select records using aggregate:
db.getCollection('stock_records').aggregate(
[
{
"$project": {
"info.created_date": 1,
"info.store_id": 1,
"info.store_name": 1,
"_id": 1
}
},
{
"$match": {
"$and": [
{
"info.store_id": "563dcf3465512285781608802a"
},
{
"info.created_date": {
$gt: ISODate("2021-07-18T21:07:42.313+00:00")
}
}
]
}
}
])
select records using find:
db.getCollection('stock_records').find(
{
'info.store_id':'563dcf3465512285781608802a',
'info.created_date':{ $gt:ISODate('2021-07-18T21:07:42.313+00:00')}
})
What is difference between these queries and which is best for select by id and date condition?
I think your question should be rephrased to "what's the difference between find and aggregate".
Before I dive into that I will say that both commands are similar and will perform generally the same at scale. If you want specific differences is that you did not add a project option to your find query so it will return the full document.
Regarding which is better, generally speaking unless you need a specific aggregation operator it's best to use find instead, it performs better
Now why is the aggregation framework performance "worse"? it's simple. it just does "more".
Any pipeline stage needs aggregation to fetch the BSON for the document then convert them to internal objects in the pipeline for processing - then at the end of the pipeline they are converted back to BSON and sent to the client.
This, especially for large queries has a very significant overhead compared to a find where the BSON is just sent back to the client.
Because of this, if you could execute your aggregation as a find query, you should.
Aggregation is slower than find.
In your example, Aggregation
In the first stage, you are returning all the documents with projected fields
For example, if your collection has 1000 documents, you are returning all 1000 documents each having specified projection fields. This will impact the performance of your query.
Now in the second stage, You are filtering the documents that match the query filter.
For example, out of 1000 documents from the stage 1 you select only few documents
In your example, find
First, you are filtering the documents that match the query filter.
For example, if your collection has 1000 documents, you are returning only the documents that match the query condition.
Here You did not specify the fields to return in the documents that match the query filter. Therefore the returned documents will have all fields.
You can use projection in find, instead of using aggregation
db.getCollection('stock_records').find(
{
'info.store_id': '563dcf3465512285781608802a',
'info.created_date': {
$gt: ISODate('2021-07-18T21:07:42.313+00:00')
}
},
{
"info.created_date": 1,
"info.store_id": 1,
"info.store_name": 1,
"_id": 1
}
)
Related
I have some data with identical layout divided over several collections, say we have collections named Jobs.Current, Jobs.Finished, Jobs.ByJames.
I have implemented a complex query using some aggregation stages on one of these collections, where the last stage is the sorting. It's something like this (but in real it's implemented in C# and additionally doing a projection):
db.ArchivedJobs.aggregate([ { $match: { Name: { $gte: "A" } } }, { $addFields: { "UpdatedTime": { $max: "$Transitions.TimeStamp" } } }, { $sort: { "__tmp": 1 } } ])
My new requirement is to include all these collections into my query. I could do it by simply running the same query on all collections in sequence - but then I still need to sort the results together. As this sort isn't so trivial (using an additional field being created by a $max on a sub-array) and I'm using skip and limit options I hope it's possible to do it in a way like:
Doing the query I already implemented on all relevant collections by defining appropriate aggregation steps
Sorting the whole result afterwards inside the same aggregation request
I found something with a $lookup stage, but couldn't apply it to my request as it needs to do some field-oriented matching (?). I need to access the complete objects.
The data is something like
{
"_id":"4242",
"name":"Stream recording Test - BBC 60 secs switch",
"transitions":[
{
"_id":"123",
"timeStamp":"2020-02-13T14:59:40.449Z",
"currentProcState":"Waiting"
},
{
"_id":"124",
"timeStamp":"2020-02-13T14:59:40.55Z",
"currentProcState":"Running"
},
{
"_id":"125",
"timeStamp":"2020-02-13T15:00:23.216Z",
"currentProcState":"Error"
} ],
"currentState":"Error"
}
I have been reading up on some mongodb documentation and ran across some confusing terminology, namely how to differentiate when a symbol will be used as an aggregate function or as an operator.
For example, the $size function either calculates the number of items in an array, or checks if the number of elements in an array is equal to a number, is there any way to know what the function will do at what time? Through trial and error I discovered that the $size will throw an error unless a number is passed to it in the $match step, but is there some rule/guideline so I can know what it will do beforehand?
db.collection.aggregate([
{
$project: {
key: 1,
number: {
$size: "$key"
}
}
},
{
$match: {
key: {
$size: 1
}
}
}
])
For querying data in MongoDB you can use the find method or the aggregate method. There are operators which you can use with these methods.
These query operators are used with the find method. Some of these can also used with the $match stage of the aggregate method (details later in the post).
These aggregation pipeline operators are used within the aggregate's stages. Some of these can also be used with the find method (details later in the post).
You will notice that, there are common operator names; for example, $eq, $gte, $or, $type, $size, etc. But, their usage and / or functionality can be different. The $eq operator has same function but different usage syntax and the $typeoperator has different functionality (and usage syntax).
And, some of these operators can be used with both the methods.
Some Usage Scenarios:
Lets consider a users collection and some queries:
{ "_id" : 1, "age" : 21, "firstname" : "John" }
{ "_id" : 2, "age" : 18, "firstname" : "John" }
{ "_id" : 3, "age" : "39", "firstname" : "Johnson" }
The query:
db.users.find( { firstname: { $eq: "John"}, age: { $gt: 20 } } )
This query's filter is same as { firstname: "John"}, age: { $gt: 20 } }. This uses the query operators $eq and $gt. The same query can be written in the aggregate method's $match stage:
db.users.aggregate([
{ $match: { firstname: "John", age: { $gt: 20 } } },
])
The operators used in this case are the same query operators. The query comparison operators can be used with the aggregate method's $match and $lookup stages.
Another Scenario:
db.users.aggregate([
{ $project: { ageGreaterThan20: { $gt: [ "$age", 20 ] } } },
])
This is the usage of aggregation comparison operator $gt. Note this is used within the aggregation query, but within the $project stage.
As you had used the query operators in the aggregation query, you can also use the aggregation operators within the find method. But, this must be used with the "special" $expr operator. For example:
db.users.find( { $expr: { $gt: [ "$age", 20 ] } } )
The advantage of the $expr is that - there are number of aggregation operators which can be used within the find queries. For example, using the $strLenCP:
db.users.find( { $expr: { $gt: [ { $strLenCP: "$firstname" }, 4 ] } } )
You can also use the $expr within the aggregation, within the $match or $lookup stages:
db.users.aggregate([
{ $match: { $expr: { $gt: [ "$age", 20 ] } } },
])
Finally:
I have been reading up on some mongodb documentation and ran across
some confusing terminology, namely how to differentiate when a symbol
will be used as an aggregate function or as an operator. ... but is
there some rule/guideline so I can know what it will do beforehand?
Reading helps, practicing helps better and experience helps the best. You use a specific operator in a specific scenario or use case. To achieve some functionality you use an appropriate method and operator(s).
Reference: Operators
As per my understanding, I'm pointing out few things :
Difference between aggregation and operator
You'll have certain basic functions for crud operations like .find() or .insert() or .delete() or .update() on MongoDB (There are few others like .count(), .distinct() but those are primary)
Versus
aggregation is a whole framework heavily used for complex reads, only two stages in aggregation is capable of writes $out and $merge.
Operators :
There are different types of operators :
Query and Projection Operators : These operators are crucial and are used to filter docs and to transform fields of docs in the response, Usually used in filter and project part of .find(filter, project) or .update(filter, update, project/options) etc.. These are also used in $match stage of aggregation pipeline ($match is similar to filter in .find()). Ex. :- $and, $or, $in, $or & more.
Update Operators : By name, these operators help to update documents in the collection. Usually used in update part of .update() or .findOneAndUpdate() etc.. Ex. :- $set, $unset, $inc & more.
Aggregation :
When it comes to aggregation they call it aggregation framework, definitely for a reason as you can do a lot of things with data using aggregation.
Aggregation has aggregation pipeline , which has syntax .aggregate([]). pipeline is an array with stages. Each stage in aggregation does certain operation on data flowing through them. Ex. :- $match, $project, $group etc..
As we know each document is independent on it's own, most of aggregation stages operate independently on each doc flowing through them.
Aggregation pipeline Operators :
These operators are generally used to achieve what you're looking for, certain operators can't be used in conjunction with other operators or in a stage.
Let's say in $match stage you would mostly use Query operators but not Aggregation operators as aggregation operators can't directly be used in $match in contrast with $project or $addFields stages.
Example with Stages & Operators :
Let's say you got to make Smoothie :
Out of a bunch of groceries you would filter needed fruits(docs in fruits collection) by matching with what you wanted to blend using $match stage ($and to match fruits/veggies, $lt to filter only fruits that hasn't expired and $size to limit no.of fruits needed).
You would peel off skin or chop into pieces to keep just keep useful parts of fruits(docs) using $project.
You would group all of the fruits into a blender using $group & add ingredients like cream & sugar using $addFields - You're too cautious about sugar quantity so you'll use operators like $size to check size and $multiply no.of nutrients based on conditions ($cond) you would $divide both sugar/nutrients to count nutrition value.
You'll iterate on adding ice pieces again and again using $map, $filter to remove un crushed ice pieces.
uhh, you always forget to add veggies (A different collection needs to be merged) based on fruits that already got blended you use $lookup to lookup for matching veggies(docs) & either blend in again with group or just put it on top.
Finally either you drink it from blender (just return the docs no more stages) or take into glass using $out or $merge stage (Remember as I said, only two stages that can write to a collection).
Off course every stage is Optional - You can eat fruits as is instead of blending (get all docs and all their fields) but you know what you wouldn't do that (for many reasons like performance, unnecessary data flowing through network) unless it's pre-prepared product (like a small configuration collection which has limited data with few docs & every doc is need & can be easily retrieved in one DB call).
Note :
Usually you can't use aggregation operators in filter part i.e; .find(filter) or in '$match' stage unless you use $expr.
Starting MongoDB v4.2 you can run aggregation pipeline in update-with-an-aggregation-pipeline where you can take advantage of aggregation stages/operators in update part.
While you search MongoDB's documentation often you would find the same operator in multiple places, Let's say if you search for $size you would find multiple references one is Query operator or as Projection operator or as aggregation operator - So depends on need/where you wanted to use you can refer their documentation for usage cause though name seems to be similar or does almost same but functionality may differ & syntax also differs.
You can always MongoDB for free at MongoDB University.
I have the following scheme in MongoDB:
collection: records
{
"profile_id":int
"fields":[
{
"marc_ff":string
}
]
}
I'd like to get the distinct fields.marc_ff values!
When I use the Eve's aggregation feature (development branch v0.7):
getuniquefields={
'datasource': {
'source': 'records',
'aggregation': {
'pipeline': [
{"$match": {
"profile_id":"$profile_value"
}},
{ "$unwind": "$fields" },
{ "$group": {
"_id": {"field_subfield":"$fields.marc_ff"},
}}
],
'options': {'allowDiskUse':True}
}
}
}
the query gets almost 48 seconds (for several thousand records).
However, when I use the PyMongo's distinct() function:
db.records.distinct('fields.marc_ff',{"profile_id":"$profile_value"})
it gets 30 seconds.
Why is it faster?
In the second case I'm initializing and authenticating a new MognoClient inside the Eve's run.py file and still it's 18secs faster.
Eve does not support (yet) the distinct aggregation command, however is there a more efficient implementation than the one presented to get the distinct values?
UPDATE
As #Asya pointed in the comments I have added the "profile_id" restriction in the PyMongo's query, so now the queries are equivalent and (oddly) distinct is even faster (from 33secs to 30secs).
I should mention that currently I only have records from one profile_id in my collection, hence the records that both the queries have to take into consideration is the same (the whole collection).
Can anyone tell me how to add a $match stage to an aggregation pipeline to filter for where a field MATCHES a query, (and may have other data in it too), rather than limiting results to entries where the field EQUALS the query?
The query specification...
var query = {hello:"world"};
...can be used to retrieve the following documents using the find() operation of MongoDb's native node driver, where the query 'map' is interpreted as a match...
{hello:"world"}
{hello:"world", extra:"data"}
...like...
collection.find(query);
The same query map can also be interpreted as a match when used with $elemMatch to retrieve documents with matching entries contained in arrays like these documents...
{
greetings:[
{hello:"world"},
]
}
{
greetings:[
{hello:"world", extra:"data"},
]
}
{
greetings:[
{hello:"world"},
{aloha:"mars"},
]
}
...using an invocation like [PIPELINE1] ...
collection.aggregate([
{$match:{greetings:{$elemMatch:query}}},
]).toArray()
However, trying to get a list of the matching greetings with unwind [PIPELINE2] ...
collection.aggregate([
{$match:{greetings:{$elemMatch:query}}},
{$unwind:"$greetings"},
]).toArray()
...produces all the array entries inside the documents with any matching entries, including the entries which don't match (simplified result)...
[
{greetings:{hello:"world"}},
{greetings:{hello:"world", extra:"data"}},
{greetings:{hello:"world"}},
{greetings:{aloha:"mars"}},
]
I have been trying to add a second match stage, but I was surprised to find that it limited results only to those where the greetings field EQUALS the query, rather than where it MATCHES the query [PIPELINE3].
collection.aggregate([
{$match:{greetings:{$elemMatch:query}}},
{$unwind:"$greetings"},
{$match:{greetings:query}},
]).toArray()
Unfortunately PIPELINE3 produces only the following entries, excluding the matching hello world entry with the extra:"data", since that entry is not strictly 'equal' to the query (simplified result)...
[
{greetings:{hello:"world"}},
{greetings:{hello:"world"}},
]
...where what I need as the result is rather...
[
{greetings:{hello:"world"}},
{greetings:{hello:"world"}},
{greetings:{"hello":"world","extra":"data"}
]
How can I add a second $match stage to PIPELINE2, to filter for where the greetings field MATCHES the query, (and may have other data in it too), rather than limiting results to entries where the greetings field EQUALS the query?
What you're seeing in the results is correct. Your approach is a bit wrong. If you want the results you're expecting, then you should use this approach:
collection.aggregate([
{$match:{greetings:{$elemMatch:query}}},
{$unwind:"$greetings"},
{$match:{"greetings.hello":"world"}},
]).toArray()
With this, you should get the following output:
[
{greetings:{hello:"world"}},
{greetings:{hello:"world"}},
{greetings:{"hello":"world","extra":"data"}
]
Whenever you're using aggregation in MongoDB and want to create an aggregation pipeline that yields documents you expect, you should always start your query with the first stage. And then eventually add stages to monitor the outputs from subsequent stages.
The output of your $unwind stage would be:
[{
greetings:{hello:"world"}
},
{
greetings:{hello:"world", extra:"data"}
},
{
greetings:{hello:"world"}
},
{
greetings:{aloha:"mars"}
}]
Now if we include the third stage that you used, then it would match for greetings key that have a value {hello:"world"} and with that exact value, it would find only two documents in the pipeline. So you would only be getting:
{ "greetings" : { "hello" : "world" } }
{ "greetings" : { "hello" : "world" } }
Let's say I have four documents in my collection:
{u'a': {u'time': 3}}
{u'a': {u'time': 5}}
{u'b': {u'time': 4}}
{u'b': {u'time': 2}}
Is it possible to sort them by the field 'time' which is common in both 'a' and 'b' documents?
Thank you
No, you should put your data into a common format so you can sort it on a common field. It can still be nested if you want but it would need to have the same path.
You can use use aggregation and the following code has been tested.
db.test.aggregate({
$project: {
time: {
"$cond": [{
"$gt": ["$a.time", null]
}, "$a.time", "$b.time"]
}
}
}, {
$sort: {
time: -1
}
});
Or if you also want the original fields returned back: gist
Alternatively you can sort once you get the result back, using a customized compare function ( not tested,for illustration purpose only)
db.eval(function() {
return db.mycollection.find().toArray().sort( function(doc1, doc2) {
var time1 = doc1.a? doc1.a.time:doc1.b.time,
time2 = doc2.a?doc2.a.time:doc2.b.time;
return time1 -time2;
})
});
You can, using the aggregation framework.
The trick here is to $project a common field to all the documents so that the $sort stage can use the value in that field to sort the documents.
The $ifNull operator can be used to check if a.time exists, it
does, then the record will be sorted by that value else, by b.time.
code:
db.t.aggregate([
{$project:{"a":1,"b":1,
"sortBy":{$ifNull:["$a.time","$b.time"]}}},
{$sort:{"sortBy":-1}},
{$project:{"a":1,"b":1}}
])
consequences of this approach:
The aggregation pipeline won't be covered by any of the index you
create.
The performance will be very poor for very large data sets.
What you could ideally do is to ask the source system that is sending you the data to standardize its format, something like:
{"a":1,"time":5}
{"b":1,"time":4}
That way your query can make use of the index if you create one on the time field.
db.t.ensureIndex({"time":-1});
code:
db.t.find({}).sort({"time":-1});