I want to implement a views counter like most forums, Youtube and several others have. So every time a user reads an article, that is stored and remembered. I also want to know who looked at the article.
My queston is: How do you implement this efficiently? What is the best practice?
One way would be to call a stored procedure for every view, but that would result in a lot of unneeded calls to the database.
Another way would be to store this to some global application object, and then store in DB every 5 minutes or so (and can you even do that in a good way?)
What's the best way to do this?
Database operations are surprisingly cheap and really are not worth worrying about. In the event that a DB operation was even marginally expensive then you can always delegate the blocking operation to a new thread thus freeing-up your page-generation thread (you can trivially do this for UPDATE and INSERT operations that return nothing from the database - they are inconsequential).
Sprocs aren't really in-fashion right now - the performance advantage they might have had from pre-computed execution plans is almost eliminated because modern servers cache plans from all previous queries, and for trivial SELECT, INSERT, and UPDATEs you begin to suffer from increased code complexity. There's nothing wrong with inline SQL commands now.
Anyway, back on-topic and in summary: your assumptions are wrong. There is nothing wrong with running UPDATE Pages SET ViewCount = ViewCount + 1 WHERE PageId = #pageId on every page-view. There is also nothing wrong with doing this either: INSERT INTO UserPageviews (UserId, PageId, DateTime) VALUES ( #userId, #pageId, NOW() ). Both operations are very cheap and will execute in under 2-3 miliseconds on even an old and aged database server.
Another way would be to store this to some global application object,
and then store in DB every 5 minutes or so (and can you even do that
in a good way?)
This method is very prone to data loss unless you use a durable queueing mechanism (like MSMQ). Unless you anticipate massive traffic, I wouldn't even think about this approach.
Writes of this nature are inexpensive and hundreds of operations per second are not a big deal. I recently built a comment/rating framework that acheives throughput of 3000+ complete transactions per second just on my local all-in-one workstation. This included processing the request, validation, and creating multiple records within a transaction.
As a note, you should take steps to ensure that your statistics data isn't vulnerable to artificial inflation/manipulation. This part of the process will probably be more complex than the view tracking itself. For example, a user should not be able to sit and hold down the F5 key and inflate the number of views on their video. Nor should these values be manipulable by HTTP (e.g. creating a small script to send an AJAX request over and over).
This suggests that each INSERT would be preceded by a SELECT to ensure that the same user ID or IP hadn't already been recorded in some period of time. Of course, this isn't foolproof (unless you invest a great deal of effort), but it errs on the side of conservatism which is usually a good approach.
One way would be to call a stored procedure for every view, but that
would result in a lot of unneeded calls to the database.
I regularly have to remind myself (and other developers) to not fear the database. People (me included) sometimes go to great lengths to avoid a few simple database calls. Keep your tables narrow and well-indexed, and operations like this are faster than you might think.
Related
Say I need to populate 4 or 5 dropdowns w/ items from a database. Each drop down will have < 15 items in it. These items almost never change.
Now I could query the DB each time the page is accessed or I could grab the values from a custom class that would check to see if they already exist in ASP.Net's cache and only if they don't query the DB to update the cache.
It's trivial for me to write but I'm unsure if the performace would be better or not. I think it would be (although not likely anything huge).
What do you think?
When dealing with performance issues you should always:
Do things the simplest way first (avoid premature optimisation)
Performance test your code with set performance goals (e.g. 200ms response time under load of N concurent users)
Then, IF your code doesn't perform then profile your code to determine what is slow, and profile your proposed performance fixes to accurately measure what the real-world performance change will be.
Having said that then yes, what you are suggesting seems sensible (you would usually expect an in-memory cache to be quicker than a database), however it also depends on what data is being returned, what the memory load of your application is, how expensive the query is, what the query parameters are etc...
You should performance test your changes before and after to determine the actual effect of your changes (including things like memory load), and you should only really be doing things like this once you have identified that these dropdowns are the cause of an unacceptable performance problem.
That's what System.Web.Helpers.WebCache class exists for.
IO is usually more expensive than memory operations (by orders of magnitude). Especially if your database is in another machine, then you would even be using network resources, and it will definitely be faster to just use the cache.
But indeed, optimize in the end when you have really identified it as a performance bottleneck by measuring.
Quick answer to your question:
Use the built in .Net cache.
Additional points to ponder over..
Preferably, retrieve all master data in a single database retrieval (think stored procedure and dataset): though, I do not advocate the used of stored procs in all scenarios.
As you rightly said, ensure that your data access layer checks the cache before making a round trip to the database
Also, as your drop down values do not change very often; do remember to keep a long expiry duration
Finally, based on your page design you could also look at Fragment Caching (partial page caching: user controls) which could give you bigger benefits since now you neither access the data cache nor the database.
Performance:
Again, the performance depends more on the application's load as compared to your direct round trips for fetching the master data. Put simply, As Thomas suggested use the cache class!
I have method in my BLL that interacts with the database and retrieves data based on the defined criteria.
The returned data is a collection of FAQ objects which is defined as follows:
FAQID,
FAQContent,
AnswerContent
I would like to cache the returned data to minimize the DB interaction.
Now, based on the user selected option, I have to return either of the below:
ShowAll: all data.
ShowAnsweredOnly: faqList.Where(Answercontent != null)
ShowUnansweredOnly: faqList.Where(AnswerContent != null)
My Question:
Should I only cache all data returned from DB (e.g. FAQ_ALL) and filter other faqList modes from cache (= interacting with DB just once and filter the data from the cache item for the other two modes)? Or should I have 3 cache items: FAQ_ALL, FAQ_ANSWERED and FAQ_UNANSWERED (=interacting with database for each mode [3 times]) and return the cache item for each mode?
I'd be pleased if anyone tells me about pros/cons of each approach.
Food for thought.
How many records are you caching, how big are the tables?
How much mid-tier resources can be reserved for caching?
How many of each type data exists?
How fast filtering on the client side will be?
How often does the data change?
how often is it changed by the same application instance?
how often is it changed by other applications or server side jobs?
What is your cache invalidation policy?
What happens if you return stale data?
Can you/Should you leverage active cache invalidation, like SqlDependency or LinqToCache?
If the dataset is large then filtering on the client side will be slow and you'll need to cache two separate results (no need for a third if ALL is the union of the other two). If the data changes often then caching will return stale items frequently w/o a proactive cache invalidation in place. Active cache invalidation is achievable in the mid-tier if you control all the updates paths and there is only one mid-tier instance application, but becomes near really hard if one of those prerequisites is not satisfied.
It basically depends how volatile the data is, how much of it there is, and how often it's accessed.
For example, if the answered data didn't change much then you'd be safe caching that for a while; but if the unanswered data changed a lot (and more often) then your caching needs might be different. If this was the case it's unlikely that caching it as one dataset will be the best option.
It's not all bad though - if the discrepancy isn't too huge then you might be ok cachnig the lot.
The other point to think about is how the data is related. If the FAQ items toggle between answered and unanswered then it'd make sense to cache the base data as one - otherwise the items would be split where you wanted it together.
Alternatively, work with the data in-memory and treat the database as an add-on...
What do I mean? Well, typically the user will hit "save" this will invoke code which saves to the DB; when the next user comes along they will invoke a call which gets the data out of the DB. In terms of design the DB is a first class citizen, everything has to go through it before anyone else gets a look in. The alternative is to base the design around data which is held in-memory (by the BLL) and then saved (perhaps asynchronously) to the DB. This removes the DB as a bottleneck but gives you a new set of problems - like what happens if the database connection goes down or the server dies with data only in-memory?
Pros and Cons
Getting all the data in one call might be faster (by making less calls).
Getting all the data at once if it's related makes sense.
Granularity: data that is related and has a similar "cachability" can be cached together, otherwise you might want to keep them in separate cache partitions.
We've developed a system with a search screen that looks a little something like this:
(source: nsourceservices.com)
As you can see, there is some fairly serious search functionality. You can use any combination of statuses, channels, languages, campaign types, and then narrow it down by name and so on as well.
Then, once you've searched and the leads pop up at the bottom, you can sort the headers.
The query uses ROWNUM to do a paging scheme, so we only return something like 70 rows at a time.
The Problem
Even though we're only returning 70 rows, an awful lot of IO and sorting is going on. This makes sense of course.
This has always caused some minor spikes to the Disk Queue. It started slowing down more when we hit 3 million leads, and now that we're getting closer to 5, the Disk Queue pegs for up to a second or two straight sometimes.
That would actually still be workable, but this system has another area with a time-sensitive process, lets say for simplicity that it's a web service, that needs to serve up responses very quickly or it will cause a timeout on the other end. The Disk Queue spikes are causing that part to bog down, which is causing timeouts downstream. The end result is actually dropped phone calls in our automated VoiceXML-based IVR, and that's very bad for us.
What We've Tried
We've tried:
Maintenance tasks that reduce the number of leads in the system to the bare minimum.
Added the obvious indexes to help.
Ran the index tuning wizard in profiler and applied most of its suggestions. One of them was going to more or less reproduce the entire table inside an index so I tweaked it by hand to do a bit less than that.
Added more RAM to the server. It was a little low but now it always has something like 8 gigs idle, and the SQL server is configured to use no more than 8 gigs, however it never uses more than 2 or 3. I found that odd. Why isn't it just putting the whole table in RAM? It's only 5 million leads and there's plenty of room.
Poured over query execution plans. I can see that at this point the indexes seem to be mostly doing their job -- about 90% of the work is happening during the sorting stage.
Considered partitioning the Leads table out to a different physical drive, but we don't have the resources for that, and it seems like it shouldn't be necessary.
In Closing...
Part of me feels like the server should be able to handle this. Five million records is not so many given the power of that server, which is a decent quad core with 16 gigs of ram. However, I can see how the sorting part is causing millions of rows to be touched just to return a handful.
So what have you done in situations like this? My instinct is that we should maybe slash some functionality, but if there's a way to keep this intact that will save me a war with the business unit.
Thanks in advance!
Database bottlenecks can frequently be improved by improving your SQL queries. Without knowing what those look like, consider creating an operational data store or a data warehouse that you populate on a scheduled basis.
Sometimes flattening out your complex relational databases is the way to go. It can make queries run significantly faster, and make it a lot easier to optimize your queries, since the model is very flat. That may also make it easier to determine if you need to scale your database server up or out. A capacity and growth analysis may help to make that call.
Transactional/highly normalized databases are not usually as scalable as an ODS or data warehouse.
Edit: Your ORM may have optimizations as well that it may support, that may be worth looking into, rather than just looking into how to optimize the queries that it's sending to your database. Perhaps bypassing your ORM altogether for the reports could be one way to have full control over your queries in order to gain better performance.
Consider how your ORM is creating the queries.
If you're having poor search performance perhaps you could try using stored procedures to return your results and, if necessary, multiple stored procedures specifically tailored to which search criteria are in use.
determine which ad-hoc queries will most likely be run or limit the search criteria with stored procedures.. can you summarize data?.. treat this
app like a data warehouse.
create indexes on each column involved in the search to avoid table scans.
create fragments on expressions.
periodically reorg the data and update statistics as more leads are loaded.
put the temporary files created by queries (result sets) in ramdisk.
consider migrating to a high-performance RDBMS engine like Informix OnLine.
Initiate another thread to start displaying N rows from the result set while the query
continues to execute.
I'm having an internal debate about where I should handle checking for changes to data and can't decide on the practice that makes the most sense:
Handling IsChanged in the GUI - This requires persistence of data between page load and posting of data which is potentially a lot of bandwidth/page delivery overhead. This isn't so bad in a win forms application, but in a website, this could start having a major financial impact for bandwidth costs.
Handling it in the DAL - This requires multiple calls to the database to check if any data has changed prior to saving it. This potentially means an extra needless call to the database potentially causing scalability issues by needless database queries.
Handling it in a Save() stored proc - This would require the stored proc to potentially make an extra needless call to the table to check, but would save the extra call from the DAL to the database. This could potentially scale better than having the DAL handle it, but my gut says this can be done better.
Handling it in a trigger - This would require using a trigger (which I'm emotionally averse to, I tend to avoid triggers except where absolutely necessary).
Don't handle IsChanged functionality at all - It not only becomes hard to handle the "LastUpdated" date, but saving data unnecessarily to the database seems like a bad practice for scalability in itself.
So each approach has its drawbacks and I'm at a loss as to which is the best of this bad bunch. Does anyone have any more scalable ideas for handling data persistence for the specific purpose of seeing if anything has changed?
Architecture: SQL Server 2005, ASP.NET MVC, IIS7, High scalability requirements for non-specific global audience.
Okay, here's another solution - I've not thought through all the repercussions, but it could work I think:
Think about the GetHashCode() comparison functionality:
At page load time, you calculate the hash code for your page data. You store the hashcode in the page data or viewstate/session if that's what you prefer.
At data post (postback) you calculate the hash code of the data that was posted and compare it to the original hash. If it's different, the user changed something and you can save it back to the database.
Pros
You don't have to store all your original data in the page load which cuts down on bandwidth/page delivery overhead.
You don't have to have your DAL do multiple calls to the database to determine if something's changed.
The record will only be updated in the database if something's changed and hence maintain your correct LastUpdated date.
Cons
You still have to load any original data from the database into your business object that wasn't stored in the "viewstate" that is necessary to save a valid record to your database.
Change of one field will change the hash, but you don't know which field unless you call the original data from the database to compare. On a side note, perhaps you don't need to. If you've gotta update any of the fields the timestamp changes and overwriting a field that hasn't changed for all intensive purposes doesn't have any effect.
You can't completely rule out the chance of collisions but they would be rare. This comes down to is the repercussion of a collision acceptable or not?
Either/Or
If you store the hash in the session, then that saves bandwidth, but increases server resources so you have a potential scalability issue in either case to consider.
Unknowns
Is the overhead of updating a single column and different than that of updating multiple/all columns in a record? I don't know what that performance overhead is.
I handle it in the DAL - it has the original values in it so no need to go to the database.
For each entity in your system introduce additional Version field. With this field you will be able to check for changes at the database level.
Since you have a web application and usually scalability matters for web application, I would suggest you to avoid IsChanged logic at the UI level. LastUpdated date can be set at the database level during Save operation.
Context
My current project is a large-ish public site (2 million pageviews per day) site running a mixture of asp classic and asp.net with a SQL Server 2005 back-end. We're heavy on reads, with occasional writes and virtually no updates/deletes. Our pages typically concern a single 'master' object with a stack of dependent (detail) objects.
I like the idea of returning all the data required for a page in a single proc (and absolutely no unnecesary data). True, this requires a dedicated proc for such pages, but some pages receive double-digit percentages of our overall site traffic so it's worth the time/maintenance hit. We typically only consume multiple-recordsets from our .net code, using System.Data.SqlClient.SqlDataReader and it's NextResult method. Oh, yeah, I'm not doing any updates/inserts in these procs either (except to table variables).
The question
SQL Server (2005) procs which return multiple recordsets are working well (in prod) for us so far but I am a little worried that multi-recordset procs are my new favourite hammer that i'm hitting every problem (nail) with. Are there any multi-recordset sql server proc gotchas I should know about? Anything that's going to make me wish I hadn't used them? Specifically anything about it affecting connection pooling, memory utilization etc.
Here's a few gotchas for multiple-recordset stored procs:
They make it more difficult to reuse code. If you're doing several queries, odds are you'd be able to reuse one of those queries on another page.
They make it more difficult to unit test. Every time you make a change to one of the queries, you have to test all of the results. If something changed, you have to dig through to see which query failed the unit test.
They make it more difficult to tune performance later. If another DBA comes in behind you to help performance improve, they have to do more slicing and dicing to figure out where the problems are coming from. Then, combine this with the code reuse problem - if they optimize one query, that query might be used in several different stored procs, and then they have to go fix all of them - which makes for more unit testing again.
They make error handling much more difficult. Four of the queries in the stored proc might succeed, and the fifth fails. You have to plan for that.
They can increase locking problems and incur load in TempDB. If your stored procs are designed in a way that need repeatable reads, then the more queries you stuff into a stored proc, the longer it's going to take to run, and the longer it's going to take to return those results back to your app server. That increased time means higher contention for locks, and the more SQL Server has to store in TempDB for row versioning. You mentioned that you're heavy on reads, so this particular issue shouldn't be too bad for you, but you want to be aware of it before you reuse this hammer on a write-intensive app.
I think multi recordset stored procedures are great in some cases, and it sounds like yours maybe one of them.
The bigger (more traffic), you site gets, the more important that 'extra' bit of performance is going to matter. If you can combine 2-3-4 calls (and possibly a new connections), to the database in one, you could be cutting down your database hits by 4-6-8 million per day, which is substantial.
I use them sparingly, but when I have, I have never had a problem.
I would recommend having invoking in one stored procedure several inner invocations of stored procedures that return 1 resultset each.
create proc foo
as
execute foobar --returns one result
execute barfoo --returns one result
execute bar --returns one result
That way when requirments change and you only need the 3rd and 5th result set, you have a easy way to invoke them without adding new stored procedures and regenerating your data access layer. My current app returns all reference tables (e.g. US states table) if I want them or not. Worst is when you need to get a reference table and the only access is via a stored procedure that also runs an expensive query as one of its six resultsets.