Update an array element with inc mongo update - mongodb

Question

HI All I have this Data in mongo,
{"articleId" : [
{
"articleId" : "9514666",
"articleCount" : 1
}
],
"count" : NumberLong(1),
"timeStamp" : NumberLong("1416634200000"),
"interval" : 1,
"tags" : "famous"
}
I want to update it using this new data
{"articleId" : [
{
"articleId" : "9514666",
"articleCount" : 3
}
{
"articleId" : "9514667",
"articleCount" : 3
}
],
"count" : NumberLong(6),
"timeStamp" : NumberLong("1416634200000"),
"interval" : 1,
"tags" : "famous"
}
What i need in the output is
{"articleId" : [
{
"articleId" : "9514666",
"articleCount" : 4
}
{
"articleId" : "9514667",
"articleCount" : 3
}
],
"count" : NumberLong(7),
"timeStamp" : NumberLong("1416634200000"),
"interval" : 1,
"tags" : "famous"
}
Could you please suggest me how can i achieve this this using update operation
My update query will have tags field as query parameter.

Answer 1

You'll never get this in a single query operation as presently there is no way for MongoDB updates to refer to the existing values of fields. The exception of course is operators such as $inc, but this has a bit more going on than can be really handled by this.
You need multiple updates, but there is a consistent model to follow and the Bulk Operations API can at least help with sending all of those updates in a single request:
var updoc = {
"articleId" : [
{
"articleId" : "9514666",
"articleCount" : 3
},
{
"articleId" : "9514667",
"articleCount" : 3
}
],
"count" : NumberLong(6),
"timeStamp" : NumberLong("1416634200000"),
"interval" : 1,
"tags" : "famous"
};
var bulk = db.collection.initializeOrderedBulkOp();
// Inspect the document variable for update
// For each array entry
updoc.articleId.forEach(function(doc) {
// First try to match the document and array entry to update
bulk.find({
"tags": updoc.tags,
"articleId.articleId": doc.articleId
}).update({
"$inc": { "articleId.$.articleCount": doc.articleCount }
});
// Then try to "push" the array entry where it does not exist
bulk.find({
"tags": updoc.tags,
"articleId.articleId": { "$ne": doc.articleId }
}).update({
"$push": { "articleId": doc }
});
})
// Finally increment the overall count
bulk.find({ "tags": updoc.tags }).update({
"$inc": { "count": updoc.count }
});
bulk.execute();
Now that is not "truly" atomic and there is a very small chance that the modified document could be read without all of the modifications in place. And the Bulk API sends these over to the server to process all at once, then that is a lot better than individual operations between the client and server where the chance of the document being read in a non-consistent state would be much higher.
So for each array member in the document to "merge" you want to both try to $inc where the
member is matched in the query and to $push a new member where it was not. Finally you just want to $inc again for the total count on the merged document with the existing one.
For this sample that is a total of 5 update operations but all sent in one package. Note that the response though will confirm that only 3 operations where applied here as 2 of the operations would not actually match a document due to the conditions specified:
BulkWriteResult({
"writeErrors" : [ ],
"writeConcernErrors" : [ ],
"nInserted" : 0,
"nUpserted" : 0,
"nMatched" : 3,
"nModified" : 3,
"nRemoved" : 0,
"upserted" : [ ]
})
So that is one way to handle it. Another may be to just submit each document individually and then periodically "merge" the data into grouped documents using the aggregation framework. It depends on how "real time" you want to do this. The above is as close to "real time" updates as you can generally get.
Delayed Processing
As mentioned, there is another approach to this where you can consider a "delayed" processing of this "merging" where you do not need the data to be updated in real time. The approach considers the use of the aggregation framework to perform the "merge", and you could even use the aggregation as the general query for the data, but you probably want to accumulate in a collection instead.
The basic premise of the aggregation is that you store each "change" document as a separate document in the collection, rather than merge in real time. So two documents in the collection would be represented like this:
{
"_id" : ObjectId("548fe1c78ad2c25d4c952eee"),
"articleId" : [
{
"articleId" : "9514666",
"articleCount" : 1
}
],
"count" : NumberLong(1),
"timeStamp" : NumberLong("1416634200000"),
"interval" : 1,
"tags" : "famous"
},
{
"_id" : ObjectId("548fe2286032bac607405eb3"),
"articleId" : [
{
"articleId" : "9514666",
"articleCount" : 3
},
{
"articleId" : "9514667",
"articleCount" : 3
}
],
"count" : NumberLong(6),
"timeStamp" : NumberLong("1416634200000"),
"interval" : 1,
"tags" : "famous"
}
In order to "merge" these results for a given "tags" value, you want an aggregation pipeline like this:
db.collection.aggregate([
// Unwinds the array members to de-normalize
{ "$unwind": "$articleId" },
// Group the elements by "tags" value and "articleId"
{ "$group": {
"_id": {
"tags": "$tags",
"articleId": "$articleId.articleId",
},
"articleCount": { "$sum": "$articleId.articleCount" },
"timeStamp": { "$max": "$timeStamp" },
"interval": { "$max": "$interval" },
}},
// Now group again creating the array of "merged" items
{ "$group": {
"_id": "$tags",
"articleId": {
"$push": {
"articleId": "$_id.articleId",
"articleCount": "$articleCount"
}
},
"count": { "$sum": "$articleCount" },
"timeStamp": { "$max": "$timeStamp" },
"interval": { "$max": "$interval" },
}}
])
So using "tags" and "articleId" ( the inner value ) you group the results together, taking the $sum of the "articleCount" fields where both of those fields are the same and the $max value for the rest of the fields, which makes sense.
In a second $group pass you then just break the result documents down to "tags", pushing each matching "articleId" value under that into an array. To avoid any duplication the document "count" is summed at this stage and the other values are just taken from the same groupings.
The result is the same "merged" document, which you could either use the above aggregation query to simply return your results from such a collection, or use those results to either just create a new collection for the merged documents ( see the $out operator for one option ) or use a similar process to the first example to "merge" these "merged" results with an existing "merged" collection.
Accumulating data like this is generally a wide topic, even though a common use case for many. There is a reference project maintained but MongoDB solutions architecture called HVDF or High Volume Data Feed. It is aimed at providing a framework or at least a reference example of handling volume feeds ( for which change document accumulation is a case ) and aggregating these in a series manner for analysis.
The actual approaches depend on the overall needs of your application. Concepts such as these are employed internally by a framework like HVDF, it's just a matter of how much complexity you need and the approach that suits your application best for how you need to access the data.