I have a document schema in a test collection like this:
{
"_id" : NumberLong("A unique number"),
"text" : {
"characters_map" : {
"a" : 4,
"f" : 3,
"b" : 6,
...
"o" : 3
}
...
}
...
}
I want to count the sum of each character in the whole collection. I tried using the aggregation framework, but it doesn't seem I do it correct.
I began by calculating each character separately ( i.e the 'a'), but with no luck. My best approach is:
> db.test.aggregate([
{ "$group" : {
_id : { a_count : "$text.characters_map.a" },
counter : { "$sum" : "$text.characters_map.a" }
}
}
])
with a_count representing the name of the id.
which results to:
{ "_id" : { "a_map" : 8 }, "counter" : 8 }
{ "_id" : { "a_map" : 5 }, "counter" : 5 }
{ "_id" : { "a_map" : 7 }, "counter" : 21 }
...
Not false entirely, but not what I want. These results mean I have 1 document with a field of: { "a" : 8, ... }, another 1 document with: { "a" : 5, ... } and 3 documents with: { "a" : 7, ... }.
Is there any way to group these fields together at "a" level? Is my approach completely wrong?
You have to change your structure to something like below.(Preferred)
[{
"text" : { "characters_map" : [{"k":"a", "v":4}, {"k":"b", "v":8}, {"k":"c", "v":5}, {"k":"d", "v":4 }] }
}]
You can use aggregation the way you tried in your post.
db.test.aggregate([
{$unwind:"$text.characters_map"},
{$group:{_id:"$text.characters_map.k", counter:{$sum:"$text.characters_map.v"}}}
])
This is the solution if you can't change your structure.
You can use 3.4.4 version and use $objectToArray & $arrayToObject to switch between dynamic keys and label value pair.
Stage 1: Convert the dynamic characters_map keys into label value pair.
Stage 2 & 3: $unwind & $group on the characters and sum their counts.
Stage 4 & 5: Group back the output from last grouping into array of key value pairs followed by $arrayToObject to convert the key value pairs into dynamic keys.
db.test.aggregate([
{$addFields: {"text.characters_map": {$objectToArray: "$text.characters_map"}}},
{$unwind:"$text.characters_map"},
{$group:{_id:"$text.characters_map.k", counter:{$sum:"$text.characters_map.v"}}},
{$group:{_id:null, arraykeyval:{$push:{k:"$_id", v:"$counter"}}}},
{$project:{result: {$arrayToObject:"$arraykeyval"}}}
])
Related
Consider the following document that is output of an aggregation pipeline:
{ "_id" : 1, "results" : [
{ "status" : "HOLD", "footage" : 43.01, "pieces" : 1 },
{ "status" : "ACCEPTED", "footage" : 80.90, "pieces" : 2 },
{ "status" : "REJECTED", "footage" : 40.00, "pieces" : 1 }
]}
I am trying to create custom keys that concatenate the status with the footage and piece fields resulting in:
{ "_id" : 1, "results" :
{
"HOLD_footage": 43.01,
"HOLD_pieces":1,
"ACCEPTED_footage": 80.90,
"ACCEPTED_pieces": 2,
"REJECTED_footage":40.00,
"REJECTED_pieces":1
}
}
You can do following:
use $map to build the k-v tuples with key you want(i.e. HOLD_footage...)
use $concatArrays to group back the arrays of k-v tuples
use $objectToArray to get back the array form you expected
Here is the Mongo playground for your reference.
I am wandering whether using $unwind operator in aggregation pipeline for document with nested array will return the deconstructed documents in the same order as the order of the items in the array.
Example:
Suppose I have the following documents
{ "_id" : 1, "item" : "foo", values: [ "foo", "foo2", "foo3"] }
{ "_id" : 2, "item" : "bar", values: [ "bar", "bar2", "bar3"] }
{ "_id" : 3, "item" : "baz", values: [ "baz", "baz2", "baz3"] }
I would like to use paging for all values in all documents in my application code. So, my idea is to use mongo aggregation framework to:
sort the documents by _id
use $unwind on values attribute to deconstruct the documents
use $skip and $limit to simulate paging
So the question using the example described above is:
Is it guaranteed that the following aggregation pipeline:
[
{$sort: {"_id": 1}},
{$unwind: "$values"}
]
will always result to the following documents with exactly the same order?:
{ "_id" : 1, "item" : "foo", values: "foo" }
{ "_id" : 1, "item" : "foo", values: "foo2" }
{ "_id" : 1, "item" : "foo", values: "foo3" }
{ "_id" : 2, "item" : "bar", values: "bar" }
{ "_id" : 2, "item" : "bar", values: "bar2" }
{ "_id" : 2, "item" : "bar", values: "bar3" }
{ "_id" : 3, "item" : "baz", values: "baz" }
{ "_id" : 3, "item" : "baz", values: "baz2" }
{ "_id" : 3, "item" : "baz", values: "baz3" }
I also asked the same question in the MongoDB community forum . An answer that confirms my assumption was posted from a member of MongoDB stuff.
Briefly:
Yes, the order of the returned documents in the example above will always be the same. It follows the order from the array field.
In the case that you do run into issues with order. You could use includeArrayIndex to guarantee order.
[
{$unwind: {
path: 'values',
includeArrayIndex: 'arrayIndex'
}},
{$sort: {
_id: 1,
arrayIndex: 1
}},
{ $project: {
index: 0
}}
]
From what I see at https://github.com/mongodb/mongo/blob/0cee67ce6909ca653462d4609e47edcc4ac5c1a9/src/mongo/db/pipeline/document_source_unwind.cpp
The cursor iterator uses getNext() method to unwind an array:
DocumentSource::GetNextResult DocumentSourceUnwind::doGetNext() {
auto nextOut = _unwinder->getNext();
while (nextOut.isEOF()) {
.....
// Try to extract an output document from the new input document.
_unwinder->resetDocument(nextInput.releaseDocument());
nextOut = _unwinder->getNext();
}
return nextOut;
}
And the getNext() implemenation relies on array's index:
DocumentSource::GetNextResult DocumentSourceUnwind::Unwinder::getNext() {
....
// Set field to be the next element in the array. If needed, this will automatically
// clone all the documents along the field path so that the end values are not shared
// across documents that have come out of this pipeline operator. This is a partial deep
// clone. Because the value at the end will be replaced, everything along the path
// leading to that will be replaced in order not to share that change with any other
// clones (or the original).
_output.setNestedField(_unwindPathFieldIndexes, _inputArray[_index]);
indexForOutput = _index;
_index++;
_haveNext = _index < length;
.....
return _haveNext ? _output.peek() : _output.freeze();
}
So unless there is anything upstream that messes with document's order the cursor should have unwound docs in the same order as subdocs were stored in the array.
I don't recall how merger works for sharded collections and I imagine there might be a case when documents from other shards are returned from between 2 consecutive unwound documents. What the snippet of the code guarantees is that unwound document with next item from the array will never be returned before unwound document with previous item from the array.
As a side note, having million items in an array is quite an extreme design. Even 20-bytes items in the array will exceed 16Mb doc limit.
For a given record id, how do I get the average of a sub document field if I have the following in MongoDB:
/* 0 */
{
"item" : "1",
"samples" : [
{
"key" : "test-key",
"value" : "1"
},
{
"key" : "test-key2",
"value" : "2"
}
]
}
/* 1 */
{
"item" : "1",
"samples" : [
{
"key" : "test-key",
"value" : "3"
},
{
"key" : "test-key2",
"value" : "4"
}
]
}
I want to get the average of the values where key = "test-key" for a given item id (in this case 1). So the average should be $avg (1 + 3) = 2
Thanks
You'll need to use the aggregation framework. The aggregation will end up looking something like this:
db.stack.aggregate([
{ $match: { "samples.key" : "test-key" } },
{ $unwind : "$samples" },
{ $match : { "samples.key" : "test-key" } },
{ $project : { "new_key" : "$samples.key", "new_value" : "$samples.value" } },
{ $group : { `_id` : "$new_key", answer : { $avg : "$new_value" } } }
])
The best way to think of the aggregation framework is like an assembly line. The query itself is an array of JSON documents, where each sub-document represents a different step in the assembly.
Step 1: $match
The first step is a basic filter, like a WHERE clause in SQL. We place this step first to filter out all documents that do not contain an array element containing test-key. Placing this at the beginning of the pipeline allows the aggregation to use indexes.
Step 2: $unwind
The second step, $unwind, is used for separating each of the elements in the "samples" array so we can perform operations across all of them. If you run the query with just that step, you'll see what I mean.
Long story short :
{ name : "bob",
children : [ {"name" : mary}, { "name" : "sue" } ]
}
becomes two documents :
{ name : "bob", children : [ { "name" : mary } ] }
{ name : "bob", children : [ { "name" : sue } ] }
Step 3: $match
The third step, $match, is an exact duplicate of the first $match stage, but has a different purpose. Since it follows $unwind, this stage filters out previous array elements, now documents, that don't match the filter criteria. In this case, we keep only documents where samples.key = "test-key"
Step 4: $project (Optional)
The fourth step, $project, restructures the document. In this case, I pulled the items out of the array so I could reference them directly. Using the example above..
{ name : "bob", children : [ { "name" : mary } ] }
becomes
{ new_name : "bob", new_child_name : mary }
Note that this step is entirely optional; later stages could be completed even without this $project after a few minor changes. In most cases $project is entirely cosmetic; aggregations have numerous optimizations under the hood such that manually including or excluding fields in a $project should not be necessary.
Step 5: $group
Finally, $group is where the magic happens. The _id value what you will be "grouping by" in the SQL world. The second field is saying to average over the value that I defined in the $project step. You can easily substitute $sum to perform a sum, but a count operation is typically done the following way: my_count : { $sum : 1 }.
The most important thing to note here is that the majority of the work being done is to format the data to a point where performing the operation is simple.
Final Note
Lastly, I wanted to note that this would not work on the example data provided since samples.value is defined as text, which can't be used in arithmetic operations. If you're interested, changing the type of a field is described here: MongoDB How to change the type of a field
I'm trying to get the highest value of a child value. If I have two documents like this
{
"_id" : ObjectId("5585b8359557d21f44e1d857"),
"test" : {
"number" : 1,
"number2" : 1
}
}
{
"_id" : ObjectId("5585b8569557d21f44e1d858"),
"test" : {
"number" : 2,
"number2" : 1
}
}
How would I get the highest value of key "number"?
Using dot notation:
db.testSOF.find().sort({'test.number': -1}).limit(1)
To get the highest value of the key "number" you could use two approaches here. You could use the aggregation framework where the pipeline would look like this
db.collection.aggregate([
{
"$group": {
"_id": 0,
"max_number": {
"$max": "$test.number"
}
}
}
])
Result:
/* 0 */
{
"result" : [
{
"_id" : 0,
"max_number" : 2
}
],
"ok" : 1
}
or you could use the find() cursor as follows
db.collection.find().sort({"test.number": -1}).limit(1)
max() does not work the way you would expect it to in SQL for Mongo.
This is perhaps going to change in future versions but as of now,
max,min are to be used with indexed keys primarily internally for
sharding.
see http://www.mongodb.org/display/DOCS/min+and+max+Query+Specifiers
Unfortunately for now the only way to get the max value is to sort the
collection desc on that value and take the first.
db.collection.find("_id" => x).sort({"test.number" => -1}).limit(1).first()
quoted from: Getting the highest value of a column in MongoDB
For a given record id, how do I get the average of a sub document field if I have the following in MongoDB:
/* 0 */
{
"item" : "1",
"samples" : [
{
"key" : "test-key",
"value" : "1"
},
{
"key" : "test-key2",
"value" : "2"
}
]
}
/* 1 */
{
"item" : "1",
"samples" : [
{
"key" : "test-key",
"value" : "3"
},
{
"key" : "test-key2",
"value" : "4"
}
]
}
I want to get the average of the values where key = "test-key" for a given item id (in this case 1). So the average should be $avg (1 + 3) = 2
Thanks
You'll need to use the aggregation framework. The aggregation will end up looking something like this:
db.stack.aggregate([
{ $match: { "samples.key" : "test-key" } },
{ $unwind : "$samples" },
{ $match : { "samples.key" : "test-key" } },
{ $project : { "new_key" : "$samples.key", "new_value" : "$samples.value" } },
{ $group : { `_id` : "$new_key", answer : { $avg : "$new_value" } } }
])
The best way to think of the aggregation framework is like an assembly line. The query itself is an array of JSON documents, where each sub-document represents a different step in the assembly.
Step 1: $match
The first step is a basic filter, like a WHERE clause in SQL. We place this step first to filter out all documents that do not contain an array element containing test-key. Placing this at the beginning of the pipeline allows the aggregation to use indexes.
Step 2: $unwind
The second step, $unwind, is used for separating each of the elements in the "samples" array so we can perform operations across all of them. If you run the query with just that step, you'll see what I mean.
Long story short :
{ name : "bob",
children : [ {"name" : mary}, { "name" : "sue" } ]
}
becomes two documents :
{ name : "bob", children : [ { "name" : mary } ] }
{ name : "bob", children : [ { "name" : sue } ] }
Step 3: $match
The third step, $match, is an exact duplicate of the first $match stage, but has a different purpose. Since it follows $unwind, this stage filters out previous array elements, now documents, that don't match the filter criteria. In this case, we keep only documents where samples.key = "test-key"
Step 4: $project (Optional)
The fourth step, $project, restructures the document. In this case, I pulled the items out of the array so I could reference them directly. Using the example above..
{ name : "bob", children : [ { "name" : mary } ] }
becomes
{ new_name : "bob", new_child_name : mary }
Note that this step is entirely optional; later stages could be completed even without this $project after a few minor changes. In most cases $project is entirely cosmetic; aggregations have numerous optimizations under the hood such that manually including or excluding fields in a $project should not be necessary.
Step 5: $group
Finally, $group is where the magic happens. The _id value what you will be "grouping by" in the SQL world. The second field is saying to average over the value that I defined in the $project step. You can easily substitute $sum to perform a sum, but a count operation is typically done the following way: my_count : { $sum : 1 }.
The most important thing to note here is that the majority of the work being done is to format the data to a point where performing the operation is simple.
Final Note
Lastly, I wanted to note that this would not work on the example data provided since samples.value is defined as text, which can't be used in arithmetic operations. If you're interested, changing the type of a field is described here: MongoDB How to change the type of a field