Let's say I have a mongo db restaurant collection that has an array of different foods, and I want to average the price of the "sandwich" and the "burger" for each restaurant i.e. to not include the steak. How do I match 2 out of the 3 types in this situation i.e. or, in other words, filter out the steak?
For example, for the match operator, I can (assuming I have already unwound the array) do something like this
{ $match : { foods : "burger" } }
but I want to do something more like this (which leaves out steak)
{ $match : { foods : ["burger", "sandwich" ]} }
except that code doesn't work.
Can you explain?
"_id" : ObjectId("50b59cd75bed76f46522c34e"),
"restaurant_id" : 0,
"foods" : [
{
"type" : "sandwich",
"price" : 6.99
},
{
"type" : "burger",
"price" : 5.99
},
{
"type" : "steak"
"price" : 9.99
}
]
Use $in to match one of multiple values:
{ $match : { foods : { $in: ["burger", "sandwich" ]}}}
JohnyHK's answer is right and concise.
For the "Can you explain?" part, when you specified the match as follows:
{ $match : { foods : ["burger", "sandwich" ]} }
You are requiring the document to have a field "foods" containing an array with "burger" and "sandwich" as elements. This is an equals comparison.
The operator $in is not directly explained with the $match, see here:
http://docs.mongodb.org/manual/reference/aggregation/match/
since $in is a query operator, which is explained here (linked from $match):
http://docs.mongodb.org/manual/tutorial/query-documents/#read-operations-query-argument
Related
Have records in my db with such structure:
{
"_id" : "YA14163134",
"discount" : "",
"retail" : "115.0000",
"cost" : "",
"description" : "Caterpillar Mens Big Twist Analog Watch",
"stock_update" : "05",
"brand" : "Kronos",
"img_url" : "image2342000.jpg",
"UPC" : "4895053708012",
"stock" : [ [ "1611292138", "5" ], [ "1612032232", "4" ], [ "1612050918", "0" ] ]
}
and looking for query to get all records that have in "stock" "1612050918" value. That is update id.
Trying something like:
db.vlc.find({stock: {$elemMatch:{$all:["1612050918"]}}})
or
db.vlc.find({stock: { $in : ['1611292138']}})
or
db.vlc.find({stock: { $all : [[1611292138]]}})
with no result. It works only if I include in request second array element like here
db.vlc.find({stock: { $all : [['1611292138', '7']]}})
but that limit my request to all items from update with qnty 7 when I need with any qnty. Thank you in advance!
use this query:
{
"stock" : {
"$elemMatch" : {
"$elemMatch" : {
"$eq" : "1611292138"
}
}
}
}
Explanation:
The first $elemMatch allows you to scan all three arrays under stock
The nex $elemMatch allows you to scan the two elements in the sub-arrays
since $elemMatch requires a query object, the $eq notation is used for a literal match.
If you know that "1611292138" will always be the first element of the sub-array, your query becomes simpler:
{ "stock" : { "$elemMatch" : { "0" : "1611292138" } } }
Explanation:
Scan all arrays under stock
Look for "1611292138" in the first slot of each sub-array
Use nested $elemMatch as below :
db.vlc.find({stock: { "$elemMatch":{"$elemMatch":{"$all":["1612050918"]}}}})
Or
db.vlc.find({stock: {"$elemMatch":{ "$elemMatch":{"$in" : ["1612050918"]}}}})
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
Aggregate, $unwind and $group is not my solution as they make query very slow, there for I am looking to get my record by db.collection.find() method.
The problem is that I need more then one value from sub array. For example from the following example I want to get the "type" : "exam" and "type" : "quiz" elements.
{
"_id" : 22,
"scores" : [
{
"type" : "exam",
"score" : 75.04996547553947
},
{
"type" : "quiz",
"score" : 10.23046475899236
},
{
"type" : "homework",
"score" : 96.72520512117761
},
{
"type" : "homework",
"score" : 6.488940333376703
}
]
}
I am looking something like
db.students.find(
// Search criteria
{ '_id': 22 },
// Projection
{ _id: 1, scores: { $elemMatch: { type: 'exam', type: 'quiz' } }}
)
The result should be like
{ "_id": 22, "scores" : [ { "type" : "exam", "type" : "quiz" } ] }
But this over ride the type: 'exam' and returns only type: 'quiz'. Have anybody any idea how to do this with db.find()?
This is not possible directly using find and elemMatch because of following limitation of elemMatch and mongo array fields.
The $elemMatch operator limits the contents of an field from the query results to contain only the first element matching the $elemMatch condition. ref. from $elemMacth
and mongo array field limitations as below
Only one positional $ operator may appear in the projection document.
The query document should only contain a single condition on the array field being projected. Multiple conditions may override each other internally and lead to undefined behavior. ref from mongo array field limitations
So either you tried following this to find out only exam or quiz
db.collectionName.find({"_id":22,"scores":{"$elemMatch":{"type":"exam"}}},{"scores.$.type":1}).pretty()
is shows only exam scores array.
Otherwise you should go through aggregation
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
Is there a way to use db.collection.find() to query for a specific value in a sub-document and find those documents that match. For example:
{
{ 'Joe' : {eyecolor : 'brown'},
{ 'Mary' : {eyecolor : 'blue'},
....
}
I want to return the names of all people whose eyecolor is blue.
You need to specify the full path to a value for search to work:
db.people.find({ "Joe.eyecolor" : "brown" })
You can't switch to an array of people instead of an associative array style you're using now, as there is no way to return only array elements that match conditions. You can use $elemMatch to return the first match, but that's not likely what you'd want. Or, you could still use arrays, but you'd need to filter the array further within your client code (not the database).
You might be able to use the Aggregation framework, but it wouldn't use indexes efficiently, as you'd need to $unwind the entire array, and then do filtering, brute force. And if the data contained is more complex, the fact that projections when using the AF require you to manually specify all fields, it becomes a bit cumbersome.
To most efficiently do the query you're showing, you'd need to not use subdocuments, and instead place the people as individual documents:
{
name: "Joe",
eyecolor: "brown"
}
Then, you could just do a simple search like:
db.people.find({eyecolor: "brown"})
Yes and no. You can query for all documents that have a matching person, but you can't query for all persons directly. In other words, subdocuments are not virtual collections, you'll always have the 'parent' document returned.
The example you posted comes with the additional complexity that you're using the name as a field key, which prevents you from using the dot notation.
In general, if you have a number of similar things, it's best to put them in a list, e.g.
{
"_id" : 132,
"ppl" : [ { "Name" : "John", "eyecolor" : "blue" },
{ "Name" : "Mary", "eyecolor" : "brown" },
...
]
}
Then, you can query using the aggregation framework:
db.collection.aggregate([
// only match documents that have a person w/ blue eyes (can use indexing)
{$match : { "ppl.eyecolor" : "blue" } },
// unwind the array of people
{$unwind : "$ppl" },
// match only those with blue eyes
{$match : { "ppl.eyecolor" : "blue" }},
// optional projection to make the result a list of people
{$project : { Name : "$ppl.Name", EyeColor: "$ppl.eyecolor" }} ]);
Which gives a result like
"result" : [
{
"_id" : 132,
"Name" : "John",
"EyeColor" : "blue"
},
{
"_id" : 12,
"Name" : "Jimmy",
"EyeColor" : "blue"
},
{
"_id" : 4312,
"Name" : "Jimmy",
"EyeColor" : "blue"
},
{
"_id" : 4312,
"Name" : "Marc",
"EyeColor" : "blue"
}
],
"ok" : 1