Spring 数据匹配和过滤嵌套数组
Spring data Match and Filter Nested Array
如何从嵌套数组中提取数据?
我想提取数组项 "values",其中 wind_speed 参数值介于 vitRange.min 和 vitRange.max 之间(twaRange 和风向的条件相同)
数据:
{
"name" : "race"
,"polaire" : [
{
"voile" : "foc"
, "matrice" :[
{
"vitRange" : { "min" : 0, "max" : 4}
,"twaRange" : { "min" : 0, "max" : 30}
,"values" : [0, 0, 0, 2.4]
},
{
"vitRange" : { "min" : 4, "max" : 6}
,"twaRange" : { "min" : 30, "max" : 33}
,"values" : [0, 0, 2.4, 3.7]
}
]
},
{
"voile" : "spi"
, "matrice" :[
{
"vitRange" : { "min" : 0, "max" : 4}
,"twaRange" : { "min" : 0, "max" : 30}
,"values" : [0, 0, 0, 1.4]
},
{
"vitRange" : { "min" : 4, "max" : 6}
,"twaRange" : { "min" : 30, "max" : 33}
,"values" : [0, 0, 1.4, 2.2]
}
]
}
]
}
第一种方法:
Query query = new Query(
Criteria.where("name").is(name)
.andOperator(
Criteria.where("polaire.voile").is(sail),
Criteria.where("polaire.matrice.twaRange.max").lt(wind_direction),
Criteria.where("polaire.matrice.twaRange.min").gte(wind_direction),
Criteria.where("polaire.matrice.vitRange.max").lt(wind_speed),
Criteria.where("polaire.matrice.vitRange.min").gte(wind_speed)
)
);
query.fields().include("polaire.matrice.values");
Polaires data = mongoTemplate.findOne(query, Polaires.class);
第二种方法:
Criteria findPolaireCriteria = Criteria.where("name").is(name);
Criteria findValueCriteria = Criteria.where("polaire").elemMatch(Criteria.where("voile").is(sail))
.andOperator(
Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("max").lt(wind_direction)),
Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("min").gte(wind_direction)),
Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("max").lt(wind_speed)),
Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("min").gte(wind_speed)));
BasicQuery query = new BasicQuery(findPolaireCriteria.getCriteriaObject(), findValueCriteria.getCriteriaObject());
query.fields().include("polaire.matrice.values");
Polaires data = mongoTemplate.findOne(query, Polaires.class);
最后一种方法:
(比照)
Aggregation aggregation = newAggregation(
match(Criteria.where("name").is(name)
.and("polaire").elemMatch(Criteria.where("voile").is(sail))),
project( "_id", "matrice")
.and(new AggregationExpression() {
@Override
public DBObject toDbObject(AggregationOperationContext aggregationOperationContext ) {
DBObject filter = new BasicDBObject("input", "$matrice")
.append("as", "result")
.append("cond",
new BasicDBObject("$and", Arrays.<Object> asList(
new BasicDBObject("$gte", Arrays.<Object> asList("$$result.vitRange.min", 0)),
new BasicDBObject("$lt", Arrays.<Object> asList("$$result.vitRange.max", 4))
)
)
);
return new BasicDBObject("$filter", filter);
}
}).as("matrice")
);
List<BasicDBObject> dbObjects = mongoTemplate.aggregate(aggregation, "collectionname", BasicDBObject.class).getMappedResults();
或者另一个...
List<AggregationOperation> list = new ArrayList<AggregationOperation>();
list.add(Aggregation.match(Criteria.where("name").is(name)));
list.add(Aggregation.unwind("polaire"));
list.add(Aggregation.match(Criteria.where("polaire.voile").is(sail)));
list.add(Aggregation.unwind("polaire.matrice"));
list.add(Aggregation.match(Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("max").lt(wind_direction))));
list.add(Aggregation.match(Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("min").gte(wind_direction))));
list.add(Aggregation.match(Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("max").lt(wind_speed))));
list.add(Aggregation.match(Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("min").gte(wind_speed))));
list.add(Aggregation.group("id", "polaire.matrice").push("polaire.matrice.values").as("values"));
list.add(Aggregation.project("polaire.matrice","values"));
TypedAggregation<Polaires> agg = Aggregation.newAggregation(Polaires.class, list);
List<BasicDBObject> dbObjects = mongoTemplate.aggregate(agg, "collectionname", BasicDBObject.class).getMappedResults();
在论坛上转来转去,但none个帮助了我。
问题可能出在 json 结构上(使其适应轻松请求)?
谢谢
我将在这里硬编码一些值以匹配 "polaire"
的 "first" 数组索引和 "matrice"
的 "second" 数组索引以进行演示。这里注意$elemMatch
in the $match
aggregation pipeline stage and the usage of $map
and $filter
in the $project
管道阶段的用法:
Aggregation aggregation = newAggregation(
match(
Criteria.where("name").is("race").and("polaire").elemMatch(
Criteria.where("voile").is("foc")
.and("matrice").elemMatch(
Criteria.where("vitRange.min").lt(5)
.and("vitRange.max").gt(5)
.and("twaRange.min").lt(32)
.and("twaRange.max").gt(32)
)
)
),
project("name")
.and(new AggregationExpression() {
@Override
public DBObject toDbObject(AggregationOperationContext context) {
return new BasicDBObject("$map",
new BasicDBObject("input",new BasicDBObject(
"$filter", new BasicDBObject(
"input", "$polaire")
.append("as","p")
.append("cond", new BasicDBObject("$eq", Arrays.asList("$$p.voile","foc")))
))
.append("as","p")
.append("in", new BasicDBObject(
"voile", "$$p.voile")
.append("matrice",new BasicDBObject(
"$filter", new BasicDBObject(
"input", "$$p.matrice")
.append("as","m")
.append("cond", new BasicDBObject(
"$and", Arrays.asList(
new BasicDBObject("$lt", Arrays.asList("$$m.vitRange.min", 5)),
new BasicDBObject("$gt", Arrays.asList("$$m.vitRange.max", 5)),
new BasicDBObject("$lt", Arrays.asList("$$m.twaRange.min", 32)),
new BasicDBObject("$gt", Arrays.asList("$$m.twaRange.max", 32))
)
))
))
)
);
}
}).as("polaire")
);
转换为此序列化:
[
{ "$match": {
"name": "race",
"polaire": {
"$elemMatch": {
"voile": "foc",
"matrice": {
"$elemMatch": {
"vitRange.min": { "$lt": 5 },
"vitRange.max": { "$gt": 5 },
"twaRange.min": { "$lt": 32 },
"twaRange.max": { "$gt": 32 }
}
}
}
}
}},
{ "$project": {
"name": 1,
"polaire": {
"$map": {
"input": {
"$filter": {
"input": "$polaire",
"as": "p",
"cond": { "$eq": [ "$$p.voile", "foc" ] }
}
},
"as": "p",
"in": {
"voile": "$$p.voile",
"matrice": {
"$filter": {
"input": "$$p.matrice",
"as": "m",
"cond": {
"$and": [
{ "$lt": [ "$$m.vitRange.min", 5 ] },
{ "$gt": [ "$$m.vitRange.max", 5 ] },
{ "$lt": [ "$$m.twaRange.min", 32 ] },
{ "$gt": [ "$$m.twaRange.max", 32 ] }
]
}
}
}
}
}
}
}}
]
并生成匹配的文档输出为:
{
"_id" : ObjectId("593bc2f15924d4206cc6e399"),
"name" : "race",
"polaire" : [
{
"voile" : "foc",
"matrice" : [
{
"vitRange" : {
"min" : 4,
"max" : 6
},
"twaRange" : {
"min" : 30,
"max" : 33
},
"values" : [
0,
0,
2.4,
3.7
]
}
]
}
]
}
$match
is important to actually select the "document(s)" that meet the conditions. Without the usage of $elemMatch
表达式的 "query" 部分实际上可以匹配相同内部元素上没有正确条件的文档,并且实际上会分布在文档中存在的所有数组元素中.
过滤嵌套的数组首先使用$map
since the "inner" array element is also going to be subject to its own "filtering". So both the "input"
source for the $map
as well as the "output" as "in"
make reference to $filter
条件来匹配数组的特定元素。
作为 "conditions" ( "cond"
) 到 $filter
we make use of "logical aggregation expressions" such as the boolean $and
as well as the other "comparison operators" 模仿其 "query operator" 同行的相同条件。这些负责将正确的数组项与 "filtered" 结果中的 return 相匹配的逻辑。
作为参考,这是从中获得结果的源数据,应该与问题中发布的相同:
{
"_id" : ObjectId("593bc2f15924d4206cc6e399"),
"name" : "race",
"polaire" : [
{
"voile" : "foc",
"matrice" : [
{
"vitRange" : {
"min" : 0,
"max" : 4
},
"twaRange" : {
"min" : 0,
"max" : 30
},
"values" : [
0,
0,
0,
2.4
]
},
{
"vitRange" : {
"min" : 4,
"max" : 6
},
"twaRange" : {
"min" : 30,
"max" : 33
},
"values" : [
0,
0,
2.4,
3.7
]
}
]
},
{
"voile" : "spi",
"matrice" : [
{
"vitRange" : {
"min" : 0,
"max" : 4
},
"twaRange" : {
"min" : 0,
"max" : 30
},
"values" : [
0,
0,
0,
1.4
]
},
{
"vitRange" : {
"min" : 4,
"max" : 6
},
"twaRange" : {
"min" : 30,
"max" : 33
},
"values" : [
0,
0,
1.4,
2.2
]
}
]
}
]
}
如何从嵌套数组中提取数据?
我想提取数组项 "values",其中 wind_speed 参数值介于 vitRange.min 和 vitRange.max 之间(twaRange 和风向的条件相同)
数据:
{
"name" : "race"
,"polaire" : [
{
"voile" : "foc"
, "matrice" :[
{
"vitRange" : { "min" : 0, "max" : 4}
,"twaRange" : { "min" : 0, "max" : 30}
,"values" : [0, 0, 0, 2.4]
},
{
"vitRange" : { "min" : 4, "max" : 6}
,"twaRange" : { "min" : 30, "max" : 33}
,"values" : [0, 0, 2.4, 3.7]
}
]
},
{
"voile" : "spi"
, "matrice" :[
{
"vitRange" : { "min" : 0, "max" : 4}
,"twaRange" : { "min" : 0, "max" : 30}
,"values" : [0, 0, 0, 1.4]
},
{
"vitRange" : { "min" : 4, "max" : 6}
,"twaRange" : { "min" : 30, "max" : 33}
,"values" : [0, 0, 1.4, 2.2]
}
]
}
]
}
第一种方法:
Query query = new Query(
Criteria.where("name").is(name)
.andOperator(
Criteria.where("polaire.voile").is(sail),
Criteria.where("polaire.matrice.twaRange.max").lt(wind_direction),
Criteria.where("polaire.matrice.twaRange.min").gte(wind_direction),
Criteria.where("polaire.matrice.vitRange.max").lt(wind_speed),
Criteria.where("polaire.matrice.vitRange.min").gte(wind_speed)
)
);
query.fields().include("polaire.matrice.values");
Polaires data = mongoTemplate.findOne(query, Polaires.class);
第二种方法:
Criteria findPolaireCriteria = Criteria.where("name").is(name);
Criteria findValueCriteria = Criteria.where("polaire").elemMatch(Criteria.where("voile").is(sail))
.andOperator(
Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("max").lt(wind_direction)),
Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("min").gte(wind_direction)),
Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("max").lt(wind_speed)),
Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("min").gte(wind_speed)));
BasicQuery query = new BasicQuery(findPolaireCriteria.getCriteriaObject(), findValueCriteria.getCriteriaObject());
query.fields().include("polaire.matrice.values");
Polaires data = mongoTemplate.findOne(query, Polaires.class);
最后一种方法:
(比照
Aggregation aggregation = newAggregation(
match(Criteria.where("name").is(name)
.and("polaire").elemMatch(Criteria.where("voile").is(sail))),
project( "_id", "matrice")
.and(new AggregationExpression() {
@Override
public DBObject toDbObject(AggregationOperationContext aggregationOperationContext ) {
DBObject filter = new BasicDBObject("input", "$matrice")
.append("as", "result")
.append("cond",
new BasicDBObject("$and", Arrays.<Object> asList(
new BasicDBObject("$gte", Arrays.<Object> asList("$$result.vitRange.min", 0)),
new BasicDBObject("$lt", Arrays.<Object> asList("$$result.vitRange.max", 4))
)
)
);
return new BasicDBObject("$filter", filter);
}
}).as("matrice")
);
List<BasicDBObject> dbObjects = mongoTemplate.aggregate(aggregation, "collectionname", BasicDBObject.class).getMappedResults();
或者另一个...
List<AggregationOperation> list = new ArrayList<AggregationOperation>();
list.add(Aggregation.match(Criteria.where("name").is(name)));
list.add(Aggregation.unwind("polaire"));
list.add(Aggregation.match(Criteria.where("polaire.voile").is(sail)));
list.add(Aggregation.unwind("polaire.matrice"));
list.add(Aggregation.match(Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("max").lt(wind_direction))));
list.add(Aggregation.match(Criteria.where("polaire.matrice.twaRange").elemMatch(Criteria.where("min").gte(wind_direction))));
list.add(Aggregation.match(Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("max").lt(wind_speed))));
list.add(Aggregation.match(Criteria.where("polaire.matrice.vitRange").elemMatch(Criteria.where("min").gte(wind_speed))));
list.add(Aggregation.group("id", "polaire.matrice").push("polaire.matrice.values").as("values"));
list.add(Aggregation.project("polaire.matrice","values"));
TypedAggregation<Polaires> agg = Aggregation.newAggregation(Polaires.class, list);
List<BasicDBObject> dbObjects = mongoTemplate.aggregate(agg, "collectionname", BasicDBObject.class).getMappedResults();
在论坛上转来转去,但none个帮助了我。 问题可能出在 json 结构上(使其适应轻松请求)?
谢谢
我将在这里硬编码一些值以匹配 "polaire"
的 "first" 数组索引和 "matrice"
的 "second" 数组索引以进行演示。这里注意$elemMatch
in the $match
aggregation pipeline stage and the usage of $map
and $filter
in the $project
管道阶段的用法:
Aggregation aggregation = newAggregation(
match(
Criteria.where("name").is("race").and("polaire").elemMatch(
Criteria.where("voile").is("foc")
.and("matrice").elemMatch(
Criteria.where("vitRange.min").lt(5)
.and("vitRange.max").gt(5)
.and("twaRange.min").lt(32)
.and("twaRange.max").gt(32)
)
)
),
project("name")
.and(new AggregationExpression() {
@Override
public DBObject toDbObject(AggregationOperationContext context) {
return new BasicDBObject("$map",
new BasicDBObject("input",new BasicDBObject(
"$filter", new BasicDBObject(
"input", "$polaire")
.append("as","p")
.append("cond", new BasicDBObject("$eq", Arrays.asList("$$p.voile","foc")))
))
.append("as","p")
.append("in", new BasicDBObject(
"voile", "$$p.voile")
.append("matrice",new BasicDBObject(
"$filter", new BasicDBObject(
"input", "$$p.matrice")
.append("as","m")
.append("cond", new BasicDBObject(
"$and", Arrays.asList(
new BasicDBObject("$lt", Arrays.asList("$$m.vitRange.min", 5)),
new BasicDBObject("$gt", Arrays.asList("$$m.vitRange.max", 5)),
new BasicDBObject("$lt", Arrays.asList("$$m.twaRange.min", 32)),
new BasicDBObject("$gt", Arrays.asList("$$m.twaRange.max", 32))
)
))
))
)
);
}
}).as("polaire")
);
转换为此序列化:
[
{ "$match": {
"name": "race",
"polaire": {
"$elemMatch": {
"voile": "foc",
"matrice": {
"$elemMatch": {
"vitRange.min": { "$lt": 5 },
"vitRange.max": { "$gt": 5 },
"twaRange.min": { "$lt": 32 },
"twaRange.max": { "$gt": 32 }
}
}
}
}
}},
{ "$project": {
"name": 1,
"polaire": {
"$map": {
"input": {
"$filter": {
"input": "$polaire",
"as": "p",
"cond": { "$eq": [ "$$p.voile", "foc" ] }
}
},
"as": "p",
"in": {
"voile": "$$p.voile",
"matrice": {
"$filter": {
"input": "$$p.matrice",
"as": "m",
"cond": {
"$and": [
{ "$lt": [ "$$m.vitRange.min", 5 ] },
{ "$gt": [ "$$m.vitRange.max", 5 ] },
{ "$lt": [ "$$m.twaRange.min", 32 ] },
{ "$gt": [ "$$m.twaRange.max", 32 ] }
]
}
}
}
}
}
}
}}
]
并生成匹配的文档输出为:
{
"_id" : ObjectId("593bc2f15924d4206cc6e399"),
"name" : "race",
"polaire" : [
{
"voile" : "foc",
"matrice" : [
{
"vitRange" : {
"min" : 4,
"max" : 6
},
"twaRange" : {
"min" : 30,
"max" : 33
},
"values" : [
0,
0,
2.4,
3.7
]
}
]
}
]
}
$match
is important to actually select the "document(s)" that meet the conditions. Without the usage of $elemMatch
表达式的 "query" 部分实际上可以匹配相同内部元素上没有正确条件的文档,并且实际上会分布在文档中存在的所有数组元素中.
过滤嵌套的数组首先使用$map
since the "inner" array element is also going to be subject to its own "filtering". So both the "input"
source for the $map
as well as the "output" as "in"
make reference to $filter
条件来匹配数组的特定元素。
作为 "conditions" ( "cond"
) 到 $filter
we make use of "logical aggregation expressions" such as the boolean $and
as well as the other "comparison operators" 模仿其 "query operator" 同行的相同条件。这些负责将正确的数组项与 "filtered" 结果中的 return 相匹配的逻辑。
作为参考,这是从中获得结果的源数据,应该与问题中发布的相同:
{
"_id" : ObjectId("593bc2f15924d4206cc6e399"),
"name" : "race",
"polaire" : [
{
"voile" : "foc",
"matrice" : [
{
"vitRange" : {
"min" : 0,
"max" : 4
},
"twaRange" : {
"min" : 0,
"max" : 30
},
"values" : [
0,
0,
0,
2.4
]
},
{
"vitRange" : {
"min" : 4,
"max" : 6
},
"twaRange" : {
"min" : 30,
"max" : 33
},
"values" : [
0,
0,
2.4,
3.7
]
}
]
},
{
"voile" : "spi",
"matrice" : [
{
"vitRange" : {
"min" : 0,
"max" : 4
},
"twaRange" : {
"min" : 0,
"max" : 30
},
"values" : [
0,
0,
0,
1.4
]
},
{
"vitRange" : {
"min" : 4,
"max" : 6
},
"twaRange" : {
"min" : 30,
"max" : 33
},
"values" : [
0,
0,
1.4,
2.2
]
}
]
}
]
}