[英]Aggregations in Elasticsearch
我有一個elasticsearch查詢,它返回一堆看起來像這樣的對象:
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"_index": "searchdb",
"_type": "profile",
"_id": "1825",
"_score": 1,
"_source": {
"id": 1825,
"market": "Chicago",
"geo_location": {
"lat": 41.1234,
"lon": -87.5678
},
"hourly_values": [
{
"datetime": "1997-07-16T19:00:00.00+00:00",
"seconds": 1200
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 1200
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 1200
}
]
}
},
{
"_index": "searchdb",
"_type": "profile",
"_id": "1808",
"_score": 1,
"_source": {
"id": 1808,
"market": "Chicago",
"geo_location": {
"lat": 41.1234,
"lon": -87.5678
},
"hourly_values": [
{
"datetime": "1997-07-16T19:00:00.00+00:00",
"seconds": 900
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 1200
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 800
}
]
}
}
]
}
我想返回相同的結果,但返回的每個對象的秒字段匯總在一起。
我的查詢現在看起來像這樣:
{
"query": {
"filtered":{
"filter":{
"geo_distance":{
"distance":"1km",
"geo_location":{
"lat":"41.1234",
"lon":"-87.5678"
}
}
}
}
},
"aggregations": {
"seconds_sum": {
"sum": {
"field": "hourly_values.seconds"
}
}
}
}
上面只是將所有對象的所有秒匯總在一起。 我無法弄清楚如何僅聚合每個對象的秒數,並隨該對象返回該聚合,因此我可以得到如下結果:
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"_index": "searchdb",
"_type": "profile",
"_id": "1825",
"_score": 1,
"_source": {
"id": 1825,
"market": "Chicago",
"geo_location": {
"lat": 41.1234,
"lon": -87.5678
},
"seconds":3600
}
},
{
"_index": "searchdb",
"_type": "profile",
"_id": "1808",
"_score": 1,
"_source": {
"id": 1808,
"market": "Chicago",
"geo_location": {
"lat": 41.1234,
"lon": -87.5678
},
"seconds":2900
}
}
]
}
或類似的東西 ...
那很容易。 首先,您需要將hourly_values
存儲為嵌套對象 。
您必須使用條款按唯一值進行匯總,在這種情況下,它可能會是id,只有這樣您才可以求和 。 總結一下:
PUT /test
{
"mappings": {
"data": {
"properties": {
"id": {
"type": "integer"
},
"geo_location": {
"type": "geo_point"
},
"hourly_values": {
"type": "nested",
"properties": {
"datetime": {
"type": "date"
},
"seconds": {
"type": "integer"
}
}
}
}
}
}
}
PUT /test/data/1
{
"id": 1825,
"market": "Chicago",
"geo_location": {
"lat": 41.1234,
"lon": -87.5678
},
"hourly_values": [
{
"datetime": "1997-07-16T19:00:00.00+00:00",
"seconds": 1200
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 1200
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 1200
}
]
}
PUT /test/data/2
{
"id": 1808,
"market": "Chicago",
"geo_location": {
"lat": 41.1234,
"lon": -87.5678
},
"hourly_values": [
{
"datetime": "1997-07-16T19:00:00.00+00:00",
"seconds": 900
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 1200
},
{
"datetime": "1997-07-16T19:20:00.00+00:00",
"seconds": 800
}
]
}
POST /test/_search
{
"size": 0,
"aggs": {
"Ids": {
"terms": {
"field": "id",
"size": 0
},
"aggs": {
"Nesting": {
"nested": {
"path": "hourly_values"
},
"aggs": {
"SumSeconds": {
"sum": {
"field": "hourly_values.seconds"
}
}
}
}
}
}
}
}
{
"took": 7,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0,
"hits": []
},
"aggregations": {
"Ids": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 1808,
"doc_count": 1,
"Nesting": {
"doc_count": 3,
"SumSeconds": {
"value": 2900
}
}
},
{
"key": 1825,
"doc_count": 1,
"Nesting": {
"doc_count": 3,
"SumSeconds": {
"value": 3600
}
}
}
]
}
}
}
如果您也想將文檔返回到它們旁邊,則可以將Top Hit的聚合與嵌套總和一起使用:
POST /test/_search
{
"size": 0,
"aggs": {
"Ids": {
"terms": {
"field": "id",
"size": 0
},
"aggs": {
"Objects": {
"top_hits": {
"_source": ["id", "market", "geo_location"],
"size": 1
}
},
"Nesting": {
"nested": {
"path": "hourly_values"
},
"aggs": {
"SumSeconds": {
"sum": {
"field": "hourly_values.seconds"
}
}
}
}
}
}
}
}
這會帶回它:
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0,
"hits": []
},
"aggregations": {
"Ids": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 1808,
"doc_count": 1,
"Nesting": {
"doc_count": 3,
"SumSeconds": {
"value": 2900
}
},
"Objects": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "data",
"_id": "2",
"_score": 1,
"_source": {
"market": "Chicago",
"geo_location": {
"lon": -87.5678,
"lat": 41.1234
},
"id": 1808
}
}
]
}
}
},
{
"key": 1825,
"doc_count": 1,
"Nesting": {
"doc_count": 3,
"SumSeconds": {
"value": 3600
}
},
"Objects": {
"hits": {
"total": 1,
"max_score": 1,
"hits": [
{
"_index": "test",
"_type": "data",
"_id": "1",
"_score": 1,
"_source": {
"market": "Chicago",
"geo_location": {
"lon": -87.5678,
"lat": 41.1234
},
"id": 1825
}
}
]
}
}
}
]
}
}
}
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