For example, we have an iOS application that looks like facebook. In which there is a pullToRefresh and incremental loading and using realm for it.
If you do not delete data from the database, it will grow infinitely. This will affect performance.
When is it worth cleaning bd? And should it be completely cleaned? Or partially? What are the best practices?
best practices with realm?
It's hard to recommend any single best practice since all of this depends on your app, the size of the data it manages, and what would be a good user experience.
It's definitely a good idea to periodically clear out extremely old entries in a local cache database. But deleting a lot of data can also hurt performance, so it's not a good idea to do it too aggressively. There's nothing wrong with having a large local database, if it means it's providing a better user experience. Realm's zero-copy mechanism means it's very efficient, even when there's a lot of content.
I recommend you take a look at your app, and work out how old the data needs to be before the user will absolutely not care about it anymore. Depending on how much data your app downloads, this could be up to a day, a week or a month. Conversely, you could also set a size tolerance, so when the database grows to a size you don't like, you can then make the app start looking at shrinking it.
Since deletes can be time consuming, I'd definitely recommend you do any sort of cleaning in a background thread. You could easily set up a small class that runs an NSOperation periodically (Maybe once a week), queries for the oldest data in your database, and then deletes it.
Related
I am currently implementing a web application in .net core(C#) using entity framework. While working on the project, I actually encountered quite a few challenges but I will start with the one which I think are most important. My questions are as follows:
Instead of frequent loading data from the database, I am having a set of static objects which is a mirror of the data in the database. However, it is tedious and error prone when I want to ensure any changes, i.e., adding/deleting/modifying of objects are being saved to the database at real time. Is there any good example or advice that I can refer to improve my approach to do this?
Another thing is that value of some objects' properties will be changed on the fly according to the value of some other objects' properties. Something like a spreadsheet where a cell's value will be changed automatically if the value in the cell that the formula is referring to changes. I do not have a solution to do this yet. Appreciate if anyone has any example that I can refer to. But this will add another layer of complexity to sync the changes of the objects in memory to database.
At the moment, I am unsure if there is any better approach. Appreciate if anyone can help. Thanks!
Basically, you're facing a problem that's called eventual consistency. Something changes and two or more systems need to be aware at the same time. The problem here is that both changes need to be applied in order to consider the operation successful. If either one fails, you need to know.
In your case, I would use the Azure Service Bus. You can create queues and put messages on a queue. An Azure Function would handle these queue messages. You would create two queues, one for database updates, and one for the in-memory update (I think changing this to a cache service may be something to think off). Now the advantage of these queues is that you can easily drop messages on these queues from anywhere. Because you mentioned the object is going to evolve, you may need to update these objects either in the database or in memory (cache).
Once you've done that, I'd create a topic, with two subscriptions. One forwarding messages to Queue 1, and the other to Queue 2. This will solve your primary problem. In case an object changes, just send it to the topic. Both changes (database and memory) will be executed automagically.
The only problem you have now, it that you mentioned you wanted to update the database in real-time. With this scenario, you're going to have to leave that.
Also, you need to make sure you have proper alerts in place for the queues so in case you did miss a message, or your functions didn't handle it well enough, you'll receive an alert to check & correct errors.
I'm totally agree with #nineedm's and answer, but there are also other solutions.
If you introduce cache, you will always face cache revalidation problem - you have to mark cache as invalid when data were changed. Sometimes it is easy, depending on nature of cached data and how often data are changed.
If you have just single application, MemoryCache can be enough with proper specified expiration options.
If there is a cluster - you have to look at Distributed Cache solutions, for example Redis. There is MS article about that Distributed caching in ASP.NET Core
I am building a rails app, have a site wide counter variable (counter) that is used (read and write) by different pages, basically many pages can cause the counter increment, what is a multi-thread-safe way to store this variable so that
1) it is thread-safe, I may have many concurrent user access might read and write this variable
2) high performing, I originally thought about persist this variable in DB, but wondering is there a better way given there can be high volume of request and I don't want to make this DB query the bottleneck of my app...
suggestions?
It depends whether it has to be perfectly accurate. Assuming not, you can store it in memcached and sync to the database occasionally. If memcached crashes, it expires (shouldn't happen if configured properly), you have to shutdown, etc., reload it from the database on startup.
You could also look at membase. I haven't tried it, but to simplify, it's a distributed memcached server that automatically persists to disk.
For better performance and accuracy, you could look at a sharded approach.
Well you need persistence, so you have to store it in the Database/Session/File, AFAIK.
I am looking for either a sample app or a more architectural discussion to build an app, which maintains a local persistent store (CoreData) and keeps it sync against a Web-Service like Flickr. In my case it is Salesforce, but the pattern should be similar to many apps for Flickr, Twitter, IMAP and so on.
Sample questions:
where are the best points to invoke the syncing?
what are proven datastructures to maintain local changes
- maintain a "changed" BOOL in the local store for every unsynched change; I would prefer a field level flag against a record level flag)?
Of course I have to optimize this on my own, knowing the amount of records (100's) and changes (10's per day) and the probability of conflicts (low in my case on a field level).
Here's how I would approach this:
Start by modeling a local CoreData/Sqlite database that mirrors your online database.
Add an NSDate lastModified property to every row of each table. This will allow me to track changes at the record level, instead of field level. This helps reduce sync complexity, and in most real world scenarios, record level syncing is sufficient.
Perform an automatic sync when the app starts, and also provide a prominent "Sync" button in your navigation bar. This way the user always has an updated dataset when the app launches after a long period, and can sync the latest changes over the course of a day. I would avoid doing background sync while the app is being used. This will make your app more complex and error-prone when you're trying to tackle other things. So postpone working on background/automatic sync until you have the rest working.
Once I have my sync working reasonably well at launch and on-demand, I would try and support background sync. I would also try and eliminate the "Sync" button, so the user never has to think about syncing - (it's always up to date as far as the user is concerned). But this would be a longer term enhancement, which i would attempt only after I have "on-demand" syncing working rock solid.
Hope this helps you get started. I would love to hear if you think differently about any of this.
Caching is something that I kind of ignored for a long time, as projects that I worked on were on local intranets with very little activity. I'm working on a much larger Rails 3 personal project now, and I'm trying to work out what and when I should cache things.
How do people generally determine this?
If I know a site is going to be relatively low-activity, should I just cache every single page?
If I have a page that calls several partials, is it better to do fragment caching in those partials, or page caching on those partials?
The Ruby on Rails guides did a fine job of explaining how caching in Rails 3 works, but I'm having trouble understanding the decision-making process associated with it.
Don't ever cache for the sake of it, cache because there's a need (with the exception of something like the homepage, which you know is going to be super popular.) Launch the site, and either parse your logs or use something like NewRelic to see what's slow. From there, you can work out what's worth caching.
Generally though, if something takes 500ms to complete, you should cache, and if it's over 1 second, you're probably doing too much in the request, and you should farm whatever you're doing to a background process…for example, fetching a Twitter feed, or manipulating images.
EDIT: See apneadiving's answer too, he links to some great screencasts (albeit based on Rails 2, but the theory is the same.)
You'll want to think about caching several kinds of things:
Requests that are hit a lot, and seldom change
Requests that are "expensive" to draw, lots of database calls, etc. Also hopefully these seldom change.
The other side of caching that shouldn't go without mention, is expiration. Its also often the harder part. You have to know when a cache is no longer good, and clear it out so fresh content will be generated. Sweepers, or Observers, depending on how you implement your cache can help you with this. You could also do it just based on a time value, allow caches to have a max-age and clear them after that no matter what.
As for fragment vs full page caching, think of it in terms of how often those parts are updated. If 3 partials of a page are never updated, and one is, maybe you want to cache those 3, and allow that 1 to be fetched live for so you can have up to the second accuracy. Or if the different partials of a page should have different caching rules: maybe a "timeline" section is cached, but has a cache-age of 1 minute. While the "friends" partial is cached for 12 hours.
Hope this helps!
If the site is relatively low activity you shouldn't cache any page. You cache because of performance problems, and performance problems come about because you have too much data to query, too many users, or worse, both of those situations at the same time.
Before you even think about caching, the first thing you do is look through your application for the requests that are taking up the most time. Not the slowest requests, but the requests your application spends the most aggregate time performing. That is if you have a request A that runs 10 times at 1500ms and request B that runs 5000 times at 250ms you work on optimizing B first.
It's actually pretty easy to grep through your production.log and extract rendering times and URLs to combine them into a simple report. You can even do that in real-time if you want.
Once you've identified a problematic request, you go about picking apart what it's doing to service the request. The first thing is to look for any queries that can be combined by using eager loading or by looking ahead a bit more to anticipate what you'll need. The next thing is to ensure you're not loading data that isn't used.
So many times you'll see code to list users and it's loading 50KB per person of biographical data, their Facebook and Twitter handles, literally everything about them, and all you use is their name.
Fetch as little as you need, and fetch it in the most efficient way you can. Use connection.select_rows when you don't need models.
The next step is to look at what kind of queries you're running, and how they're under-performing. Ensure your indexes are all set properly and are being used. Check that you're not doing complicated JOIN operations that could be resolved by a bit of tactical de-normalization.
Have a look at what data you are storing in your application, and try and find things that can be removed from your production database and warehoused somewhere else. Cycle your data out regularly when it's no longer relevant, preserve it in a separate database if you need to.
Then go over and have a look at how your database server is tuned. Does it have sufficiently large buffers? Is it on hardware that could be upgraded with more memory at a nominal cost? Too many people are running a completely un-tuned database server and with a few simple settings they can get ten-fold performance increases.
If, and only if, you still have a performance problem at this point then you might want to consider caching.
You know why you don't cache first? It's because once you cache something, that cached data is immediately stale. If parts of your application use this data under the assumption it's always up to date, you will have problems. If you don't expire this cache when the data does change, you will have problems. If you cache the data and never use it again, you're just clogging up your cache and you will have problems. Basically you'll have lots of problems when you use caching, so it's often a last resort.
A bit of backstory: I am working on an web application that requires quite a bit of time to prep / crunch data before giving it to the user to edit / manipulate. The data request task ~ 15 / 20 secs to complete and a couple secs to process. Once there, the user can manipulate vaules on the fly. Any manipulation of values will require the data to be reprocessed completely.
Update: To avoid confusion, I am only making the data call 1 time (the 15 sec hit) and then wanting to keep the results in memory so that I will not have to call it again until the user is 100% done working with it. So, the first pull will take a while, but, using Ajax, I am going to hit the in-memory data to constantly update and keep the response time to around 2 secs or so (I hope).
In order to make this efficient, I am moving the intial data into memory and using Ajax calls back to the server so that I can reduce processing time to handle the recalculation that occurs w/ this user's updates.
Here is my question, with performance in mind, what would be the best way to storing this data, assuming that only 1 user will be working w/ this data at any given moment.
Also, the user could potentially be working in this process for a few hours. When the user is working w/ the data, I will need some kind of failsafe to save the user's current data (either in a db or in a serialized binary file) should their session be interrupted in some way. In other words, I will need a solution that has an appropriate hook to allow me to dump out the memory object's data in the case that the user gets disconnected / distracted for too long.
So far, here are my musings:
Session State - Pros: Locked to one user. Has the Session End event which will meet my failsafe requirements. Cons: Slowest perf of the my current options. The Session End event is sometimes tricky to ensure it fires properly.
Caching - Pros: Good Perf. Has access to dependencies which could be a bonus later down the line but not really useful in current scope. Cons: No easy failsafe step other than a write based on time intervals. Global in scope - will have to ensure that users do not collide w/ each other's work.
Static - Pros: Best Perf. Easies to maintain as I can directly leverage my current class structures. Cons: No easy failsafe step other than a write based on time intervals. Global in scope - will have to ensure that users do not collide w/ each other's work.
Does anyone have any suggestions / comments on what I option I should choose?
Thanks!
Update: Forgot to mention, I am using VB.Net, Asp.Net, and Sql Server 2005 to perform this task.
I'll vote for secret option #4: use the database for this. If you're talking about a 20+ second turnaround time on the data, you are not going to gain anything by trying to do this in-memory, given the limitations of the options you presented. You might as well set this up in the database (give it a table of its own, or even a separate database if the requirements are that large).
I'd go with the caching method of for storing the data across any page loads. You can name the cache you want to store the data in to avoid conflicts.
For tracking user-made changes, I'd go with a more old-school approach: append to a text file each time the user makes a change and then sweep that file at intervals to save changes back to DB. If you name the files based on the user/account or some other session-unique indicator then there's no issue with conflict and the app (or some other support app, which might be a better idea in general) can sweep through all such files and update the DB even if the session is over.
The first part of this can be adjusted to stagger the write out more: save changes to Session, then write that to file at intervals, then sweep the file at larger intervals. you can tune it to performance and choose what level of possible user-change loss will be possible.
Use the Session, but don't rely on it.
Simply, let the user "name" the dataset, and make a point of actively persisting it for the user, either automatically, or through something as simple as a "save" button.
You can not rely on the session simply because it is (typically) tied to the users browser instance. If they accidentally close the browser (click the X button, their PC crashes, etc.), then they lose all of their work. Which would be nasty.
Once the user has that kind of control over the "persistent" state of the data, you can rely on the Session to keep it in memory and leverage that as a cache.
I think you've pretty much just answered your question with the pros/cons. But if you are looking for some peer validation, my vote is for the Session. Although the performance is slower (do you know by how much slower?), your processing is going to take a long time regardless. Do you think the user will know the difference between 15 seconds and 17 seconds? Both are "forever" in web terms, so go with the one that seems easiest to implement.
perhaps a bit off topic. I'd recommend putting those long processing calls in asynchronous (not to be confused with AJAX's asynchronous) pages.
Take a look at this article and ping me back if it doesn't make sense.
http://msdn.microsoft.com/en-us/magazine/cc163725.aspx
I suggest to create a copy of the data in a new database table (let's call it EDIT) as you send the initial results to the user. If performance is an issue, do this in a background thread.
As the user edits the data, update the table (also in a background thread if performance becomes an issue). If you have to use threads, you must make sure that the first thread is finished before you start updating the rows.
This allows a user to walk away, come back, even restart the browser and commit whenever she feels satisfied with the result.
One possible alternative to what the others mentioned, is to store the data on the client.
Assuming the dataset is not too large, and the code that manipulates it can be handled client side. You could store the data as an XML data island or JSON object. This data could then be manipulated/processed and handled all client side with no round trips to the server. If you need to persist this data back to the server the end resulting data could be posted via an AJAX or standard postback.
If this does not work with your requirements I'd go with just storing it on the SQL server as the other comment suggested.