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How to search for a value in two different type of field or index or heading of mongodb using python?

I am new to any kind of programming. This is an issue I encountered when using mongodb. Below is the collection structure of the document I imported from two different csv files.
{
"_id": {
"$oid": "61bc4217ed94f9d5fe6a350c"
},
"Telephone Number": "8429950810",
"Date of Birth": "01/01/1945"
}
{
"_id": {
"$oid": "61bc4217ed94f9d5fe6a350c"
},
"Telephone Number": "8129437810",
"Date of Birth": "01/01/1998"
}
{
"_id": {
"$oid": "61bd98d36cc90a9109ab253c"
},
"TELEPHONE_NUMBER": "9767022829",
"DATE_OF_BIRTH": "16-Jun-98"
}
{
"_id": {
"$oid": "61bd98d36cc9090109ab253c"
},
"TELEPHONE_NUMBER": "9567085829",
"DATE_OF_BIRTH": "16-Jan-91"
}
The first two entries are from a csv and the next two entries from another csv file. Now I am creating a user interface where users can search for a telephone number. How to write the query to search the telephone number value in both the index ( Telephone Number and TELEPHONE_NUMBER) using find() in the above case. If not possible is there a way to change the index's to a desired format while importing csv to db. Or is there a way where I create two different collection and then import csv to each collections and then perform a collective search of both the collections. Or can we create a compound index and then search the compound index instead. I am using pymongo for all the operations.
Thankyou.

You can use or query if different key is used to store same type of data.
yourmongocoll.find({"$or":[ {"Telephone Number":"8429950810"}, {"TELEPHONE_NUMBER":8429950810}]})

Assuming you have your connection string to connect via pymongo. Then the following is an example of how to query for the telephone number "8429950810":
from pymongo import MongoClient
client = MongoClient("connection_string")
db = client["db"]
collection = db["collection"]
results = collection.find({"Telephone Number":"8429950810"})
Please note this will return as type cursor, if you would like your documents in a list consider wrapping the query in list() like so:
results = list(collection.find({"Telephone Number":"8429950810"}))

Elasticsearch prevent indexing of Markdown hyperlinks

I am building a Markdown file content search using Elasticsearch. Currently the whole content inside the MD file is indexed in Elasticsearch. But the problem is it shows results like this [Mylink](https://link-url-here.org), [Mylink2](another_page.md)
in the search results.
I would like to prevent indexing of hyperlinks and reference to other pages. When someone search for "Mylink" it should only return the text without the URL. It would be great if someone could help me with the right solution for this.

You need to render Markdown in your indexing application, then remove HTML tags and save it alongside with the markdown source.

I think you have two main solutions for this problem.
first: clean the data in your source code before indexing it into Elasticsearch.
second: use the Elasticsearch filter to clean the data for you.
the first solution is the easy one but if you need to do this process inside the Elasticsearch you need to create a ingest pipeline.
then you can use the Script processor to clean the data you need by a ruby script that can find your regex and remove it

You could use an ingest pipeline with a script processor to extract the link text:
1. Set up the pipeline
PUT _ingest/pipeline/clean_links
{
"description": "...",
"processors": [
{
"script": {
"source": """
if (ctx["content"] == null) {
// nothing to do here
return
}
def content = ctx["content"];
Pattern pattern = /\[([^\]\[]+)\](\(((?:[^\()]+)+)\))/;
Matcher matcher = pattern.matcher(content);
def purged_content = matcher.replaceAll("$1");
ctx["purged_content"] = purged_content;
"""
}
}
]
}
The regex can be tested here and is inspired by this.
2. Include the pipeline when ingesting the docs
POST my-index/_doc?pipeline=clean_links
{
"content": "[Mylink](https://link-url-here.org) [anotherLink](http://dot.com)"
}
POST my-index/_doc?pipeline=clean_links
{
"content": "[Mylink2](another_page.md)"
}
The python docs are here.
3. Verify
GET my-index/_search?filter_path=hits.hits._source
should yield
{
"hits" : {
"hits" : [
{
"_source" : {
"purged_content" : "Mylink anotherLink",
"content" : "[Mylink](https://link-url-here.org) [anotherLink](http://dot.com)"
}
},
{
"_source" : {
"purged_content" : "Mylink2",
"content" : "[Mylink2](another_page.md)"
}
}
]
}
}
You could instead replace the original content if you want to fully discard them from your _source.
In contrast, you could go a step further in the other direction and store the text + link pairs in a nested field of the form:
{
"content": "...",
"links": [
{
"text": "Mylink",
"href": "https://link-url-here.org"
},
...
]
}
so that when you later decide to make them searchable, you'll be able to do so with precision.
Shameless plug: you can find other hands-on ingestion guides in my Elasticsearch Handbook.

Update all MongoDB fields with their own values with PyMongo

I want to update every field of my MongoDB collection using the field's own value to do so.
Example: if I have this document: "string": "foo", a possible update would do this: "string": $string.lower(). Here, $string would be "foo", but I don't know how to do this with PyMongo.
I've tried this:
user_collection.update_many({}, { "$set": { "word": my_func("$word")}})
Which replaces everything with "$word".
I've been able to do it successfully iterating each document but it takes too long.

As I know you can't find and update in one statement using python function. You can either use mongo query language:
user_collection.update_many({}, { "$set": {"name": { "$concat": ["$name", "_2"]}}})
or use separate functions of pymongo:
for obj in user_collection.find({some query here}):
user_collection.update({"_id": obj['_id']}, { "$set": {"name": my_func(obj['name']) } })

Is it possible to use references in JSON?

I have this JSON:
{
"app_name": "my_app",
"version": {
"1.0": {
"path": "/my_app/1.0"
},
"2.0": {
"path": "/my_app/2.0"
}
}
}
Is it somehow possible to reference the keywords app_name and the key of version so that I don't have to repeat "my_app" and the version numbering?
I was thinking something along the lines of... (code totally made up):
{
"#app_name": "my_app",
"version": {
"1.0": {
"path": "/{{$app_name}}/{{key[-1]]}}"
},
"2.0": {
"path": "/{{$app_name}}/{{key[-1]}}"
}
}
}
Or is this something that could instead be handled better using YAML?
In the end, I intend to read this data into a Python dictionary.

No, JSON does not have references. (The functionality you request here, with substring expansion, would open itself to memory attacks against the parser; by not supporting this functionality, JSON avoids vulnerability to such attacks).
If you want such functionality, you need to implement it yourself.

Not in pure JSON, but you could performs string substitution after you parse the JSON.

Elasticsearch relative time range query in Python

I have searched and searched but cannot find an answer for this. I am new to using Elasticsearch with Python and trying to do a simple Python query against my Elasticsearch index which will return a count of the results matching a specific set of criteria in the past hour. I'm getting all the results back using the following (sanitized) code:
hits = es.count(index='myindex-*',q=thing.rstrip() )
Simple enough right? So is there a way to include a relative time range in this query, or do I need to write some Python to figure out the times to insert as a time range?
Thanks in advance for the help!

Yes, everything you need is a time-based key in your index and then query that key with:
{
"query" : {
"range" : {
"<time_based_key>" : {
"gte" : "now-1h"
}
}
}
}
To define your time-based key:
curl -XPUT localhost:9200/<database>/<index>/_mapping?pretty -d '
{
"<index>" : {
"properties": {
"<time_based_key>" : {
"type" : "date",
"index": "not_analyzed"
}
}
}
}'