范围字段分桶的细微之处
编辑范围字段分桶的细微之处
编辑文档计入其所属的每个桶
编辑由于范围表示多个值，因此对范围字段运行桶聚合可能会导致同一文档落入多个桶中。这可能导致意外行为，例如桶计数之和高于匹配文档的数量。例如，考虑以下索引
resp = client.indices.create(
index="range_index",
settings={
"number_of_shards": 2
},
mappings={
"properties": {
"expected_attendees": {
"type": "integer_range"
},
"time_frame": {
"type": "date_range",
"format": "yyyy-MM-dd||epoch_millis"
}
}
},
)
print(resp)
resp1 = client.index(
index="range_index",
id="1",
refresh=True,
document={
"expected_attendees": {
"gte": 10,
"lte": 20
},
"time_frame": {
"gte": "2019-10-28",
"lte": "2019-11-04"
}
},
)
print(resp1)
response = client.indices.create(
index: 'range_index',
body: {
settings: {
number_of_shards: 2
},
mappings: {
properties: {
expected_attendees: {
type: 'integer_range'
},
time_frame: {
type: 'date_range',
format: 'yyyy-MM-dd||epoch_millis'
}
}
}
}
)
puts response
response = client.index(
index: 'range_index',
id: 1,
refresh: true,
body: {
expected_attendees: {
gte: 10,
lte: 20
},
time_frame: {
gte: '2019-10-28',
lte: '2019-11-04'
}
}
)
puts response
const response = await client.indices.create({
index: "range_index",
settings: {
number_of_shards: 2,
},
mappings: {
properties: {
expected_attendees: {
type: "integer_range",
},
time_frame: {
type: "date_range",
format: "yyyy-MM-dd||epoch_millis",
},
},
},
});
console.log(response);
const response1 = await client.index({
index: "range_index",
id: 1,
refresh: "true",
document: {
expected_attendees: {
gte: 10,
lte: 20,
},
time_frame: {
gte: "2019-10-28",
lte: "2019-11-04",
},
},
});
console.log(response1);
PUT range_index
{
"settings": {
"number_of_shards": 2
},
"mappings": {
"properties": {
"expected_attendees": {
"type": "integer_range"
},
"time_frame": {
"type": "date_range",
"format": "yyyy-MM-dd||epoch_millis"
}
}
}
}
PUT range_index/_doc/1?refresh
{
"expected_attendees" : {
"gte" : 10,
"lte" : 20
},
"time_frame" : {
"gte" : "2019-10-28",
"lte" : "2019-11-04"
}
}
在以下聚合中，范围大于区间，因此文档将落入多个桶中。
resp = client.search(
index="range_index",
size="0",
aggs={
"range_histo": {
"histogram": {
"field": "expected_attendees",
"interval": 5
}
}
},
)
print(resp)
response = client.search(
index: 'range_index',
size: 0,
body: {
aggregations: {
range_histo: {
histogram: {
field: 'expected_attendees',
interval: 5
}
}
}
}
)
puts response
const response = await client.search({
index: "range_index",
size: 0,
aggs: {
range_histo: {
histogram: {
field: "expected_attendees",
interval: 5,
},
},
},
});
console.log(response);
POST /range_index/_search?size=0
{
"aggs": {
"range_histo": {
"histogram": {
"field": "expected_attendees",
"interval": 5
}
}
}
}
由于区间为 5（默认情况下偏移量为 0），我们预期桶为 10、15 和 20。我们的范围文档将落入所有这三个桶中。
{
...
"aggregations" : {
"range_histo" : {
"buckets" : [
{
"key" : 10.0,
"doc_count" : 1
},
{
"key" : 15.0,
"doc_count" : 1
},
{
"key" : 20.0,
"doc_count" : 1
}
]
}
}
}
文档不能部分存在于桶中；例如，上述文档不能在上述三个桶中的每一个中各计数三分之一。在本例中，由于文档的范围落入多个桶中，因此该文档的完整值也将计入每个桶的任何子聚合中。
查询边界不是聚合过滤器
编辑当使用查询过滤要聚合的字段时，可能会出现另一种意外行为。在这种情况下，文档可能匹配查询，但其范围的端点之一或两者都可能在查询之外。考虑对上述文档进行以下聚合
resp = client.search(
index="range_index",
size="0",
query={
"range": {
"time_frame": {
"gte": "2019-11-01",
"format": "yyyy-MM-dd"
}
}
},
aggs={
"november_data": {
"date_histogram": {
"field": "time_frame",
"calendar_interval": "day",
"format": "yyyy-MM-dd"
}
}
},
)
print(resp)
response = client.search(
index: 'range_index',
size: 0,
body: {
query: {
range: {
time_frame: {
gte: '2019-11-01',
format: 'yyyy-MM-dd'
}
}
},
aggregations: {
november_data: {
date_histogram: {
field: 'time_frame',
calendar_interval: 'day',
format: 'yyyy-MM-dd'
}
}
}
}
)
puts response
const response = await client.search({
index: "range_index",
size: 0,
query: {
range: {
time_frame: {
gte: "2019-11-01",
format: "yyyy-MM-dd",
},
},
},
aggs: {
november_data: {
date_histogram: {
field: "time_frame",
calendar_interval: "day",
format: "yyyy-MM-dd",
},
},
},
});
console.log(response);
POST /range_index/_search?size=0
{
"query": {
"range": {
"time_frame": {
"gte": "2019-11-01",
"format": "yyyy-MM-dd"
}
}
},
"aggs": {
"november_data": {
"date_histogram": {
"field": "time_frame",
"calendar_interval": "day",
"format": "yyyy-MM-dd"
}
}
}
}
即使查询只考虑11月份的天数，聚合也会生成8个桶（10月份4个，11月份4个），因为聚合是根据所有匹配文档的范围计算的。
{
...
"aggregations" : {
"november_data" : {
"buckets" : [
{
"key_as_string" : "2019-10-28",
"key" : 1572220800000,
"doc_count" : 1
},
{
"key_as_string" : "2019-10-29",
"key" : 1572307200000,
"doc_count" : 1
},
{
"key_as_string" : "2019-10-30",
"key" : 1572393600000,
"doc_count" : 1
},
{
"key_as_string" : "2019-10-31",
"key" : 1572480000000,
"doc_count" : 1
},
{
"key_as_string" : "2019-11-01",
"key" : 1572566400000,
"doc_count" : 1
},
{
"key_as_string" : "2019-11-02",
"key" : 1572652800000,
"doc_count" : 1
},
{
"key_as_string" : "2019-11-03",
"key" : 1572739200000,
"doc_count" : 1
},
{
"key_as_string" : "2019-11-04",
"key" : 1572825600000,
"doc_count" : 1
}
]
}
}
}
根据用例，CONTAINS 查询可以将文档限制为仅完全落入查询范围内的那些文档。在本例中，该文档不会被包含，聚合将为空。聚合后过滤桶也是一种选择，用于文档应被计数但可以安全忽略超出范围的数据的用例。