Mongodb - 查询深层嵌套数组
sha*_*ker 0 mongodb aggregation-framework
我已经在SO和谷歌上提出了类似的问题2天了.这是文件
{
"rows" : [
{
"company" : "1 Gables Inn Bed & Breakfast",
"address" : " 318 Quapaw Avenue Hot Springs",
"a_url" : " AR 71901 Phone: 501-623-7576 ",
"json" : {
"results" : [
{
"address_components" : [
{
"long_name" : "318",
"short_name" : "318",
"types" : [ "street_number" ]
},
{
"long_name" : "Quapaw Avenue",
"short_name" : "Quapaw Ave",
"types" : [ "route" ]
},
{
"long_name" : "Hot Springs",
"short_name" : "Hot Springs",
"types" : [ "locality", "political" ]
},
{
"long_name" : "Hot Springs",
"short_name" : "Hot Springs",
"types" : [ "administrative_area_level_3", "political" ]
},
{
"long_name" : "Garland County",
"short_name" : "Garland County",
"types" : [ "administrative_area_level_2", "political" ]
},
{
"long_name" : "Arkansas",
"short_name" : "AR",
"types" : [ "administrative_area_level_1", "political" ]
},
{
"long_name" : "United States",
"short_name" : "US",
"types" : [ "country", "political" ]
},
{
"long_name" : "71901",
"short_name" : "71901",
"types" : [ "postal_code" ]
}
],
"formatted_address" : "318 Quapaw Avenue, Hot Springs, AR 71901, USA",
"geometry" : {
"bounds" : {
"northeast" : {
"lat" : 34.508227,
"lng" : -93.05744779999999
},
"southwest" : {
"lat" : 34.50822,
"lng" : -93.05746420000001
}
},
"location" : {
"lat" : 34.508227,
"lng" : -93.05746420000001
},
"location_type" : "RANGE_INTERPOLATED",
"viewport" : {
"northeast" : {
"lat" : 34.5095724802915,
"lng" : -93.05610701970851
},
"southwest" : {
"lat" : 34.5068745197085,
"lng" : -93.05880498029151
}
}
},
"types" : [ "street_address" ]
}
],
"status" : "OK"
}
},
{
"company" : "1000 Acres Retreat House",
"address" : " 122 Russell Street Hot Springs ",
"a_url" : " AR 71901 501-318-4117 Website ",
"json" : {
"results" : [
{
"address_components" : [
{
"long_name" : "122",
"short_name" : "122",
"types" : [ "street_number" ]
},
{
"long_name" : "Russell Street",
"short_name" : "Russell St",
"types" : [ "route" ]
},
{
"long_name" : "Hot Springs",
"short_name" : "Hot Springs",
"types" : [ "locality", "political" ]
},
{
"long_name" : "Hot Springs",
"short_name" : "Hot Springs",
"types" : [ "administrative_area_level_3", "political" ]
},
{
"long_name" : "Garland County",
"short_name" : "Garland County",
"types" : [ "administrative_area_level_2", "political" ]
},
{
"long_name" : "Arkansas",
"short_name" : "AR",
"types" : [ "administrative_area_level_1", "political" ]
},
{
"long_name" : "United States",
"short_name" : "US",
"types" : [ "country", "political" ]
},
{
"long_name" : "71901",
"short_name" : "71901",
"types" : [ "postal_code" ]
}
],
"formatted_address" : "122 Russell Street, Hot Springs, AR 71901, USA",
"geometry" : {
"location" : {
"lat" : 34.5313788,
"lng" : -93.05564799999999
},
"location_type" : "ROOFTOP",
"viewport" : {
"northeast" : {
"lat" : 34.5327277802915,
"lng" : -93.05429901970848
},
"southwest" : {
"lat" : 34.5300298197085,
"lng" : -93.0569969802915
}
}
},
"types" : [ "street_address" ]
}
],
"status" : "OK"
}
}
]
}
我想知道,这些行来自Google地理编码结果
我要做的是找到并提取每个地址的"postal_code"."postal_code"的位置可能因请求而异,因此我们不能依赖于数组中固定的预期位置.相反,我们必须识别等于"postal_code"的"类型",然后提取相应的"long_name".最终,"postal_code"将重新回到MySQL,所以我想这里的目标是"压扁"文档.
万一它可以帮助任何人,这是Mongodb中的记录:
每个记录都在它自己的数组行中,这似乎使查询复杂化,因为我尝试了以下变体:
db.AR.find({ 'results.address_components.types': { $in: ["postal_code"] } }).toArray()
db.AR.find({ 'rows.results.address_components.types': { $in: ["postal_code"] } }).toArray()
......并且似乎无法获得任何结果.没什么,齐尔奇.纳达.
顺便说一下,这是我第一次使用MongoDB,因为它似乎是最适合此类问题的工具.我也尝试过MySQL"json_extract"UDF函数,但我似乎也无法取得任何进展.所以我想我所说的是,如果我让它变得比它需要的更困难 - 而且有人有更好的方法 - 我愿意接受建议.
不要脸
你真正想要的是聚合框架,它不仅用于"聚合"结果,还用于"文档重塑",这是你问题的一部分.您也没有按要求通过正确的路径查询
db.AR.aggregate([
{ "$unwind": "$rows" },
{ "$unwind": "$rows.json.results" },
{ "$unwind": "$rows.json.results.address_components" },
{ "$match": {
"rows.json.results.address_components.types": "postal_code"
}},
{ "$project": {
"company": "$rows.company",
"address": "$rows.address",
"postal_code": "$rows.json.results.address_components.long_name"
}}
])
给你一个结果:
{
"_id" : ObjectId("5383eebcdef83b11e3d71ec4"),
"company" : "1 Gables Inn Bed & Breakfast",
"address" : " 318 Quapaw Avenue Hot Springs",
"postal_code" : "71901"
}
{
"_id" : ObjectId("5383eebcdef83b11e3d71ec4"),
"company" : "1000 Acres Retreat House",
"address" : " 122 Russell Street Hot Springs ",
"postal_code" : "71901"
}
由于文档包含嵌套数组,因此$unwind在使用之前必须将所有这些数据$match过滤到types最内层数组中字段中包含"postal_code"的结果.该$unwind声明"反规范化"的文件,基本上是建立为每个阵列条目的文档"复制".
最后,您可以使用$project将文档重新塑造或"展平"为您想要的结果样式.
您还可以阅读文档中有关聚合框架运算符的更多信息.