I have a data set which I can represent by this toy example of a list of dictionaries: Here is an algorithm in Python which collects each of the keys in each ‘collection’ and whenever there is a key change, the algorithm adds those keys to output. The correct output given here records each change of field name: Foo, Bar,
Tag: mongodb-query
Perform $gte and $lt on the same field _id in MongoDB
db.comments.find({“_id” : {“$gte”: ObjectId(“6225f932a7bce76715a9f3bd”), “$lt”:ObjectId(“6225f932a7bce76715a9f3bd”)}}).sort({“created_datetime”:1}).limit(10).pretty() I am using this query which should give me the current “6225f932a7bce76715a9f3bd” doc, 4 docs inserted before this and 5 docs inserted after this. But currently when i run this query, i get null result. Where am i going wrong ?? Answer I had no other option but to seperate my queries in order to
Query by computed property in python mongoengine
I wondered if it is possible to query documents in MongoDB by computed properties using mongoengine in python. Currently, my model looks like this: When I do for example SnapshotIndicatorKeyValue .objects().first().snapshot, I can access the snapshotproperty. But when I try to query it, it doesn’t work. For example: I get the error `mongoengine.errors.InvalidQueryError: Cannot resolve field “snapshot”“ Is there any
Updating a pre-exiting fields datatype(string=> date) in a mongoDb collection
I am trying to update a field that was initially captured as a string instead of a date type. Currently, the query that insert into the collection has been modified , so that future insert to that field is date. data type However, I am trying to update the previously inserted data, before query modification that still has the string
pymongo: remove duplicates (map reduce?)
I do have a Database with several collections (overall ~15mil documents) and documents look like this (simplified): They all have an unique _id field as well, but I want to delete duplicates accodring to another field (the external ID field). First, I tried a very manual approach with lists and deleting afterwards, but the DB seems too big, takes very