Develop Reference

Would giving response to client while letting asynchronous operation continue to run a good idea?

So I need to implement an "expensive" API endpoint. Basically, the user/client would need to be able to create a "group" of existing users.
So this "create group" API would need to check that each users fulfill the criteria, i.e. all users in the same group would need to be from the same region, same gender, within an age group etc. This operation can be quite expensive, especially since there are no limit on how many users in one group, so its possible that the client requests group of 1000 users for example.
My idea is that the endpoint will just create entry in database and mark the "group" as pending, while the checking process is still happening, then after its completed, it will update the group status to "completed" or "error" with error message, then the client would need to periodically fetch the status if its still pending.
My implementation idea is something along this line
const createGroup = async (req, res) => {
const { ownerUserId, userIds } = req.body;
// This will create database entry of group with "pending" status and return the primary key
const groupId = await insertGroup(ownerUserId, 'pending');
// This is an expensive function which will do checking over the network, and would take 0.5s per user id for example
// I would like this to keep running after this API endpoint send the response to client
checkUser(userIds)
.then((isUserIdsValid) => {
if (isUserIdsValid) {
updateGroup(groupId, 'success');
} else {
updateGroup(groupId, 'error');
}
})
.catch((err) => {
console.error(err);
updateGroup(groupId, 'error');
});
// The client will receive a groupId to check periodically whether its ready via separate API
res.status(200).json({ groupId });
};
My question is, is it a good idea to do this? Do I missing something important that I should consider?

Yes, this is the standard approach to long-running operations. Instead of offering a createGroup API that creates and returns a group, think of it as having an addGroupCreationJob API that creates and returns a job.
Instead of polling (periodically fetching the status to check whether it's still pending), you can use a notification API (events via websocket, SSE, webhooks etc) and even subscribe to the progress of processing. But sure, a check-status API (via GET request on the job identifier) is the lowest common denominator that all kinds of clients will be able to use.
Did I not consider something important?
Failure handling is getting much more complicated. Since you no longer create the group in a single transaction, you might find your application left in some intermediate state, e.g. when the service crashed (due to unrelated things) during the checkUser() call. You'll need something to ensure that there are no pending groups in your database for which no actual creation process is running. You'll need to give users the ability to retry a job - will insertGroup work if there already is a group with the same identifier in the error state? If you separate the group and the jobs into independent entities, do you need to ensure that no two pending jobs are trying to create the same group? Last but not least you might want to allow users to cancel a currently running job.

Efficient DB design with PouchDB/CouchDB

So I was reading a lot about how to actually store and fetch data in an efficient way. Basically my application is about time management/capturing for projects. I am very happy for any opinion on which strategy I should use or even suggestions for other strategies. The main concern is about the limited resources for local storage on the different Browsers.
This is the main data I have to store:
db_projects: This is a database where the projects itself are stored.
db_timestamps: Here go the timestamps per project whenever a project is running.
I came up with the following strategies:
1: Storing the status of the project in the timestamps
When a project is started, there is addad a timestamp to db_timestamps like so:
db_timestamps.put({
_id: String(Date.now()),
title: projectID,
status: status //could be: 1=active/2=inactive/3=paused
})...
This follows the strategy to only add data to the db and not modify any entries. The problem I see here is that if I want to get all active projects for example, I would need to query the whole db_timestamp which can contain thousands of entries. Since I can not use the ID to search all active projects, this could result in a quite heavy DB query.
2: Storing the status of the project in db_projects
Each time a project changes it's status, there is a update to the project itself. So the "get all active projects"-query would be much resource friendly, since there are a lot less projects than timestamps. But this would also mean that each time a status change happens, the project entry would be revisioned and therefor would produce "a lot" of overhead. I'm also not sure if the compaction feature would do a good job, since not all revision data is deleted (the documents are, but the leaf revisions not). This means for a state change we have at least the _rev information which is still a string of 34 chars for changing only the status (1 char). Or can I delete the leaf revisions after conflict resolution?
3: Storing the status in a separate DB like db_status
This leads to the same problem as in #2 since status changes lead to revisions on this DB. Or if the states would be added in "only add data"-mode (like in #1), it would just quickly fill with entries.

The general problem is that you have a limited amount of space that you could put into indexedDB. On the other hand the principle of ChouchDB is that storage space is cheap (which it is indeed true when you store on the server side only). Here an interesting discussion about that.
So this is the solution that I use for now. I am using a mix between solution 1 and solution 2 from above with the following additions:
Storing only the timesamps in a synced Database (db_timestamps) with the "only add data" principle.
Storing the projects and their states in a separate local (not
synced) database (db_projects). Therefor I still use pouchDB since
it has a lot simpler API than indexedDB.
Storing the new/changed
project status in each timestamp aswell (so you could rebuild db_projects
out of db_timestams if needed)
Deleting db_projects every so often and repopulate it, so the
revision data (overhead for this db in my case) is eliminated and the size is acceptable.
I use the following code to rebuild my DB:
//--------------------------------------------------------------------
function rebuild_db_project(){
db_project.allDocs({
include_docs: true,
//attachments: true
}).then(function (result) {
// do stuff
console.log('I have read the DB and delete it now...');
deleteDB('db_project', '_pouch_DB_Projekte');
return result;
}).then(function (result) {
console.log('Creating the new DB...'+result);
db_project = new PouchDB('DB_Projekte');
var dbContentArray = [];
for (var row in result.rows) {
delete result.rows[row].doc._rev; //delete the revision of the doc. else it would raise an error on the bulkDocs() operation
dbContentArray.push(result.rows[row].doc);
}
return db_project.bulkDocs(dbContentArray);
}).then(function(response){
console.log('I have successfully populated the DB with: '+JSON.stringify(response));
}).catch(function (err) {
console.log(err);
});
}
//--------------------------------------------------------------------
function deleteDB(PouchDB_Name, IndexedDB_Name){
console.log('DELETE');
new PouchDB(PouchDB_Name).destroy().then(function () {
// database destroyed
console.log("pouchDB destroyed.");
}).catch(function (err) {
// error occurred
});
var DBDeleteRequest = window.indexedDB.deleteDatabase(IndexedDB_Name);
DBDeleteRequest.onerror = function(event) {
console.log("Error deleting database.");
};
DBDeleteRequest.onsuccess = function(event) {
console.log("IndexedDB deleted successfully");
console.log(request.result); // should be null
};
}
So I not only use the pouchDB.destroy() command but also the indexedDB.deleteDatabase() command to get the storage freed nearly completely (there is still some 4kB that are not freed, but this is insignificant to me.)
The timings are not really proper but it works for me. I'm happy if somone has an idea to make the timing work properly (The problem for me is that indexedDB does not support promises).

Which is worse? One more db acess or one outdated value?

PROBLEM
I'm running an application with AngularJS, Node JS, Express and MongoDB. I'm acessing MongoDB trought mongoose. My problem is that I have a lists of swords, each forged by someone. But, to access someone's profile, I need to use the ID of someone. The ID is unique. But I can't display the link as something link '352384b685326vyad6'. So, when someone create's a sword, I will store his or her name within the sword info.
To display a list of swords, for example, I could do something like this:
<div ng-repeat='sword in swords'>
<p> Sword name: {{sword.name}} </p>
<p> Author: <a href='#/user/{{sword.createdByID}}'> {{sword.createdByName}} </a> </p>
</div>
But, if the User changes his name, the sword will not update his creator name accordingly. What should I do? I have thought of some solutions, but I don't know which and if any could solve this in a good manner.
When someone changes own name, I could make a POST request with the new username and the ID, updating all sword that has createdByID equals to user ID. But I see this too strange.
SwordModel.find({ createdByID: req.body.id}, [...]);
When loading the swords in the controller via GET request, make another GET request for each sword and update sword.username based on the sword.createdById.
UserModel.findById(req.body.id), [...]);
Forget UX and use ugly links.
I want to know how can I maintain the username of each sword updated without affecting too much my DB Thanks for any advice.
MODELS - Just for reference.
sword.js
var mongoose = require('mongoose');
var SwordModel = mongoose.model('SwordModel',
{
name: String, //Sword's name
createdById: String, //ID of the user who created.
createdByName: String //Name of the user
});
module.exports = mongoose.model('SwordModel', SwordModel);
user.js
var mongoose = require('mongoose');
var UserModel = mongoose.model('UserModel',
{
name: String, //Name of the user.
ID: String //ID of the user.
});
module.exports = mongoose.model('UserModel', UserModel);

You can do it by referencing the User inside Swords if you make use of Mongoose schema.
After you make a reference to another schema you can use populate method to get the desired results.
Example (May be not exactly, but something similar to following) :
Sword Schema:
var swordSchema = new Schema({
name: String,
createdBy: {type: mongoose.Schema.Types.ObjectId, ref: 'User'}
});
Make model using the schema :
var swordModel = mongoose.model('Sword', swordSchema);
Find what you are looking for using populate.
See full documentation for populate here - http://mongoosejs.com/docs/populate.html
EDIT: note that I am recommending to keep user's name only in User model and just reference it.

I think we have less of a technical problem and more of a conceptual problem here.
Constraints
I take it for granted that...
users may change their usernames
usernames are unique
you only need to provide a link to a user, displayed by name
Furthermore, I will use plain JSON and MongoDB and trust that you can translate this to Mongoose.
The solution
Albeit users can change their usernames, this is not going to happen often. The more common use case is that you need to link to a username. So we first need to find out how to efficiently deal with that use case.
Since you only need the name of the smith for a given sword, there is nothing wrong with a sword model like
{
_id: new ObjectId(),
name: "Libertas",
smith: "Foobar"
}
to efficiently find "Foobar" in the users collection, we simply add an index here (if not already done):
db.users.createIndex({username:1}, {unique:true})
and your service can query efficiently by using
db.users.find({name: "Foobar"})
No need to save the _id of the user within the sword document, but still you can query for it efficiently.
Dealing with a change in the username is a rather rarely executed use case, so optimizing here does not make sense. However, if a user changes his or her username, your service can easily achieve that through
db.swords.update(
{ smith:"Foobar" },
{ $set:{ smith: "CoolNewUsername" },
{ multi: true, writeConcern: { w:1, j:true }
)
The last line of the above needs a little explanation. The multi: true option tells MongoDB to change all documents matching {smith: "Foobar"}, not only the first one found, that's easy to understand. But why to set the write concern to journaled? The first reason for it is that regardless of the write concern configured for the connection (which may even be unacknowledged), we need those changes to be durable. However, we usually do not need to have the changes to be propagated to more replica set members, so the chosen write concern gives you the best performance while still you can be sure that the changes were synced to a disk. If you need higher durability, of course you can set the write concern to {w:2} or {w:"majority"}.
Advantages
For the most common use case of this relationship (displaying a link to the user who forged a given sword), all information needed to do this is included in the sword's document, preventing possibly unnecessary queries.
Still, the smith of a given sword can be queried efficiently if a user clicks said link.
Changing a users name is possible and can be achieved pretty efficiently and durable
Disadvantages
The main disadvantage here is that you actually have to modify all affected swords when a user changes his or her username, whereas with a Mongoose reference that would be unnecessary. However, since this is a rare use case and using populate would result in the whole user document to be loaded where only the users name is needed, I see this disadvantage as negligible. Basically you are trading to cut down the queries needed for a common use case by half against the need for a manual update which occurs rather rarely.
I fail to see any other disadvantage.

How to write denormalized data in Firebase

I've read the Firebase docs on Stucturing Data. Data storage is cheap, but the user's time is not. We should optimize for get operations, and write in multiple places.
So then I might store a list node and a list-index node, with some duplicated data between the two, at very least the list name.
I'm using ES6 and promises in my javascript app to handle the async flow, mainly of fetching a ref key from firebase after the first data push.
let addIndexPromise = new Promise( (resolve, reject) => {
let newRef = ref.child('list-index').push(newItem);
resolve( newRef.key()); // ignore reject() for brevity
});
addIndexPromise.then( key => {
ref.child('list').child(key).set(newItem);
});
How do I make sure the data stays in sync in all places, knowing my app runs only on the client?
For sanity check, I set a setTimeout in my promise and shut my browser before it resolved, and indeed my database was no longer consistent, with an extra index saved without a corresponding list.
Any advice?

Great question. I know of three approaches to this, which I'll list below.
I'll take a slightly different example for this, mostly because it allows me to use more concrete terms in the explanation.
Say we have a chat application, where we store two entities: messages and users. In the screen where we show the messages, we also show the name of the user. So to minimize the number of reads, we store the name of the user with each chat message too.
users
so:209103
name: "Frank van Puffelen"
location: "San Francisco, CA"
questionCount: 12
so:3648524
name: "legolandbridge"
location: "London, Prague, Barcelona"
questionCount: 4
messages
-Jabhsay3487
message: "How to write denormalized data in Firebase"
user: so:3648524
username: "legolandbridge"
-Jabhsay3591
message: "Great question."
user: so:209103
username: "Frank van Puffelen"
-Jabhsay3595
message: "I know of three approaches, which I'll list below."
user: so:209103
username: "Frank van Puffelen"
So we store the primary copy of the user's profile in the users node. In the message we store the uid (so:209103 and so:3648524) so that we can look up the user. But we also store the user's name in the messages, so that we don't have to look this up for each user when we want to display a list of messages.
So now what happens when I go to the Profile page on the chat service and change my name from "Frank van Puffelen" to just "puf".
Transactional update
Performing a transactional update is the one that probably pops to mind of most developers initially. We always want the username in messages to match the name in the corresponding profile.
Using multipath writes (added on 20150925)
Since Firebase 2.3 (for JavaScript) and 2.4 (for Android and iOS), you can achieve atomic updates quite easily by using a single multi-path update:
function renameUser(ref, uid, name) {
var updates = {}; // all paths to be updated and their new values
updates['users/'+uid+'/name'] = name;
var query = ref.child('messages').orderByChild('user').equalTo(uid);
query.once('value', function(snapshot) {
snapshot.forEach(function(messageSnapshot) {
updates['messages/'+messageSnapshot.key()+'/username'] = name;
})
ref.update(updates);
});
}
This will send a single update command to Firebase that updates the user's name in their profile and in each message.
Previous atomic approach
So when the user change's the name in their profile:
var ref = new Firebase('https://mychat.firebaseio.com/');
var uid = "so:209103";
var nameInProfileRef = ref.child('users').child(uid).child('name');
nameInProfileRef.transaction(function(currentName) {
return "puf";
}, function(error, committed, snapshot) {
if (error) {
console.log('Transaction failed abnormally!', error);
} else if (!committed) {
console.log('Transaction aborted by our code.');
} else {
console.log('Name updated in profile, now update it in the messages');
var query = ref.child('messages').orderByChild('user').equalTo(uid);
query.on('child_added', function(messageSnapshot) {
messageSnapshot.ref().update({ username: "puf" });
});
}
console.log("Wilma's data: ", snapshot.val());
}, false /* don't apply the change locally */);
Pretty involved and the astute reader will notice that I cheat in the handling of the messages. First cheat is that I never call off for the listener, but I also don't use a transaction.
If we want to securely do this type of operation from the client, we'd need:
security rules that ensure the names in both places match. But the rules need to allow enough flexibility for them to temporarily be different while we're changing the name. So this turns into a pretty painful two-phase commit scheme.
change all username fields for messages by so:209103 to null (some magic value)
change the name of user so:209103 to 'puf'
change the username in every message by so:209103 that is null to puf.
that query requires an and of two conditions, which Firebase queries don't support. So we'll end up with an extra property uid_plus_name (with value so:209103_puf) that we can query on.
client-side code that handles all these transitions transactionally.
This type of approach makes my head hurt. And usually that means that I'm doing something wrong. But even if it's the right approach, with a head that hurts I'm way more likely to make coding mistakes. So I prefer to look for a simpler solution.
Eventual consistency
Update (20150925): Firebase released a feature to allow atomic writes to multiple paths. This works similar to approach below, but with a single command. See the updated section above to read how this works.
The second approach depends on splitting the user action ("I want to change my name to 'puf'") from the implications of that action ("We need to update the name in profile so:209103 and in every message that has user = so:209103).
I'd handle the rename in a script that we run on a server. The main method would be something like this:
function renameUser(ref, uid, name) {
ref.child('users').child(uid).update({ name: name });
var query = ref.child('messages').orderByChild('user').equalTo(uid);
query.once('value', function(snapshot) {
snapshot.forEach(function(messageSnapshot) {
messageSnapshot.update({ username: name });
})
});
}
Once again I take a few shortcuts here, such as using once('value' (which is in general a bad idea for optimal performance with Firebase). But overall the approach is simpler, at the cost of not having all data completely updated at the same time. But eventually the messages will all be updated to match the new value.
Not caring
The third approach is the simplest of all: in many cases you don't really have to update the duplicated data at all. In the example we've used here, you could say that each message recorded the name as I used it at that time. I didn't change my name until just now, so it makes sense that older messages show the name I used at that time. This applies in many cases where the secondary data is transactional in nature. It doesn't apply everywhere of course, but where it applies "not caring" is the simplest approach of all.
Summary
While the above are just broad descriptions of how you could solve this problem and they are definitely not complete, I find that each time I need to fan out duplicate data it comes back to one of these basic approaches.

To add to Franks great reply, I implemented the eventual consistency approach with a set of Firebase Cloud Functions. The functions get triggered whenever a primary value (eg. users name) gets changed, and then propagate the changes to the denormalized fields.
It is not as fast as a transaction, but for many cases it does not need to be.

How to perform sql "LIKE" operation on firebase?

I am using firebase for data storage. The data structure is like this:
products:{
product1:{
name:"chocolate",
}
product2:{
name:"chochocho",
}
}
I want to perform an auto complete operation for this data, and normally i write the query like this:
"select name from PRODUCTS where productname LIKE '%" + keyword + "%'";
So, for my situation, for example, if user types "cho", i need to bring both "chocolate" and "chochocho" as result. I thought about bringing all data under "products" block, and then do the query at the client, but this may need a lot of memory for a big database. So, how can i perform sql LIKE operation?
Thanks

Update: With the release of Cloud Functions for Firebase, there's another elegant way to do this as well by linking Firebase to Algolia via Functions. The tradeoff here is that the Functions/Algolia is pretty much zero maintenance, but probably at increased cost over roll-your-own in Node.
There are no content searches in Firebase at present. Many of the more common search scenarios, such as searching by attribute will be baked into Firebase as the API continues to expand.
In the meantime, it's certainly possible to grow your own. However, searching is a vast topic (think creating a real-time data store vast), greatly underestimated, and a critical feature of your application--not one you want to ad hoc or even depend on someone like Firebase to provide on your behalf. So it's typically simpler to employ a scalable third party tool to handle indexing, searching, tag/pattern matching, fuzzy logic, weighted rankings, et al.
The Firebase blog features a blog post on indexing with ElasticSearch which outlines a straightforward approach to integrating a quick, but extremely powerful, search engine into your Firebase backend.
Essentially, it's done in two steps. Monitor the data and index it:
var Firebase = require('firebase');
var ElasticClient = require('elasticsearchclient')
// initialize our ElasticSearch API
var client = new ElasticClient({ host: 'localhost', port: 9200 });
// listen for changes to Firebase data
var fb = new Firebase('<INSTANCE>.firebaseio.com/widgets');
fb.on('child_added', createOrUpdateIndex);
fb.on('child_changed', createOrUpdateIndex);
fb.on('child_removed', removeIndex);
function createOrUpdateIndex(snap) {
client.index(this.index, this.type, snap.val(), snap.name())
.on('data', function(data) { console.log('indexed ', snap.name()); })
.on('error', function(err) { /* handle errors */ });
}
function removeIndex(snap) {
client.deleteDocument(this.index, this.type, snap.name(), function(error, data) {
if( error ) console.error('failed to delete', snap.name(), error);
else console.log('deleted', snap.name());
});
}
Query the index when you want to do a search:
<script src="elastic.min.js"></script>
<script src="elastic-jquery-client.min.js"></script>
<script>
ejs.client = ejs.jQueryClient('http://localhost:9200');
client.search({
index: 'firebase',
type: 'widget',
body: ejs.Request().query(ejs.MatchQuery('title', 'foo'))
}, function (error, response) {
// handle response
});
</script>
There's an example, and a third party lib to simplify integration, here.

I believe you can do :
admin
.database()
.ref('/vals')
.orderByChild('name')
.startAt('cho')
.endAt("cho\uf8ff")
.once('value')
.then(c => res.send(c.val()));
this will find vals whose name are starting with cho.
source

The elastic search solution basically binds to add set del and offers a get by wich you can accomplish text searches.
It then saves the contents in mongodb.
While I love and reccomand elastic search for the maturity of the project, the same can be done without another server, using only the firebase database.
That's what I mean:
(https://github.com/metaschema/oxyzen)
for the indexing part basically the function:
JSON stringifies a document.
removes all the property names and JSON to leave only the data
(regex).
removes all xml tags (therefore also html) and attributes (remember
old guidance, "data should not be in xml attributes") to leave only
the pure text if xml or html was present.
removes all special chars and substitute with space (regex)
substitutes all instances of multiple spaces with one space (regex)
splits to spaces and cycles:
for each word adds refs to the document in some index structure in
your db tha basically contains childs named with words with childs
named with an escaped version of "ref/inthedatabase/dockey"
then inserts the document as a normal firebase application would do
in the oxyzen implementation, subsequent updates of the document ACTUALLY reads the index and updates it, removing the words that don't match anymore, and adding the new ones.
subsequent searches of words can directly find documents in the words child. multiple words searches are implemented using hits

SQL"LIKE" operation on firebase is possible
let node = await db.ref('yourPath').orderByChild('yourKey').startAt('!').endAt('SUBSTRING\uf8ff').once('value');

This query work for me, it look like the below statement in MySQL
select * from StoreAds where University Like %ps%;
query = database.getReference().child("StoreAds").orderByChild("University").startAt("ps").endAt("\uf8ff");