Możesz użyć struktury agregacji
import pymongo
conn = pymongo.MongoClient()
db = conn.test
col = db.collection
for doc in col.aggregate([{'$unwind': '$impressions'},
{'$match': {'impressions.service': 'furniture'}},
{'$group': {'_id': '$impressions.id', 'impressions_count': {'$sum': 1}}},
]):
print(doc)
Lub wydajniej używając $map
i $setDifference
operatorów.
col.aggregate([
{ "$project": { "impressions": {"$setDifference": [{ "$map": { "input": "$impressions", "as": "imp", "in": { "$cond": { "if": { "$eq": [ "$$imp.service", "furniture" ] }, "then": "$$imp.id", "else": 0 }}}}, [0]]}}},
{ "$unwind": "$impressions" },
{ "$group": { "_id": "$impressions", "impressions_count": { "$sum": 1 }}}
])
Co daje:
{'_id': 122.0, 'impressions_count': 1}
{'_id': 124.0, 'impressions_count': 1}
{'_id': 127.0, 'impressions_count': 1}
{'_id': 123.0, 'impressions_count': 2}
