How to Retrieve a Random Object from Firebase Using a Sequential Id

How I can store sequential record id for an object in firebase

You now add these objects to Firebase using something like:

var groups = [
  { GroupDescription: Test, GroupName: test, Id: 1 },
  { GroupDescription: Another test, GroupName: Simple, Id: 2 }
];
groups.forEach(function(group) {
  ref.push(group);
});

Using push tells Firebase to create a new child and automatically create a unique, ordered ID for you.

If you want to keep control of the name of the new children, you can simply specify your own child name and use set:

groups.forEach(function(group) {
  ref.child(group.Id).set(group);
});

Firestore: How to get random documents in a collection

Using randomly generated indexes and simple queries, you can randomly select documents from a collection or collection group in Cloud Firestore.

This answer is broken into 4 sections with different options in each section:

1. How to generate the random indexes
2. How to query the random indexes
3. Selecting multiple random documents
4. Reseeding for ongoing randomness

How to generate the random indexes

The basis of this answer is creating an indexed field that when ordered ascending or descending, results in all the document being randomly ordered. There are different ways to create this, so let's look at 2, starting with the most readily available.

Auto-Id version

If you are using the randomly generated automatic ids provided in our client libraries, you can use this same system to randomly select a document. In this case, the randomly ordered index is the document id.

Later in our query section, the random value you generate is a new auto-id (iOS, Android, Web) and the field you query is the __name__ field, and the 'low value' mentioned later is an empty string. This is by far the easiest method to generate the random index and works regardless of the language and platform.

By default, the document name (__name__) is only indexed ascending, and you also cannot rename an existing document short of deleting and recreating. If you need either of these, you can still use this method and just store an auto-id as an actual field called random rather than overloading the document name for this purpose.

Random Integer version

When you write a document, first generate a random integer in a bounded range and set it as a field called random. Depending on the number of documents you expect, you can use a different bounded range to save space or reduce the risk of collisions (which reduce the effectiveness of this technique).

You should consider which languages you need as there will be different considerations. While Swift is easy, JavaScript notably can have a gotcha:

• 32-bit integer: Great for small (~10K unlikely to have a collision) datasets
• 64-bit integer: Large datasets (note: JavaScript doesn't natively support, yet)

This will create an index with your documents randomly sorted. Later in our query section, the random value you generate will be another one of these values, and the 'low value' mentioned later will be -1.

How to query the random indexes

Now that you have a random index, you'll want to query it. Below we look at some simple variants to select a 1 random document, as well as options to select more than 1.

For all these options, you'll want to generate a new random value in the same form as the indexed values you created when writing the document, denoted by the variable random below. We'll use this value to find a random spot on the index.

Wrap-around

Now that you have a random value, you can query for a single document:

let postsRef = db.collection("posts")
queryRef = postsRef.whereField("random", isGreaterThanOrEqualTo: random)
                   .order(by: "random")
                   .limit(to: 1)

Check that this has returned a document. If it doesn't, query again but use the 'low value' for your random index. For example, if you did Random Integers then lowValue is 0:

let postsRef = db.collection("posts")
queryRef = postsRef.whereField("random", isGreaterThanOrEqualTo: lowValue)
                   .order(by: "random")
                   .limit(to: 1)

As long as you have a single document, you'll be guaranteed to return at least 1 document.

Bi-directional

The wrap-around method is simple to implement and allows you to optimize storage with only an ascending index enabled. One downside is the possibility of values being unfairly shielded. E.g if the first 3 documents (A,B,C) out of 10K have random index values of A:409496, B:436496, C:818992, then A and C have just less than 1/10K chance of being selected, whereas B is effectively shielded by the proximity of A and only roughly a 1/160K chance.

Rather than querying in a single direction and wrapping around if a value is not found, you can instead randomly select between >= and <=, which reduces the probability of unfairly shielded values by half, at the cost of double the index storage.

If one direction returns no results, switch to the other direction:

queryRef = postsRef.whereField("random", isLessThanOrEqualTo: random)
                   .order(by: "random", descending: true)
                   .limit(to: 1)

queryRef = postsRef.whereField("random", isGreaterThanOrEqualTo: random)
                   .order(by: "random")
                   .limit(to: 1)

Selecting multiple random documents

Often, you'll want to select more than 1 random document at a time. There are 2 different ways to adjust the above techniques depending on what trade offs you want.

Rinse & Repeat

This method is straight forward. Simply repeat the process, including selecting a new random integer each time.

This method will give you random sequences of documents without worrying about seeing the same patterns repeatedly.

The trade-off is it will be slower than the next method since it requires a separate round trip to the service for each document.

Keep it coming

In this approach, simply increase the number in the limit to the desired documents. It's a little more complex as you might return 0..limit documents in the call. You'll then need to get the missing documents in the same manner, but with the limit reduced to only the difference. If you know there are more documents in total than the number you are asking for, you can optimize by ignoring the edge case of never getting back enough documents on the second call (but not the first).

The trade-off with this solution is in repeated sequences. While the documents are randomly ordered, if you ever end up overlapping ranges you'll see the same pattern you saw before. There are ways to mitigate this concern discussed in the next section on reseeding.

This approach is faster than 'Rinse & Repeat' as you'll be requesting all the documents in the best case a single call or worst case 2 calls.

Reseeding for ongoing randomness

While this method gives you documents randomly if the document set is static the probability of each document being returned will be static as well. This is a problem as some values might have unfairly low or high probabilities based on the initial random values they got. In many use cases, this is fine but in some, you may want to increase the long term randomness to have a more uniform chance of returning any 1 document.

Note that inserted documents will end up weaved in-between, gradually changing the probabilities, as will deleting documents. If the insert/delete rate is too small given the number of documents, there are a few strategies addressing this.

Multi-Random

Rather than worrying out reseeding, you can always create multiple random indexes per document, then randomly select one of those indexes each time. For example, have the field random be a map with subfields 1 to 3:

{'random': {'1': 32456, '2':3904515723, '3': 766958445}}

Now you'll be querying against random.1, random.2, random.3 randomly, creating a greater spread of randomness. This essentially trades increased storage to save increased compute (document writes) of having to reseed.

Reseed on writes

Any time you update a document, re-generate the random value(s) of the random field. This will move the document around in the random index.

Reseed on reads

If the random values generated are not uniformly distributed (they're random, so this is expected), then the same document might be picked a dispropriate amount of the time. This is easily counteracted by updating the randomly selected document with new random values after it is read.

Since writes are more expensive and can hotspot, you can elect to only update on read a subset of the time (e.g, if random(0,100) === 0) update;).

Swift. How to choose a random user from Firebase Database?

You could make a call to users, take the ids given back to you, and pick one at random with a basic random number. If after making a call to users you have 10 user ids in an array you would want to get a random number between 0-9 and then make a call to firebase with the userId. I don't believe Firebase has any built in code for this.

Firebase: Pull random data from Firebase to RecyclerView (android)

I suggest you also add another variable to your Model class. Something like an "id". And you store random Long values in it. (From 0 to 15 for example).
And then create a method that will generate a random value (lets say the method's name is generateRandom()). This way, when you want to get the random data, you can use your query like this:

query = mDatabase.orderByChild("id").startAt(generateRandom()).limitToFirst(6); 

how can i point to the random key( firebase user ID)

There are more than a single problem in your code.

I haven't found any other method instead of creating a child of every user to save creation date and time sort by using orderByChild("Create")

That's the recommended approach to create a separate node for every user. First problem is that Firebase realtime database queries are immutable, which means that you cannot change the properties of an existing query. If you change the value by calling .orderByChild("Create") method, it becomes a new query. That's the exact same behaviour as in case of the String class. So to solve this, please chain all method calls and store them in a single Query object:

Query userref = FirebaseDatabase.getInstance().getReference()
    .child("Connection")
    .child("Admin")
    .orderByChild("Create");

Or you can create a new Query object like this:

Query createQuery = userref.orderByChild("Create");
createQuery.addValueEventListener(/* ... */);

Second problem, to have relevant results, please note that the timestamp should not be stored as a String:

Create: "2019/09/11 02.32:54"

Because the order in this case would be lexicographically. So you definitely should store them as a ServerValue.TIMESTAMP, as explained in my answer from the following post:

• How to save the current date/time when I add new value to Firebase Realtime Database

The third problem is that the properties in your database are starting with a capital letter. If the fields in your Users class are lowercase or even if are starting with a capital letter, you might get the following error:

W/ClassMapper: No setter/field for Create found on class Users

To solve this, you should either use the answer from the following post:

• how to read firestore sub-collection and pass it to FirestoreRecyclerOptions

Or from the following post:

• Firebase Android ListView not being displayed

Edit:

When you query, there is no need to add the uid 0yozyNJzCvTWtRU8ufe5Zx8TPvu1 as child. You should only get a reference to Admin and then loop through the children (which are actually user object). When you call .orderByChild("Create") you are transforming the DatabaseReference object into a Query object and you are ordering all users according to the Create property.

You should add an explicit call to child("0yozyNJzCvTWtRU8ufe5Zx8TPvu1") only if you want to get a particular user obect, otherwise you don't need to do it.

or you're .orderByChild() can work for sub child too ??

Yes, it will work. Give it a try ;)

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