We have an affiliate system which counts millions of banner Impressions/Clicks per day.
Currently it writes to SQL every Impression/Click that occurs in real time on each request.
Web application serves these requests.
We are facing two problems:
If we have a lot of concurrent requests per second, the SQL is
starting to work very hard to insert the Impressons/Clicks data and
as a result lead to problem #2.
If SQL is slow at the moment, the requests are being accumulated and
are waiting in the queue on web server. As a result we have a
slowness on a web application and requests are not being processed.
Design we thought of in high level:
We are now considering changing the design by taking out the writing to SQL logic out of web application (write it to some local storage instead) and making a stand alone service which will read from local storage and eventually write the aggregated Impressions/Clicks data (not in real time) to SQL in background.
Our constraints:
10 web servers (load balanced)
1 SQL server
What do you think of suggested design?
Would you use NoSQL as local storage for each web server?
Suggest your alternative.
Your problem seems to be that your front-end code is synchronusly blocking while waiting for the back-end code to update the database.
Decouple front-end and back-end, e.g. by putting a queue inbetween where the front-end can write to the queue with low latency and high throughput. The back-end then can take its time to process the queued data into their destinations.
It may or may not be necessary to make the queue restartable (i.e. not losing data after a crash). Depending on this, you have various options:
In-memory queue, speedy but not crash-proof.
Database queue, makes sense if writing the raw request data to a simple data structure is faster than writing the final data into its target data structures.
Renundant queues, to cover for crashes.
I'm with Bernd, but I'm not sure about using a queue specifically.
All you need is something asynchronous that you can call; that way the act of logging the impression is pretty much redundant.
Related
Currently I am realising that I come to the limits of relational databases. Having n>>1000 clients periodically (each 10 seconds) requesting a simple microservice endpoint (signals its activity), I just have to make a decision if a client is active or not (3 periods of requests missing).
The naive approach would be to make a database update for each clients request (which has bad performance impacts and hence would not be a suitable solution).
Now, having more instances of those services (for future service scaling), any bulk processing (e.g. by using a periodically processed queue structure) would automatically introduce database locking issues due to subsequent requests of the same clients by different instances (this client id would be part of multiple database bulk updates by different services). Hence, those updates would partially fail even for clients not beeing processed in parallel.
Is there any way to make this processing reliable and high-performant using other (e.g. in-memory) database solutions like Redis or somehing else? In Redis I don't get the trick to manage keys and values in a way to retrieve all keys that were not changed during the last x periods.
Do you have any ideas about well-established technologies to cope with that issue?
I have a CRUD webservice, and have been tasked with trying to figure out a way to ensure that we don't lose data when the database goes down. Everyone is aware that if the database goes down we won't be able to get "reads" but for a specific subset of the operations we want to make sure that we don't lose data.
I've been given the impression that this is something that is covered by services like 0MQ, RabbitMQ, or one of the Microsoft MQ services. Although after a few days of reading and research, I'm not even certain that the messages we're talking about in MQ services include database operations. I am however 100% certain that I can queue up as many hello worlds as I could ever hope for.
If I can use a message queue for adding a layer of protection to the database, I'd lean towards Rabbit (because it appears to persist through crashes) but since the target is a Microsoft SQL server databse, perhaps one of their solutions (such as SQL Service Broker, or MSMQ) is more appropriate.
The real fundamental question that I'm not yet sure of though is whether I'm even playing with the right deck of cards (so to speak).
With the desire for a high-availablity webservice, that continues to function if the database goes down, does it make sense to put a Rabbit MQ instance "between" the webservice and the database? Maybe the right link in the chain is to have RabbitMQ send messages to the webserver?
Or is there some other solution for achieving this? There are a number of lose ideas at the moment around finding a way to roll up weblogs in the event of database outage or something... but we're still in early enough stages that (at least I) have no idea what I'm going to do.
Is message queue the right solution?
Introducing message queuing in between a service and it's database operations is certainly one way of improving service availability. Writing to a local temporary queue in a store-and-forward scenario will always be more available than writing to a remote database server, simply by being a local operation.
Additionally by using queuing you gain greater control over the volume and nature of database traffic your database has to handle at peak. Database writes can be queued, routed, and even committed in a different order.
However, in order to do this you need to be aware that when a database write is performed it is processed off-line. Even under conditions where this happens almost instantaneously, you are losing a benefit that the synchronous nature of your current service gives you, which is that your service consumers can always know if the database write operation is successful or not.
I have written about this subject before here. The user posting the question had similar concerns to you. Whether you do this or not is a decision you have to make based on whether this is something your consumers care about or not.
As for the technology stacks you are thinking of this off-line model is implementable with any of them pretty much, with the possible exception of Service broker, which doesn't integrate well with code (see my answer here: https://stackoverflow.com/a/45690344/569662).
If you're using Windows and unlikely to need to migrate, I would go for MSMQ (which supports durable messaging via transactional queues) as it's lightweight and part of Windows.
I am curious as to how caching works in Google App Engine or any cloud based application. Since there is no guarantee that requests are sent to same sever, does that mean that if data is cached on 1st request on Server A, then on 2nd requests which is processed by Server B, it will not be able to access the cache?
If thats the case (cache only local to server), won't it be unlikely (depending on number of users) that a request uses the cache? eg. Google probably has thousands of servers
With App Engine you cache using memcached. This means that a cache server will hold the data in memory (rather than each application server). The application servers (for a given application) all talk the same cache server (conceptually, there could be sharding or replication going on under the hoods).
In-memory caching on the application server itself will potentially not be very effective, because there is more than one of those (although for your given application there are only a few instances active, it is not spread out over all of Google's servers), and also because Google is free to shut them down all the time (which is a real problem for Java apps that take some time to boot up again, so now you can pay to keep idle instances alive).
In addition to these performance/effectiveness issues, in-memory caching on the application server could lead to consistency problems (every refresh shows different data when the caches are not in sync).
Depends on the type of caching you want to achieve.
Caching on the application server itself can be interesting if you have complex in-memory object structure that takes time to rebuild from data loaded from the database. In that specific case, you may want to cache the result of the computation. It will be faster to use a local cache than a shared memcache to load if the structure is large.
If having consistent value between in-memory and the database is paramount, you can do some checksum/timestamp check with a stored value on the datastore, every time you use the cached value. Storing checksum/timestamp on a small object or in a global cache will fasten the process.
One big issue using global memcache is ensuring proper synchronization on "refilling" it, when a value is not yet present or has been flushed. If you have multiple servers doing the check at the exact same time and refilling value in cache, you may end-up having several distinct servers doing the refill at the same time. If the operation is idem-potent, this is not a problem; if not, a potential and very hard to trace bug.
Setup
I have web service that takes its inputs through a REST interface. The REST call does not return any meaningful data, so whatever is passed in to the web service is just recorded in the database and that is it. It is an analytics service which my company is using internally to do some special processing on web requests that are received on their web page. So it is very important the response take as little time to return as possible.
I have pretty much optimized the code down as much as possible, to make the response as fast as possible. However, the time the database stays open still keeps the connection open for longer than I want before a response is sent back to the web client.
The code looks basically like this, by the way it is ASP.NET MVC, using Entity Framework, running on IIS 7, if that matters.
public ActionResult Add(/*..bunch of parameters..*/) {
using (var db = new Entities()) {
var log = new Log {
// populate Log from parameters
}
db.AddToLogs(log);
db.SaveChanges();
}
return File(pixelImage, "image/gif");
}
Question
Is there a way to off load the database insert in to another process, so the response to the client is returned almost instantly?
I was thinking about wrapping everything in the using block in another thread, to make the database insert asynchronous, but didn't know if that was the best way to free up the response back to the client.
What would you recommend if you were trying to accomplish this goal?
If the request has to be reliable then you need to write it into the database. Eg. if your return means 'I have paid the merchant' then you can't return before you actually commit in the database. If the processing is long then there are database based asynchronous patterns, using a table as a queue or using built-in queuing like Asynchronous procedure execution. But these apply when heavy and lengthy processing is needed, not for a simple log insert.
When you want just to insert a log record (visitor/url tracking stuff) then the simplest solution is to use CLR's thread pools and just queue the work, something like:
...
var log = new Log {// populate Log from parameters}
ThreadPool.QueueUserWorkItem(stateInfo=>{
var queueLog = stateInfo as Log;
using (var db = new Entities())
{
db.AddToLogs(queuedLog);
db.SaveChanges();
}
}, log);
...
This is quick and easy and it frees the ASP handler thread to return the response as soon as possible. But it has some drawbacks:
If the incomming rate of requests exceeds the thread pool processing rate then the in memory queue will grow until it will trigger an app pool 'recycle', thus loosing all items 'in progress' (as well as warm caches and other goodies).
The order of requests is not preserved (may or may not be important)
It consumes a CLR pool thread on doing nothing but waiting for a response from the DB
The last concern can be addressed by using a true asynchronous database call, via SqlCommand.BeginExecuteXXX and setting the AsynchronousProcessing on the connection to true. Unfortunately AFAIK EF doesn't yet have true asynchronous execution, so you would have to resort to the SqlClient layer (SqlConnection, SqlCommand). But this solution would not address the first concern, when the rate of page hits is so high that this logging (= writes on every page hit) becomes a critical bottle neck.
If the first concern is real then and no threading and/or producer/consumer wizardry can aleviate it. If you trully have an incomming rate vs. write rate scalability concern ('pending' queue grows in memory) you have to either make the writes faster in the DB layer (faster IO, special log flush IO) and/or you have to aggregate the writes. Instead of logging every requests, just increment in memory counters and write them periodically as aggregates.
I've been working on multi-tier solutions mostly for the last year or so that require this sort of functionality, and that's exactly how I've been doing it.
I have a singleton that takes care of running tasks in the background based on an ITask interface. Then I just register a new ITask with my singleton and pass control from my main thread back to the client.
Create a separate thread that monitors a global, in memory queue. Have your request put it's information on the queue and return, the thread then takes the item off the queue and posts it to the DB.
Under heavy load, if the thread lags the requests, your queue will grow.
Also, if you lose the machine, you will lose any unprocessed queue entries.
Whether these limitations are acceptable to you, you'd need to decide that.
A more formal mechanism is using some actual middleware messaging system (JMS in Java land, dunno the equivalent in .NET, but there's certainly something).
It depends: When you return to the client do you need to be 100% sure that the data is stored in the database?
Take this scenario:
Request comes in
A thread is started to save to the database
Response is sent to the client
Server crashes
Data was not saved to the database
You also need to check how many milliseconds you save by starting a new thread instead of saving to the database.
The added complexity and maintainence cost is probably too high compared with the savings in response time. And the savings in response time are probably so low that they will not be noticed.
Before I spent a lot of time on the optimization I'd be sure of where the time is going. Connections like these have significant latency overhead (check this out). Just for grins, make your service a NOP and see how it performs.
It seems to me that the 'async-ness' needs to be on the client - it should fire off the call to your service and move on, especially since it doesn't care about the result?
I also suspect that if the NOP performance is good-to-tolerable on your LAN it will be a different story in the wild.
Has anyone had any experience scaling out SQL Server in a multi reader single writer fashion. If not can anyone suggest a suitable alternative for a read intensive web application, that they have experience with
It depends on probably 2 things:
How big each single write is?
Do readers need real time data?
A write will block readers when writing, but if each write is small and fast then readers won't notice.
If you offload, say, end of day reporting then you batch your load onto a separate server because readers do not require real time data. This makes sense
A write on your primary server must be synched to your offload secondary server... which will block there as part of the synch process anyway + you add an overhead load to manage the synch.
Most apps are 95%+ read anyway all the time. For example, an update or delete is a read followed by a write.
My choice would be (probably, based on the low write volume and it's a web app) to scale up and stuff as much RAM as I could in the DB server with separate disk paths for the data and log files of the database.
I don't have any experience with scaling out SQL Server for your scenario.
However for a Read-Intensive application, I would be looking at reducing the load on the database and employ a Cache Strategy using something like Memcache or MS Velocity
There are two approaches that I'm aware of:
Have the entire database loaded into the Cache and manage Adding and Updating of items in the cache.
Add items to the cache only when they are requested and remove them when a write operation is performed.
Some kind of replication would do the trick.
http://msdn.microsoft.com/en-us/library/ms151827.aspx
You of course need to change your app code.
Some people use partitioned tables, with different row ranges being stored on different servers - united with views. This would be invisible to the app. Federation for this practice, I think.
By designing your database, application and server configuration (SQL particulars - location of data/log/system/sql binaries/tempdb), you should be able to handle a pretty good load. Try not to complicate things if you don't have to.