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Can I create a map in Firestore Rules?

I have for every document an array of admins that are allowed access to that document. The items in the array are all objects similar to this:
[
{user_ID : "Wfdwwwrdfsdfsdf",
avatar: "www.dfsfsd.com/dfdfd"
name: "Ben Ben"
},
{user_ID : "Hdfsdbbf",
avatar: "www.dfsfsd.com/popo"
name: "Josh Josh"
}
]
In my Firestore Rules I want to check the user making the request is an admin, so I need to check if their uid is part of this array. In JS, I'd just create a new array from the array admins that would only include the IDs, using a map, and check if the ID is there. In Firestore Rules that doesn't seem to be an option. How can I get around this?
Do I have to create another array that only stores the IDs of admins for every document? That seems excessive.
Can't really find all the methods and functions that I can use when workign with Firestore. All I find are examples for certain operations.

There is no way to do loops in the rules, so you wont be able to go through the objects and create an array of ID. Having this array of admins pre-calculated seems the best option then you would just do
allow update: if request.auth.uid in resource.data.admins
The other option is to transform your array of admins into a map with the uid as the key. Then you dont need to duplicate the keys.
{
Wfdwwwrdfsdfsdf : {
avatar: "www.dfsfsd.com/dfdfd"
name: "Ben Ben"
},
Hdfsdbbf: {
avatar: "www.dfsfsd.com/popo"
name: "Josh Josh"
}
}
The rule remains the same
The reference containing all the functions you can use is here

Mongodb check If field exists in an sub-document of an array

I am trying to check If a field exists in a sub-document of an array and if it does, it will only provide those documents in the callback. But every time I log the callback document it gives me all values in my array instead of ones based on the query.
I am following this tutorial
And the only difference is I am using the findOne function instead of find function but it still gives me back all values. I tried using find and it does the same thing.
I am also using the same collection style as the example in the link above.
Example
In the image above you can see in the image above I have a document with a uid field and a contacts array. What I am trying to do is first select a document based on the inputted uid. Then after selecting that document then I want to display the values from the contacts array where contacts.uid field exists. So from the image above only values that would be displayed is contacts[0] and contacts[3] because contacts1 doesn't have a uid field.
Contact.contactModel.findOne({$and: [
{uid: self.uid},
{contacts: {
$elemMatch: {
uid: {
$exists: true,
$ne: undefined,
}
}
}}
]}

You problems come from a misconception about data modeling in MongoDB, not uncommon for developers coming from other DBMS. Let me illustrate this with the example of how data modeling works with an RDBMS vs MongoDB (and a lot of the other NoSQL databases as well).
With an RDBMS, you identify your entities and their properties. Next, you identify the relations, normalize the data model and bang your had against the wall for a few to get the UPPER LEFT ABOVE AND BEYOND JOIN™ that will answer the questions arising from use case A. Then, you pretty much do the same for use case B.
With MongoDB, you would turn this upside down. Looking at your use cases, you would try to find out what information you need to answer the questions arising from the use case and then model your data so that those questions can get answered in the most efficient way.
Let us stick with your example of a contacts database. A few assumptions to be made here:
Each user can have an arbitrary number of contacts.
Each contact and each user need to be uniquely identified by something other than a name, because names can change and whatnot.
Redundancy is not a bad thing.
With the first assumption, embedding contacts into a user document is out of question, since there is a document size limit. Regarding our second assumption: the uid field becomes not redundant, but simply useless, as there already is the _id field uniquely identifying the data set in question.
The use cases
Let us look at some use cases, which are simplified for the sake of the example, but it will give you the picture.
Given a user, I want to find a single contact.
Given a user, I want to find all of his contacts.
Given a user, I want to find the details of his contact "John Doe"
Given a contact, I want to edit it.
Given a contact, I want to delete it.
The data models
User
{
"_id": new ObjectId(),
"name": new String(),
"whatever": {}
}
Contact
{
"_id": new ObjectId(),
"contactOf": ObjectId(),
"name": new String(),
"phone": new String()
}
Obviously, contactOf refers to an ObjectId which must exist in the User collection.
The implementations
Given a user, I want to find a single contact.
If I have the user object, I have it's _id, and the query for a single contact becomes as easy as
db.contacts.findOne({"contactOf":self._id})
Given a user, I want to find all of his contacts.
Equally easy:
db.contacts.find({"contactOf":self._id})
Given a user, I want to find the details of his contact "John Doe"
db.contacts.find({"contactOf":self._id,"name":"John Doe"})
Now we have the contact one way or the other, including his/her/undecided/choose not to say _id, we can easily edit/delete it:
Given a contact, I want to edit it.
db.contacts.update({"_id":contact._id},{$set:{"name":"John F Doe"}})
I trust that by now you get an idea on how to delete John from the contacts of our user.
Notes
Indices
With your data model, you would have needed to add additional indices for the uid fields - which serves no purpose, as we found out. Furthermore, _id is indexed by default, so we make good use of this index. An additional index should be done on the contact collection, however:
db.contact.ensureIndex({"contactOf":1,"name":1})
Normalization
Not done here at all. The reasons for this are manifold, but the most important is that while John Doe might have only have the mobile number of "Mallory H Ousefriend", his wife Jane Doe might also have the email address "janes_naughty_boy#censored.com" - which at least Mallory surely would not want to pop up in John's contact list. So even if we had identity of a contact, you most likely would not want to reflect that.
Conclusion
With a little bit of data remodeling, we reduced the number of additional indices we need to 1, made the queries much simpler and circumvented the BSON document size limit. As for the performance, I guess we are talking of at least one order of magnitude.

In the tutorial you mentioned above, they pass 2 parameters to the method, one for filter and one for projection but you just passed one, that's the difference. You can change your query to be like this:
Contact.contactModel.findOne(
{uid: self.uid},
{contacts: {
$elemMatch: {
uid: {
$exists: true,
$ne: undefined,
}
}
}}
)

The agg framework makes filtering for existence of a field a little tricky. I believe the OP wants all docs where a field exists in an array of subdocs and then to return ONLY those subdocs where the field exists. The following should do the trick:
var inputtedUID = "0"; // doesn't matter
db.foo.aggregate(
[
// This $match finds the docs with our input UID:
{$match: {"uid": inputtedUID }}
// ... and the $addFields/$filter will strip out those entries in contacts where contacts.uid does NOT exist. We wish we could use {cond: {$zz.name: {$exists:true} }} but
// we cannot use $exists here so we need the convoluted $ifNull treatment. Note we
// overwrite the original contacts with the filtered contacts:
,{$addFields: {contacts: {$filter: {
input: "$contacts",
as: "zz",
cond: {$ne: [ {$ifNull:["$$zz.uid",null]}, null]}
}}
}}
,{$limit:1} // just get 1 like findOne()
]);
show(c);
{
"_id" : 0,
"uid" : 0,
"contacts" : [
{
"uid" : "buzz",
"n" : 1
},
{
"uid" : "dave",
"n" : 2
}
]
}

Algolia search on nested objects in a record - multiple facetFilters in one object

I’m migrating from Mongo to Firebase with Algolia on top to provide the search. But hitting a snag coming up with a comparable way to search in individual elements of a record.
I have an object that stores when a room is available: from and to. Each record can have many individual from/to combos (see the sample below with 2). I want to be able to run a search something like:
roomavailable.from <= 1522195200 AND roomavailable.to >=1522900799
But only have the query search a match within each element, not any facet in all elements. An element query in Mongo works like that. But if I run that query on the record listed below, it will return the record, because the two roomavailable objects satisfy the .from and .to query. I think.
Is there a way to ensure the search is looking only at matching a pair of .from and .to in an individual object/element?
Below is the pertinent part of the record stored in Algolia so you can see the structure.
"roomavailable": [
{
"_id": "rJbdWvY9M",
"from": 1522195200,
"to": 1522799999
},
{
"_id": "r1H_-vKqz",
"from": 1523923200,
"to": 1524268799
}
],
And here is the Mongo (mongoose) equivalent where its searching inside individual elements (this works):
$elemMatch: {
from: {
$lte: moment(dateArray[0]).utc().startOf('day').format()
},
to: {
$gte: moment(dateArray[1]).utc().endOf('day').format()
}
}
I have also tried this query but it seems to still match either the .from AND .to but in any of the the individual roomavailable elements:
index.search({
query: '',
filters: filters,
facetFilters: [roomavailable.from: 1522195200, roomavailable.to: 1524268799],
attributesToRetrieve: [
"roomavailable",
],
restrictHighlightAndSnippetArrays: true
})
I found a couple posts on Algolia discussing using 1 bracket vs. 2 brackets in the facetFilters. I've tried both. Neither work.
Any suggestions would be awesome. Thanks!

Edit: See discussion on Algolia Discourse:
https://discourse.algolia.com/t/how-to-match-multiple-attributes-in-nested-object-with-numericfilters/4887/8
Hi #kanec, thanks for clarifying your question!
Indeed what #Alefort suggested (using roomavailable in a separate index) would be the easiest option since the query I mentioned above will definitely return the results you want. This will mean that you'll have to query the room availability index separately in order to get which IDs are available, so you'll have to use multiple-queries:
https://www.algolia.com/doc/api-reference/api-methods/multiple-queries/
That said, I asked our core API team to see if there's a more reasonable way to approach this issue, but I fear that this is a filter limit due to performance reasons with arrays. You could transform your data structure in the following and index your rooms as an object instead:
[
{
"roomavailable": {
"0": {
"_id": "rJbdWvY9M",
"from": 1522195200,
"to": 1522799999
},
"1": {
"_id": "r1H_-vKqz",
"from": 1523923200,
"to": 1524268799
}
}
}
]
So you can apply the following filter:
{
"filters": "roomavailable.0.from <= 1522195200 AND roomavailable.0.to >= 1522799999 AND roomavailable.1.from <= 1522195200 AND roomavailable.1.to >=1522900799"
}
The downside of this is that you'll need to know the length of roomavailable in order to build the search query on the front-end (you can do so at indexing time by adding a roomavailable_count property) and also this will probably will be less performant with a considerable number of rooms per item; in this case, switching to a dedicated index makes totally sense for the following reasons:
If in your backend you frequently update available rooms you won't impact the other indices' build time
Filters will perform better (as explained above)
Indexing strategy will be simpler to handle
Let me know what you think about this and if it helps you out.

How to optimize performance of searching in two array of object

There are two array of objects one from database and one from csv. I required to compare both array object by their relative properties of Phones and emails and find duplicate array among them. Due to odd database object structure I required to compare both array with Javascript. I wanted to know what is the best algorithm and best way of compare and find duplicates?
I explain simple calculations.
There are 5000 contacts in my database and user may upload another 3000 contacts from csv. Everytime we requires to find duplicate contacts from database and if they find then it may overwrite and rest should be insert. If I compare contact row by row then it may loop 5000 database contacts x 3000 csv contacts = 15000000 time traverse.
This is my present scenario I face due to this system goes stuck. I require some efficient solution of this issue.
I develop the stuff in NodeJS, RethinkDB.
Database object structure exactly represent like that way and it may duplicate entry of emails and phones in other contacts also.
[{
id: 2349287349082734,
name: "ABC",
phones: [
{
id: 2234234,
flag: true,
value: 982389679823
},
{
id: 65234234,
flag: false,
value: 2979023423
}
],
emails: [
{
id: 22346234,
flag: true,
value: "test#domain.com"
},
{
id: 609834234,
flag: false,
value: "test2#domain.com"
}
]
}]
Please review fiddle code, if you want: https://jsfiddle.net/dipakchavda2912/eua1truj/
I have already did indexing. The problem is looking very easy and known in first sight but when we talk about concurrency it is really very critical and CPU intensive.

If understand the question you can use the lodash method differenceWith
let csvContacts = [] //fill it with your values;
let databaseContacts = .... //from your database
let diffArray = [] //the non duplicated object;
const l = require("lodash");
diffArray = l.differenceWith(csvContact,
databaseContacts,
(firstValue,secValue)=>firstValue.email == secValue.email

Aggregation the data from mongodb, map reduce or any other ways?

Well i am struggling with the aggregation problems. I thought the easiest way to solve problem is to use map reduce or make separate find queries and then loop through with the async library help.
The schema is here:
db.keyword
keyword: String
start: Date
source: String(Only one of these (‘google’,’yahoo’,’bing’,’duckduckgo’) )
job: ref db.job
results: [
{
title: String
url: String
position: Number
}
]
db.job
name: String
keywords: [ String ]
urls: [ String ]
sources: [ String(‘google’,’yahoo’,’bing’,’duckduckgo’) ]
Now i need to take the data to this form:
data = {
categories: [ 'keyword1', 'keyword2', 'keyword3' ],
series: [
{
name: 'google',
data: [33, 43, 22]
},
{
name: 'yahoo',
data: [12, 5, 3]
}
]
}
Well the biggest problem is that the series[0].data array is made of really difficult find, matching the db.job.urls against the db.keyword.results.url and then get the position.
Is there any way to simplify the query_? I have looked through many of the map reduce examples, but I cant find the correct way what data to map and which to reduce.

It looks as though you are trying to combine data from two separate collections (keyword and job).
Map Reduce as well as the new Aggregation Framework can only operate on a single collection at a time.
Your best bet is probably to query each collection separately and programmatically combine the results, saving them in whichever form is best suited to your application.
If you would like to experiment with Map Reduce, here is a link to a blog post written by a user who used an incremental Map Reduce operation to combine values from two collections.
http://tebros.com/2011/07/using-mongodb-mapreduce-to-join-2-collections/
For more information on using Map Reduce with MongoDB, please see the Mongo Documentation:
http://www.mongodb.org/display/DOCS/MapReduce
(The section on incremental Map Reduce is here: http://www.mongodb.org/display/DOCS/MapReduce#MapReduce-IncrementalMapreduce)
There are some additional Map Reduce examples in the MongoDB Cookbook:
http://cookbook.mongodb.org/
For a step-by-step walkthrough of how a Map Reduce operation is run, please see the "Extras" section of the MongoDB Cookbook recipe "Finding Max And Min Values with Versioned Documents" http://cookbook.mongodb.org/patterns/finding_max_and_min/
Hopefully the above will give you some ideas for how to achieve your desired results. As I mentioned, I believe that the most straightforward solution is simply to combine the results programmatically. However, if you are successful writing a Map Reduce operation that does this, please post your solution, so that the Community may gain the benefit of your experience.