I have problems when an json attribute contain how value one document on base64. Document data is truncated to 32745 characters.
I have this code, but the value for documentPDF was modified because max body question is limited to 30000 chars:
strResponse = '{
"NameFlow": "Certificates",
"MessageDate": "2023-02-02 10:14:24",
"Pagination": {
"Pages": 1,
"ActualPage": 1,
"Quantity": 1
},
"Certificates": [
{
"Type": "DRFI",
"Number": "SV-000014-23",
"Date": "2023-01-31",
"TypeId": "CC",
"Id": "9999900000",
"DRFI": "SV-000014-23",
"DocumentPDF": "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"
}]}';
dynamic objObject = new ExpandoObject();
objObject = JsonConvert.DeserializeObject(strResponse);
I Need data document complete for next visualizations.
Related
I'm very new to mongodb. I'm trying to do an aggregation pipeline for lookup kinda like SQL left join. Given the following document's schema:
Mongo playground: https://mongoplayground.net/p/yAIwH5V2yv8
Characters:
{
"_id": 1,
"account_id": 1,
"world_id": 0,
"name": "hello"
}
Inventories:
{
"_id": 7,
"character_id": 2,
"type": "EQUIPPED"
}
Items:
{
"_id": 1,
"inventory_id": 7
}
I want to query for characters and look up inventories as well as items in inventories. I was able to achieve this however I would like to separate the inventories field in characters result document.
Current result:
{
"_id": 2,
"account_id": 1,
"world_id": 0,
"name": "hello",
"inventories: [
{
"_id": 1,
"character_id": "2",
"type": "EQUIPPED",
"items: [...]
}
]
}
What I want is based on the type of inventory I want it to be a separate field of the resulted character document something like this:
{
"_id": 2,
"account_id": 1,
"world_id": 0,
"name": "hello",
"equippedInventory: {
"_id": 1,
"character_id": "2",
"type": "EQUIPPED",
"items: [...]
},
"equipInventory: {
"_id": 2,
"character_id": "2",
"type": "EQUIP",
"items: [...]
},
}
Also, is my pipeline the best way to achieve this?
I need to create couple of columns in Dataframe where I want to parse and store the json string. Here is one json which I need to store in one column. Other json are also similar.Can you please help in how to transform and store this json string in the column. The values section needs to be filled from the values from other dataframe columns within the same data frame.
{
"name": "",
"headers": [
{
"name": "A",
"dataType": "number"
},
{
"name": "B",
"dataType": "string"
},
{
"name": "C",
"dataType": "string"
}
],
"values": [
[
2,
"some value",
"some value"
]
]
}
I have a series of deeply nested json strings in a pyspark dataframe column. I need to explode and filter based on the contents of these strings and would like to add them as columns. I've tried defining the StructTypes but each time it continues to return an empty DF.
Tried using json_tuples to parse but there are no common keys to rejoin the dataframes and the row numbers dont match up? I think it might have to do with some null fields
The sub field can be nullable
Sample JSON
{
"TIME": "datatime",
"SID": "yjhrtr",
"ID": {
"Source": "Person",
"AuthIFO": {
"Prov": "Abc",
"IOI": "123",
"DETAILS": {
"Id": "12345",
"SId": "ABCDE"
}
}
},
"Content": {
"User1": "AB878A",
"UserInfo": "False",
"D": "ghgf64G",
"T": "yjuyjtyfrZ6",
"Tname": "WE ARE THE WORLD",
"ST": null,
"TID": "BPV 1431: 1",
"src": "test",
"OT": "test2",
"OA": "test3",
"OP": "test34
},
"Test": false
}
How to import these file in mongodb, here is my json file
[{
"_id": ObjectId(),
"title": "How long running and abdominal excersises?",
"author": {
"userid": ObjectId(),
"name": "clark Loews",
},
"postdatetime": Date(),
},
{
"_id": ObjectId(),
"title": "How long will it take me to lose 15-20 pounds with running and abdominal excersises?",
"author": {
"userid": ObjectId(),
"name": "Bart Loews",
},
"postdatetime": Date(),
}
]
It give me this error:-Failed: error unmarshaling bytes on document #0: expected 1 argument to ObjectId constructor, but 0 received
Hmm... You'll need actual data to import. Having the ObjectId() and Date() functions in a json file will not work. How would the userid fields actually match anything in your database?
Note that you can skip the _id field when importing and MongoDB will create unique values for you, but the other fields in your document will need to be explicit.
Records i'm pushing to algolia are showing as string however it's an integer in my local database.
Well you didn't put any code so I assume your error is here : Algolia is schemaless. You have to do it like said in this page :
{
"objectID": 42, // record identifier
"title": "Breaking Bad", // string attribute
"episodes": [ // array of strings attribute
"Crazy Handful of Nothin'",
"Gray Matter"
],
"like_count": 978, // integer attribute
"avg_rating": 1.23456, // float attribute
"featured": true, // boolean attribute
"actors": [ // nested objects attribute
{
"name": "Walter White",
"portrayed_by": "Bryan Cranston"
},
{
"name": "Skyler White",
"portrayed_by": "Anna Gunn"
}
]
}