I want to declare a winner of a game when ever its time expires.
is there any functionality in parse to perform an action on a specific time.
Qaiser,
As far as I am aware, there is no way to use a timer in the CloudCode on Parse. They have a 3 second timeout on cloud operations. I am assuming from your question statement that the time expires on a client device and then you want to use Parse to send a message to all other participants of the game that the time has expired and a winner has been named.
Personally, I would use a single timer in the "Leader" of the game to update "gameOver" and "winner" keys within a "game" object that holds all relevant data for your game. However, this structure is subject to the moving pieces within your specific game (without more information, I cannot suggest an alternative). In each client, I would use a timer to request an update of the "game" object every couple of seconds to keep non-leader users up to date. Once those fields have been set, then their clients know that the game has ended and a winner has been named.
I am unaware of any functionality in Parse to implement a model-observer scheme where Parse would automatically send an update to every observing client. If this is key to your game, then perhaps looking at Firebase would be helpful because that is integral to the way that Firebase structures its database scheme. However, the system is slightly more rudimentary and hierarchal than Parse, so it might not suit the rest of your application quite as well.
Hope this helps!
Related
I am working on one chatroom [all to all] application in Elixir using OTP Genserver and getting messages from js client as user gets registered with their names as first phase. Now, just bit not sure what would be the best approach to store these names at my elixir server somehow and send regular updates to client with list of users online or database storage. Please suggest the best approach.
I agree with bitwalker that ETS is a good fit.
Here's a short summary of what I did in production. It wasn't a chat server, but a server push with a couple of thousand of users connecting via long polling. Pushed data was divided in some 50 categories, and users were able to choose which ones they want. At peak times the server pushed new messages each 2 secs, and processed > 2000 reqs/sec.
Essentially, I kept a gen_server for each user, where I held pending messages and user's configuration (basically a list of selected channels). This was beneficial with long polling, since user's data is decoupled from the user's request, so the data remains while requests are transient. However, I think this approach is also good for permanent connections, such as websockets, since there might still be occasional disconnections, and keeping a more stable user's data gives you a chance of resuming after reconnect.
Obviously, when a request arrives, you need to find the user specific process, and for this, ETS is a good fit, since you don't have a single process bottleneck. Instead of manually working with ETS, I'd recommend using gproc in conjunction with via tuples. Basically, when starting a user's gen_server, you can provide name: {:via, :gproc, {:n, :l, key}} where key is some custom key (arbitrary term) you make based on your internal user's id(:n and :l indicate a unique name on the local node). You can then use that same via tuple when issuing calls/casts, and gen_server will use gproc to find the corresponding process.
Finally, you need to have some timeout/disconnect logic to cleanup user processes. In my case, I simply terminated a user's process if there was no activity from the web layer (no end-user came for data in some time). Gproc will automatically remove entries for terminated process from its internal ETS table. It's probably best to supervise user processes under a temporary strategy.
I realize all of this is still a bit vague, but I hope it makes some sense. Keep in mind that this is not the ultimate pattern (there's no such thing of course), but I think it's a reasonable first attempt.
You may also want to take a look at Phoenix web framework that has an interesting pub-sub facility in form ofTopics. I didn't try this out myself yet, but it seems interesting, and may even simplify some of the stuff I discussed above, or at least help for pushing notifications from chatroom to all users.
Sounds like a good use case for ETS.
A simpler approach might be to use an Agent to store the online users information, but it depends quite a lot on what you need from the storage mechanism you choose.
I am using HP Project and Portfolio Management(PPM) tool and I am adding a custom field in my request type which has date as value. Now my requirement is to send the email notifications to the users once the date mentioned in the custom field crosses the system date.
I had tried to set the notification for field level from Notification tab but not getting the custom fields in the list. All the fields, which is available, are pre-configured fields.
So, can anyone suggest me how to implement this requirement? And also where the changes need to be done If any required to implement this?
Please answer in detail and also reply soon.
Thanks in advance!!
PPM notifications can be configured on pre-defined events, like a certain transition, or timeout etc...
One possible solution for this scenario is to have timeouts on your decision step. Time out goes to an execution step, which checks for the date condition. If the condition is met it fires the notification. Else it just returns to the original decision step.
The downside of this work around is that there will be transaction details added once daily. Also the last update date of the request keeps updating daily, which is not ideal if you want to track what was the last time an end user update the request.
It is a workaround cause of the restrictions around notification events.
And if you have a large workflow, I would not recommend this work around.
After my user logs in the app pulls down a list of their meetings and adds them as UILocalNotification.
Each time the user launches the app and logs in this happens.
Which of the following is better "practice" when it comes to doing this over and over:
Pull all the location notifications with scheduledLocalNotifications then as I cycle through the downloaded meetings check to see if a local notification already exist for that meeting. If so, don't add it.
Use cancelAllLocalNotifications to clear all the local notifications and add them over.
It seems that #1 would be the right way but I'm concerned that if I have a meeting at the same time as another (it happens), it will view it as already added and won't add the new one.
Personally I would pull down the locations and check through them. It just feels tidier to me rather than destroying everything and creating it all again. Then again, #2 might be (negligibly) faster as you don't need to loop and check.
If you're concerned about #1 not matching where two meetings have the same time, you could add your own unique identifier to the UILocalNotification object via the userInfo property, and query this to see if the meeting is the same event, rather than basing it on the time of the notification.
If you have an unique token, say a UUID, for each meeting, the double-booking situation can be avoided. Put this token in the userInfo dictionary for the notification and then you can see precisely which meeting the notification is for. Comparison to determine the if this meeting has a notification queued already would best be done by this unique token, making sure the time of the meeting did not change.
I'm trying to decouple some of the bits of our big-ball-of-mud architecture, and identified several boundaries that are obvious candidates for using CQRS to provide a more resilient and scalable solution.
Typical example: when a customer places an order, at the moment we block their thread whilst the order is submitted for payment, approved by the sales system, etc, etc.
This can all be handled asynchronously - allowing us to accept and queue orders whilst the payment processing system is unavailable, etc. - but I'm not sure how I should manage the UI data for the customer.
In other words - they place an order. Their order goes in a queue. If they log back into their account five seconds later and click "review orders" - what happens?
If I draw it from the central repo (or from a cache that's updated based on that repo), then the user won't see their order and will probably try and place it again - or phone us and panic.
If I draw it from a local database, then I have the overhead of maintaining another database of orders - which will need to be synchronised in a load-balanced environment, and seems to undermine a lot of the advantages of CQRS.
I want to do this in lots of places - and not all of them are actions as significant as confirming an order; in some cases it's as simple as a customer changing a phone number or something - so they're not all cases where I can just say "thanks a lot, we'll send you a confirmation e-mail" - because sending confirmation e-mails for every modification to a record strikes me as a little excessive.
Any patterns or solutions I should look at to help with this?
Something worth considering is a 'user' inbox: a place in your app the user can consult 'in-progress' commands at. You could also 'push' notifications back to the user's UI when he has already moved onto another screen, but still resides in your app. This might also be an option when the user logs back on.
Another option could be faking the synchronous experience, i.e. wait around and do polling while in the background everything happens asynchronously. Granted, this might involve including timeouts as well, but I'd argue that those are embraced in today's synchronous processing as well.
On top of all this, you may want to both inform and solicit feedback from your end users about how they experience your app and its behavior.
Regardless what anybody tells you, if you want to handle this elegantly, it will take some effort on your part.
The best thing to do is lie!
The user should have no idea that their transaction is in fact a little like Schrödinger's cat, either dead or alive. From their perspective the transaction was a success, because you just indicate to them that it was successful and queue the job away for offline processing.
Because the vast majority of transactions are successful you can then handle those that are not with an appropriate compensationary mechanism.
Insignificant cases, like modification of some record:
Send the user to a confirmation page telling him something around the lines of "Thanks, your input is being processed. What do you want to do next?" and a couple of links.
If you absolutely have to send the user back to the edited record or a list thereof, in non-distributed systems we're probably talking about milliseconds until the read store has been updated. As long as it takes longer to redirect the user to the new page, from the user's POV everything's fine.
If in some cases the user actually doesn't see his update "immediately", he might call user support. They tell him to hit F5. What? It's there now? Great! Guess what he does next time before reaching for the phone.
Significant cases like offline order processing:
There might be an implicit concept of a Received Order or Pending Order in your domain. If you make this concept explicit, you can present the user with accurate information.
"Thank you very much! Your order has been received an we'll keep you updated once it has been shipped. [Click here] to see a list of your pending orders..."
I think the simplest thing, doing nothing, can often be good enough. If user changes phone number, and the system processes this command in 1-2s, it is a good chance user has not had the opportunity to see old data in-between this operation.
If that is not satisfactory, and your user must absolutely know that his request was fulfilled, your UI can subscribe to domain events. Once the command is executed successfully, your UI gets notification and can inform the user. There are various ways you could do this in UI. You could simply block until the success notification arrives. Or you can say "we received your request", and once you get confirmation, show the notification window "your request was fulfilled" somewhere in the corner.
Currently I am developing an API and within that API I want the signed in users to be able to like/unlike or favorite/unfavorite two resources.
My "Like" model (it's a Ruby on Rails 3 application) is polymorphic and belongs to two different resources:
/api/v1/resource-a/:id/likes
and
/api/v1/resource-a/:resource_a_id/resource-b/:id/likes
The thing is: I am in doubt what way to choose to make my resources as RESTful as possible. I already tried the next two ways to implement like/unlike structure in my URL's:
Case A: (like/unlike being the member of the "resource")
PUT /api/v1/resource/:id/like maps to Api::V1::ResourceController#like
PUT /api/v1/resource/:id/unlike maps to Api::V1::ResourceController#unlike
and case B: ("likes" is a resource on it's own)
POST /api/v1/resource/:id/likes maps to Api::V1::LikesController#create
DELETE /api/v1/resource/:id/likes maps to Api::V1::LikesController#destroy
In both cases I already have a user session, so I don't have to mention the id of the corresponding "like"-record when deleting/"unliking".
I would like to know how you guys have implemented such cases!
Update April 15th, 2011: With "session" I mean HTTP Basic Authentication header being sent with each request and providing encrypted username:password combination.
I think the fact that you're maintaining application state on the server (user session that contains the user id) is one of the problems here. It's making this a lot more difficult than it needs to be and it's breaking a REST's statelessness constraint.
In Case A, you've given URIs to operations, which again is not RESTful. URIs identify resources and state transitions should be performed using a uniform interface that is common to all resources. I think Case B is a lot better in this respect.
So, with these two things in mind, I'd propose something like:
PUT /api/v1/resource/:id/likes/:userid
DELETE /api/v1/resource/:id/likes/:userid
We also have the added benefit that a user can only register one 'Like' (they can repeat that 'Like' as many times as they like, and since the PUT is idempotent it has the same result no matter how many times it's performed). DELETE is also idempotent, so if an 'Unlike' operation is repeated many times for some reason then the system remains in a consistent state. Of course you can implement POST in this way, but if we use PUT and DELETE we can see that the rules associated with these verbs seem to fit our use-case really well.
I can also imagine another useful request:
GET /api/v1/resource/:id/likes/:userid
That would return details of a 'Like', such as the date it was made or the ordinal (i.e. 'This was the 50th like!').
case B is better, and here have a good sample from GitHub API.
Star a repo
PUT /user/starred/:owner/:repo
Unstar a repo
DELETE /user/starred/:owner/:repo
You are in effect defining a "like" resource, a fact that a user resource likes some other resource in your system. So in REST, you'll need to pick a resource name scheme that uniquely identifies this fact. I'd suggest (using songs as the example):
/like/user/{user-id}/song/{song-id}
Then PUT establishes a liking, and DELETE removes it. GET of course finds out if someone likes a particular song. And you could define GET /like/user/{user-id} to see a list of the songs a particular user likes, and GET /like/song/{song-id} to see a list of the users who like a particular song.
If you assume the user name is established by the existing session, as #joelittlejohn points out, and is not part of the like resource name, then you're violating REST's statelessness constraint and you lose some very important advantages. For instance, a user can only get their own likes, not their friends' likes. Also, it breaks HTTP caching, because one user's likes are indistinguishable from another's.