将$ graphLookup的ObjectId与String匹配 [英] Matching ObjectId to String for $graphLookup
问题描述
我正在尝试运行$graphLookup，如打印波纹管中所示:
目标是在给定特定记录(在此处注释为$match)的情况下，通过immediateAncestors属性检索其完整的路径".如您所见，它没有发生.
我在这里介绍了$convert来处理集合中的_id作为string，因为相信可以与immediateAncestors记录列表(这是一个string)中的_id匹配".
因此，我确实使用不同的数据(不涉及ObjectId)进行了另一项测试:
db.nodos.insert({"id":5,"name":"cinco","children":[{"id":4}]})
db.nodos.insert({"id":4,"name":"quatro","ancestors":[{"id":5}],"children":[{"id":3}]})
db.nodos.insert({"id":6,"name":"seis","children":[{"id":3}]})
db.nodos.insert({"id":1,"name":"um","children":[{"id":2}]})
db.nodos.insert({"id":2,"name":"dois","ancestors":[{"id":1}],"children":[{"id":3}]})
db.nodos.insert({"id":3,"name":"três","ancestors":[{"id":2},{"id":4},{"id":6}]})
db.nodos.insert({"id":7,"name":"sete","children":[{"id":5}]})
查询:
db.nodos.aggregate( [
{ $match: { "id": 3 } },
{ $graphLookup: {
from: "nodos",
startWith: "$ancestors.id",
connectFromField: "ancestors.id",
connectToField: "id",
as: "ANCESTORS_FROM_BEGINNING"
}
},
{ $project: {
"name": 1,
"id": 1,
"ANCESTORS_FROM_BEGINNING": "$ANCESTORS_FROM_BEGINNING.id"
}
}
] )
...这将输出我期望的结果(这5条记录直接和间接地与id 3的一条记录相连):
{
"_id" : ObjectId("5afe270fb4719112b613f1b4"),
"id" : 3.0,
"name" : "três",
"ANCESTORS_FROM_BEGINNING" : [
1.0,
4.0,
6.0,
5.0,
2.0
]
}
问题是:有一种方法可以实现我一开始提到的对象?
我正在运行Mongo 3.7.9(来自官方Docker)
提前谢谢！
您当前正在使用MongoDB的开发版本，该版本具有启用的某些功能，这些功能预计将与MongoDB 4.0一起作为正式发行版发布.请注意，某些功能可能会在最终版本发布之前进行更改，因此，在提交产品代码之前，生产代码应该已经意识到这一点.
为什么$ convert在这里失败
可能最好的解释方法是查看更改后的样本，但用代替ObjectId的ObjectId 值和数组下的那些值的字符串":
{
"_id" : ObjectId("5afe5763419503c46544e272"),
"name" : "cinco",
"children" : [ { "_id" : "5afe5763419503c46544e273" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e273"),
"name" : "quatro",
"ancestors" : [ { "_id" : "5afe5763419503c46544e272" } ],
"children" : [ { "_id" : "5afe5763419503c46544e277" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e274"),
"name" : "seis",
"children" : [ { "_id" : "5afe5763419503c46544e277" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e275"),
"name" : "um",
"children" : [ { "_id" : "5afe5763419503c46544e276" } ]
}
{
"_id" : ObjectId("5afe5763419503c46544e276"),
"name" : "dois",
"ancestors" : [ { "_id" : "5afe5763419503c46544e275" } ],
"children" : [ { "_id" : "5afe5763419503c46544e277" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e277"),
"name" : "três",
"ancestors" : [
{ "_id" : "5afe5763419503c46544e273" },
{ "_id" : "5afe5763419503c46544e274" },
{ "_id" : "5afe5763419503c46544e276" }
]
},
{
"_id" : ObjectId("5afe5764419503c46544e278"),
"name" : "sete",
"children" : [ { "_id" : "5afe5763419503c46544e272" } ]
}
这应该可以大致模拟您要使用的工具.
您试图通过_id值转换为字符串" > $project ，然后输入 $graphLookup 舞台.失败的原因是您进行了初始 $project 在此管道中，问题是"from"选项中的>仍然是未更改的集合，因此您在后续的查找"迭代中未获得正确的详细信息.
db.strcoll.aggregate([
{ "$match": { "name": "três" } },
{ "$addFields": {
"_id": { "$toString": "$_id" }
}},
{ "$graphLookup": {
"from": "strcoll",
"startWith": "$ancestors._id",
"connectFromField": "ancestors._id",
"connectToField": "_id",
"as": "ANCESTORS_FROM_BEGINNING"
}},
{ "$project": {
"name": 1,
"ANCESTORS_FROM_BEGINNING": "$ANCESTORS_FROM_BEGINNING._id"
}}
])
因此与查找"不匹配:
{
"_id" : "5afe5763419503c46544e277",
"name" : "três",
"ANCESTORS_FROM_BEGINNING" : [ ]
}
修补"问题
但这是核心问题，不是 $convert 或它本身就是别名.为了使它真正起作用，我们可以改为创建视图" 出于输入目的将其呈现为一个集合.
我将采用另一种方法，将字符串"转换为 c9> 通过 $toObjectId :
db.createView("idview","strcoll",[
{ "$addFields": {
"ancestors": {
"$ifNull": [
{ "$map": {
"input": "$ancestors",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
},
"children": {
"$ifNull": [
{ "$map": {
"input": "$children",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
}
}}
])
使用视图" 表示数据是始终与转换后的值一致.因此，以下使用视图进行聚合:
db.idview.aggregate([
{ "$match": { "name": "três" } },
{ "$graphLookup": {
"from": "idview",
"startWith": "$ancestors._id",
"connectFromField": "ancestors._id",
"connectToField": "_id",
"as": "ANCESTORS_FROM_BEGINNING"
}},
{ "$project": {
"name": 1,
"ANCESTORS_FROM_BEGINNING": "$ANCESTORS_FROM_BEGINNING._id"
}}
])
返回预期的输出:
{
"_id" : ObjectId("5afe5763419503c46544e277"),
"name" : "três",
"ANCESTORS_FROM_BEGINNING" : [
ObjectId("5afe5763419503c46544e275"),
ObjectId("5afe5763419503c46544e273"),
ObjectId("5afe5763419503c46544e274"),
ObjectId("5afe5763419503c46544e276"),
ObjectId("5afe5763419503c46544e272")
]
}
解决问题
话虽如此，这里的真正问题是您拥有一些看起来像 ObjectId 值，实际上作为 ObjectId ，但是它已被记录为字符串".一切正常工作的基本问题是两个类型"不相同，并且在尝试联接"时导致相等不匹配.
因此，实际的修复方法仍然与以前一样，而是遍历数据并对其进行修复，以使字符串"实际上也是实际上使用 "$toObjectId" 以便编写新的集合，与我们之前创建视图"的几乎相同:
db.strcoll.aggregate([
{ "$addFields": {
"ancestors": {
"$ifNull": [
{ "$map": {
"input": "$ancestors",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
},
"children": {
"$ifNull": [
{ "$map": {
"input": "$children",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
}
}}
{ "$out": "fixedcol" }
])
或者，当然，如果您需要"保持相同的集合，那么传统的循环和更新"仍然与始终需要的相同:
var updates = [];
db.strcoll.find().forEach(doc => {
var update = { '$set': {} };
if ( doc.hasOwnProperty('children') )
update.$set.children = doc.children.map(e => ({ _id: new ObjectId(e._id) }));
if ( doc.hasOwnProperty('ancestors') )
update.$set.ancestors = doc.ancestors.map(e => ({ _id: new ObjectId(e._id) }));
updates.push({
"updateOne": {
"filter": { "_id": doc._id },
update
}
});
if ( updates.length > 1000 ) {
db.strcoll.bulkWrite(updates);
updates = [];
}
})
if ( updates.length > 0 ) {
db.strcoll.bulkWrite(updates);
updates = [];
}
由于实际上一次覆盖了整个阵列，因此实际上有点大锤".对于生产环境而言，这不是一个好主意，但对于本练习的目的而言，足以作为一个演示.
结论
因此，尽管MongoDB 4.0将添加这些确实非常有用的广播"功能，但它们的实际意图并非真正适用于此类情况.实际上，如使用聚合管道转换"到新集合中所展示的那样，它们比大多数其他可能的用途要有用得多.
我们可以" 创建一个视图"，该视图可以转换数据类型以启用诸如 $graphLookup 可以在实际收集数据有所不同的地方工作，这实际上只是真正问题上的创可贴" ，因为数据类型实际上应该没有区别，应该在事实将被永久转换.
使用视图"实际上意味着用于构建的聚合管道需要有效地每次每次运行集合"(实际上是视图")，这会产生实际的开销. /p>
避免开销通常是设计目标，因此，纠正此类数据存储错误对于使应用程序获得真正的性能至关重要，而不仅仅是使用只会使事情变慢的蛮力".
更安全的转换"脚本，该脚本将匹配的"更新应用于每个数组元素.这里的代码需要NodeJS v10.x和最新版本的MongoDB节点驱动程序3.1.x:
const { MongoClient, ObjectID: ObjectId } = require('mongodb');
const EJSON = require('mongodb-extended-json');
const uri = 'mongodb://localhost/';
const log = data => console.log(EJSON.stringify(data, undefined, 2));
(async function() {
try {
const client = await MongoClient.connect(uri);
let db = client.db('test');
let coll = db.collection('strcoll');
let fields = ["ancestors", "children"];
let cursor = coll.find({
$or: fields.map(f => ({ [`${f}._id`]: { "$type": "string" } }))
}).project(fields.reduce((o,f) => ({ ...o, [f]: 1 }),{}));
let batch = [];
for await ( let { _id, ...doc } of cursor ) {
let $set = {};
let arrayFilters = [];
for ( const f of fields ) {
if ( doc.hasOwnProperty(f) ) {
$set = { ...$set,
...doc[f].reduce((o,{ _id },i) =>
({ ...o, [`${f}.$[${f.substr(0,1)}${i}]._id`]: ObjectId(_id) }),
{})
};
arrayFilters = [ ...arrayFilters,
...doc[f].map(({ _id },i) =>
({ [`${f.substr(0,1)}${i}._id`]: _id }))
];
}
}
if (arrayFilters.length > 0)
batch = [ ...batch,
{ updateOne: { filter: { _id }, update: { $set }, arrayFilters } }
];
if ( batch.length > 1000 ) {
let result = await coll.bulkWrite(batch);
batch = [];
}
}
if ( batch.length > 0 ) {
log({ batch });
let result = await coll.bulkWrite(batch);
log({ result });
}
await client.close();
} catch(e) {
console.error(e)
} finally {
process.exit()
}
})()
针对七个文档生成并执行以下批量操作:
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e272"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e273"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e273"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e273"
}
},
"update": {
"$set": {
"ancestors.$[a0]._id": {
"$oid": "5afe5763419503c46544e272"
},
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e277"
}
}
},
"arrayFilters": [
{
"a0._id": "5afe5763419503c46544e272"
},
{
"c0._id": "5afe5763419503c46544e277"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e274"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e277"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e277"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e275"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e276"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e276"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e276"
}
},
"update": {
"$set": {
"ancestors.$[a0]._id": {
"$oid": "5afe5763419503c46544e275"
},
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e277"
}
}
},
"arrayFilters": [
{
"a0._id": "5afe5763419503c46544e275"
},
{
"c0._id": "5afe5763419503c46544e277"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e277"
}
},
"update": {
"$set": {
"ancestors.$[a0]._id": {
"$oid": "5afe5763419503c46544e273"
},
"ancestors.$[a1]._id": {
"$oid": "5afe5763419503c46544e274"
},
"ancestors.$[a2]._id": {
"$oid": "5afe5763419503c46544e276"
}
}
},
"arrayFilters": [
{
"a0._id": "5afe5763419503c46544e273"
},
{
"a1._id": "5afe5763419503c46544e274"
},
{
"a2._id": "5afe5763419503c46544e276"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5764419503c46544e278"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e272"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e272"
}
]
}
}
I'm trying to run a $graphLookup like demonstrated in print bellow:
The objective is to, given a specific record (commented $match there), retrieve it's full "path" throught immediateAncestors property. As you can see, it's not happening.
I introduced $convert here to deal with _id from collection as string, believing it could be possible to "match" with _id from immediateAncestors records list (which is a string).
So, I did run another test with different data (no ObjectIds involved):
db.nodos.insert({"id":5,"name":"cinco","children":[{"id":4}]})
db.nodos.insert({"id":4,"name":"quatro","ancestors":[{"id":5}],"children":[{"id":3}]})
db.nodos.insert({"id":6,"name":"seis","children":[{"id":3}]})
db.nodos.insert({"id":1,"name":"um","children":[{"id":2}]})
db.nodos.insert({"id":2,"name":"dois","ancestors":[{"id":1}],"children":[{"id":3}]})
db.nodos.insert({"id":3,"name":"três","ancestors":[{"id":2},{"id":4},{"id":6}]})
db.nodos.insert({"id":7,"name":"sete","children":[{"id":5}]})
And the query:
db.nodos.aggregate( [
{ $match: { "id": 3 } },
{ $graphLookup: {
from: "nodos",
startWith: "$ancestors.id",
connectFromField: "ancestors.id",
connectToField: "id",
as: "ANCESTORS_FROM_BEGINNING"
}
},
{ $project: {
"name": 1,
"id": 1,
"ANCESTORS_FROM_BEGINNING": "$ANCESTORS_FROM_BEGINNING.id"
}
}
] )
...which outputs what I was expecting (the five records directly and indirectly connected to the one with id 3):
{
"_id" : ObjectId("5afe270fb4719112b613f1b4"),
"id" : 3.0,
"name" : "três",
"ANCESTORS_FROM_BEGINNING" : [
1.0,
4.0,
6.0,
5.0,
2.0
]
}
The question is: there is a way to achieve the objetive I mentioned in the beginning?
I'm running Mongo 3.7.9 (from official Docker)
Thanks in advance!
You are currently using a development version of MongoDB which has some features enabled expected to be released with MongoDB 4.0 as an official release. Note that some features may be subject to change before the final release, so production code should be aware of this before you commit to it.
Why $convert fails here
Probably the best way to explain this is to look at your altered sample but replacing with ObjectId values for _id and "strings" for those under the the arrays:
{
"_id" : ObjectId("5afe5763419503c46544e272"),
"name" : "cinco",
"children" : [ { "_id" : "5afe5763419503c46544e273" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e273"),
"name" : "quatro",
"ancestors" : [ { "_id" : "5afe5763419503c46544e272" } ],
"children" : [ { "_id" : "5afe5763419503c46544e277" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e274"),
"name" : "seis",
"children" : [ { "_id" : "5afe5763419503c46544e277" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e275"),
"name" : "um",
"children" : [ { "_id" : "5afe5763419503c46544e276" } ]
}
{
"_id" : ObjectId("5afe5763419503c46544e276"),
"name" : "dois",
"ancestors" : [ { "_id" : "5afe5763419503c46544e275" } ],
"children" : [ { "_id" : "5afe5763419503c46544e277" } ]
},
{
"_id" : ObjectId("5afe5763419503c46544e277"),
"name" : "três",
"ancestors" : [
{ "_id" : "5afe5763419503c46544e273" },
{ "_id" : "5afe5763419503c46544e274" },
{ "_id" : "5afe5763419503c46544e276" }
]
},
{
"_id" : ObjectId("5afe5764419503c46544e278"),
"name" : "sete",
"children" : [ { "_id" : "5afe5763419503c46544e272" } ]
}
That should give a general simulation of what you were trying to work with.
What you attempted was to convert the _id value into a "string" via $project before entering the $graphLookup stage. The reason this fails is whilst you did an initial $project "within" this pipeline, the problem is that the source for $graphLookup in the "from" option is still the unaltered collection and therefore you don't get the correct details on the subsequent "lookup" iterations.
db.strcoll.aggregate([
{ "$match": { "name": "três" } },
{ "$addFields": {
"_id": { "$toString": "$_id" }
}},
{ "$graphLookup": {
"from": "strcoll",
"startWith": "$ancestors._id",
"connectFromField": "ancestors._id",
"connectToField": "_id",
"as": "ANCESTORS_FROM_BEGINNING"
}},
{ "$project": {
"name": 1,
"ANCESTORS_FROM_BEGINNING": "$ANCESTORS_FROM_BEGINNING._id"
}}
])
Does not match on the "lookup" therefore:
{
"_id" : "5afe5763419503c46544e277",
"name" : "três",
"ANCESTORS_FROM_BEGINNING" : [ ]
}
"Patching" the problem
However that is the core problem and not a failing of $convert or it's aliases itself. In order to make this actually work we can instead create a "view" which presents itself as a collection for the sake of input.
I'll do this the other way around and convert the "strings" to ObjectId via $toObjectId:
db.createView("idview","strcoll",[
{ "$addFields": {
"ancestors": {
"$ifNull": [
{ "$map": {
"input": "$ancestors",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
},
"children": {
"$ifNull": [
{ "$map": {
"input": "$children",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
}
}}
])
Using the "view" however means that the data is consistently seen with the values converted. So the following aggregation using the view:
db.idview.aggregate([
{ "$match": { "name": "três" } },
{ "$graphLookup": {
"from": "idview",
"startWith": "$ancestors._id",
"connectFromField": "ancestors._id",
"connectToField": "_id",
"as": "ANCESTORS_FROM_BEGINNING"
}},
{ "$project": {
"name": 1,
"ANCESTORS_FROM_BEGINNING": "$ANCESTORS_FROM_BEGINNING._id"
}}
])
Returns the expected output:
{
"_id" : ObjectId("5afe5763419503c46544e277"),
"name" : "três",
"ANCESTORS_FROM_BEGINNING" : [
ObjectId("5afe5763419503c46544e275"),
ObjectId("5afe5763419503c46544e273"),
ObjectId("5afe5763419503c46544e274"),
ObjectId("5afe5763419503c46544e276"),
ObjectId("5afe5763419503c46544e272")
]
}
Fixing the problem
With all of that said, the real issue here is that you have some data which "looks like" an ObjectId value and is in fact valid as an ObjectId, however it has been recorded as a "string". The basic issue to everything working as it should is that the two "types" are not the same and this results in an equality mismatch as the "joins" are attempted.
So the real fix is still the same as it always has been, which is to instead go through the data and fix it so that the "strings" are actually also ObjectId values. These will then match the _id keys which they are meant to refer to, and you are saving a considerable amount of storage space since an ObjectId takes up a lot less space to store than it's string representation in hexadecimal characters.
Using MongoDB 4.0 methods, you "could" actually use the "$toObjectId" in order to write a new collection, just in much the same matter that we created the "view" earlier:
db.strcoll.aggregate([
{ "$addFields": {
"ancestors": {
"$ifNull": [
{ "$map": {
"input": "$ancestors",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
},
"children": {
"$ifNull": [
{ "$map": {
"input": "$children",
"in": { "_id": { "$toObjectId": "$$this._id" } }
}},
"$$REMOVE"
]
}
}}
{ "$out": "fixedcol" }
])
Or of course where you "need" to keep the same collection, then the traditional "loop and update" remains the same as what has always been required:
var updates = [];
db.strcoll.find().forEach(doc => {
var update = { '$set': {} };
if ( doc.hasOwnProperty('children') )
update.$set.children = doc.children.map(e => ({ _id: new ObjectId(e._id) }));
if ( doc.hasOwnProperty('ancestors') )
update.$set.ancestors = doc.ancestors.map(e => ({ _id: new ObjectId(e._id) }));
updates.push({
"updateOne": {
"filter": { "_id": doc._id },
update
}
});
if ( updates.length > 1000 ) {
db.strcoll.bulkWrite(updates);
updates = [];
}
})
if ( updates.length > 0 ) {
db.strcoll.bulkWrite(updates);
updates = [];
}
Which is actually a bit of a "sledgehammer" due to actually overwriting the entire array in a single go. Not a great idea for a production environment, but enough as a demonstration for the purposes of this exercise.
Conclusion
So whilst MongoDB 4.0 will add these "casting" features which can indeed be very useful, their actual intent is not really for cases such as this. They are in fact much more useful as demonstrated in the "conversion" to a new collection using an aggregation pipeline than most other possible uses.
Whilst we "can" create a "view" which transforms the data types to enable things like $lookup and $graphLookup to work where the actual collection data differs, this really is only a "band-aid" on the real problem as the data types really should not differ, and should in fact be permanently converted.
Using a "view" actually means that the aggregation pipeline for construction needs to effectively run every time the "collection" ( actually a "view" ) is accessed, which creates a real overhead.
Avoiding overhead is usually a design goal, therefore correcting such data storage mistakes is imperative to getting real performance out of your application, rather than just working with "brute force" that will only slow things down.
A much safer "conversion" script which applied "matched" updates to each array element. The code here requires NodeJS v10.x and a latest release MongoDB node driver 3.1.x:
const { MongoClient, ObjectID: ObjectId } = require('mongodb');
const EJSON = require('mongodb-extended-json');
const uri = 'mongodb://localhost/';
const log = data => console.log(EJSON.stringify(data, undefined, 2));
(async function() {
try {
const client = await MongoClient.connect(uri);
let db = client.db('test');
let coll = db.collection('strcoll');
let fields = ["ancestors", "children"];
let cursor = coll.find({
$or: fields.map(f => ({ [`${f}._id`]: { "$type": "string" } }))
}).project(fields.reduce((o,f) => ({ ...o, [f]: 1 }),{}));
let batch = [];
for await ( let { _id, ...doc } of cursor ) {
let $set = {};
let arrayFilters = [];
for ( const f of fields ) {
if ( doc.hasOwnProperty(f) ) {
$set = { ...$set,
...doc[f].reduce((o,{ _id },i) =>
({ ...o, [`${f}.$[${f.substr(0,1)}${i}]._id`]: ObjectId(_id) }),
{})
};
arrayFilters = [ ...arrayFilters,
...doc[f].map(({ _id },i) =>
({ [`${f.substr(0,1)}${i}._id`]: _id }))
];
}
}
if (arrayFilters.length > 0)
batch = [ ...batch,
{ updateOne: { filter: { _id }, update: { $set }, arrayFilters } }
];
if ( batch.length > 1000 ) {
let result = await coll.bulkWrite(batch);
batch = [];
}
}
if ( batch.length > 0 ) {
log({ batch });
let result = await coll.bulkWrite(batch);
log({ result });
}
await client.close();
} catch(e) {
console.error(e)
} finally {
process.exit()
}
})()
Produces and executes bulk operations like these for the seven documents:
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e272"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e273"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e273"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e273"
}
},
"update": {
"$set": {
"ancestors.$[a0]._id": {
"$oid": "5afe5763419503c46544e272"
},
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e277"
}
}
},
"arrayFilters": [
{
"a0._id": "5afe5763419503c46544e272"
},
{
"c0._id": "5afe5763419503c46544e277"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e274"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e277"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e277"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e275"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e276"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e276"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e276"
}
},
"update": {
"$set": {
"ancestors.$[a0]._id": {
"$oid": "5afe5763419503c46544e275"
},
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e277"
}
}
},
"arrayFilters": [
{
"a0._id": "5afe5763419503c46544e275"
},
{
"c0._id": "5afe5763419503c46544e277"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5763419503c46544e277"
}
},
"update": {
"$set": {
"ancestors.$[a0]._id": {
"$oid": "5afe5763419503c46544e273"
},
"ancestors.$[a1]._id": {
"$oid": "5afe5763419503c46544e274"
},
"ancestors.$[a2]._id": {
"$oid": "5afe5763419503c46544e276"
}
}
},
"arrayFilters": [
{
"a0._id": "5afe5763419503c46544e273"
},
{
"a1._id": "5afe5763419503c46544e274"
},
{
"a2._id": "5afe5763419503c46544e276"
}
]
}
},
{
"updateOne": {
"filter": {
"_id": {
"$oid": "5afe5764419503c46544e278"
}
},
"update": {
"$set": {
"children.$[c0]._id": {
"$oid": "5afe5763419503c46544e272"
}
}
},
"arrayFilters": [
{
"c0._id": "5afe5763419503c46544e272"
}
]
}
}
这篇关于将$ graphLookup的ObjectId与String匹配的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
