Develop Reference

Modify the value of a field of a specific nested object (its index) depending on a condition

I would like to modify the value of a field on a specific index of a nested type depending on another value of the same nested object or a field outside of the nested object.
As example, I have the current mapping of my index feed:
{
"feed": {
"mappings": {
"properties": {
"attacks_ids": {
"type": "keyword"
},
"created_by": {
"type": "keyword"
},
"date": {
"type": "date"
},
"groups_related": {
"type": "keyword"
},
"indicators": {
"type": "nested",
"properties": {
"date": {
"type": "date"
},
"description": {
"type": "text"
},
"role": {
"type": "keyword"
},
"type": {
"type": "keyword"
},
"value": {
"type": "keyword"
}
}
},
"malware_families": {
"type": "keyword"
},
"published": {
"type": "boolean"
},
"references": {
"type": "keyword"
},
"tags": {
"type": "keyword"
},
"targeted_countries": {
"type": "keyword"
},
"title": {
"type": "text"
},
"tlp": {
"type": "keyword"
}
}
}
}
}
Take the following document as example:
{
"took": 194,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"max_score": 1,
"hits": [
{
"_index": "feed",
"_type": "_doc",
"_id": "W3CS7IABovFpcGfZjfyu",
"_score": 1,
"_source": {
"title": "Test",
"date": "2022-05-22T16:21:09.159711",
"created_by": "finch",
"tlp": "white",
"published": true,
"references": [
"test",
"test"
],
"tags": [
"tag1",
"tag2"
],
"targeted_countries": [
"Italy",
"Germany"
],
"malware_families": [
"family1",
"family2"
],
"groups_related": [
"group1",
"griup2"
],
"attacks_ids": [
""
],
"indicators": [
{
"value": "testest",
"description": "This is a test",
"type": "sha256",
"role": "file",
"date": "2022-05-22T16:21:09.159560"
},
{
"value": "testest2",
"description": "This is a test 2",
"type": "ipv4",
"role": "c2",
"date": "2022-05-22T16:21:09.159699"
}
]
}
}
]
}
}
I would like to make this update: indicators[0].value = 'changed'
if _id == 'W3CS7IABovFpcGfZjfyu'
or if title == 'some_title'
or if indicators[0].role == 'c2'
I already tried with a script, but it seems I can't manage to get it work, I hope the explanation is clear, ask any question if not, thank you.
Edit 1:
I managed to make it work, however it needs the _id, still looking for a way to do that without it.
My partial solution:
update = Pulse.get(id="XHCz7IABovFpcGfZWfz9") #Pulse is my document
update.update(script="for (indicator in ctx._source.indicators) {if (indicator.value=='changed2') {indicator.value='changed3'}}")
# Modify depending on the value of a field inside the same nested object

Recursive: Differences in JSON files not printing as expected

I am creating code to go through JSON files and print the differences. I created two JSON files with generic data for the sake of sample files to compare.
My Code:
import json
from recursive_diff import recursive_eq
lhs = json.loads('sample1.json')
rhs = json.loads('sample2.json')
def test1():
recursive_eq(lhs, rhs)
Output:
(Personal info redacted.)
(env) --$ /Users/--/env/bin/python3.9 /Users/c/Desktop/--/json_compare_recursive.py
Traceback (most recent call last):
File "/Users/--/Desktop/--/json_compare_recursive.py", line 4, in <module>
lhs = json.loads('sample1.json')
File "/usr/local/Cellar/python#3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
File "/usr/local/Cellar/python#3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/local/Cellar/python#3.9/3.9.7_1/Frameworks/Python.framework/Versions/3.9/lib/python3.9/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
Desired output:
I want it to print explicitly each difference between the two JSON files. For example purposes (actual output would be different):
6 differences found
Ln 5, Col 22: , not in lhs
Ln 37, Col 30: "batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
{ "id": "1002", "type": "Chocolate" }
]
}, not in lhs
Ln 50, Col 28: 5 not in rhs
Any suggestions on the most efficient way to accomplish this?
sample1.json code:
[
{
"id": "0001",
"type": "donut",
"name": "Cake",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
{ "id": "1002", "type": "Chocolate" },
{ "id": "1003", "type": "Blueberry" },
{ "id": "1004", "type": "Devil's Food" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5005", "type": "Sugar" },
{ "id": "5007", "type": "Powdered Sugar" },
{ "id": "5006", "type": "Chocolate with Sprinkles" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
]
}
]
sample2.json code:
[
{
"id": "0001",
"type": "donut",
"name": "Cake",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
{ "id": "1002", "type": "Chocolate" },
{ "id": "1003", "type": "Blueberry" },
{ "id": "1004", "type": "Devil's Food" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5005", "type": "Sugar" },
{ "id": "5007", "type": "Powdered Sugar" },
{ "id": "5006", "type": "Chocolate with Sprinkles" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
]
},
{
"id": "0002",
"type": "donut",
"name": "Raised",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5005", "type": "Sugar" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
]
},
{
"id": "0003",
"type": "donut",
"name": "Old Fashioned",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
{ "id": "1002", "type": "Chocolate" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
]
}
]

Extract IF cases from AST tree which is stored as Dictionary in Python

I have an AST tree of javascript in the form of json (Dictionary).
I need to extract only the information about the ifstatement (entire if condition block) in the AST tree using python, so that I can tokenize the extracted data and use for some deep learning tasks.
{
"type": "Program",
"body": [
{
"type": "ExpressionStatement",
"expression": {
"type": "AssignmentExpression",
"operator": "=",
"left": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "MemberExpression",
"computed": true,
"object": {
"type": "Identifier",
"name": "Template"
},
"property": {
"type": "CallExpression",
"callee": {
"type": "Identifier",
"name": "getTemplate"
},
"arguments": [
{
"type": "Literal",
"value": "layout",
"raw": "'layout'"
}
]
}
},
"property": {
"type": "Identifier",
"name": "rendered"
}
},
"right": {
"type": "FunctionExpression",
"id": null,
"params": [],
"body": {
"type": "BlockStatement",
"body": [
{
"type": "IfStatement",
"test": {
"type": "AssignmentExpression",
"operator": "=",
"left": {
"type": "Identifier",
"name": "currentScroll"
},
"right": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "Session"
},
"property": {
"type": "Identifier",
"name": "get"
}
},
"arguments": [
{
"type": "Literal",
"value": "currentScroll",
"raw": "'currentScroll'"
}
]
}
},
"consequent": {
"type": "BlockStatement",
"body": [
{
"type": "ExpressionStatement",
"expression": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "CallExpression",
"callee": {
"type": "Identifier",
"name": "$"
},
"arguments": [
{
"type": "Literal",
"value": "body",
"raw": "'body'"
}
]
},
"property": {
"type": "Identifier",
"name": "scrollTop"
}
},
"arguments": [
{
"type": "Identifier",
"name": "currentScroll"
}
]
}
},
{
"type": "ExpressionStatement",
"expression": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "Session"
},
"property": {
"type": "Identifier",
"name": "set"
}
},
"arguments": [
{
"type": "Literal",
"value": "currentScroll",
"raw": "'currentScroll'"
},
{
"type": "Literal",
"value": null,
"raw": "null"
}
]
}
}
]
},
"alternate": null
},
{
"type": "VariableDeclaration",
"declarations": [
{
"type": "VariableDeclarator",
"id": {
"type": "Identifier",
"name": "link"
},
"init": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "document"
},
"property": {
"type": "Identifier",
"name": "createElement"
}
},
"arguments": [
{
"type": "Literal",
"value": "link",
"raw": "'link'"
}
]
}
}
],
"kind": "var"
},
{
"type": "ExpressionStatement",
"expression": {
"type": "AssignmentExpression",
"operator": "=",
"left": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "link"
},
"property": {
"type": "Identifier",
"name": "type"
}
},
"right": {
"type": "Literal",
"value": "image/x-icon",
"raw": "'image/x-icon'"
}
}
},
{
"type": "ExpressionStatement",
"expression": {
"type": "AssignmentExpression",
"operator": "=",
"left": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "link"
},
"property": {
"type": "Identifier",
"name": "rel"
}
},
"right": {
"type": "Literal",
"value": "shortcut icon",
"raw": "'shortcut icon'"
}
}
},
{
"type": "ExpressionStatement",
"expression": {
"type": "AssignmentExpression",
"operator": "=",
"left": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "link"
},
"property": {
"type": "Identifier",
"name": "href"
}
},
"right": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "Settings"
},
"property": {
"type": "Identifier",
"name": "get"
}
},
"arguments": [
{
"type": "Literal",
"value": "faviconUrl",
"raw": "'faviconUrl'"
},
{
"type": "Literal",
"value": "/img/favicon.ico",
"raw": "'/img/favicon.ico'"
}
]
}
}
},
{
"type": "ExpressionStatement",
"expression": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "MemberExpression",
"computed": true,
"object": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "document"
},
"property": {
"type": "Identifier",
"name": "getElementsByTagName"
}
},
"arguments": [
{
"type": "Literal",
"value": "head",
"raw": "'head'"
}
]
},
"property": {
"type": "Literal",
"value": 0,
"raw": "0"
}
},
"property": {
"type": "Identifier",
"name": "appendChild"
}
},
"arguments": [
{
"type": "Identifier",
"name": "link"
}
]
}
},
{
"type": "ExpressionStatement",
"expression": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "CallExpression",
"callee": {
"type": "Identifier",
"name": "$"
},
"arguments": [
{
"type": "Literal",
"value": "a.category-silent-hangout",
"raw": "'a.category-silent-hangout'"
}
]
},
"property": {
"type": "Identifier",
"name": "after"
}
},
"arguments": [
{
"type": "Literal",
"value": "<span class=\"silent-icons\"> <img src=\"http://codebuddies.org/images/icon-video-off.png\" alt=\"turn off video\" width=\"25\" height=\"25\"> <img src=\"http://codebuddies.org/images/icon-mute.png\" alt=\"turn off microphone\" width=\"25\" height=\"25\"></span>",
"raw": "'<span class=\"silent-icons\"> <img src=\"http://codebuddies.org/images/icon-video-off.png\" alt=\"turn off video\" width=\"25\" height=\"25\"> <img src=\"http://codebuddies.org/images/icon-mute.png\" alt=\"turn off microphone\" width=\"25\" height=\"25\"></span>'"
}
]
}
}
]
},
"generator": false,
"expression": false,
"async": false
}
}
},
{
"type": "ExpressionStatement",
"expression": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "MemberExpression",
"computed": true,
"object": {
"type": "Identifier",
"name": "Template"
},
"property": {
"type": "CallExpression",
"callee": {
"type": "Identifier",
"name": "getTemplate"
},
"arguments": [
{
"type": "Literal",
"value": "layout",
"raw": "'layout'"
}
]
}
},
"property": {
"type": "Identifier",
"name": "events"
}
},
"arguments": [
{
"type": "ObjectExpression",
"properties": [
{
"type": "Property",
"key": {
"type": "Literal",
"value": "click .inner-wrapper",
"raw": "'click .inner-wrapper'"
},
"computed": false,
"value": {
"type": "FunctionExpression",
"id": null,
"params": [
{
"type": "Identifier",
"name": "e"
}
],
"body": {
"type": "BlockStatement",
"body": [
{
"type": "IfStatement",
"test": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "CallExpression",
"callee": {
"type": "Identifier",
"name": "$"
},
"arguments": [
{
"type": "Literal",
"value": "body",
"raw": "'body'"
}
]
},
"property": {
"type": "Identifier",
"name": "hasClass"
}
},
"arguments": [
{
"type": "Literal",
"value": "mobile-nav-open",
"raw": "'mobile-nav-open'"
}
]
},
"consequent": {
"type": "BlockStatement",
"body": [
{
"type": "ExpressionStatement",
"expression": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "Identifier",
"name": "e"
},
"property": {
"type": "Identifier",
"name": "preventDefault"
}
},
"arguments": []
}
},
{
"type": "ExpressionStatement",
"expression": {
"type": "CallExpression",
"callee": {
"type": "MemberExpression",
"computed": false,
"object": {
"type": "CallExpression",
"callee": {
"type": "Identifier",
"name": "$"
},
"arguments": [
{
"type": "Literal",
"value": "body",
"raw": "'body'"
}
]
},
"property": {
"type": "Identifier",
"name": "removeClass"
}
},
"arguments": [
{
"type": "Literal",
"value": "mobile-nav-open",
"raw": "'mobile-nav-open'"
}
]
}
}
]
},
"alternate": null
}
]
},
"generator": false,
"expression": false,
"async": false
},
"kind": "init",
"method": false,
"shorthand": false
}
]
}
]
}
}
],
"sourceType": "script"
}
I want the subtree of below mentioned IF cases.
if(currentScroll=Session.get('currentScroll'))
if ($('body').hasClass('mobile-nav-open'))
Is there an easy way to extract this information in Python?
I am looking for some methods or packages in python to solve this problem, instead of completely traversing entire dictionary.

You can use the tree-sitter library for this purpose.
Check out the Usage section in the README file to setup the package.
This is what you need to do at a high-level:
from tree_sitter import Language, Parser
JS_LANGUAGE = Language('build/my-languages.so', 'javascript')
parser = Parser()
parser.set_language(JS_LANGUAGE)
parser.parse(bytes(<code string>, 'utf-8'))

How to Remove Outer Layer of JSON in Python

I am trying to remove the outer (parent) layer of a JSON file so that I can process it, however I have no idea how.
As you will see by the code below, the outer 2 most layers are 2 dictionaries, however, python says the 2nd dictionary ("item") is just a string when I call its type. Am I incorrect in how I interpret the structure?
sample_object6 = {
"items":
{
"item":
[
{
"id": "0001",
"type": "donut",
"name": "Cake",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
{ "id": "1002", "type": "Chocolate" },
{ "id": "1003", "type": "Blueberry" },
{ "id": "1004", "type": "Devil's Food" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5005", "type": "Sugar" },
{ "id": "5007", "type": "Powdered Sugar" },
{ "id": "5006", "type": "Chocolate with Sprinkles" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
]
},
{
"id": "0002",
"type": "donut",
"name": "Raised",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5005", "type": "Sugar" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
]
},
{
"id": "0003",
"type": "donut",
"name": "Old Fashioned",
"ppu": 0.55,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
{ "id": "1002", "type": "Chocolate" }
]
},
"topping":
[
{ "id": "5001", "type": "None" },
{ "id": "5002", "type": "Glazed" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
]
},
{
"id": "0004",
"type": "bar",
"name": "Bar",
"ppu": 0.75,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
]
},
"topping":
[
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
],
"fillings":
{
"filling":
[
{ "id": "7001", "name": "None", "addcost": 0 },
{ "id": "7002", "name": "Custard", "addcost": 0.25 },
{ "id": "7003", "name": "Whipped Cream", "addcost": 0.25 }
]
}
},
{
"id": "0005",
"type": "twist",
"name": "Twist",
"ppu": 0.65,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
]
},
"topping":
[
{ "id": "5002", "type": "Glazed" },
{ "id": "5005", "type": "Sugar" },
]
},
{
"id": "0006",
"type": "filled",
"name": "Filled",
"ppu": 0.75,
"batters":
{
"batter":
[
{ "id": "1001", "type": "Regular" },
]
},
"topping":
[
{ "id": "5002", "type": "Glazed" },
{ "id": "5007", "type": "Powdered Sugar" },
{ "id": "5003", "type": "Chocolate" },
{ "id": "5004", "type": "Maple" }
],
"fillings":
{
"filling":
[
{ "id": "7002", "name": "Custard", "addcost": 0 },
{ "id": "7003", "name": "Whipped Cream", "addcost": 0 },
{ "id": "7004", "name": "Strawberry Jelly", "addcost": 0 },
{ "id": "7005", "name": "Rasberry Jelly", "addcost": 0 }
]
}
}
]
}
}
I thought that it might be possible to store the nested portion starting at the first list (right after 'item') in a variable and then work with this but if I can't get python to see that item is a dictionary inside the items dictionary, then I fear I am at a loss with how to proceed.
Does anyone know what I am doing wrong?
Thank you in advance!
As far as the processing goes, there has been none because I could not even get the string to read as a dictionary appropriately.
This is what I tried to test if it was a dictionary:
for i in sample_object6:
print(i + str(type(i)))
for n in i["item"]:
print(n + str(type(n)))

After submitting the same code that I thought I had already submitted, I noticed that python is interpreting the object correctly. I have some obvious fundamental gaps in how to work in python and I'm sorry I took it to the forum.
For the record (and for future python newbies out there like me), I used the following code which returned the proper class types:
#this returned a class type of dictionary
print(type(sample_object6["items"]))
#this returned a class type of list
print(type(sample_object6["items"]["item"]))
Thank you SungJin Steve Yoo & Pm2Ring for your help.

Split a large json file into multiple smaller files

I have a large JSON file, about 5 million records and a file size of about 32GB, that I need to get loaded into our Snowflake Data Warehouse. I need to get this file broken up into chunks of about 200k records (about 1.25GB) per file. I'd like to do this in either Node.JS or Python for deployment to an AWS Lambda function, unfortunately I haven't coded in either, yet. I have C# and a lot of SQL experience, and learning both node and python are on my to do list, so why not dive right in, right!?
My first question is "Which language would better serve this function? Python, or Node.JS?"
I know I don't want to read this entire JSON file into memory (or even the output smaller file). I need to be able to "stream" it in and out into the new file based on a record count (200k), properly close up the json objects, and continue into a new file for another 200k, and so on. I know Node can do this, but if Python can also do this, I feel like it would be easier to quickly start using for other ETL stuff I'll be doing soon.
My second question is "Based on your recommendation above, can you also recommend what modules I should require/import to help me get started? Primarily as it relates to not pulling the entire json file into memory? Maybe some tips, tricks, or 'How would you do it's? And if you're feeling really generous, some code example to help push me into the deep end on this?
I can't include a sample of the JSON data, as it contains personal information. But I can provide the JSON schema ...
{
"$schema": "http://json-schema.org/draft-04/schema#",
"items": {
"properties": {
"activities": {
"properties": {
"activity_id": {
"items": {
"type": "integer"
},
"type": "array"
},
"frontlineorg_id": {
"items": {
"type": "integer"
},
"type": "array"
},
"import_id": {
"items": {
"type": "integer"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"is_source": {
"items": {
"type": "boolean"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"address": {
"properties": {
"city": {
"items": {
"type": "string"
},
"type": "array"
},
"congress_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"congress_dist_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"congress_end_yr": {
"items": {
"type": "integer"
},
"type": "array"
},
"congress_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"congress_start_yr": {
"items": {
"type": "integer"
},
"type": "array"
},
"county": {
"items": {
"type": "string"
},
"type": "array"
},
"formatted": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"latitude": {
"items": {
"type": "number"
},
"type": "array"
},
"longitude": {
"items": {
"type": "number"
},
"type": "array"
},
"number": {
"items": {
"type": "string"
},
"type": "array"
},
"observes_dst": {
"items": {
"type": "boolean"
},
"type": "array"
},
"post_directional": {
"items": {
"type": "null"
},
"type": "array"
},
"pre_directional": {
"items": {
"type": "null"
},
"type": "array"
},
"school_district": {
"items": {
"properties": {
"school_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"school_dist_type": {
"items": {
"type": "string"
},
"type": "array"
},
"school_grade_high": {
"items": {
"type": "string"
},
"type": "array"
},
"school_grade_low": {
"items": {
"type": "string"
},
"type": "array"
},
"school_lea_code": {
"items": {
"type": "integer"
},
"type": "array"
}
},
"type": "object"
},
"type": "array"
},
"secondary_number": {
"items": {
"type": "null"
},
"type": "array"
},
"secondary_unit": {
"items": {
"type": "null"
},
"type": "array"
},
"state": {
"items": {
"type": "string"
},
"type": "array"
},
"state_house_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"state_house_dist_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"state_senate_dist_name": {
"items": {
"type": "string"
},
"type": "array"
},
"state_senate_dist_number": {
"items": {
"type": "integer"
},
"type": "array"
},
"street": {
"items": {
"type": "string"
},
"type": "array"
},
"suffix": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"timezone": {
"items": {
"type": "string"
},
"type": "array"
},
"utc_offset": {
"items": {
"type": "integer"
},
"type": "array"
},
"zip": {
"items": {
"type": "integer"
},
"type": "array"
}
},
"type": "object"
},
"age": {
"type": "integer"
},
"anniversary": {
"properties": {
"date": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"baptism": {
"properties": {
"church_id": {
"type": "null"
},
"date": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"birth_dd": {
"type": "integer"
},
"birth_mm": {
"type": "integer"
},
"birth_yyyy": {
"type": "integer"
},
"church_attendance": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"cohabiting": {
"properties": {
"confidence": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood": {
"items": {
"type": "null"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"dating": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"divorced": {
"properties": {
"bool": {
"items": {
"type": "null"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood_considering": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"education": {
"properties": {
"est_level": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"email": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"is_work_school": {
"items": {
"type": "boolean"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"engaged": {
"properties": {
"insert_datetime_utc": {
"type": "null"
},
"likelihood": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"est_income": {
"properties": {
"est_level": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"ethnicity": {
"type": "string"
},
"first_name": {
"type": "string"
},
"formatted_birthdate": {
"type": "string"
},
"gender": {
"type": "string"
},
"head_of_household": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"home_church": {
"properties": {
"church_id": {
"type": "null"
},
"group_participant": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"is_coaching": {
"type": "null"
},
"is_giving": {
"type": "null"
},
"is_serving": {
"type": "null"
},
"membership_date": {
"type": "null"
},
"regular_attendee": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"hub_poid": {
"type": "integer"
},
"insert_datetime_utc": {
"type": "string"
},
"ip_address": {
"properties": {
"insert_datetime_utc": {
"type": "null"
},
"string": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"last_name": {
"type": "string"
},
"marriage_segment": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"married": {
"properties": {
"bool": {
"items": {
"type": "boolean"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"middle_name": {
"type": "string"
},
"miscellaneous": {
"properties": {
"attribute": {
"items": {
"type": "string"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"value": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"name_suffix": {
"type": "null"
},
"name_title": {
"type": "null"
},
"newlywed": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"parent": {
"properties": {
"bool": {
"items": {
"type": "boolean"
},
"type": "array"
},
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"likelihood_expecting": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"person_id": {
"type": "integer"
},
"phone": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"number": {
"items": {
"type": "integer"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"type": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"property_rights": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"psychographic_cluster": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"religion": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"religious_segment": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"separated": {
"properties": {
"bool": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"significant_other": {
"properties": {
"first_name": {
"type": "null"
},
"insert_datetime_utc": {
"type": "null"
},
"last_name": {
"type": "null"
},
"middle_name": {
"type": "null"
},
"name_suffix": {
"type": "null"
},
"name_title": {
"type": "null"
},
"suppressed_datetime_utc": {
"type": "null"
}
},
"type": "object"
},
"suppressed_datetime_utc": {
"type": "string"
},
"target_group": {
"properties": {
"insert_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
},
"string": {
"items": {
"type": "string"
},
"type": "array"
},
"suppressed_datetime_utc": {
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
}
},
"type": "object"
},
"type": "array"
}

Use this code in linux command prompt
split -b 53750k <your-file>
cat xa* > <your-file>
Refer to this link:
https://askubuntu.com/questions/28847/text-editor-to-edit-large-4-3-gb-plain-text-file

Answering the question whether Python or Node will be better for the task would be an opinion and we are not allowed to voice our opinions on Stack Overflow. You have to decide yourself what you have more experience in and what you want to work with - Python or Node.
If you go with Node, there are some modules that can help you with that task, that do streaming JSON parsing. E.g. those modules:
https://www.npmjs.com/package/JSONStream
https://www.npmjs.com/package/stream-json
https://www.npmjs.com/package/json-stream
If you go with Python, there are streaming JSON parsers here as well:
https://github.com/kashifrazzaqui/json-streamer
https://github.com/danielyule/naya
http://www.enricozini.org/blog/2011/tips/python-stream-json/

consider to use jq to preprocessing your json files
it could split and stream your large json files
jq is like sed for JSON data - you can use it to slice
and filter and map and transform structured data with
the same ease that sed, awk, grep and friends let you play with text.
see the official documentation and this questions for more.
extra: for your first questions jq is written by C, it's faster than python/node isn't it ?

Snowflake has a very special treatment for JSON and if we understand them, it would be easy to draw the design.
JSON/Parquet/Avro/XML is considered as semi-structure data
They are stored as Variant data type in Snowflake.
While loading the JSON data into stage location, flag the strip_outer_array=true
copy into <table>
from #~/<file>.json
file_format = (type = 'JSON' strip_outer_array = true);
Each row size can not exceed 16Mb compressed when loaded in snowflake.
Snowflake data loading works well if the file size is split in the range of 10-100Mb in size.
Use the utilities which can split the file based on per line and have the file size note more than 100Mb and that brings the power of parallelism as well as accuracy for your data.
As per your data set size, you will get around 31K small files (of 100Mb size).
It means that the 31k parallel process run, however, it is not possible.
So choose an x-large size warehouse (16 v-core & 32 threads)
31k/32 = (approximately) 1000 rounds
This will not take more than a few minutes to load data based on your network bandwidth. Even if we think of 3sec per round, it may load the data in 50min.
Look at the warehouse configuration & throughput details and refer semi-structured data loading best practice.

The easiest approach that worked for me was this:
json_file = <your_file>
chunks = 200
for i in range(0,len(json_file), chunks):
print(json_file[i:i+chunks])

To split and compress at the same time with bash, resulting in files of ~100MB each:
cat bigfile.json | split -C 1000000000 -d -a4 - output_prefix --filter='gzip > $FILE.gz'
See more: https://stackoverflow.com/a/68718176/132438

You can use Python3 with the following script:
import json
def split_json(file_path):
with open(file_path, 'r') as json_file:
data = json.load(json_file)
chunk_size = len(data) // 3
for i in range(3):
with open(f"part{i}.json", 'w') as outfile:
outfile.write(json.dumps(data[i*chunk_size:(i+1)*chunk_size]))
file_path = input("Enter the file path of the JSON file: ")
split_json(file_path)