I have the following documents in my collection. Each document contains historical weather data about a specific location:
{
'location':'new york',
'history':[
{'timestamp':1524542400, 'temp':79, 'wind_speed':1, 'wind_direction':'SW'}
{'timestamp':1524548400, 'temp':80, 'wind_speed':2, 'wind_direction':'SW'}
{'timestamp':1524554400, 'temp':82, 'wind_speed':3, 'wind_direction':'S'}
{'timestamp':1524560400, 'temp':78, 'wind_speed':4, 'wind_direction':'S'}
]
},
{
'location':'san francisco',
'history':[
{'timestamp':1524542400, 'temp':80, 'wind_speed':5, 'wind_direction':'SW'}
{'timestamp':1524548400, 'temp':81, 'wind_speed':6, 'wind_direction':'SW'}
{'timestamp':1524554400, 'temp':82, 'wind_speed':7, 'wind_direction':'S'}
{'timestamp':1524560400, 'temp':73, 'wind_speed':8, 'wind_direction':'S'}
]
},
{
'location':'miami',
'history':[
{'timestamp':1524542400, 'temp':84, 'wind_speed':9, 'wind_direction':'SW'}
{'timestamp':1524548400, 'temp':85, 'wind_speed':10, 'wind_direction':'SW'}
{'timestamp':1524554400, 'temp':86, 'wind_speed':11, 'wind_direction':'S'}
{'timestamp':1524560400, 'temp':87, 'wind_speed':12, 'wind_direction':'S'}
]
}
I would like to get a list of the most recent weather data for each location (more or less) like so:
{
'location':'new york',
'history':{'timestamp':1524560400, 'temp':78, 'wind_speed':4, 'wind_direction':'S'}
},
{
'location':'san francisco',
'history':{'timestamp':1524560400, 'temp':73, 'wind_speed':8, 'wind_direction':'S'}
},
{
'location':'miami',
'history':{'timestamp':1524560400, 'temp':87, 'wind_speed':12, 'wind_direction':'S'}
}
I was pretty sure it needed some sort of $group aggregate but can't figure out how to select an entire object by $max:<field>. For example the below query only returns the max timestamp itself, without any of the accompanying fields.
db.collection.aggregate([{
'$unwind': '$history'
}, {
'$group': {
'_id': '$name',
'timestamp': {
'$max': '$history.timestamp'
}
}
}])
returns
{ "_id" : "new york", "timestamp" : 1524560400 }
{ "_id" : "san franciscoeo", "timestamp" : 1524560400 }
{ "_id" : "miami", "timestamp" : 1524560400 }
The actual collection and arrays are very large so client side processing won't be ideal. Any help would be much appreciated.
Well as the author of the answer you found, I think we can actually do a bit better with modern MongoDB versions.
Single match per document
In short we can actually apply $max to your particular case, used with $indexOfArray and $arrayElemAt to extract the matched value:
db.collection.aggregate([
{ "$addFields": {
"history": {
"$arrayElemAt": [
"$history",
{ "$indexOfArray": [ "$history.timestamp", { "$max": "$history.timestamp" } ] }
]
}
}}
])
Which will return you:
{
"_id" : ObjectId("5ae9175564de8a00a66b3974"),
"location" : "new york",
"history" : {
"timestamp" : 1524560400,
"temp" : 78,
"wind_speed" : 4,
"wind_direction" : "S"
}
}
{
"_id" : ObjectId("5ae9175564de8a00a66b3975"),
"location" : "san francisco",
"history" : {
"timestamp" : 1524560400,
"temp" : 73,
"wind_speed" : 8,
"wind_direction" : "S"
}
}
{
"_id" : ObjectId("5ae9175564de8a00a66b3976"),
"location" : "miami",
"history" : {
"timestamp" : 1524560400,
"temp" : 87,
"wind_speed" : 12,
"wind_direction" : "S"
}
}
That is of course without actually needing to "group" anything and simply find the $max value from within each document, as you seem to be trying to do. This avoids you needing to "mangle" any other document output by forcing it through a $group or indeed an $unwind.
The usage essentially is that the $max returns the "maximum" value from the specified array property since $history.timestamp is a short way of notating to extract "just those values" from within the objects of the array.
This is used in comparison with the same "list of values" to determine the matching "index" via $indexOfArray, which takes an array as it's first argument and the value to match as the second.
The $arrayElemAt operator also takes an array as it's first argument, here we use the full "$history" array since we want to extract the "full object". Which we do by the "returned index" value of the $indexOfArray operator.
"Multiple" matches per document
Of course that's fine for "single" matches, but if you wanted to expand that to "multiple" matches of the same $max value, then you would use $filter instead:
db.collection.aggregate([
{ "$addFields": {
"history": {
"$filter": {
"input": "$history",
"cond": { "$eq": [ "$$this.timestamp", { "$max": "$history.timestamp" } ] }
}
}
}}
])
Which would output:
{
"_id" : ObjectId("5ae9175564de8a00a66b3974"),
"location" : "new york",
"history" : [
{
"timestamp" : 1524560400,
"temp" : 78,
"wind_speed" : 4,
"wind_direction" : "S"
}
]
}
{
"_id" : ObjectId("5ae9175564de8a00a66b3975"),
"location" : "san francisco",
"history" : [
{
"timestamp" : 1524560400,
"temp" : 73,
"wind_speed" : 8,
"wind_direction" : "S"
}
]
}
{
"_id" : ObjectId("5ae9175564de8a00a66b3976"),
"location" : "miami",
"history" : [
{
"timestamp" : 1524560400,
"temp" : 87,
"wind_speed" : 12,
"wind_direction" : "S"
}
]
}
The main difference being of course that the "history" property is still an "array" since that is what $filter will produce. Also noting of course that if there were in fact "multiple" entries with the same timestamp value, then this would of course return them all and not just the "first index" matched.
The comparison is basically done instead against "each" array element to see if the "current" ( "$$this" ) object has the specified property which matches the $max result, and ultimately returning only those array elements which are a match for the supplied condition.
These are essentially your "modern" approaches which avoid the overhead of $unwind, and indeed $sort and $group where they may not be needed. Of course they are not needed for just dealing with individual documents.
If however you really need to $group across "multiple documents" by a specific grouping key and consideration of values "inside" the array, then the initial approach outlined as you discovered is actually the fit for that scenario, as ultimately you "must" $unwind to deal with items "inside" an array in such a way. And also with consideration "across documents".
So be mindful to use stages like $group and $unwind only where you actually need to and where "grouping" is your actual intent. If you are just looking to find something "in the document", then there are far more efficient ways to do this without all the additional overhead that those stages bring with them to processing.
Related
I have a large DB with various inconsistencies. One of the items I would like to clear up is changing the country status based on the population.
A Sample of the data is:
{ "_id" : "D", "name" : "Deutschland", "pop" : 70000000, "country" : "Large Western" }
{ "_id" : "E", "name" : "Eire", "pop" : 4500000, "country" : "Small Western" }
{ "_id" : "G", "name" : "Greenland", "pop" : 30000, "country" : "Dependency" }
{ "_id" : "M", "name" : "Mauritius", "pop" : 1200000, "country" : "Small island"}
{ "_id" : "L", "name" : "Luxembourg", "pop" : 500000, "country" : "Small Principality" }
Obviously I would like to change the country field go something more uniform, based on population size.
I've tried this approach, but obviously missing some way of tying into an update of the country field.
db.country.updateMany( { case : { $lt : ["$pop" : 20000000] }, then : "Small country" }, { case : { $gte : ["$pop" : 20000000] }, then : "Large country" }
Edit: Posted before I was finished writing.
I was thinking to use $cond functionality, to basically return if true, do X, if false, do y, while using the updateMany.
Is this possible, or is there a workaround?
You really want want bulkWrite() using two "updateMany" statements within it instead. Aggregation expressions cannot be used to do "alternate selection" in any form of update statement.
db.country.bulkWrite([
{ "updateMany": {
"filter": { "pop": { "$lt": 20000000 } },
"update": { "$set": { "country": "Small Country" } }
}},
{ "updateMany": {
"filter": { "pop": { "$gt": 20000000 } },
"update": { "$set": { "country": "Large Country" } }
}}
])
There is still an outstanding "feature request" on SERVER-6566 for "conditional syntax", but this is not yet resolved. The "bulk" API was actually introduced after this request was raised, and really can be adapted as shown to do more or less the same thing.
Also using $out in an aggregation statement as was otherwise suggested is not an option to "update" and can only write to a "new collection" at present. The slated change from MongoDB 4.2 onwards would allow $out to actually "update" an existing collection, however this would only be where the collection to be updated is different from any other collection used within the gathering of data from the aggregation pipeline. So it is not possible to use an aggregation pipeline to update the same collection as what you are reading from.
In short, use bulkWrite().
I am using an aggregation pipeline with the MongoDB Java driver version 3.6. If I have documents that look something like:
doc1 --
{
"CAR": {
"VIN": "ASDF1234",
"YEAR": "2018",
"MAKE": "Honda",
"MODEL": "Accord"
},
"FEATURES": [
{
"AUDIO": "MP3",
"TIRES": "All Season",
"BRAKES": "ABS"
}
]
}
doc2 --
{
"CAR": {
"VIN": "ASDF1234",
"AVAILABILITY": "In Stock"
}
}
And if I submit a query like:
collection.aggregate(
Arrays.asList(
Aggregates.match(
and(
in("CAR.VIN", vinList),
or(
eq("CAR.MAKE", carMake),
eq("CAR.AVAILABILITY", carAvailability),
)
)
)
)
)
Let us assume that there are exactly two different records for which the "CAR.VIN" criteria match for every VIN, and I am going to get two results. Rather than deal with two results each time, I would like to merge the documents so that the result looks like this:
{
"CAR": {
"VIN": "ASDF1234",
"YEAR": "2018",
"MAKE": "Honda",
"MODEL": "Accord",
"AVAILABILITY": "In Stock"
},
"FEATURES": [
{
"AUDIO": "MP3",
"TIRES": "All Season",
"BRAKES": "ABS"
}
]
}
The example where I have two and only two results trivializes my need for this. Imagine that vinList is a list of 10000 values, and it might return 2 x 10000 documents. When I return an AggregateIterable to the client that is calling my code, I do not want to impose the requirement that they have to group or collate the results in any way, but that they will receive one document for each result that has all of the information that they will want to parse, cleanly and easily.
Of course, people will suggest that the data is simply combined into one document with all of the data in the MongoDB collection. For reasons that I cannot control, there are two separate documents corresponding to each VIN in the same collection, and that is something that I am unable to change. There is a value in our system that makes this more reasonable than it might seem, so please don't focus on this apparent problem with the data.
I am trying, with not much luck, to utilize the Aggretes.group() operation to merge the fields in my aggregation pipeline. Accumulators.push seems to be the closest operation to what I need, but I do not want to complicate the document structure with extra arrays, etc. Is there a straightforward approach that I am not seeing?
you can try $mergeObjects added in mongo v3.6
db.cc.aggregate(
[
{
$group: {
_id : "$CAR.VIN",
CAR : {$mergeObjects : "$CAR"},
FEATURES : {$mergeObjects : {$arrayElemAt : ["$FEATURES", 0 ]}}
}
}
]
).pretty()
result
{
"_id" : "ASDF1234",
"CAR" : {
"VIN" : "ASDF1234",
"YEAR" : "2018",
"MAKE" : "Honda",
"MODEL" : "Accord",
"AVAILABILITY" : "In Stock"
},
"FEATURES" : {
"AUDIO" : "MP3",
"TIRES" : "All Season",
"BRAKES" : "ABS"
}
}
>
to get features as array
db.cc.aggregate(
[
{
$group: {
_id : "$CAR.VIN",
CAR : {$mergeObjects : "$CAR"},
FEATURES : {$push : {$arrayElemAt : ["$FEATURES", 0 ]}}
}
}
]
).pretty()
result
{
"_id" : "ASDF1234",
"CAR" : {
"VIN" : "ASDF1234",
"YEAR" : "2018",
"MAKE" : "Honda",
"MODEL" : "Accord",
"AVAILABILITY" : "In Stock"
},
"FEATURES" : [
{
"AUDIO" : "MP3",
"TIRES" : "All Season",
"BRAKES" : "ABS"
},
null
]
}
>
In my Meteor app, I have a collection of documents with an array of subdocuments that look like this:
/* 1 */
{
"_id" : "5xF9iDTj3reLDKNHh",
"name" : "Lorem ipsum",
"revisions" : [
{
"number" : 0,
"comment" : "Dolor sit amet",
"created" : ISODate("2016-02-11T01:22:45.588Z")
}
],
"number" : 1
}
/* 2 */
{
"_id" : "qTF8kEphNoB3eTNRA",
"name" : "Consecitur quinam",
"revisions" : [
{
"comment" : "Hoste ad poderiquem",
"number" : 1,
"created" : ISODate("2016-02-11T23:25:46.033Z")
},
{
"number" : 0,
"comment" : "Fagor questibilus",
"created" : ISODate("2016-02-11T01:22:45.588Z")
}
],
"number" : 2
}
What I want to do is query this collection and sort the result set by the maximum date in the created field of the revisions array. Something I haven't been able to pull off yet. Some constraints I have are:
Just sorting by revisions.created doesn't cut it, because the date used from the collection depends on the sort direction. I have to use the maximum date in the set regardless of sort order.
I cannot rely on post-query manipulation of an unsorted result set, so, this must be done by a proper query or aggregation by the database.
There's no guarantee that the revisions array will be pre-sorted.
There may be extra fields in some documents and those have to come along, so careful with $project.
Meteor is still using MongoDB 2.6, newer API features are no good :(
The basic problem with what you are asking here comes down to the fact that the data in question is within an "array", and therefore there are some basic assumptions made by MongoDB as to how this gets handled.
If you applied a sort in "descending order", then MongoDB will do exactly what you ask and sort the documents by the "largest" value of the specified field within the array:
.sort({ "revisions.created": -1 ))
But if instead you sort in "ascending" order then of course the reverse is true and the "smallest" value is considered.
.sort({ "revisions.created": 1 })
So the only way of doing this means working out which is the maximum date from the data in the array, and then sorting on that result. This basically means applying .aggregate(), which for meteor is a server side operation, being unfortunately something like this:
Collection.aggregate([
{ "$unwind": "$revisions" },
{ "$group": {
"_id": "$_id",
"name": { "$first": "$name" },
"revisions": { "$push": "$revisions" },
"number": { "$first": "$number" }
"maxDate": { "$max": "$revisions.created" }
}},
{ "$sort": { "maxDate": 1 }
])
Or at best with MongoDB 3.2, where $max can be applied directly to an array expression:
Collection.aggregate([
{ "$project": {
"name": 1,
"revisions": 1,
"number": 1,
"maxDate": {
"$max": {
"$map": {
"input": "$revisions",
"as": "el",
"in": "$$el.created"
}
}
}
}},
{ "$sort": { "maxDate": 1 } }
])
But really both are not that great, even if the MongoDB 3.2 approach has way less overhead than what is available to prior versions, it's still not as good as you can get in terms of performance due to the need to pass through the data and work out the value to sort on.
So for best performance, "always" keep such data you are going to need "outside" of the array. For this there is the $max "update" operator, which will only replace a value within the document "if" the provided value is "greater than" the existing value already there. i.e:
Collection.update(
{ "_id": "qTF8kEphNoB3eTNRA" },
{
"$push": {
"revisions": { "created": new Date("2016-02-01") }
},
"$max": { "maxDate": new Date("2016-02-01") }
}
)
This means that the value you want will "always" be already present within the document with the expected value, so it is just now a simple matter of sorting on that field:
.sort({ "maxDate": 1 })
So for my money, I would go though the existing data with either of the .aggregate() statements available, and use those results to update each doccument to contain a "maxDate" field. Then change the coding of all additions and revisions of array data to apply that $max "update" on every change.
Having a solid field rather than a calculation always makes much more sense if you are using it often enough. And the maintenance is quite simple.
In any case, considering the above applied example date, which is "less than" the other maximum dates present would return for me in all forms:
{
"_id" : "5xF9iDTj3reLDKNHh",
"name" : "Lorem ipsum",
"revisions" : [
{
"number" : 0,
"comment" : "Dolor sit amet",
"created" : ISODate("2016-02-11T01:22:45.588Z")
}
],
"number" : 1,
"maxDate" : ISODate("2016-02-11T01:22:45.588Z")
}
{
"_id" : "qTF8kEphNoB3eTNRA",
"name" : "Consecitur quinam",
"revisions" : [
{
"comment" : "Hoste ad poderiquem",
"number" : 1,
"created" : ISODate("2016-02-11T23:25:46.033Z")
},
{
"number" : 0,
"comment" : "Fagor questibilus",
"created" : ISODate("2016-02-11T01:22:45.588Z")
},
{
"created" : ISODate("2016-02-01T00:00:00Z")
}
],
"number" : 2,
"maxDate" : ISODate("2016-02-11T23:25:46.033Z")
}
Which correctly places the first document at the top of the sort order with consideration to the "maxDate".
I know how to sort the embedded document after the find results but how do I sort before the find so that the query itself is run on the sorted array ? I know this must be possible if I use aggregate but i really like to know if this is possible without that so that I understand it better how it works.
This is my embedded document
"shipping_charges" : [
{
"region" : "region1",
"weight" : 500,
"rate" : 10
},
{
"region" : "Bangalore HQ",
"weight" : 200,
"rate" : 40
},
{
"region" : "region2",
"weight" : 1500,
"rate" : 110
},
{
"region" : "region3",
"weight" : 100,
"rate" : 50
},
{
"region" : "Bangalore HQ",
"weight" : 100,
"rate" : 150
}
]
This is the query i use to match the 'region' and the 'weight' to get the pricing for that match ..
db.clients.find( { "shipping_charges.region" : "Bangalore HQ" , "shipping_charges.weight" : { $gte : 99 } }, { "shipping_charges.$" : 1 } ).pretty()
This query currently returns me the
{
"shipping_charges" : [
{
"region" : "Bangalore HQ",
"weight" : 200,
"rate" : 40
}
]
}
The reason it possibly returns this set is because of the order in which it appears(& matches) in the embedded document.
But, I want this to return me the last set that best matches to closest slab of the weight(100grams)
What changes required in my existing query so that I can sort the embedded document before the find runs on them to get the results as I want it ?
If for any reasons you are sure this cant be done without a MPR, let me know so that i can stay away from this method and focus only on MPR to get the desired results as I want it .
You can use an aggregation pipeline instead of map-reduce:
db.clients.aggregate([
// Filter the docs to what we're looking for.
{$match: {
'shipping_charges.region': 'Bangalore HQ',
'shipping_charges.weight': {$gte: 99}
}},
// Duplicate the docs, once per shipping_charges element
{$unwind: '$shipping_charges'},
// Filter again to get the candidate shipping_charges.
{$match: {
'shipping_charges.region': 'Bangalore HQ',
'shipping_charges.weight': {$gte: 99}
}},
// Sort those by weight, ascending.
{$sort: {'shipping_charges.weight': 1}},
// Regroup and take the first shipping_charge which will be the one closest to 99
// because of the sort.
{$group: {_id: '$_id', shipping_charges: {$first: '$shipping_charges'}}}
])
You could also use find, but you'd need to pre-sort the shipping_charges array by weight in the documents themselves. You can do that by using a $push update with the $sort modifier:
db.clients.update({}, {
$push: {shipping_charges: {$each: [], $sort: {weight: 1}}}
}, {multi: true})
After doing that, your existing query will return the right element:
db.clients.find({
"shipping_charges.region" : "Bangalore HQ",
"shipping_charges.weight" : { $gte : 99 }
}, { "shipping_charges.$" : 1 } )
You would, of course, need to consistently include the $sort modifier on any further updates to your docs' shipping_charges array to ensure it stays sorted.
I have a collection :
gStats : {
"_id" : "id1",
"criteria" : ["key1":"value1", "key2":"value2"],
"groups" : [
{"id":"XXXX", "visited":100, "liked":200},
{"id":"YYYY", "visited":30, "liked":400}
]
}
I want to be able to update a document of the stats Array of a given array of criteria (exact match).
I try to do this on 2 steps :
Pull the stat document from the array of a given "id" :
db.gStats.update({
"criteria" : {$size : 2},
"criteria" : {$all : [{"key1" : "2096955"},{"value1" : "2015610"}]}
},
{
$pull : {groups : {"id" : "XXXX"}}
}
)
Push the new document
db.gStats.findAndModify({
query : {
"criteria" : {$size : 2},
"criteria" : {$all : [{"key1" : "2015610"}, {"key2" : "2096955"}]}
},
update : {
$push : {groups : {"id" : "XXXX", "visited" : 29, "liked" : 144}}
},
upsert : true
})
The Pull query works perfect.
The Push query gives an error :
2014-12-13T15:12:58.571+0100 findAndModifyFailed failed: {
"value" : null,
"errmsg" : "exception: Cannot create base during insert of update. Cause
d by :ConflictingUpdateOperators Cannot update 'criteria' and 'criteria' at the
same time",
"code" : 12,
"ok" : 0
} at src/mongo/shell/collection.js:614
Neither query is working in reality. You cannot use a key name like "criteria" more than once unless under an operator such and $and. You are also specifying different fields (i.e groups) and querying elements that do not exist in your sample document.
So hard to tell what you really want to do here. But the error is essentially caused by the first issue I mentioned, with a little something extra. So really your { "$size": 2 } condition is being ignored and only the second condition is applied.
A valid query form should look like this:
query: {
"$and": [
{ "criteria" : { "$size" : 2 } },
{ "criteria" : { "$all": [{ "key1": "2015610" }, { "key2": "2096955" }] } }
]
}
As each set of conditions is specified within the array provided by $and the document structure of the query is valid and does not have a hash-key name overwriting the other. That's the proper way to write your two conditions, but there is a trick to making this work where the "upsert" is failing due to those conditions not matching a document. We need to overwrite what is happening when it tries to apply the $all arguments on creation:
update: {
"$setOnInsert": {
"criteria" : [{ "key1": "2015610" }, { "key2": "2096955" }]
},
"$push": { "stats": { "id": "XXXX", "visited": 29, "liked": 144 } }
}
That uses $setOnInsert so that when the "upsert" is applied and a new document created the conditions specified here rather than using the field values set in the query portion of the statement are used instead.
Of course, if what you are really looking for is truly an exact match of the content in the array, then just use that for the query instead:
query: {
"criteria" : [{ "key1": "2015610" }, { "key2": "2096955" }]
}
Then MongoDB will be happy to apply those values when a new document is created and does not get confused on how to interpret the $all expression.