I have an asp.net-mvc website and people manage a list of projects. Based on some algorithm, I can tell if a project is out of date. When a user logs in, i want it to show the number of stale projects (similar to when i see a number of updates in the inbox).
The algorithm to calculate stale projects is kind of slow so if everytime a user logs in, i have to:
Run a query for all project where they are the owner
Run the IsStale() algorithm
Display the count where IsStale = true
My guess is that will be real slow. Also, on everything project write, i would have to recalculate the above to see if changed.
Another idea i had was to create a table and run a job everything minutes to calculate stale projects and store the latest count in this metrics table. Then just query that when users log in. The issue there is I still have to keep that table in sync and if it only recalcs once every minute, if people update projects, it won't change the value until after a minute.
Any idea for a fast, scalable way to support this inbox concept to alert users of number of items to review ??
The first step is always proper requirement analysis. Let's assume I'm a Project Manager. I log in to the system and it displays my only project as on time. A developer comes to my office an tells me there is a delay in his activity. I select the developer's activity and change its duration. The system still displays my project as on time, so I happily leave work.
How do you think I would feel if I receive a phone call at 3:00 AM from the client asking me for an explanation of why the project is no longer on time? Obviously, quite surprised, because the system didn't warn me in any way. Why did that happen? Because I had to wait 30 seconds (why not only 1 second?) for the next run of a scheduled job to update the project status.
That just can't be a solution. A warning must be sent immediately to the user, even if it takes 30 seconds to run the IsStale() process. Show the user a loading... image or anything else, but make sure the user has accurate data.
Now, regarding the implementation, nothing can be done to run away from the previous issue: you will have to run that process when something that affects some due date changes. However, what you can do is not unnecessarily run that process. For example, you mentioned that you could run it whenever the user logs in. What if 2 or more users log in and see the same project and don't change anything? It would be unnecessary to run the process twice.
Whatsmore, if you make sure the process is run when the user updates the project, you won't need to run the process at any other time. In conclusion, this schema has the following advantages and disadvantages compared to the "polling" solution:
Advantages
No scheduled job
No unneeded process runs (this is arguable because you could set a dirty flag on the project and only run it if it is true)
No unneeded queries of the dirty value
The user will always be informed of the current and real state of the project (which is by far, the most important item to address in any solution provided)
Disadvantages
If a user updates a project and then upates it again in a matter of seconds the process would be run twice (in the polling schema the process might not even be run once in that period, depending on the frequency it has been scheduled)
The user who updates the project will have to wait for the process to finish
Changing to how you implement the notification system in a similar way to StackOverflow, that's quite a different question. I guess you have a many-to-many relationship with users and projects. The simplest solution would be adding a single attribute to the relationship between those entities (the middle table):
Cardinalities: A user has many projects. A project has many users
That way when you run the process you should update each user's Has_pending_notifications with the new result. For example, if a user updates a project and it is no longer on time then you should set to true all users Has_pending_notifications field so that they're aware of the situation. Similarly, set it to false when the project is on time (I understand you just want to make sure the notifications are displayed when the project is no longer on time).
Taking StackOverflow's example, when a user reads a notification you should set the flag to false. Make sure you don't use timestamps to guess if a user has read a notification: logging in doesn't mean reading notifications.
Finally, if the notification itself is complex enough, you can move it away from the relationship between users and projects and go for something like this:
Cardinalities: A user has many projects. A project has many users. A user has many notifications. A notifications has one user. A project has many notifications. A notification has one project.
I hope something I've said has made sense, or give you some other better idea :)
You can do as follows:
To each user record add a datetime field sayng the last time the slow computation was done. Call it LastDate.
To each project add a boolean to say if it has to be listed. Call it: Selected
When you run the Slow procedure set you update the Selected fileds
Now when the user logs if LastDate is enough close to now you use the results of the last slow computation and just take all project with Selected true. Otherwise yourun again the slow computation.
The above procedure is optimal, becuase it re-compute the slow procedure ONLY IF ACTUALLY NEEDED, while running a procedure at fixed intervals of time...has the risk of wasting time because maybe the user will neber use the result of a computation.
Make a field "stale".
Run a SQL statement that updates stale=1 with all records where stale=0 AND (that algorithm returns true).
Then run a SQL statement that selects all records where stale=1.
The reason this will work fast is because SQL parsers, like PHP, shouldn't do the second half of the AND statement if the first half returns true, making it a very fast run through the whole list, checking all the records, trying to make them stale IF NOT already stale. If it's already stale, the algorithm won't be executed, saving you time. If it's not, the algorithm will be run to see if it's become stale, and then stale will be set to 1.
The second query then just returns all the stale records where stale=1.
You can do this:
In the database change the timestamp every time a project is accessed by the user.
When the user logs in, pull all their projects. Check the timestamp and compare it with with today's date, if it's older than n-days, add it to the stale list. I don't believe that comparing dates will result in any slow logic.
I think the fundamental questions need to be resolved before you think about databases and code. The primary of these is: "Why is IsStale() slow?"
From comments elsewhere it is clear that the concept that this is slow is non-negotiable. Is this computation out of your hands? Are the results resistant to caching? What level of change triggers the re-computation.
Having written scheduling systems in the past, there are two types of changes: those that can happen within the slack and those that cause cascading schedule changes. Likewise, there are two types of rebuilds: total and local. Total rebuilds are obvious; local rebuilds try to minimize "damage" to other scheduled resources.
Here is the crux of the matter: if you have total rebuild on every update, you could be looking at 30 minute lags from the time of the change to the time that the schedule is stable. (I'm basing this on my experience with an ERP system's rebuild time with a very complex workload).
If the reality of your system is that such tasks take 30 minutes, having a design goal of instant gratification for your users is contrary to the ground truth of the matter. However, you may be able to detect schedule inconsistency far faster than the rebuild. In that case you could show the user "schedule has been overrun, recomputing new end times" or something similar... but I suspect that if you have a lot of schedule changes being entered by different users at the same time the system would degrade into one continuous display of that notice. However, you at least gain the advantage that you could batch changes happening over a period of time for the next rebuild.
It is for this reason that most of the scheduling problems I have seen don't actually do real time re-computations. In the context of the ERP situation there is a schedule master who is responsible for the scheduling of the shop floor and any changes get funneled through them. The "master" schedule was regenerated prior to each shift (shifts were 12 hours, so twice a day) and during the shift delays were worked in via "local" modifications that did not shuffle the master schedule until the next 12 hour block.
In a much simpler situation (software design) the schedule was updated once a day in response to the day's progress reporting. Bad news was delivered during the next morning's scrum, along with the updated schedule.
Making a long story short, I'm thinking that perhaps this is an "unask the question" moment, where the assumption needs to be challenged. If the re-computation is large enough that continuous updates are impractical, then aligning expectations with reality is in order. Either the algorithm needs work (optimizing for local changes), the hardware farm needs expansion or the timing of expectations of "truth" needs to be recalibrated.
A more refined answer would frankly require more details than "just assume an expensive process" because the proper points of attack on that process are impossible to know.
Related
We are having an issue where we need event relations for people(s), and are having problems with this very large group of people having almost 400 total event relations in this one week we are testing on... When trying to grab this large groups event relations, it will take forever and possibly time out. However, if you try again right after a timeout it goes in a couple seconds and is great. I was thinking this was salesforce just chaching the soql query/information and so it could act very quickly the second time. I tried to kind of trick it into having this query cached and ready by having a batch job that ran regularly to query every members event relations so when they tried to access our app the timeout issue would stop.
However, this is not even appearing to work. Even though the batch is running correctly and querying all these event relations, when you go to the app after a while without using it, it will still timeout or take very long the first time then be very quick after that.
Is there a way to successfully keep this cached so it will run very quickly when a user goes and tries to see all the event relations of a large group of people? With the developer console we saw that the event relation query was the huge time suck in the code and the real issue. I have been kind of looking into the Platform Cache of salesforce. Would storing this data there provide the solution I am looking for?
You should look into updating your query to be selective by using indexes in the where cause and custom indexes if necessary.
I'm creating an app on GAE with Java, and looking for advice on how to handle scheduling user notifications (which will be email, text, push, whatever). There are a couple ways notifications will generated: when a producer creates content, and on a consumer's schedule. The later is the tricky part, because a consumer can change its schedule at any time. Here are the options I have considered and my concerns so far:
Keep an entry in the datastore for each consumer, indexed by the time until the next notification. My concern is over the lag for an eventually-consistent index. The longest lag I've seen reported is about 4 hours, which would be unacceptable for this use-case. A user should not delay their schedule by a week, then 4 hours later receive a notification from the old schedule.
The same as above, but with each entry sharing a common parent so that I can use an ancestor query to eliminate its eventual-ness. My concern is that there could be enough consumers to cause a problem with contention. In my wildest dreams I could foresee something like 10,000 schedule changes per minute at peak usage.
Schedule a task for each consumer. When changing the schedule, it could delete the old task and create a new one at the new time. My concern has to do with the interaction of tasks and datastore transactions, since the schedule will be stored in the datastore. The documentation notes that enqueing a task plays nicely with transactions, but what about deleting one? I would not want a task to be deleted only to have the add fail as part of its transaction.
Edit: I experimented with deleting tasks (for option 3), and unfortunately a delete that is part of a failed transaction still succeeds. That is a disappointing asymmetry. I will probably end up going that route anyway, but adding some extra logic and datastore flags to ensure rogue tasks that didn't get deleted properly simply do nothing when they execute.
Eventual consistency in the Datastore typically measures in seconds. As Google states:
the time delay is typically small, but may be longer (even minutes or
more in exceptional circumstances).
Save a time of next notification for each user. Run a cron job periodically (e.g. once per hour), and send notifications to all users who have to be notified at this time (i.e. now >= next notification).
Create a task for each user when a user's schedule is created with the countdown value. When a task executes, it creates the next task for this user.
The first approach is probably more efficient, especially if you choose a large enough window for your cron job.
As for transactions, I don't see why you need them. You can design your system that in the very rare fail situation a user will receive two notifications instead of one (old schedule and new schedule). This is not such a bad thing that you need to design around it.
I am developing a web-app right now, where clients will frequently (every few seconds), send read/write requests on certain data. As of right now, I have my server immediately write to the database when a user changes something, and immediately read from the database when they want to view something. This is working fine for me, but I am guessing that it would be quite slow if there were thousands of users online.
Would it be more efficient to save write requests in an object on the server side, then do a bulk update at a certain time interval? This would help in situations where the same data is edited multiple times, since it would now only require one db insert. It would also mean that I would read from the object for any data that hasn't yet been synced, which could mean increased efficiency by avoiding db reads. At the same time though, I feel like this would be a liability for two reasons: 1. A server crash would erase all data that hasn't yet been synced. 2. A bulk insert has the possibility of creating sudden spikes of lag due to mass database calls.
How should I approach this? Is my current approach ok, or should I queue inserts for a later time?
If a user makes a change to data and takes an action that (s)he expects will save the data, you should do everything you can to ensure the data is actually saved. Example: Let's say you delay the write for a while. The user is in a hurry, makes a change then closes the browser. If you don't save right when they take an action that they expect saves the data, there would be a data loss.
Web stacks generally scale horizontally. Don't start to optimize this kind of thing unless there's evidence that you really have to.
I am facing two options of how to update the database, and do not know which one is better for my situation. There are three tables in the database, which are used to read/store some user's information, such the url history or some inputs.
In real time, the database is accessible by users all the time, so the changes made to the database can be seen immediately by that user.
The batch processing hides the "update" from user, database is updated by parsing the log files, and such a process runs every X hours. So user can only see their changes after X hours.
Apart from the advantage/disadvantage of synchronized/asynchronized updates that user can see. What are the other benefits of choosing real-time or batch processing updating methods for database updating?
Thanks
It all depends on the amount of traffic you expect. If you want to scale your application, asynchronous processing is always recommended. But that does not mean that your users have to wait for X hours. You can have the process run every 5 minutes or even every minute.
This way you will reduce concurrency issues and at the same time users will be able to see their updated history with a little bit of delay.
See best practices for scalability in the book Scalability Rules
I would suggest you use EDA (Event Driven Architecture) which uses a middleware to
"glue" all of this.
http://searchsoa.techtarget.com/definition/event-driven-architecture
One advice : Keep away from batch processes.
Today, everything tends to be more and more real-time. Imagine if you would receive my answer in X hours... would you be satisfied? :)
If you give us more Info, we could also help you more.
I see that your input comes from a log file? Can this be changed?
You could also implement the observer pattern.
This is an alternative approach to solving this problem, not a duplicate.
I want my Azure role to reprocess data in case of sudden failures. I consider the following option.
For every block of data to process I have a database table row and I could add a column meaning "time of last ping from a processing node". So when a node grabs a data block for processing it sets "processing" state and that time to "current time" and then it's the node responsibility to update that time say every one minute. Then periodically some node will ask for "all blocks that have processing state and ping time larger than ten minutes" and consider those blocks as abandoned and somehow queue them for reprocessing.
I have one very serious concern. The above approach requires that nodes have more or less the same time. Looks like I should not make assumptions about global time.
But all nodes talk to the very same database. What if I use that database time - with functions like GETUTCDATE() in SQL requests I will do exactly the same thing as I planned, but not I seem to not care whether nodes time is in sync - they will all use the database time.
Will this approach work reliably if I use the database time functions?
As a general rule this approach should work. If there is only one source for the current time then time synchronisation issues are not going to affect you.
But you have to consider what happens if your database server goes down. Azure will switch you over to another copy, on another server, in another rack and this database server is not guaranteed to be synchronised at least with regard to time with the original one.
Also this approach will undoubtably get you in trouble if ever you have to scale out your database.
I think i still prefer the Queue based approach.