I have a database collection that has objects like this:
{
"_id": ObjectId("something"),
"name_lower": "total",
"name": "Total",
"mounts": [
[
"mount1",
"instance1"
],
[
"mount2",
"instance1"
],
[
"mount1",
"instance2"
],
[
"mount2",
"instance2"
]
]
}
Say I want to remove every mount that has the instance instance2, How would I go about doing that? I have been searching for quite a while.
You can do something like this
[
{
$unwind: "$mounts"
},
{
$match: {
"mounts": {
$ne: "instance2"
}
}
},
{
$group: {
_id: "$_id",
name: {
$first: "$name"
},
mounts: {
$push: "$mounts"
}
}
}
]
Working Mongo playground
This answer is based on #varman answer but more pythonic and efficient.
The first stage should be a $match condition to filter out documents that don't need to be updated.
Since the mounts key consists of a nested array, we have to $unwind it, so that we can remove array elements that need to be removed.
We have to apply the $match condition again to filter out the element that has to be removed.
Finally, we have to $group the pipeline by _id key, so that the documents which got $unwind in the previous stage will be groupped into a single document.
from pymongo import MongoClient
client = MongoClient("<URI-String>")
col = client["<DB-Name"]["<Collection-Name>"]
count = 0
for cursor in col.aggregate([
{
"$match": {
"mounts": {"$ne": "instance2"}
}
},
{
"$unwind": "$mounts"
},
{
"$match": {
"mounts": {"$ne": "instance2"}
}
},
{
"$group": {
"_id": "$_id",
"newMounts": {
"$push": "$mounts"
}
}
},
]):
# print(cursor)
col.update_one({
"_id": cursor["_id"]
}, {
"$set": {
"mounts": cursor["newMounts"]
}
})
count += 1
print("\r", count, end="")
print("\n\nDone!!!")
Related
I have the following document structure.
{
_id: ...,
unique_id: 1234,
config_no: 1,
configs: [
{
data: "qwertyuiop" // random string
},
{
data: "asdfghjkl" // random string
}
]
}
I want to update value of data from one of the configs. The index of the config that needs to be updated is available in the config_no key.
Is there any way to update the value without querying the document.
This is what I am currently doing
doc = db.collection.findOne({"unique_id": 1234})
config_no = doc.config_no
db.collection.updateOne(
{"unique_id": 1234},
{"$set": {"configs."+config_no+".data": "zxcvbnm"}} //"configs.1.data"
)
Following is something what i would like to achive.
db.collection.updateOne(
{"unique_id": 1234},
{"$set": {"configs.${config_no}.data": "zxcvbnm"}}
)
You can $unwind with includeArrayIndex option. Use the index to perform conditional update and $merge back into the collection.
db.collection.aggregate([
{
$match: {
unique_id: 1234
}
},
{
"$unwind": {
path: "$configs",
includeArrayIndex: "idx"
}
},
{
$set: {
"configs.data": {
"$cond": {
"if": {
$eq: [
"$config_no",
"$idx"
]
},
"then": "zxcvbnm",
"else": "$configs.data"
}
}
}
},
{
$group: {
_id: "$_id",
config_no: {
$first: "$config_no"
},
configs: {
$push: "$configs"
},
unique_id: {
$first: "$unique_id"
}
}
},
{
"$merge": {
"into": "collection",
"on": "_id",
"whenMatched": "merge"
}
}
])
Mongo Playground
With a schema like this
{
"doc1": {
"items": [
{
"item_id": 1
},
{
"item_id": 2
},
{
"item_id": 3
},
]
},
"doc2": {
"items": [
{
"item_id": 1
},
{
"item_id": 2
},
{
"item_id": 1
},
]
}
}
I want to query for documents that contain a duplicate item in their items array field. A duplicate means items with the same item_id field.
So the result for the example above should return doc2 only, because it has two items with the same item_id
Something like this?
qry = {
"items": {
"$size": {
"$ne": {
"items.unique_count" # obviously this doesn't exist, not sure how to do it
}
}
}
}
result = MyDocument.find(qry)
One option similar to #rickhg12hs and your suggestions is:
db.collection.aggregate([
{$match: {
$expr: {
$ne: [
{$size: "$items"},
{$size: {
$reduce: {
input: "$items",
initialValue: [],
in: {$setUnion: ["$$value", ["$$this.item_id"]]}
}
}
}
]
}
}
}
])
See how it works on the playground example
I was wondering if it was possible to somehow use the $match operator within the $sum function for aggregation.
{ "$unwind": "$info.avatarInfoList" },
{ "$unwind": "$info.avatarInfoList.equipList" },
{ "$unwind": "$info.avatarInfoList.equipList.flat.reliquarySubstats" },
{
"$project": {
"name" : "$name",
"character" : "$info.avatarInfoList.avatarId",
"artifact" : "$info.avatarInfoList.equipList.itemId",
"statValue" : {
"$sum": [
{"$match" : { "$info.avatarInfoList.equipList.flat.reliquarySubstats.appendPropId" : "FIGHT_PROP_CRITICAL_HURT" } },
{"$multiply": [2, {"$match" : { "$info.avatarInfoList.equipList.flat.reliquarySubstats.appendPropId" : "FIGHT_PROP_CRITICAL" } }]}
]
},
}
},
{ "$sort": { "statValue": -1 }},
{ '$limit' : 30 }
]).to_list(length=None)
print(data)
I want to be able to use the value of the $sum operator within the project fields somehow, I just don't really understand what the right approach would be for this.
Sample Input (may be too long):
https://www.toptal.com/developers/hastebin/ixamekaxoq.json
Sample Output:
( 2 * FIGHT_PROP_CRITICAL ) + FIGHT_PROP_CRITICAL_HURT sorted from highest to lowest for each item.
{name: hat, character: Slayer, artifact: 13, statValue : 25.6}
There are still a few ambiguities about how you want to aggregate your data, but using the full document from your link, here's one way to produce the output you want.
N.B.: Weapons in the "equipList" don't have "reliquarySubstats" so they show a "statValue" of null in the output.
db.collection.aggregate([
{"$unwind": "$info.avatarInfoList"},
{"$unwind": "$info.avatarInfoList.equipList"},
{
"$project": {
"_id": 0,
"name": 1,
"character": "$info.avatarInfoList.avatarId",
"artifact": "$info.avatarInfoList.equipList.itemId",
"statValue": {
"$reduce": {
"input": "$info.avatarInfoList.equipList.flat.reliquarySubstats",
"initialValue": 0,
"in": {
"$switch": {
"branches": [
{
"case": {"$eq": ["$$this.appendPropId", "FIGHT_PROP_CRITICAL"]},
"then": {
"$add": [
"$$value",
{"$multiply": [2, "$$this.statValue"]}
]
}
},
{
"case": {"$eq": ["$$this.appendPropId", "FIGHT_PROP_CRITICAL_HURT"]},
"then": {"$add": ["$$value", "$$this.statValue"]}
}
],
"default": "$$value"
}
}
}
}
}
},
{"$sort": {"statValue": -1}}
])
Try it on mongoplayground.net.
It's not quite clear what you want to achieve, but as mentioned you want to be using $cond here.
like so:
{
"$project": {
"statValue": {
"$sum": [
{
$cond: [
{ // if this condition is true (prop id = prop critical hurt )
$eq: [
"$info.avatarInfoList.equipList.flat.reliquarySubstats.appendPropId",
"FIGHT_PROP_CRITICAL_HURT"
]
},
{ // then use this value for the "$sum"
"$multiply": [
2,
"$info.avatarInfoList.equipList.flat.reliquarySubstats.statValue"
]
},
0 // otherwise use this value for the sum.
]
}
]
}
}
Mongo Playground
generate unique id in nested document - Pymongo
my database looks like this...
{
"_id":"5ea661d6213894a6082af6d1",
"blog_id":"blog_one",
"comments": [
{
"user_id":"1",
"comment":"comment for blog one this is good"
},
{
"user_id":"2",
"comment":"other for blog one"
},
]
}
I want to add unique id in each and every comment,
I want it to output like this,
{
"_id":"5ea661d6213894a6082af6d1",
"blog_id":"blog_one",
"comments": [
{
"id" : "something" (auto generate unique),
"user_id":"1",
"comment":"comment for blog one this is good"
},
{
"id" : "something" (auto generate unique),
"user_id":"2",
"comment":"other for blog one"
},
]
}
I'm using PyMongo, is there a way to update this kind of document?
it's possible or not?
This update will add an unique id value to each of the comments array with nested documents. The id value is calculated based upon the present time as milliseconds. This value is incremented for each array element to get the new id value for the nested documents of the array.
The code runs with MongoDB version 4.2 and PyMongo 3.10.
pipeline = [
{
"$set": {
"comments": {
"$map": {
"input": { "$range": [ 0, { "$size": "$comments" } ] },
"in": {
"$mergeObjects": [
{ "id": { "$add": [ { "$toLong" : datetime.datetime.now() }, "$$this" ] } },
{ "$arrayElemAt": [ "$comments", "$$this" ] }
]
}
}
}
}
}
]
collection.update_one( { }, pipeline )
The updated document:
{
"_id" : "5ea661d6213894a6082af6d1",
"blog_id" : "blog_one",
"comments" : [
{
"id" : NumberLong("1588179349566"),
"user_id" : "1",
"comment" : "comment for blog one this is good"
},
{
"id" : NumberLong("1588179349567"),
"user_id" : "2",
"comment" : "other for blog one"
}
]
}
[ EDIT ADD ]
The following works from mongo shell. It adds unique id for the comments array's nested documents - unique across the documents.
db.collection.aggregate( [
{
"$unwind": "$comments" },
{
"$group": {
"_id": null,
"count": { "$sum": 1 },
"docs": { "$push": "$$ROOT" },
"now": { $first: "$$NOW" }
}
},
{
"$addFields": {
"docs": {
"$map": {
"input": { "$range": [ 0, "$count" ] },
"in": {
"$mergeObjects": [
{ "comments_id": { "$add": [ { "$toLong" : "$now" }, "$$this" ] } },
{ "$arrayElemAt": [ "$docs", "$$this" ] }
]
}
}
}
}
},
{
"$unwind": "$docs"
},
{
"$addFields": {
"docs.comments.comments_id": "$docs.comments_id"
}
},
{
"$replaceRoot": { "newRoot": "$docs" }
},
{
"$group": {
"_id": { "_id": "$_id", "blog_id": "$blog_id" },
"comments": { "$push": "$comments" }
}
},
{
$project: {
"_id": 0,
"_id": "$_id._id",
"blog_id": "$_id.blog_id",
"comments": 1
}
}
] ).forEach(doc => db.blogs.updateOne( { _id: doc._id }, { $set: { comments: doc.comments } } ) )
You can use ObjectId constructor to create the ids and place them in your nested documents.
I want to iterate Mongodb database Arraylist items(TRANSACTION list) and remove Arraylist specific(TRANSACTION List) item using pymongo ?
I create Mongo collection as above using python pymongo. I want to iterate array list item using pymongo and remove final item only in Arraylist?
Data insert query using Python pymongo
# added new method create block chain_structure
def addCoinWiseTransaction(self, senz, coin, format_date):
self.collection = self.db.block_chain
coinValexists = self.collection.find({"_id": str(coin)}).count()
print('coin exists : ', coinValexists)
if (coinValexists > 0):
print('coin hash exists')
newTransaction = {"$push": {"TRANSACTION": {"SENDER": senz.attributes["#SENDER"],
"RECIVER": senz.attributes["#RECIVER"],
"T_NO_COIN": int(1),
"DATE": datetime.datetime.utcnow()
}}}
self.collection.update({"_id": str(coin)}, newTransaction)
else:
flag = senz.attributes["#f"];
print flag
if (flag == "ccb"):
print('new coin mined othir minner')
root = {"_id": str(coin)
, "S_ID": int(senz.attributes["#S_ID"]), "S_PARA": senz.attributes["#S_PARA"],
"FORMAT_DATE": format_date,
"NO_COIN": int(1),
"TRANSACTION": [{"MINER": senz.attributes["#M_S_ID"],
"RECIVER": senz.attributes["#RECIVER"],
"T_NO_COIN": int(1),
"DATE": datetime.datetime.utcnow()
}
]
}
self.collection.insert(root)
else:
print('new coin mined')
root = {"_id": str(coin)
, "S_ID": int(senz.attributes["#S_ID"]), "S_PARA": senz.attributes["#S_PARA"],
"FORMAT_DATE": format_date,
"NO_COIN": int(1),
"TRANSACTION": [{"MINER": "M_1",
"RECIVER": senz.sender,
"T_NO_COIN": int(1),
"DATE": datetime.datetime.utcnow()
}
]
}
self.collection.insert(root)
return 'DONE'
To remove the last entry, the general idea (as you have mentioned) is to iterate the array and grab the index of the last element as denoted by its DATE field, then update the collection by removing it using $pull. So the crucial piece of data you need for this to work is the DATE value and the document's _id.
One approach you could take is to first use the aggregation framework to get this data. With this, you can run a pipeline where the first step if filtering the documents in the collection by using the $match operator which uses standard MongoDB queries.
The next stage after filtering the documents is to flatten the TRANSACTION array i.e. denormalise the documents in the list so that you can filter the final item i.e. get the last document by the DATE field. This is made possible with the $unwind operator, which for each input document, outputs n documents where n is the number of array elements and can be zero for an empty array.
After deconstructing the array, in order to get the last document, use the $group operator where you can regroup the flattened documents and in the process use the group accumulator operators to obtain
the last TRANSACTION date by using the $max operator applied to its embedded DATE field.
So in essence, run the following pipeline and use the results to update the collection. For example, you can run the following pipeline:
mongo shell
db.block_chain.aggregate([
{ "$match": { "_id": coin_id } },
{ "$unwind": "$TRANSACTION" },
{
"$group": {
"_id": "$_id",
"last_transaction_date": { "$max": "$TRANSACTION.DATE" }
}
}
])
You can then get the document with the update data from this aggregate operation using the toArray() method or the aggregate cursor and update your collection:
var docs = db.block_chain.aggregate([
{ "$match": { "_id": coin_id } },
{ "$unwind": "$TRANSACTION" },
{
"$group": {
"_id": "$_id",
"LAST_TRANSACTION_DATE": { "$max": "$TRANSACTION.DATE" }
}
}
]).toArray()
db.block_chain.updateOne(
{ "_id": docs[0]._id },
{
"$pull": {
"TRANSACTION": {
"DATE": docs[0]["LAST_TRANSACTION_DATE"]
}
}
}
)
python
def remove_last_transaction(self, coin):
self.collection = self.db.block_chain
pipe = [
{ "$match": { "_id": str(coin) } },
{ "$unwind": "$TRANSACTION" },
{
"$group": {
"_id": "$_id",
"last_transaction_date": { "$max": "$TRANSACTION.DATE" }
}
}
]
# run aggregate pipeline
cursor = self.collection.aggregate(pipeline=pipe)
docs = list(cursor)
# run update
self.collection.update_one(
{ "_id": docs[0]["_id"] },
{
"$pull": {
"TRANSACTION": {
"DATE": docs[0]["LAST_TRANSACTION_DATE"]
}
}
}
)
Alternatively, you can run a single aggregate operation that will also update your collection using the $out pipeline which writes the results of the pipeline to the same collection:
If the collection specified by the $out operation already
exists, then upon completion of the aggregation, the $out stage atomically replaces the existing collection with the new results collection. The $out operation does not
change any indexes that existed on the previous collection. If the
aggregation fails, the $out operation makes no changes to
the pre-existing collection.
For example, you could run this pipeline:
mongo shell
db.block_chain.aggregate([
{ "$match": { "_id": coin_id } },
{ "$unwind": "$TRANSACTION" },
{ "$sort": { "TRANSACTION.DATE": 1 } }
{
"$group": {
"_id": "$_id",
"LAST_TRANSACTION": { "$last": "$TRANSACTION" },
"FORMAT_DATE": { "$first": "$FORMAT_DATE" },
"NO_COIN": { "$first": "$NO_COIN" },
"S_ID": { "$first": "$S_ID" },
"S_PARA": { "$first": "$S_PARA" },
"TRANSACTION": { "$push": "$TRANSACTION" }
}
},
{
"$project": {
"FORMAT_DATE": 1,
"NO_COIN": 1,
"S_ID": 1,
"S_PARA": 1,
"TRANSACTION": {
"$setDifference": ["$TRANSACTION", ["$LAST_TRANSACTION"]]
}
}
},
{ "$out": "block_chain" }
])
python
def remove_last_transaction(self, coin):
self.db.block_chain.aggregate([
{ "$match": { "_id": str(coin) } },
{ "$unwind": "$TRANSACTION" },
{ "$sort": { "TRANSACTION.DATE": 1 } },
{
"$group": {
"_id": "$_id",
"LAST_TRANSACTION": { "$last": "$TRANSACTION" },
"FORMAT_DATE": { "$first": "$FORMAT_DATE" },
"NO_COIN": { "$first": "$NO_COIN" },
"S_ID": { "$first": "$S_ID" },
"S_PARA": { "$first": "$S_PARA" },
"TRANSACTION": { "$push": "$TRANSACTION" }
}
},
{
"$project": {
"FORMAT_DATE": 1,
"NO_COIN": 1,
"S_ID": 1,
"S_PARA": 1,
"TRANSACTION": {
"$setDifference": ["$TRANSACTION", ["$LAST_TRANSACTION"]]
}
}
},
{ "$out": "block_chain" }
])
Whilst this approach can be more efficient than the first, it requires knowledge of the existing fields first so in some cases the solution cannot be practical.