I have table "products" in mongodb example:
{
"_id": "62ab02ebd3e608133c947798",
"status": true,
"name": "Meat",
"type": "62918ab4cab3b0249cbd2de3",
"price": 34400,
"inventory": [
{
"_id": "62af007abb78a63a44e88561",
"locator": "62933b3fe744ac34445c4fc0",
"imports": [
{
"quantity": 150,
"_id": "62aefddcd5b52c1da07521f2",
"date_manufacture": "2022-03-01T10:43:11.842Z",
"date_expiration": "2023-05-20T10:43:20.431Z"
},
{
"quantity": 200,
"_id": "62af007abb78a63a44e88563",
"date_manufacture": "2022-04-01T10:45:01.711Z",
"date_expiration": "2023-05-11T10:45:06.882Z"
}
]
},
{
"_id": "62b3c2545a78fb4414dd718f",
"locator": "62933e07c224b41fc48a1182",
"imports": [
{
"quantity": 120,
"_id": "62b3c2545a78fb4414dd7190",
"date_manufacture": "2022-03-01T01:30:07.053Z",
"date_expiration": "2023-05-01T10:43:20.431Z"
}
]
}
],
}
I want to decrease quantity in one locator
by id in imports
of inventory
with multiple product (bulkWrite). And can I decrease quantity sort by date_expiration
?
Example: when customer order product with quantity 300 and locator 62933b3fe744ac34445c4fc0
, I want to product update belike:
{
...
"name": "Meat",
"price": 34400,
"inventory": [
{
"_id": "62af007abb78a63a44e88561",
"locator": "62933b3fe744ac34445c4fc0",
"imports": [
{
"quantity": 50,
"_id": "62aefddcd5b52c1da07521f2",
"date_manufacture": "2022-03-01T10:43:11.842Z",
"date_expiration": "2023-05-20T10:43:20.431Z"
}
]
},
{
"_id": "62b3c2545a78fb4414dd718f",
"locator": "62933e07c224b41fc48a1182",
"imports": [
{
"quantity": 120,
"_id": "62b3c2545a78fb4414dd7190",
"date_manufacture": "2022-03-01T01:30:07.053Z",
"date_expiration": "2023-05-01T10:43:20.431Z"
}
]
}
],
}
Thank you so much!
You should refactor your schema as nesting array as it is considered an anti-pattern and introduces unnecessary complexity to query.
One of the options:
db={
"products": [
{
"_id": "62ab02ebd3e608133c947798",
"status": true,
"name": "Meat",
"type": "62918ab4cab3b0249cbd2de3",
"price": 34400,
"inventory": [
"62af007abb78a63a44e88561",
"62b3c2545a78fb4414dd718f"
]
}
],
"inventory": [
{
"_id": "62af007abb78a63a44e88561",
"locator": "62933b3fe744ac34445c4fc0",
"imports": [
{
"quantity": 150,
"_id": "62aefddcd5b52c1da07521f2",
"date_manufacture": ISODate("2022-03-01T10:43:11.842Z"),
"date_expiration": ISODate("2023-05-20T10:43:20.431Z")
},
{
"quantity": 200,
"_id": "62af007abb78a63a44e88563",
"date_manufacture": ISODate("2022-04-01T10:45:01.711Z"),
"date_expiration": ISODate("2023-05-11T10:45:06.882Z")
}
]
},
{
"_id": "62b3c2545a78fb4414dd718f",
"locator": "62933e07c224b41fc48a1182",
"imports": [
{
"quantity": 120,
"_id": "62b3c2545a78fb4414dd7190",
"date_manufacture": ISODate("2022-03-01T01:30:07.053Z"),
"date_expiration": ISODate("2023-05-01T10:43:20.431Z")
}
]
}
]
}
You can then do something relatively simple. Use $sortArray
to sort the date_expiration
and start to iterate through the arrays using $reduce
.
db.inventory.aggregate([
{
$match: {
locator: "62933b3fe744ac34445c4fc0"
}
},
{
"$set": {
"imports": {
$sortArray: {
input: "$imports",
sortBy: {
date_expiration: 1
}
}
}
}
},
{
$set: {
result: {
"$reduce": {
"input": "$imports",
"initialValue": {
"qtyToDecrease": 300,
"arr": []
},
"in": {
"qtyToDecrease": {
$subtract: [
"$$value.qtyToDecrease",
{
$min: [
"$$value.qtyToDecrease",
"$$this.quantity"
]
}
]
},
"arr": {
"$concatArrays": [
"$$value.arr",
[
{
"$mergeObjects": [
"$$this",
{
"quantity": {
$subtract: [
"$$this.quantity",
{
$min: [
"$$value.qtyToDecrease",
"$$this.quantity"
]
}
]
}
}
]
}
]
]
}
}
}
}
}
},
{
$set: {
imports: "$result.arr",
result: "$$REMOVE"
}
},
{
"$merge": {
"into": "inventory",
"on": "_id"
}
}
])
Here is another version that keeps your original schema. You can see it is much more complex.