My actors collection contains an array-of-documents field, called acted_in. Instead of returning the size of acted_in.idmovies like so: {$size: $acted_in.idmovies}, I want to return the number of distinct values inside $acted_in.idmovies. How can I do that ?
c1 = actors.aggregate([{"$match": {'$and': [{'fname': f_name},
{'lname': l_name}]}},
{"$project": {'first_name': '$fname',
'last_name': '$lname',
'gender': '$gender',
'distinct_movies_played_in': {'$size': '$acted_in.idmovies'}}}])
You basically need to include $setDifference in there to obtain the "distinct" items. All "sets" are "distinct" by design and by obtaining the "difference" from the present array to an empty one [] you get the desired result. Then you can apply the $size.
You also have some common mistakes/misconceptions. Firstly when using $match or any MongoDB query expression you do not need to use $and unless there is an explicit case to do so. All query expression arguments are "already" AND conditions unless explicitly stated otherwise, as with $or. So don't explicitly use for this case.
Secondly your $project was using the explicit field path variables for every field. You do not need to do that just to return the field, and outside of usage in an "expression", you can simply use a 1 to notate you want it included:
c1 = actors.aggregate([
{ "$match": { "fname"': f_name, "lname": l_name } },
{ "$project": {
"first_name": 1,
"last_name": 1,
"gender": 1,
"distinct_movies_played_in": {
"$size": { "$setDifference": [ "$acted_in.idmovies", [] ] }
}
}}
])
In fact, if you are actually using MongoDB 3.4 or greater ( and your notation of an element within an array "$acted_in.idmovies" says you have at least MongoDB 3.2 ) which has support for $addFields then use that instead of specifying all other fields in the document.
c1 = actors.aggregate([
{ "$match": { "fname"': f_name, "lname": l_name } },
{ "$addFields": {
"distinct_movies_played_in": {
"$size": { "$setDifference": [ "$acted_in.idmovies", [] ] }
}
}}
])
Unless you explicitly need to just specify "some" other fields.
The basic case here is do not use $unwind for array operations unless you specifically need to perform a $group operation on with it's _id key pointing at a value obtained from "within" the array.
In all other cases, MongoDB has far more efficient operators for working with arrays that what $unwind does.
This should give you what you want:
actors.aggregate([
{
$match: {fname: f_name, lname: l_name}
},
{
$unwind: '$tags'
},
{
$group: {
_id: '$_id',
first_name: {$first: '$fname'},
last_name: {$last: '$lname'},
gender: {$first: '$gender'},
tags: {$addToSet: '$tags'}
}
},
{
$project: {
first_name: 1,
last_name: 1,
gender: 1,
distinct: {$size: '$tags'}
}
}
])
After the tags array is deconstructed and then put back into a set of itself, then you just need to get the number of items or length of that set.
Related
need to get specific fields from Mongo,
The DB is huge so I prefer getting the values in right format and
not post processing it .
in example,
There are 2 fields which need to convert the format:
1_id: ObjectId('604e0dbc96a0c93a45bfc5b0') to string as "604e0dbc96a0c93a45bfc5b0:
2.birthdate: ISODate('1999-11-10T00:00:00.000Z') - to string in date format "10/11/1999".
Example of json in MongoDB:
{
_id: ObjectId('604e0dbc96a0c93a45bfc5b0'),
address: 'BOB addrees',
name: 'BOB',
last_name: 'Habanero',
birthdate: ISODate('1000-11-10T00:00:00.000Z')
}
retrieving the Jsons specific fields:
customers_cursor = DB.customer.find({},{"_id": 1,"name" :1 ,"last_name":1 ,"customer_type":1,"address.0":1 ,"email":1 ,"birthdate" :1 ,"customer_status":1} )
Is there an option to use convert functions for returning values in find() ?
case not , what are my best option to do it while i have several fields required to format the values and there are MILLION of records in the MongoDB?
Demo - https://mongoplayground.net/p/4OcF0O74PvU
You've to use an aggregation query to do that.
convert object to string using $toString
Use $dateToString to format your date
db.collection.aggregate([
{
"$project": {
"_id": {
"$toString": "$_id"
},
"name": 1,
"last_name": 1,
"customer_type": 1,
"address.0": 1,
"email": 1,
"birthdate": {
"$dateToString": {
"format": "%d/%m/%Y",
"date": "$birthdate"
}
},
"customer_status": 1
}
}
])
For example, if this is my record
{
"_id":"123",
"name":"google",
"ip_1":"10.0.0.1",
"ip_2":"10.0.0.2",
"ip_3":"10.0.1",
"ip_4":"10.0.1",
"description":""}
I want to get only those fields starting with 'ip_'. Consider I have 500 fields & only 15 of them start with 'ip_'
Can we do something like this to get the output -
db.collection.find({id:"123"}, {'ip*':1})
Output -
{
"ip_1":"10.0.0.1",
"ip_2":"10.0.0.2",
"ip_3":"10.0.1",
"ip_4":"10.0.1"
}
The following aggregate query, using PyMongo, returns documents with the field names starting with "ip_".
Note the various aggregation operators used: $filter, $regexMatch, $objectToArray, $arrayToObject. The aggregation pipeline the two stages $project and $replaceWith.
pipeline = [
{
"$project": {
"ipFields": {
"$filter" : {
"input": { "$objectToArray": "$$ROOT" },
"cond": { "$regexMatch": { "input": "$$this.k" , "regex": "^ip" } }
}
}
}
},
{
"$replaceWith": { "$arrayToObject": "$ipFields" }
}
]
pprint.pprint(list(collection.aggregate(pipeline)))
I am unaware of a way to specify an expression that would decide which hash keys would be projected. MongoDB has projection operators but they deal with arrays and text search.
If you have a fixed possible set of ip fields, you can simply request all of them regardless of which fields are present in a particular document, e.g. project with
{ip_1: true, ip_2: true, ...}
A document format I ingest into ElasticSearch looks like this:
{
'id':'514d4e9f-09e7-4f13-b6c9-a0aa9b4f37a0'
'created':'2019-09-06 06:09:33.044433',
'meta':{
'userTags':[
{
'intensity':'1',
'sentiment':'0.84',
'keyword':'train'
},
{
'intensity':'1',
'sentiment':'-0.76',
'keyword':'amtrak'
}
]
}
}
...ingested with python:
r = requests.put(itemUrl, auth = authObj, json = document, headers = headers)
The idea here is that ElasticSearch will treat keyword, intensity and sentiment as fields that can be later queried. However, on ElasticSearch side I can observe that this is not happening (I use Kibana for search UI) -- instead, I see field "meta.userTags" with the value that is the whole list of objects.
How can I make ElasticSearch index elements within a list?
I used the document body you provided to create a new index 'testind' and type 'testTyp' using the Postman REST client.:
POST http://localhost:9200/testind/testTyp
{
"id":"514d4e9f-09e7-4f13-b6c9-a0aa9b4f37a0",
"created":"2019-09-06 06:09:33.044433",
"meta":{
"userTags":[
{
"intensity":"1",
"sentiment":"0.84",
"keyword":"train"
},
{
"intensity":"1",
"sentiment":"-0.76",
"keyword":"amtrak"
}
]
}
}
When I queried for the index's mapping this is what i get :
GET http://localhost:9200/testind/testTyp/_mapping
{
"testind":{
"mappings":{
"testTyp":{
"properties":{
"created":{
"type":"text",
"fields":{
"keyword":{
"type":"keyword",
"ignore_above":256
}
}
},
"id":{
"type":"text",
"fields":{
"keyword":{
"type":"keyword",
"ignore_above":256
}
}
},
"meta":{
"properties":{
"userTags":{
"properties":{
"intensity":{
"type":"text",
"fields":{
"keyword":{
"type":"keyword",
"ignore_above":256
}
}
},
"keyword":{
"type":"text",
"fields":{
"keyword":{
"type":"keyword",
"ignore_above":256
}
}
},
"sentiment":{
"type":"text",
"fields":{
"keyword":{
"type":"keyword",
"ignore_above":256
}
}
}
}
}
}
}
}
}
}
}
}
As you can see in the mapping the fields are part of the mapping and can be queried as per need in future, so I don't see the problem here as long as the field names are not one of these - https://www.elastic.co/guide/en/elasticsearch/reference/6.4/sql-syntax-reserved.html ( you might want to avoid the term 'keyword' as it might be confusing later when writing search queries as the fieldname and type are both same - 'keyword') . Also, note one thing, the mapping gets created via dynamic mapping (https://www.elastic.co/guide/en/elasticsearch/reference/6.3/dynamic-field-mapping.html#dynamic-field-mapping ) in Elasticsearch and so the data types are determined by elasticsearch based on the values you have provided.However, this may not be always accurate , so to prevent that you can use the PUT _mapping API to define your own mapping for the index, and then prevent new fields within a type from being added to mappings.
You don't need a special mapping to index a list - every field can contain one or more values of the same type. See array datatype.
In the case of a list of objects, they can be indexed as object or nested datatype. Per default elastic uses object datatype. In this case you can query meta.userTags.keyword or/and meta.userTags.sentiment. The result will allways contains whole documents with values matched independently, ie. searching keyword=train and sentiment=-0.76 you WILL find document with id=514d4e9f-09e7-4f13-b6c9-a0aa9b4f37a0.
If this is not what you want, you need to define nested datatype mapping for field userTags and use a nested query.
I got two class on Mongoengine:
class UserPoints(EmbeddedDocument):
user = ReferenceField(User, verbose_name='user')
points = IntField(verbose_name='points', required=True)
def __unicode__(self):
return self.points
And
class Local(Document):
token = StringField(max_length=250,verbose_name='token_identifier',unique=True)
points = ListField(EmbeddedDocumentField(UserPoints),required=False)
def __unicode__(self):
return self.name
If i do something like: "LP = Local.objects.filter(points__user=user)" I got all the locals with userpoints from my user. But i Want all the UserPoints from a User. How can i?
I try also: "lUs = UserPoints.objects.filter(user=user)" but i got an empty Array.
PD: I do something like this to solve the problem, but it's not efficient.
LDPoints = []
LP = Local.objects.filter(points__user=user)
print 'List P: '+str(len(LP))
for local in LP:
for points in local.points:
if points.user == user:
dPoints = parsePoints(points)
lDPoints.append(dPoints)
Adding to the original and getting venerable answer is that the aggregation framework has $filter now for some time, which is a lot cleaner that the $map and $setDifference method used in the original answer.
Local._get_collection().aggregate([
{ "$match": { "points.user": user } },
{ "$project": {
"token": 1,
"points": {
"$filter": {
"input": "$points",
"as": "el",
"cond": { "$eq": [ "$$el.user", user ] }
}
}
}}
])
The same principles apply though for obtaining "multiple" matches from an array in the collection you use the aggregate() method of the underlying driver, as called from _get_collection().
Original
The answer to avoid "filtering" your embedded documents for the selected "user" only is to use the aggregation framework. This allows you to manipulate the "array content" on the database server rather than filtering the results in your client code.
Aggregation is done with the raw pymongo driver methods, but since Mongoengine is built on top of this driver you access the raw collection object from your class with the ._get_collection() method:
Local._get_collection().aggregate([
# Match the documents that have the required user
{ "$match": {
"points.user": user
}},
# unwind the embedded array to de-normalize
{ "$unwind": "$points" },
# Matching now filters the elements
{ "$match": {
"points.user": user
}},
# Group back as an array
{ "$group": {
"_id": "$_id",
"token": { "$first": "$token" },
"points": { "$push": "$points" }
}}
])
If you have MongoDB 2.6 or greater on your server and your "user/points" combination is always unique you can alternately filter without the $unwind|$match|$group cycle using the $map and $setDifference operators available there:
Local._get_collection().aggregate([
# Match the documents that have the required user
{ "$match": {
"points.user": user
}},
# Filter the array in place
{ "$project": {
"token": 1,
"points": {
"$setDifference": [
{
"$map": {
"input": "$points",
"as": "el",
"in": {
"$cond": [
{ "$eq": [ "$$el.user", user ] },
"$$el",
false
]
}
}
},
[false]
]
}
}}
])
In the second case there the $cond is a ternary operator which takes a logical expression as it's first argument and the values to return when that expression is either true or false as it's other arguments. Inside the $map, each element is tested to see if the condition is true, in this case "is the user field equal to the selected user".
Either the content of that array position is returned or otherwise false. The $setDifference takes the resulting array and "filters" the false values out, so only the matching elements are returned.
In the legacy approach, the $unwind pipeline operator is used to effectively turn each array element into it's own document with all other parent properties. This allows you to apply the same $match condition, which unlike the initial query actually removes the documents which now as single elements no longer match your condition. You always want the first stage as there is no point processing this $unwind|$match combination on all of the documents that might not contain your matching condition.
The $group stage brings everything back into line per document. Using the $first option to return all other fields that were essentially duplicated by the $unwind and the $push operator to rebuild the array with the matching elements.
So while there no "built-in" methods to MongoEngine to do this sort of query, you can do this the MongoDB way by accessing the raw driver.
Also note that if you only expected one element to match in any array for your given "user" or other query, then you could alternately use the field projection form available to the raw driver as well. But the aggregation method is required for any more than one matching element of the array.
I can pass a single sort parameter to the search query in pyes like this:
s = MatchAllQuery()
conn.search(query=Search(s), indexes=["test"], sort='_score')
But I need to pass an extra parameter to sort the docs with the same score, like this:
{
"sort": [
"_score",
{
"extra_param": {
"order": "asc"
}
}
],
"query": {
"term": {
"match_all": {}
}
}
}
How can I do this in pyes?
Thanks
If you'd like the results in the result set with the same score to be ordered by price, append price to the sort string:
s = MatchAllQuery()
conn.search(query=Search(s), indexes=["test"], sort='_score,price')
By default the sort order is ascending. To pass the sort order append :asc or :desc to the sort parameter
s = MatchAllQuery()
conn.search(query=Search(s), indexes=["test"], sort='_score,price:desc')
If you want to do more detailed sorting that what's available via es.search's sort keyword, you can just pass the search dict into the es.Search constructor.
s = Search({'term': {'foo.monkey': 'george'}},
sort=[{'_geo_distance': {'unit': 'mi',
'order': 'desc',
'monkey.location': '81,20'}}])
conn.search(s)