我对MongoDB还不太熟悉,我正在尝试将嵌入数组合并到MongoDB集合中,我的项目集合架构如下:
Projects:
{
_id: ObjectId(),
client_id: String,
description: String,
samples: [
{
location: String, //Unique
name: String,
}
...
]
}
用户可以上传以下形式的json文件:
[
{
location: String, //Same location as in above schema
concentration: float
}
...
]
样本数组的长度与上载的数据数组的长度相同。我试图找出如何将数据字段添加到samples数组的每个元素中,但是我无法根据mongodb文档找到如何添加的方法。我可以将json数据作为“data”加载到中,并希望基于公共“location”字段进行合并:
db.projects.update({_id: myId}, {$set : {samples.$[].data : data[location]}});
但是我想不出如何在update query中获取json数组的索引,而且我还没有在mongodb文档中找到任何示例,或者类似这样的问题。
任何帮助都将不胜感激!
最佳答案
MongoDB 3.6位置过滤更新
所以您实际上是在正确的“ballpark”中使用了positional all $[]
操作符,但问题是它只适用于“every”数组元素。因为您想要的是“匹配”条目,所以实际上您想要的是positional filtered $[<identifier>]
运算符。
正如您所注意到的,您的"location"
将是唯一的并且在数组中。使用“索引位置”对于原子更新来说确实不可靠,但实际上匹配“唯一”属性是可靠的。基本上你需要从这样的事情:
let input = [
{ location: "A", concentration: 3, other: "c" },
{ location: "C", concentration: 4, other: "a" }
];
对此:
{
"$set": {
"samples.$[l0].concentration": 3,
"samples.$[l0].other": "c",
"samples.$[l1].concentration": 4,
"samples.$[l1].other": "a"
},
"arrayFilters": [
{
"l0.location": "A"
},
{
"l1.location": "C"
}
]
}
实际上,这只是将一些基本函数应用于所提供的输入数组的问题:
let arrayFilters = input.map(({ location },i) => ({ [`l${i}.location`]: location }));
let $set = input.reduce((o,{ location, ...e },i) =>
({
...o,
...Object.entries(e).reduce((oe,[k,v]) => ({ ...oe, [`samples.$[l${i}].${k}`]: v }),{})
}),
{}
);
log({ $set, arrayFilters });
Array.map()
只需获取input
的值并创建一个标识符列表,以匹配location
中的arrayFilters
值。$set
语句的构造使用Array.reduce()
进行两次迭代,以便在从考虑中删除location
之后,合并处理的每个数组元素和该数组元素中存在的每个键,因为这不会被更新。或者,使用
for..of
循环:let arrayFilters = [];
let $set = {};
for ( let [i, { location, ...e }] of Object.entries(input) ) {
arrayFilters.push({ [`l${i}.location`]: location });
for ( let [k,v] of Object.entries(e) ) {
$set[`samples.$[l${i}].${k}`] = v;
}
}
注意我们在这里使用
Object.entries()
以及在构造中使用"object spread" ...。如果您发现自己处于一个没有这种支持的javascript环境中,那么Object.keys()
和Object.assign()
基本上是无变化的替换。然后可以在更新中实际应用,如下所示:
Project.update({ client_id: 'ClientA' }, { $set }, { arrayFilters });
因此positional filtered
$[<identifier>]
实际上用于在$set
修饰符内和arrayFilters
选项内创建条目的“匹配对”。因此,对于每个update()
我们创建一个与"location"
中的值匹配的标识符,然后在实际的arrayFilters
语句中使用相同的标识符,以便只更新与标识符条件匹配的数组条目。唯一有“标识符”的真正规则是不能以数字开头,它们“应该”是唯一的,但这不是一个规则,你只需要得到第一个匹配就行了。但更新只触及那些实际符合条件的条目。
ealier mongodb固定索引
如果没有这种支持,那么你基本上就要回到“指数头寸”了,这确实不太可靠。通常,在更新之前,您实际上需要阅读每个文档并确定数组中已经存在的内容。但至少假定指数持仓的“平价”,那么:
let input = [
{ location: "A", concentration: 3 },
{ location: "B", concentration: 5 },
{ location: "C", concentration: 4 }
];
let $set = input.reduce((o,e,i) =>
({ ...o, [`samples.${i}.concentration`]: e.concentration }),{}
);
log({ $set });
生成更新语句,如:
{
"$set": {
"samples.0.concentration": 3,
"samples.1.concentration": 5,
"samples.2.concentration": 4
}
}
或者没有平价:
let input = [
{ location: "A", concentration: 3, other: "c" },
{ location: "C", concentration: 4, other: "a" }
];
// Need to get the document to compare without parity
let doc = await Project.findOne({ "client_id": "ClientA" });
let $set = input.reduce((o,e,i) =>
({
...o,
...Object.entries(e).filter(([k,v]) => k !== "location")
.reduce((oe,[k,v]) =>
({
...oe,
[`samples.${doc.samples.map(c => c.location).indexOf(e.location)}`
+ `.${k}`]: v
}),
{}
)
}),
{}
);
log({ $set });
await Project.update({ client_id: 'ClientA' },{ $set });
生成与索引匹配的语句(在实际阅读文档之后):
{
"$set": {
"samples.0.concentration": 3,
"samples.0.other": "c",
"samples.2.concentration": 4,
"samples.2.other": "a"
}
}
当然要注意的是,对于每个“更新集”,除了首先从文档中读取以确定要更新哪些索引之外,您没有其他选择。这通常不是一个好主意,因为除了在写入之前需要读取每个文档的开销之外,不能绝对保证数组本身在读取和写入之间由其他进程保持不变,因此使用“硬索引”可以假定g仍然是相同的,但事实上可能并非如此。
早期MongoDB位置匹配
在数据允许的情况下,通常最好循环标准positional matched
$set
更新。这里$
确实是唯一的,因此它是一个很好的候选,而且最重要的是您不需要阅读现有文档来比较索引的数组:let input = [
{ location: "A", concentration: 3, other: "c" },
{ location: "C", concentration: 4, other: "a" }
];
let batch = input.map(({ location, ...e }) =>
({
updateOne: {
filter: { client_id: "ClientA", 'samples.location': location },
update: {
$set: Object.entries(e)
.reduce((oe,[k,v]) => ({ ...oe, [`samples.$.${k}`]: v }), {})
}
}
})
);
log({ batch });
await Project.bulkWrite(batch);
a
location
发送多个更新操作,但它只发送一个请求和响应,就像其他任何更新操作一样。实际上,如果您正在处理一个“更改列表”,那么返回文档以对每个更改进行比较,然后构造一个大的bulkWrite()
是要进入的方向,而不是单个写入,而且这实际上也适用于前面的所有示例。最大的区别是更改集中的“每个数组元素一个更新指令”。这是在没有“位置筛选”支持的版本中执行操作的安全方法,即使它意味着更多的写操作。
示范
下面是演示中的完整列表。注意我在这里使用“mongoose”是为了简单起见,但是对于实际的更新本身并没有什么真正的“mongoose特有的”。这同样适用于任何实现,特别是在本例中,使用
bulkWrite()
和Array.map()
处理构建列表的javascript示例。const { Schema } = mongoose = require('mongoose');
const uri = 'mongodb://localhost/test';
mongoose.Promise = global.Promise;
mongoose.set('debug',true);
const sampleSchema = new Schema({
location: String,
name: String,
concentration: Number,
other: String
});
const projectSchema = new Schema({
client_id: String,
description: String,
samples: [sampleSchema]
});
const Project = mongoose.model('Project', projectSchema);
const log = data => console.log(JSON.stringify(data, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri);
await Promise.all(Object.entries(conn.models).map(([k,m]) => m.remove()));
await Project.create({
client_id: "ClientA",
description: "A Client",
samples: [
{ location: "A", name: "Location A" },
{ location: "B", name: "Location B" },
{ location: "C", name: "Location C" }
]
});
let input = [
{ location: "A", concentration: 3, other: "c" },
{ location: "C", concentration: 4, other: "a" }
];
let arrayFilters = input.map(({ location },i) => ({ [`l${i}.location`]: location }));
let $set = input.reduce((o,{ location, ...e },i) =>
({
...o,
...Object.entries(e).reduce((oe,[k,v]) => ({ ...oe, [`samples.$[l${i}].${k}`]: v }),{})
}),
{}
);
log({ $set, arrayFilters });
await Project.update(
{ client_id: 'ClientA' },
{ $set },
{ arrayFilters }
);
let project = await Project.findOne();
log(project);
mongoose.disconnect();
} catch(e) {
console.error(e)
} finally {
process.exit()
}
})()
对于那些懒得运行的人,输出将显示更新的匹配数组元素:
Mongoose: projects.remove({}, {})
Mongoose: projects.insertOne({ _id: ObjectId("5b1778605c59470ecaf10fac"), client_id: 'ClientA', description: 'A Client', samples: [ { _id: ObjectId("5b1778605c59470ecaf10faf"), location: 'A', name: 'Location A' }, { _id: ObjectId("5b1778605c59470ecaf10fae"), location: 'B', name: 'Location B' }, { _id: ObjectId("5b1778605c59470ecaf10fad"), location: 'C', name: 'Location C' } ], __v: 0 })
{
"$set": {
"samples.$[l0].concentration": 3,
"samples.$[l0].other": "c",
"samples.$[l1].concentration": 4,
"samples.$[l1].other": "a"
},
"arrayFilters": [
{
"l0.location": "A"
},
{
"l1.location": "C"
}
]
}
Mongoose: projects.update({ client_id: 'ClientA' }, { '$set': { 'samples.$[l0].concentration': 3, 'samples.$[l0].other': 'c', 'samples.$[l1].concentration': 4, 'samples.$[l1].other': 'a' } }, { arrayFilters: [ { 'l0.location': 'A' }, { 'l1.location': 'C' } ] })
Mongoose: projects.findOne({}, { fields: {} })
{
"_id": "5b1778605c59470ecaf10fac",
"client_id": "ClientA",
"description": "A Client",
"samples": [
{
"_id": "5b1778605c59470ecaf10faf",
"location": "A",
"name": "Location A",
"concentration": 3,
"other": "c"
},
{
"_id": "5b1778605c59470ecaf10fae",
"location": "B",
"name": "Location B"
},
{
"_id": "5b1778605c59470ecaf10fad",
"location": "C",
"name": "Location C",
"concentration": 4,
"other": "a"
}
],
"__v": 0
}
或按硬索引:
const { Schema } = mongoose = require('mongoose');
const uri = 'mongodb://localhost/test';
mongoose.Promise = global.Promise;
mongoose.set('debug',true);
const sampleSchema = new Schema({
location: String,
name: String,
concentration: Number,
other: String
});
const projectSchema = new Schema({
client_id: String,
description: String,
samples: [sampleSchema]
});
const Project = mongoose.model('Project', projectSchema);
const log = data => console.log(JSON.stringify(data, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri);
await Promise.all(Object.entries(conn.models).map(([k,m]) => m.remove()));
await Project.create({
client_id: "ClientA",
description: "A Client",
samples: [
{ location: "A", name: "Location A" },
{ location: "B", name: "Location B" },
{ location: "C", name: "Location C" }
]
});
let input = [
{ location: "A", concentration: 3, other: "c" },
{ location: "C", concentration: 4, other: "a" }
];
// Need to get the document to compare without parity
let doc = await Project.findOne({ "client_id": "ClientA" });
let $set = input.reduce((o,e,i) =>
({
...o,
...Object.entries(e).filter(([k,v]) => k !== "location")
.reduce((oe,[k,v]) =>
({
...oe,
[`samples.${doc.samples.map(c => c.location).indexOf(e.location)}`
+ `.${k}`]: v
}),
{}
)
}),
{}
);
log({ $set });
await Project.update(
{ client_id: 'ClientA' },
{ $set },
);
let project = await Project.findOne();
log(project);
mongoose.disconnect();
} catch(e) {
console.error(e)
} finally {
process.exit()
}
})()
以及输出:
Mongoose: projects.remove({}, {})
Mongoose: projects.insertOne({ _id: ObjectId("5b1778e0f7be250f2b7c3fc8"), client_id: 'ClientA', description: 'A Client', samples: [ { _id: ObjectId("5b1778e0f7be250f2b7c3fcb"), location: 'A', name: 'Location A' }, { _id: ObjectId("5b1778e0f7be250f2b7c3fca"), location: 'B', name: 'Location B' }, { _id: ObjectId("5b1778e0f7be250f2b7c3fc9"), location: 'C', name: 'Location C' } ], __v: 0 })
Mongoose: projects.findOne({ client_id: 'ClientA' }, { fields: {} })
{
"$set": {
"samples.0.concentration": 3,
"samples.0.other": "c",
"samples.2.concentration": 4,
"samples.2.other": "a"
}
}
Mongoose: projects.update({ client_id: 'ClientA' }, { '$set': { 'samples.0.concentration': 3, 'samples.0.other': 'c', 'samples.2.concentration': 4, 'samples.2.other': 'a' } }, {})
Mongoose: projects.findOne({}, { fields: {} })
{
"_id": "5b1778e0f7be250f2b7c3fc8",
"client_id": "ClientA",
"description": "A Client",
"samples": [
{
"_id": "5b1778e0f7be250f2b7c3fcb",
"location": "A",
"name": "Location A",
"concentration": 3,
"other": "c"
},
{
"_id": "5b1778e0f7be250f2b7c3fca",
"location": "B",
"name": "Location B"
},
{
"_id": "5b1778e0f7be250f2b7c3fc9",
"location": "C",
"name": "Location C",
"concentration": 4,
"other": "a"
}
],
"__v": 0
}
当然还有标准的"positional"
Array.reduce()
语法和更新:const { Schema } = mongoose = require('mongoose');
const uri = 'mongodb://localhost/test';
mongoose.Promise = global.Promise;
mongoose.set('debug',true);
const sampleSchema = new Schema({
location: String,
name: String,
concentration: Number,
other: String
});
const projectSchema = new Schema({
client_id: String,
description: String,
samples: [sampleSchema]
});
const Project = mongoose.model('Project', projectSchema);
const log = data => console.log(JSON.stringify(data, undefined, 2));
(async function() {
try {
const conn = await mongoose.connect(uri);
await Promise.all(Object.entries(conn.models).map(([k,m]) => m.remove()));
await Project.create({
client_id: "ClientA",
description: "A Client",
samples: [
{ location: "A", name: "Location A" },
{ location: "B", name: "Location B" },
{ location: "C", name: "Location C" }
]
});
let input = [
{ location: "A", concentration: 3, other: "c" },
{ location: "C", concentration: 4, other: "a" }
];
let batch = input.map(({ location, ...e }) =>
({
updateOne: {
filter: { client_id: "ClientA", 'samples.location': location },
update: {
$set: Object.entries(e)
.reduce((oe,[k,v]) => ({ ...oe, [`samples.$.${k}`]: v }), {})
}
}
})
);
log({ batch });
await Project.bulkWrite(batch);
let project = await Project.findOne();
log(project);
mongoose.disconnect();
} catch(e) {
console.error(e)
} finally {
process.exit()
}
})()
输出:
Mongoose: projects.remove({}, {})
Mongoose: projects.insertOne({ _id: ObjectId("5b179142662616160853ba4a"), client_id: 'ClientA', description: 'A Client', samples: [ { _id: ObjectId("5b179142662616160853ba4d"), location: 'A', name: 'Location A' }, { _id: ObjectId("5b179142662616160853ba4c"), location: 'B', name: 'Location B' }, { _id: ObjectId("5b179142662616160853ba4b"), location: 'C', name: 'Location C' } ], __v: 0 })
{
"batch": [
{
"updateOne": {
"filter": {
"client_id": "ClientA",
"samples.location": "A"
},
"update": {
"$set": {
"samples.$.concentration": 3,
"samples.$.other": "c"
}
}
}
},
{
"updateOne": {
"filter": {
"client_id": "ClientA",
"samples.location": "C"
},
"update": {
"$set": {
"samples.$.concentration": 4,
"samples.$.other": "a"
}
}
}
}
]
}
Mongoose: projects.bulkWrite([ { updateOne: { filter: { client_id: 'ClientA', 'samples.location': 'A' }, update: { '$set': { 'samples.$.concentration': 3, 'samples.$.other': 'c' } } } }, { updateOne: { filter: { client_id: 'ClientA', 'samples.location': 'C' }, update: { '$set': { 'samples.$.concentration': 4, 'samples.$.other': 'a' } } } } ], {})
Mongoose: projects.findOne({}, { fields: {} })
{
"_id": "5b179142662616160853ba4a",
"client_id": "ClientA",
"description": "A Client",
"samples": [
{
"_id": "5b179142662616160853ba4d",
"location": "A",
"name": "Location A",
"concentration": 3,
"other": "c"
},
{
"_id": "5b179142662616160853ba4c",
"location": "B",
"name": "Location B"
},
{
"_id": "5b179142662616160853ba4b",
"location": "C",
"name": "Location C",
"concentration": 4,
"other": "a"
}
],
"__v": 0
}
关于node.js - 为数组中的每个元素添加唯一值,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/50688190/