小贝子编程

弹性搜索查询筛选器和聚合不起作用



我有下面的文档和搜索查询，当我执行查询时，弹性正在获取不匹配的文档。场景是针对匹配的例外。它应该进行聚合，但它正在获取所有文档，聚合也不起作用。

1( 项目

2( 位置

3( 运输模式

4( 源位置

5( 发货日期

6( 到达日期

映射：

{
"mappings": {
"recommendations": {
"properties": {
"recommendations": {
"type": "nested"
}
}
}
}
}

文件：

{
"recommendations": [
{
"id": "578bd845-3ca9-495f-bc13-dbcc48e8f415",
"tenantId": "0d474f0a-f060-4828-b2ec-a81c4b27fa2e",
"clusterId": "1",
"eventId": "1",
"exceptionId": "1",
"eventType": "Delayed",
"item": "Item1",
"location": "DC1",
"dueDate": "2019-01-10T05:30:00.000+0530",
"exceptionQuantity": 100,
"recommendationType": "stockTransfer",
"customerName": "Walmart",
"primaryRecommendation": true,
"priority": 1,
"sourceLocation": "DC2",
"transferQuantity": 100,
"shipDate": "2019-01-09T05:30:00.000+0530",
"arrivalDate": "2019-01-10T05:30:00.000+0530",
"transportMode": "Road",
"transferCost": 500,
"maxQtyAvailableForTransfer": 100,
"totalQtyAtSource": 100,
"operation": "Road-Item1-from-DC2-to-DC1",
"peggedStockDemandId": "1",
"revenueRecovered": "20000.0"
},
{
"id": "578bd845-3ca9-495f-bc13-dbcc48e8f415",
"tenantId": "0d474f0a-f060-4828-b2ec-a81c4b27fa2e",
"clusterId": "1",
"eventId": "1",
"exceptionId": "1",
"eventType": "Delayed",
"item": "Item1",
"location": "DC1",
"dueDate": "2019-01-10T05:30:00.000+0530",
"exceptionQuantity": 100,
"recommendationType": "stockTransfer",
"customerName": "Walmart",
"primaryRecommendation": true,
"priority": 1,
"sourceLocation": "DC2",
"transferQuantity": 100,
"shipDate": "2019-01-09T05:30:00.000+0530",
"arrivalDate": "2019-01-10T05:30:00.000+0530",
"transportMode": "Road",
"transferCost": 500,
"maxQtyAvailableForTransfer": 100,
"totalQtyAtSource": 100,
"operation": "Road-Item1-from-DC2-to-DC1",
"peggedStockDemandId": "1",
"revenueRecovered": "20000.0"
},
{
"id": "578bd845-3ca9-495f-bc13-dbcc48e8f415",
"tenantId": "0d474f0a-f060-4828-b2ec-a81c4b27fa2e",
"clusterId": "1",
"eventId": "1",
"exceptionId": "2",
"eventType": "Delayed",
"item": "Item1",
"location": "DC1",
"dueDate": "2019-01-10T05:30:00.000+0530",
"exceptionQuantity": 100,
"recommendationType": "stockTransfer",
"customerName": "Walmart",
"primaryRecommendation": true,
"priority": 1,
"sourceLocation": "DC2",
"transferQuantity": 100,
"shipDate": "2019-01-09T05:30:00.000+0530",
"arrivalDate": "2019-01-10T05:30:00.000+0530",
"transportMode": "Road",
"transferCost": 500,
"maxQtyAvailableForTransfer": 100,
"totalQtyAtSource": 100,
"operation": "Road-Item1-from-DC2-to-DC1",
"peggedStockDemandId": "1",
"revenueRecovered": "20000.0"
}
]
}

查询：

{
"aggregations": {
"exceptionIds": {
"filter": {
"terms": {
"exceptionId": [
"1"
],
"boost": 1
}
},
"aggregations": {
"by_exceptionId": {
"terms": {
"field": "recommendations.exceptionId.keyword",
"size": 10,
"min_doc_count": 1,
"shard_min_doc_count": 0,
"show_term_doc_count_error": false,
"order": [
{
"_count": "desc"
},
{
"_key": "asc"
}
]
},
"aggregations": {
"by_item": {
"terms": {
"field": "recommendations.item.keyword",
"size": 10,
"min_doc_count": 1,
"shard_min_doc_count": 0,
"show_term_doc_count_error": false,
"order": [
{
"_count": "desc"
},
{
"_key": "asc"
}
]
},
"aggregations": {
"by_destination": {
"terms": {
"field": "recommendations.location.keyword",
"size": 10,
"min_doc_count": 1,
"shard_min_doc_count": 0,
"show_term_doc_count_error": false,
"order": [
{
"_count": "desc"
},
{
"_key": "asc"
}
]
},
"aggregations": {
"by_trans": {
"terms": {
"field": "recommendations.transportMode.keyword",
"size": 10,
"min_doc_count": 1,
"shard_min_doc_count": 0,
"show_term_doc_count_error": false,
"order": [
{
"_count": "desc"
},
{
"_key": "asc"
}
]
},
"aggregations": {
"by_sourcelocation": {
"terms": {
"field": "recommendations.sourceLocation.keyword",
"size": 10,
"min_doc_count": 1,
"shard_min_doc_count": 0,
"show_term_doc_count_error": false,
"order": [
{
"_count": "desc"
},
{
"_key": "asc"
}
]
},
"aggregations": {
"by_shipdate": {
"terms": {
"field": "recommendations.shipDate.keyword",
"size": 10,
"min_doc_count": 1,
"shard_min_doc_count": 0,
"show_term_doc_count_error": false,
"order": [
{
  "_count": "desc"
},
{
  "_key": "asc"
}
]
},
"aggregations": {
"by_arrival": {
"terms": {
  "field": "recommendations.arrivalDate.keyword",
  "size": 10,
  "min_doc_count": 1,
  "shard_min_doc_count": 0,
  "show_term_doc_count_error": false,
  "order": [
      {
          "_count": "desc"
      },
      {
          "_key": "asc"
      }
  ]
},
"aggregations": {
  "quantity": {
      "sum": {
          "field": "recommendations.stockTransfer.transferQuantity"
      }
  },
  "transfercost": {
      "sum": {
          "field": "recommendations.stockTransfer.transferCost"
      }
  },
  "revenueRecovered": {
      "sum": {
          "field": "recommendations.stockTransfer.revenueRecovered"
      }
  }
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}

回应：

{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 1.0,
"hits": [
{
"_index": "testing",
"_type": "recommendations",
"_id": "1",
"_score": 1.0,
"_source": {
"recommendations": [
{
"id": "578bd845-3ca9-495f-bc13-dbcc48e8f415",
"tenantId": "0d474f0a-f060-4828-b2ec-a81c4b27fa2e",
"clusterId": "1",
"eventId": "1",
"exceptionId": "1",
"eventType": "Delayed",
"item": "Item1",
"location": "DC1",
"dueDate": "2019-01-10T05:30:00.000+0530",
"exceptionQuantity": 100,
"recommendationType": "stockTransfer",
"customerName": "Walmart",
"primaryRecommendation": true,
"priority": 1,
"sourceLocation": "DC2",
"transferQuantity": 100,
"shipDate": "2019-01-09T05:30:00.000+0530",
"arrivalDate": "2019-01-10T05:30:00.000+0530",
"transportMode": "Road",
"transferCost": 500,
"maxQtyAvailableForTransfer": 100,
"totalQtyAtSource": 100,
"operation": "Road-Item1-from-DC2-to-DC1",
"peggedStockDemandId": "1",
"revenueRecovered": "20000.0"
},
{
"id": "578bd845-3ca9-495f-bc13-dbcc48e8f415",
"tenantId": "0d474f0a-f060-4828-b2ec-a81c4b27fa2e",
"clusterId": "1",
"eventId": "1",
"exceptionId": "2",
"eventType": "Delayed",
"item": "Item1",
"location": "DC1",
"dueDate": "2019-01-10T05:30:00.000+0530",
"exceptionQuantity": 100,
"recommendationType": "stockTransfer",
"customerName": "Walmart",
"primaryRecommendation": true,
"priority": 1,
"sourceLocation": "DC2",
"transferQuantity": 100,
"shipDate": "2019-01-09T05:30:00.000+0530",
"arrivalDate": "2019-01-10T05:30:00.000+0530",
"transportMode": "Road",
"transferCost": 500,
"maxQtyAvailableForTransfer": 100,
"totalQtyAtSource": 100,
"operation": "Road-Item1-from-DC2-to-DC1",
"peggedStockDemandId": "1",
"revenueRecovered": "20000.0"
}
]
}
}
]
},
"aggregations": {
"exceptionIds": {
"doc_count": 0,
"by_exceptionId": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": []
}
}
}
}

使用映射并创建"嵌套"类型的推荐。您需要在聚合中使用嵌套筛选器。使用嵌套类型，您可以将数组的每个对象视为一个单独的类型，否则它们将被展平。

示例。

文件1："id"："1"建议"：[{"eventId"："1"，"exceptionId"："1"}，{"eventId"："2"，"exceptionId"："2"}]

文件2:

"id"："1"建议"：[{"eventId"："2"，"exceptionId"："1"}]

考虑以上2个文件，并将推荐映射为对象。如果您将筛选eventId:2和exceptionId:1的文档，它将返回这两个文档，因为对象类型被展平，并且对象的字段之间的关系没有得到维护，它被视为单个文档。

当推荐映射为"嵌套类型"时，将返回文档2。即使您已经在查询中使用嵌套类型筛选了文档。在聚合中，它必须再次被使用，因为数组的所有对象都由查询返回。

映射

{
"index10" : {
"mappings" : {
"properties" : {
"recommendations" : {
"type" : "nested",
"properties" : {
"arrivalDate" : {
"type" : "date"
},
"clusterId" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"customerName" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"dueDate" : {
"type" : "date"
},
"eventId" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"eventType" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"exceptionId" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"exceptionQuantity" : {
"type" : "long"
},
"id" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"item" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"location" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"maxQtyAvailableForTransfer" : {
"type" : "long"
},
"operation" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"peggedStockDemandId" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"primaryRecommendation" : {
"type" : "boolean"
},
"priority" : {
"type" : "long"
},
"recommendationType" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"revenueRecovered" : {
"type" : "float"
},
"shipDate" : {
"type" : "date"
},
"sourceLocation" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"tenantId" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"totalQtyAtSource" : {
"type" : "long"
},
"transferCost" : {
"type" : "long"
},
"transferQuantity" : {
"type" : "long"
},
"transportMode" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
}
}
}
}
}

查询：

{
"size": 0, --> hits will not be returned
"aggregations": {
"exceptionIds": {
"nested": {
"path": "recommendations"  --> nested agrregation on nested
},
"aggs": {
"recomm": {
"filter": {
"terms": {
"recommendations.exceptionId.keyword": [ --> filter on exceptionId
"1"
]
}
},
"aggs": {
"revenueRecovered": {
"sum": {
"field": "recommendations.revenueRecovered"
}
},
"transfercost": {
"sum": {
"field": "recommendations.transferCost"
}
},
"transferQuantity": {
"sum": {
"field": "recommendations.transferQuantity"
}
}
}
}
}
}
}
}

结果：

"aggregations" : {
"exceptionIds" : {
"doc_count" : 2,
"recomm" : {
"doc_count" : 1,
"transferQuantity" : {
"value" : 100.0
},
"transfercost" : {
"value" : 500.0
},
"revenueRecovered" : {
"value" : 20000.0
}
}
}
}

相关内容

  • 没有找到相关文章

最新更新


热门标签：

javascript python java c# php android html jquery c++ css ios sql mysql arrays asp.net json python-3.x ruby-on-rails .net sql-server django objective-c excel regex ruby linux ajax iphone xml vba spring asp.net-mvc database wordpress string postgresql wpf windows xcode bash git oracle list vb.net multithreading eclipse algorithm macos powershell visual-studio image forms numpy scala function api selenium