$unwind an object in aggregation framework
In the MongoDB aggregation framework, I was hoping to use the $unwind operator on an object (ie. a JSON collection). Doesn't look like this is possible, is there a workaround? Are there plans to implement this?
For example, take the article collection from the aggregation documentation . Suppose there is an additional field "ratings" that is a map from user -> rating. Could you calculate the average rating for each user?
Other than this, I'm quite pleased with the aggregation framework.
Update: here's a simplified version of my JSON collection per request. I'm storing genomic data. I can't really make genotypes an array, because the most common lookup is to get the genotype for a random person.
variants: [
{
name: 'variant1',
genotypes: {
person1: 2,
person2: 5,
person3: 7,
}
},
{
name: 'variant2',
genotypes: {
person1: 3,
person2: 3,
person3: 2,
}
}
]
It is not possible to do the type of computation you are describing with the aggregation framework - and it's not because there is no $unwind
method for non-arrays. Even if the person:value objects were documents in an array, $unwind
would not help.
The "group by" functionality (whether in MongoDB or in any relational database) is done on the value of a field or column. We group by value of field and sum/average/etc based on the value of another field.
Simple example is a variant of what you suggest, ratings field added to the example article collection, but not as a map from user to rating but as an array like this:
{ title : title of article", ...
ratings: [
{ voter: "user1", score: 5 },
{ voter: "user2", score: 8 },
{ voter: "user3", score: 7 }
]
}
Now you can aggregate this with:
[ {$unwind: "$ratings"},
{$group : {_id : "$ratings.voter", averageScore: {$avg:"$ratings.score"} } }
]
But this example structured as you describe it would look like this:
{ title : title of article", ...
ratings: {
user1: 5,
user2: 8,
user3: 7
}
}
or even this:
{ title : title of article", ...
ratings: [
{ user1: 5 },
{ user2: 8 },
{ user3: 7 }
]
}
Even if you could $unwind
this, there is nothing to aggregate on here. Unless you know the complete list of all possible keys (users) you cannot do much with this. [*]
An analogous relational DB schema to what you have would be:
CREATE TABLE T (
user1: integer,
user2: integer,
user3: integer
...
);
That's not what would be done, instead we would do this:
CREATE TABLE T (
username: varchar(32),
score: integer
);
and now we aggregate using SQL:
select username, avg(score) from T group by username;
There is an enhancement request for MongoDB that may allow you to do this in the aggregation framework in the future - the ability to project values to keys to vice versa. Meanwhile, there is always map/reduce.
[*] There is a complicated way to do this if you know all unique keys (you can find all unique keys with a method similar to this) but if you know all the keys you may as well just run a sequence of queries of the form db.articles.find({"ratings.user1":{$exists:true}},{_id:0,"ratings.user1":1})
for each userX which will return all their ratings and you can sum and average them simply enough rather than do a very complex projection the aggregation framework would require.
Since 3.4.4, you can transform object to array using $objectToArray
See: https://docs.mongodb.com/manual/reference/operator/aggregation/objectToArray/
This is an old question, but I've run across a tidbit of information through trial and error that people may find useful.
It's actually possible to unwind on a dummy value by fooling the parser this way:
db.Opportunity.aggregate(
{ $project: {
Field1: 1, Field2: 1, Field3: 1,
DummyUnwindField: { $ifNull: [null, [1.0]] }
}
},
{ $unwind: "$DummyUnwindField" }
);
This will produce 1 row per document, regardless of whether or not the value exists. You may be able tinker with this to generate the results you want. I had hoped to combine this with multiple $unwinds to (sort of like emit() in map/reduce), but alas, the last $unwind wins or they combine as an intersection rather than union which makes it impossible to achieve the results I was looking for. I am sadly disappointed with the aggregate framework functionality as it doesn't fit the one use case I was hoping to use it for (and seems strangely like a lot of the questions on StackOverflow in this area are asking) - ordering results based on match rate. Improving the poor map reduce performance would have made this entire feature unnecessary.