MongoDB aggregate query to SpringDataMongoDB - mongodb

I have below MongoDB aggregate query and would like to have it's equivalent SpringData Mongodb query.
MongoDB Aggregate Query :
db.response.aggregate(
// Pipeline
[
// Stage 1 : Group by Emotion & Month
{
$group: {
_id: {
emotion: "$emotion",
category: "$category"
},
count: {
$sum: 1
},
point: {
$first: '$point'
}
}
},
// Stage 2 : Total Points
{
$addFields: {
"totalPoint": {
$multiply: ["$point", "$count"]
}
}
},
// Stage3 : Group By Category - Overall Response Total & totalFeedbacks
{
$group: {
_id: '$_id.category',
totalFeedbacks: {
$sum: "$count"
},
overallResponseTotal: {
$sum: "$totalPoint"
}
}
},
// Stage4 - Overall Response Total & totalFeedbacks
{
$project: {
_id: 1,
overallResponseTotal: '$overallResponseTotal',
maxTotalFrom: {
"$multiply": ["$totalFeedbacks", 3.0]
},
percent: {
"$multiply": [{
"$divide": ["$overallResponseTotal", "$maxTotalFrom"]
}, 100.0]
}
}
},
// Stage4 - Percentage Monthwise
{
$project: {
_id: 1,
overallResponseTotal: 1,
maxTotalFrom: 1,
percent: {
"$multiply": [{
"$divide": ["$overallResponseTotal", "$maxTotalFrom"]
}, 100.0]
}
}
}
]
);
I have tried it's equivalent in Spring Data but got stuck at Stage 2 on how to convert "$addFields" to java code. Though I search about it on multiple sites but couldn't find anything useful. Please see my equivalent java code for Stage 1.
//Stage 1 -Group By Emotion and Category and return it's count
GroupOperation groupEmotionAndCategory = Aggregation.group("emotion","category").count().as("count").first("point")
.as("point");
Aggregation aggregation = Aggregation.newAggregation(groupEmotionAndCategory);
AggregationResults<CategoryWiseEmotion> output = mongoTemplate.aggregate(aggregation, Response.class, CategoryWiseEmotion.class);
Any helps will be highly appreciated.

$addFields is not yet supported by Spring Data Mongodb.
One workaround is to pass the raw aggregation pipeline to Spring.
But since you have a limited number of fields after stage 1, you could also downgrade stage 2 to a projection:
{
$project: {
// _id is included by default
"count" : 1, // include count
"point" : 1, // include point
"totalPoint": {
$multiply: ["$point", "$count"] // compute totalPoint
}
}
}
I haven't tested it myself, but this projection should translate to something like:
ProjectionOperation p = project("count", "point").and("point").multiply(Fields.field("count")).as("totalPoint");
Then you can translate stage 3, 4 and 5 similarly and pass the whole pipeline to Aggregation.aggregate().

Related

Add number field in $project mongodb

I have an issue that need to insert index number when get data. First i have this data for example:
[
{
_id : 616efd7e56c9530018e318ac
student : {
name: "Alpha"
email: null
nisn: "0408210001"
gender : "female"
}
},
{
_id : 616efd7e56c9530018e318af
student : {
name: "Beta"
email: null
nisn: "0408210001"
gender : "male"
}
}
]
and then i need the output like this one:
[
{
no:1,
id:616efd7e56c9530018e318ac,
name: "Alpha",
nisn: "0408210001"
},
{
no:2,
id:616efd7e56c9530018e318ac,
name: "Beta",
nisn: "0408210002"
}
]
i have tried this code but almost get what i expected.
{
'$project': {
'_id': 0,
'id': '$_id',
'name': '$student.name',
'nisn': '$student.nisn'
}
}
but still confuse how to add the number of index. Is it available to do it in $project or i have to do it other way? Thank you for the effort to answer.
You can use $unwind which can return an index, like this:
db.collection.aggregate([
{
$group: {
_id: 0,
data: {
$push: {
_id: "$_id",
student: "$student"
}
}
}
},
{
$unwind: {path: "$data", includeArrayIndex: "no"}
},
{
"$project": {
"_id": 0,
"id": "$data._id",
"name": "$data.student.name",
"nisn": "$data.student.nisn",
"no": {"$add": ["$no", 1] }
}
}
])
You can see it works here .
I strongly suggest to use a $match step before these steps, otherwise you will group your entire collection into one document.
You need to run a pipeline with a $setWindowFields stage that allows you to add a new field which returns the position of a document (known as the document number) within a partition. The position number creation is made possible by the $documentNumber operator only available in the $setWindowFields stage.
The partition could be an extra field (which is constant) that can act as the window partition.
The final stage in the pipeline is the $replaceWith step which will promote the student embedded document to the top-level as well as replacing all input documents with the specified document.
Running the following aggregation will yield the desired results:
db.collection.aggregate([
{ $addFields: { _partition: 'students' }},
{ $setWindowFields: {
partitionBy: '$_partition',
sortBy: { _id: -1 },
output: { no: { $documentNumber: {} } }
} },
{ $replaceWith: {
$mergeObjects: [
{ id: '$_id', no: '$no' },
'$student'
]
} }
])

MongoDB query $group is returning null

I am learning MongoDb query and my requirement is to calculate the average time between two dates. I wrote a mongoDB query with project and group stages.
{
project: {
OrderObject:1,
date:1
}
},
{
group: {
objectId: '$OrderObject.pharmacy.companyName',
count: {
$sum: 1
},
duration: {
$avg: {
$abs: {
$divide: [
{
$subtract: [
{
$arrayElemAt: [
'$date',
0
]
},
{
$arrayElemAt: [
'$date',
1
]
}
]
},
60000
]
}
}
},
OrderIDs: {
$addToSet: '$OrderObject.orderID'
},
pharmacyName: {
$addToSet: '$OrderObject.pharmacy.companyName'
},
}
}
The output I get is
{
count: 3,
duration: 54.53004444444445,
OrderIDs: [ 'ABCDE', 'EWQSE', 'ERTRE' ],
pharmacyName: [ 'pharmacy business Name' ],
objectId: null
},
Can someone please tell me why objectId is null in this case but the value is printed in pharmacyName field. I am using this pipeline in parse server as query.aggregate(pipeline, {useMasterKey:true})
The my expectation is pharmacyName === objectId
Most probably your nested element here is with different name:
OrderObject.name.companyName
but this is not an issue for the $group stage since it make the aggregation for all elements( in total 3) in the collection when the _id is null and do not give you any errors ...
It is also interesing why in your output the "pharmacyName" appear simply as "name" ? ;)

Sum unique properties in different collection elements

I am quite new to MongoDB. Hopefully I am using the correct terminology to express my problem.
I have the following collection:
Data collection
{
"name":"ABC",
"resourceId":"i-1234",
"volumeId":"v-1234",
"data":"11/6/2013 12AM",
"cost": 0.5
},
{
"name":"ABC",
"resourceId":"v-1234",
"volumeId":"",
"data":"11/6/2013 2AM",
"cost": 1.5
}
I want to query the collection such that if a volumeId matches with another entries resourceId, then sum up the corresponding resourceId's cost together.
As a result, the cost would be 2.0 in this case.
Basically I want to match the volumeId of one entry to the resourceId of another entry and sum the costs if matched.
I hope I have explained my problem properly. Any help is appreciated. Thanks
Try this aggregation query:
db.col.aggregate([
{
$project: {
resourceId: 1,
volumeId: 1,
cost: 1,
match: {
$cond: [
{$eq: ["$volumeId", ""]},
"$resourceId",
"$volumeId"
]
}
}
},
{
$group: {
_id: '$match',
cost: {$sum: '$cost'},
resId: {
$addToSet: {
$cond: [
{$eq: ['$match', '$resourceId']},
null,
'$resourceId'
]
}
}
}
},
{$unwind: '$resId'},
{$match: {
resId: {
$ne: null
}
}
},
{
$project: {
resourseId: '$resId',
cost: 1,
_id: 0
}
}
])
And you will get the following:
{ "cost" : 2, "resourseId" : "i-1234" }
This is assuming the statement I wrote in the comment is true.

mongodb aggregation framework group + project

I have the following issue:
this query return 1 result which is what I want:
> db.items.aggregate([ {$group: { "_id": "$id", version: { $max: "$version" } } }])
{
"result" : [
{
"_id" : "b91e51e9-6317-4030-a9a6-e7f71d0f2161",
"version" : 1.2000000000000002
}
],
"ok" : 1
}
this query ( I just added projection so I can later query for the entire document) return multiple results. What am I doing wrong?
> db.items.aggregate([ {$group: { "_id": "$id", version: { $max: "$version" } }, $project: { _id : 1 } }])
{
"result" : [
{
"_id" : ObjectId("5139310a3899d457ee000003")
},
{
"_id" : ObjectId("513931053899d457ee000002")
},
{
"_id" : ObjectId("513930fd3899d457ee000001")
}
],
"ok" : 1
}
found the answer
1. first I need to get all the _ids
db.items.aggregate( [
{ '$match': { 'owner.id': '9e748c81-0f71-4eda-a710-576314ef3fa' } },
{ '$group': { _id: '$item.id', dbid: { $max: "$_id" } } }
]);
2. then i need to query the documents
db.items.find({ _id: { '$in': "IDs returned from aggregate" } });
which will look like this:
db.items.find({ _id: { '$in': [ '1', '2', '3' ] } });
( I know its late but still answering it so that other people don't have to go search for the right answer somewhere else )
See to the answer of Deka, this will do your job.
Not all accumulators are available in $project stage. We need to consider what we can do in project with respect to accumulators and what we can do in group. Let's take a look at this:
db.companies.aggregate([{
$match: {
funding_rounds: {
$ne: []
}
}
}, {
$unwind: "$funding_rounds"
}, {
$sort: {
"funding_rounds.funded_year": 1,
"funding_rounds.funded_month": 1,
"funding_rounds.funded_day": 1
}
}, {
$group: {
_id: {
company: "$name"
},
funding: {
$push: {
amount: "$funding_rounds.raised_amount",
year: "$funding_rounds.funded_year"
}
}
}
}, ]).pretty()
Where we're checking if any of the funding_rounds is not empty. Then it's unwind-ed to $sort and to later stages. We'll see one document for each element of the funding_rounds array for every company. So, the first thing we're going to do here is to $sort based on:
funding_rounds.funded_year
funding_rounds.funded_month
funding_rounds.funded_day
In the group stage by company name, the array is getting built using $push. $push is supposed to be part of a document specified as the value for a field we name in a group stage. We can push on any valid expression. In this case, we're pushing on documents to this array and for every document that we push it's being added to the end of the array that we're accumulating. In this case, we're pushing on documents that are built from the raised_amount and funded_year. So, the $group stage is a stream of documents that have an _id where we're specifying the company name.
Notice that $push is available in $group stages but not in $project stage. This is because $group stages are designed to take a sequence of documents and accumulate values based on that stream of documents.
$project on the other hand, works with one document at a time. So, we can calculate an average on an array within an individual document inside a project stage. But doing something like this where one at a time, we're seeing documents and for every document, it passes through the group stage pushing on a new value, well that's something that the $project stage is just not designed to do. For that type of operation we want to use $group.
Let's take a look at another example:
db.companies.aggregate([{
$match: {
funding_rounds: {
$exists: true,
$ne: []
}
}
}, {
$unwind: "$funding_rounds"
}, {
$sort: {
"funding_rounds.funded_year": 1,
"funding_rounds.funded_month": 1,
"funding_rounds.funded_day": 1
}
}, {
$group: {
_id: {
company: "$name"
},
first_round: {
$first: "$funding_rounds"
},
last_round: {
$last: "$funding_rounds"
},
num_rounds: {
$sum: 1
},
total_raised: {
$sum: "$funding_rounds.raised_amount"
}
}
}, {
$project: {
_id: 0,
company: "$_id.company",
first_round: {
amount: "$first_round.raised_amount",
article: "$first_round.source_url",
year: "$first_round.funded_year"
},
last_round: {
amount: "$last_round.raised_amount",
article: "$last_round.source_url",
year: "$last_round.funded_year"
},
num_rounds: 1,
total_raised: 1,
}
}, {
$sort: {
total_raised: -1
}
}]).pretty()
In the $group stage, we're using $first and $last accumulators. Right, again we can see that as with $push - we can't use $first and $last in project stages. Because again, project stages are not designed to accumulate values based on multiple documents. Rather they're designed to reshape documents one at a time. Total number of rounds is calculated using the $sum operator. The value 1 simply counts the number of documents passed through that group together with each document that matches or is grouped under a given _id value. The project may seem complex, but it's just making the output pretty. It's just that it's including num_rounds and total_raised from the previous document.

How do I use aggregation operators in a $match in MongoDB (for example $year or $dayOfMonth)?

I have a collection full of documents with a created_date attribute. I'd like to send these documents through an aggregation pipeline to do some work on them. Ideally I would like to filter them using a $match before I do any other work on them so that I can take advantage of indexes however I can't figure out how to use the new $year/$month/$dayOfMonth operators in my $match expression.
There are a few examples floating around of how to use the operators in a $project operation but I'm concerned that by placing a $project as the first step in my pipeline then I've lost access to my indexes (MongoDB documentation indicates that the first expression must be a $match to take advantage of indexes).
Sample data:
{
post_body: 'This is the body of test post 1',
created_date: ISODate('2012-09-29T05:23:41Z')
comments: 48
}
{
post_body: 'This is the body of test post 2',
created_date: ISODate('2012-09-24T12:34:13Z')
comments: 10
}
{
post_body: 'This is the body of test post 3',
created_date: ISODate('2012-08-16T12:34:13Z')
comments: 10
}
I'd like to run this through an aggregation pipeline to get the total comments on all posts made in September
{
aggregate: 'posts',
pipeline: [
{$match:
/*Can I use the $year/$month operators here to match Sept 2012?
$year:created_date : 2012,
$month:created_date : 9
*/
/*or does this have to be
created_date :
{$gte:{$date:'2012-09-01T04:00:00Z'},
$lt: {$date:'2012-10-01T04:00:00Z'} }
*/
},
{$group:
{_id: '0',
totalComments:{$sum:'$comments'}
}
}
]
}
This works but the match loses access to any indexes for more complicated queries:
{
aggregate: 'posts',
pipeline: [
{$project:
{
month : {$month:'$created_date'},
year : {$year:'$created_date'}
}
},
{$match:
{
month:9,
year: 2012
}
},
{$group:
{_id: '0',
totalComments:{$sum:'$comments'}
}
}
]
}
As you already found, you cannot $match on fields that are not in the document (it works exactly the same way that find works) and if you use $project first then you will lose the ability to use indexes.
What you can do instead is combine your efforts as follows:
{
aggregate: 'posts',
pipeline: [
{$match: {
created_date :
{$gte:{$date:'2012-09-01T04:00:00Z'},
$lt: {date:'2012-10-01T04:00:00Z'}
}}
}
},
{$group:
{_id: '0',
totalComments:{$sum:'$comments'}
}
}
]
}
The above only gives you aggregation for September, if you wanted to aggregate for multiple months, you can for example:
{
aggregate: 'posts',
pipeline: [
{$match: {
created_date :
{ $gte:'2012-07-01T04:00:00Z',
$lt: '2012-10-01T04:00:00Z'
}
},
{$project: {
comments: 1,
new_created: {
"yr" : {"$year" : "$created_date"},
"mo" : {"$month" : "$created_date"}
}
}
},
{$group:
{_id: "$new_created",
totalComments:{$sum:'$comments'}
}
}
]
}
and you'll get back something like:
{
"result" : [
{
"_id" : {
"yr" : 2012,
"mo" : 7
},
"totalComments" : 5
},
{
"_id" : {
"yr" : 2012,
"mo" : 8
},
"totalComments" : 19
},
{
"_id" : {
"yr" : 2012,
"mo" : 9
},
"totalComments" : 21
}
],
"ok" : 1
}
Let's look at building some pipelines that involve operations that are already familiar to us. So, we're going to look at the following stages:
match - this is filtering stage, similar to find.
project
sort
skip
limit
We might ask ourself why these stages are necessary, given that this functionality is already provided in the MongoDB query language, and the reason is because we need these stages to support the more complex analytics-oriented functionality that's included with the aggregation framework. The below query is simply equal to a find:
db.companies.aggregate([{
$match: {
founded_year: 2004
}
}, ])
Let's introduce a project stage in this aggregation pipeline:
db.companies.aggregate([{
$match: {
founded_year: 2004
}
}, {
$project: {
_id: 0,
name: 1,
founded_year: 1
}
}])
We use aggregate method for implementing aggregation framework. The aggregation pipelines are merely an array of documents. Each of the document should stipulate a particular stage operator. So, in the above case we've an aggregation pipeline with two stages. The $match stage is passing the documents one at a time to $project stage.
Let's extend to limit stage:
db.companies.aggregate([{
$match: {
founded_year: 2004
}
}, {
$limit: 5
}, {
$project: {
_id: 0,
name: 1
}
}])
This gets the matching documents and limits to five before projecting out the fields. So, projection is working only on 5 documents. Assume, if we were to do something like this:
db.companies.aggregate([{
$match: {
founded_year: 2004
}
}, {
$project: {
_id: 0,
name: 1
}
}, {
$limit: 5
}])
This gets the matching documents and projects those large number of documents and finally limits to five. So, projection is working on large number of documents and finally limiting to 5. This gives us a lesson that we should limit the documents to those which are absolutely necessary to be passed to the next stage. Now, let's look at sort stage:
db.companies.aggregate([{
$match: {
founded_year: 2004
}
}, {
$sort: {
name: 1
}
}, {
$limit: 5
}, {
$project: {
_id: 0,
name: 1
}
}])
This will sort all documents by name and give only 5 out of them. Assume, if we were to do something like this:
db.companies.aggregate([{
$match: {
founded_year: 2004
}
}, {
$limit: 5
}, {
$sort: {
name: 1
}
}, {
$project: {
_id: 0,
name: 1
}
}])
This will take first 5 documents and sort them. Let's add the skip stage:
db.companies.aggregate([{
$match: {
founded_year: 2004
}
}, {
$sort: {
name: 1
}
}, {
$skip: 10
}, {
$limit: 5
}, {
$project: {
_id: 0,
name: 1
}
}, ])
This will sort all the documents and skip the initial 10 documents and return to us. We should try to include $match stages as early as possible in the pipeline. To filter documents using a $match stage, we use the same syntax for constructing query documents (filters) as we do for find().
Try this;
db.createCollection("so");
db.so.remove();
db.so.insert([
{
post_body: 'This is the body of test post 1',
created_date: ISODate('2012-09-29T05:23:41Z'),
comments: 48
},
{
post_body: 'This is the body of test post 2',
created_date: ISODate('2012-09-24T12:34:13Z'),
comments: 10
},
{
post_body: 'This is the body of test post 3',
created_date: ISODate('2012-08-16T12:34:13Z'),
comments: 10
}
]);
//db.so.find();
db.so.ensureIndex({"created_date":1});
db.runCommand({
aggregate:"so",
pipeline:[
{
$match: { // filter only those posts in september
created_date: { $gte: ISODate('2012-09-01'), $lt: ISODate('2012-10-01') }
}
},
{
$group: {
_id: null, // no shared key
comments: { $sum: "$comments" } // total comments for all the posts in the pipeline
}
},
]
//,explain:true
});
Result is;
{ "result" : [ { "_id" : null, "comments" : 58 } ], "ok" : 1 }
So you could also modify your previous example to do this, although I'm not sure why you'd want to, unless you plan on doing something else with month and year in the pipeline;
{
aggregate: 'posts',
pipeline: [
{$match: { created_date: { $gte: ISODate('2012-09-01'), $lt: ISODate('2012-10-01') } } },
{$project:
{
month : {$month:'$created_date'},
year : {$year:'$created_date'}
}
},
{$match:
{
month:9,
year: 2012
}
},
{$group:
{_id: '0',
totalComments:{$sum:'$comments'}
}
}
]
}