  if (dtHistResult.isSuccess) dtHistResult.result.aggregations.dateHistogram("sales_per_month").buckets .foreach(db => println(s"${db.date},${db.docCount}")) else println(s"error: ${dtHistResult.error.causedBy.getOrElse("unknown")}") ... POST:/cartxns/_search? StringEntity({"aggs":{"sales_per_month":{"date_histogram":{"calendar_interval":"1M","min_doc_count":1,"format":"yyyy-MM-dd","field":"sold"}}}},Some(application/json)) 2014-01-01,1
2014-02-01,1
2014-05-01,1
2014-07-01,1
2014-08-01,1
2014-10-01,1
2014-11-01,2

在以月划分bucket后能够再进行每一个月的深度聚合：

POST /cartxns/_search { "aggs": { "sales_per_month": { "date_histogram": { "field": "sold", "calendar_interval":"1M", "format": "yyyy-MM-dd" }, "aggs": { "per_make_sum": { "terms": { "field": "make.keyword", "size": 10 }, "aggs": { "sum_price": { "sum": {"field": "price"} } } }, "total_sum": { "sum": { "field": "price" } } } } } }

咱们能够获得每一个月的销售总额、每一个车款每一个月的销售，以下：

"aggregations" : { "sales_per_month" : { "buckets" : [ { "key_as_string" : "2014-01-01", "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" : { "value" : 80000.0 } } ] }, "total_sum" : { "value" : 80000.0 } }, { "key_as_string" : "2014-02-01", "key" : 1391212800000, "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" : 25000.0 } } ] }, "total_sum" : { "value" : 25000.0 } }, { "key_as_string" : "2014-03-01", "key" : 1393632000000, "doc_count" : 0, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ ] }, "total_sum" : { "value" : 0.0 } }, { "key_as_string" : "2014-04-01", "key" : 1396310400000, "doc_count" : 0, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ ] }, "total_sum" : { "value" : 0.0 } }, { "key_as_string" : "2014-05-01", "key" : 1398902400000, "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" : "2014-06-01", "key" : 1401580800000, "doc_count" : 0, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ ] }, "total_sum" : { "value" : 0.0 } }, { "key_as_string" : "2014-07-01", "key" : 1404172800000, "doc_count" : 1, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "toyota", "doc_count" : 1, "sum_price" : { "value" : 15000.0 } } ] }, "total_sum" : { "value" : 15000.0 } }, { "key_as_string" : "2014-08-01", "key" : 1406851200000, "doc_count" : 1, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "toyota", "doc_count" : 1, "sum_price" : { "value" : 12000.0 } } ] }, "total_sum" : { "value" : 12000.0 } }, { "key_as_string" : "2014-09-01", "key" : 1409529600000, "doc_count" : 0, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ ] }, "total_sum" : { "value" : 0.0 } }, { "key_as_string" : "2014-10-01", "key" : 1412121600000, "doc_count" : 1, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "honda", "doc_count" : 1, "sum_price" : { "value" : 10000.0 } } ] }, "total_sum" : { "value" : 10000.0 } }, { "key_as_string" : "2014-11-01", "key" : 1414800000000, "doc_count" : 2, "per_make_sum" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "honda", "doc_count" : 2, "sum_price" : { "value" : 40000.0 } } ] }, "total_sum" : { "value" : 40000.0 } } ] } }

用elastic4s能够这样写：

  val aggMonthSales= search("cartxns").aggregations( dateHistogramAggregation("sales_per_month") .field("sold") .calendarInterval(DateHistogramInterval.Month) .format("yyyy-MM-dd") .minDocCount(1).subAggregations( termsAgg("month_make","make.keyword").subAggregations( sumAggregation("month_total_per_make").field("price") ), sumAggregation("monthly_total").field("price") ) ) println(aggMonthSales.show) val monthSalesResult = client.execute(aggMonthSales).await

  if (monthSalesResult.isSuccess) monthSalesResult.result.aggregations.dateHistogram("sales_per_month").buckets .foreach { sb => println(s"${sb.date},${sb.docCount},${sb.sum("monthly_total").value}") sb.terms("month_make").buckets .foreach(mb => println(s"${mb.key},${mb.docCount},${mb.sum("month_total_per_make").value}")) } else println(s"error: ${monthSalesResult.error.causedBy.getOrElse("unknown")}") ... POST:/cartxns/_search? StringEntity({"aggs":{"sales_per_month":{"date_histogram":{"calendar_interval":"1M","min_doc_count":1,"format":"yyyy-MM-dd","field":"sold"},"aggs":{"month_make":{"terms":{"field":"make.keyword"},"aggs":{"month_total_per_make":{"sum":{"field":"price"}}}},"monthly_total":{"sum":{"field":"price"}}}}}},Some(application/json)) 2014-01-01,1,80000.0 bmw,1,80000.0
2014-02-01,1,25000.0 ford,1,25000.0
2014-05-01,1,30000.0 ford,1,30000.0
2014-07-01,1,15000.0 toyota,1,15000.0
2014-08-01,1,12000.0 toyota,1,12000.0
2014-10-01,1,10000.0 honda,1,10000.0
2014-11-01,2,40000.0 honda,2,40000.0