Elasticsearch：查詢嵌套對象
[英]Elasticsearch: Querying nested objects
問題描述
尊敬的Elasticsearch專家，
我在查詢嵌套對象時遇到問題。 讓我們使用以下簡化的映射：
{
"mappings" : {
"_doc" : {
"properties" : {
"companies" : {
"type": "nested",
"properties" : {
"company_id": { "type": "long" },
"name": { "type": "text" }
}
},
"title": { "type": "text" }
}
}
}
}
並將一些文檔放在索引中：
PUT my_index/_doc/1
{
"title" : "CPU release",
"companies" : [
{ "company_id" : 1, "name" : "AMD" },
{ "company_id" : 2, "name" : "Intel" }
]
}
PUT my_index/_doc/2
{
"title" : "GPU release 2018-01-10",
"companies" : [
{ "company_id" : 1, "name" : "AMD" },
{ "company_id" : 3, "name" : "Nvidia" }
]
}
PUT my_index/_doc/3
{
"title" : "GPU release 2018-03-01",
"companies" : [
{ "company_id" : 3, "name" : "Nvidia" }
]
}
PUT my_index/_doc/4
{
"title" : "Chipset release",
"companies" : [
{ "company_id" : 2, "name" : "Intel" }
]
}
現在我想執行這樣的查詢：
{
"query": {
"bool": {
"must": [
{ "match": { "title": "GPU" } },
{ "nested": {
"path": "companies",
"query": {
"bool": {
"must": [
{ "match": { "companies.name": "AMD" } }
]
}
},
"inner_hits" : {}
}
}
]
}
}
}
結果，我想獲得具有匹配文件數量的匹配公司。 所以上面的查詢應該給我：
[
{ "company_id" : 1, "name" : "AMD", "matched_documents:": 1 }
]
以下查詢：
{
"query": {
"bool": {
"must": [
{ "match": { "title": "GPU" } }
{ "nested": {
"path": "companies",
"query": { "match_all": {} },
"inner_hits" : {}
}
}
]
}
}
}
應該給我所有分配給文檔的公司，該公司的標題包含“ GPU”以及匹配的文檔數：
[
{ "company_id" : 1, "name" : "AMD", "matched_documents:": 1 },
{ "company_id" : 3, "name" : "Nvidia", "matched_documents:": 2 }
]
是否有可能具有良好的性能來達到此結果？ 我顯然對匹配的文檔不感興趣，僅對匹配的文檔和嵌套對象的數量不感興趣。
謝謝你的幫助。
1 個解決方案
解決方案1
0 2018-09-03 21:49:18
關於Elasticsearch，您需要做的是：
- 根據所需條件過濾“父”文檔（例如，在
title使用GPU或在companies列表中提及Nvidia）； - 按照一定的標准將“嵌套”文檔分組，例如存儲桶 （例如
company_id）； - 計算每個存儲桶中有多少“嵌套”文檔。
數組中的每個nested對象都被索引為一個單獨的隱藏文檔 ，這使生活變得有些復雜。 讓我們看看如何匯總它們。
那么如何匯總和計算nested文檔呢？
您可以結合使用nested ， term和top_hits聚合來實現此目的 ：
POST my_index/doc/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"title": "GPU"
}
},
{
"nested": {
"path": "companies",
"query": {
"match_all": {}
}
}
}
]
}
},
"aggs": {
"Extract nested": {
"nested": {
"path": "companies"
},
"aggs": {
"By company id": {
"terms": {
"field": "companies.company_id"
},
"aggs": {
"Examples of such company_id": {
"top_hits": {
"size": 1
}
}
}
}
}
}
}
}
這將給出以下輸出：
{
...
"hits": { ... },
"aggregations": {
"Extract nested": {
"doc_count": 4, <== How many "nested" documents there were?
"By company id": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 3, <== this bucket's key: "company_id": 3
"doc_count": 2, <== how many "nested" documents there were with such company_id?
"Examples of such company_id": {
"hits": {
"total": 2,
"max_score": 1.5897496,
"hits": [ <== an example, "top hit" for such company_id
{
"_nested": {
"field": "companies",
"offset": 1
},
"_score": 1.5897496,
"_source": {
"company_id": 3,
"name": "Nvidia"
}
}
]
}
}
},
{
"key": 1,
"doc_count": 1,
"Examples of such company_id": {
"hits": {
"total": 1,
"max_score": 1.5897496,
"hits": [
{
"_nested": {
"field": "companies",
"offset": 0
},
"_score": 1.5897496,
"_source": {
"company_id": 1,
"name": "AMD"
}
}
]
}
}
}
]
}
}
}
}
注意，對於Nvidia我們有"doc_count": 2 。
但是，如果我們要計算擁有Nvidia與Intel的“父”對象的數量呢？
如果我們想基於nested存儲桶計算父對象怎么辦？
可以使用reverse_nested聚合來實現。
我們只需要稍微更改一下查詢：
POST my_index/doc/_search
{
"query": { ... },
"aggs": {
"Extract nested": {
"nested": {
"path": "companies"
},
"aggs": {
"By company id": {
"terms": {
"field": "companies.company_id"
},
"aggs": {
"Examples of such company_id": {
"top_hits": {
"size": 1
}
},
"original doc count": { <== we ask ES to count how many there are parent docs
"reverse_nested": {}
}
}
}
}
}
}
}
結果將如下所示：
{
...
"hits": { ... },
"aggregations": {
"Extract nested": {
"doc_count": 3,
"By company id": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 3,
"doc_count": 2,
"original doc count": {
"doc_count": 2 <== how many "parent" documents have such company_id
},
"Examples of such company_id": {
"hits": {
"total": 2,
"max_score": 1.5897496,
"hits": [
{
"_nested": {
"field": "companies",
"offset": 1
},
"_score": 1.5897496,
"_source": {
"company_id": 3,
"name": "Nvidia"
}
}
]
}
}
},
{
"key": 1,
"doc_count": 1,
"original doc count": {
"doc_count": 1
},
"Examples of such company_id": {
"hits": {
"total": 1,
"max_score": 1.5897496,
"hits": [
{
"_nested": {
"field": "companies",
"offset": 0
},
"_score": 1.5897496,
"_source": {
"company_id": 1,
"name": "AMD"
}
}
]
}
}
}
]
}
}
}
}
我如何發現差異？
為了使區別變得明顯，讓我們稍微更改一下數據，然后在文檔列表中添加另一個Nvidia項目：
PUT my_index/doc/2
{
"title" : "GPU release 2018-01-10",
"companies" : [
{ "company_id" : 1, "name" : "AMD" },
{ "company_id" : 3, "name" : "Nvidia" },
{ "company_id" : 3, "name" : "Nvidia" }
]
}
最后一個查詢（帶有reverse_nested查詢）將為我們提供以下內容：
"By company id": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 3,
"doc_count": 3, <== 3 "nested" documents with Nvidia
"original doc count": {
"doc_count": 2 <== but only 2 "parent" documents
},
"Examples of such company_id": {
"hits": {
"total": 3,
"max_score": 1.5897496,
"hits": [
{
"_nested": {
"field": "companies",
"offset": 2
},
"_score": 1.5897496,
"_source": {
"company_id": 3,
"name": "Nvidia"
}
}
]
}
}
},
如您所見，這是一個難以理解的細微差別，但它完全改變了語義。
表現如何？
雖然在大多數情況下， nested查詢和聚合的性能應該足夠，但是當然要付出一定的代價。 因此，建議在調整搜索速度時避免使用nested或父子類型。
在Elasticsearch中，盡管沒有單一的配方，但通常可以通過反規范化來獲得最佳性能，您應該根據需要選擇數據模型。
希望這可以為您澄清一下nested東西！
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