Question:
Is there a way to force the Task Parallel Library to run multiple tasks simultaneously? Even if it means making the whole process run slower with all the added context switching on each core?
Background:
I'm fairly new to multithreading, so I could use some assistance. My initial research hasn't turned up much, but I also doubt I know what exactly to search for. Perhaps someone more experienced with multithreading can help me better understand TPL and/or find a better solution.
Our company is planning on deploying a piece of software to all users' machines that will connect to a central server a few times a day, and synchronize some files and MS Access data back to the user's machine. We would like to load-test this concept first and see how the Access DB holds up to lots of simultaneous connections.
I've been tasked with writing a .NET application that behaves like the client app (connecting & syncing with a network location), but does this on multiple threads simultaneously.
I've been getting familiar with the Task Parallel Library (TPL), as this seems like the best (newest) way to handle multithreading, and get return values back from each thread easily. However as I understand it, TPL decides how to run each "task" for the fastest execution possible, splitting the work among the available cores. So lets say I want to run 30 sync jobs on a 2-core machine... the TPL would run 15 on each core, sequentially. This would mean my load test would only be hitting the Access DB with at most 2 connections at the same time. I want to hit the database with lots of simultaneous connections.
You can force the TPL to do this by specifying TaskOptions.LongRunning. According to Reflector (not according to the docs, though) this always creates a new thread. I consider relying on this safe production use.
Normal tasks will not do, because they don't guarantee execution. Setting MinThreads is a horrible solution (for production) because you are changing a process global setting to solve a local problem. And still, you are not guaranteed success.
Of course, you can also start threads. Tasks are more convenient though because of error handling. Nothing wrong with using threads for this use case.
Based on your comment, I think you should reconsider using Access in the first place. It doesn't scale well and has problems once the database grows to a certain size. Especially if this is simply served off some file share on your network.
You can try and simulate load from your single machine but I don't think that would be very representative of what you are trying to accomplish.
Have you considered using SQL Server Express? It's basically a de-tuned version of the full-blown SQL Server which might suit your needs better.
Related
I am fairly new to asynchronous programming so I need help.
What I need to do is, create a windows service that constantly checks the database for menu updates (insert/updates), tables updates (insert/updates), menu category updates (insert/updates) and so on and if any change is detected the service will then need to POST those said changes to separate APIs one by one. Keeping in mind that the service will be used for just this purpose and the database that I need to check for updates is SQL Server.
So, how do I approach this scenario efficiently ? Do I create new Tasks (System.Threading.Tasks) or create new Threads (System.Threading.Thread) for each pieces like UpdateMenu that checks the menu updates and upload to api, UpdateTable, UpdateDishes and so on and how do I go about the Posting to the API part I mean do I create a new Task for each and every API call? I want the application to be as efficient as possible and pick the changes and post them to API as soon as possible.
Thanks in advance.
It seems that you are worried about the overhead of the mechanism that you are going to use, in order to fetch data from the database and post these data to APIs. You are thinking that maybe Threads are fast and Tasks are slower, or vice versa. In fact choosing between these two mechanisms is likely to have no measurable impact to your service's demand for CPU, memory or other system resources.
What is likely to be impactful, is the pattern of communication of your service with the database and the APIs. For example if your threads/tasks are not coordinated with each other, and query the database all at the same time, the database might be slow to respond, and might consume larger amounts of memory while preparing the response. That's not because your threads/tasks are slow. It's because your service is querying the database with a pattern that makes it harder for the database to respond. The same might be true for the pattern of communication with the APIs. If your workers are not coordinated, the network connectivity might become a bottleneck, or the remote machines that host the APIs might suffer.
So my advice is to focus on the usability factor of the mechanisms, and not on their supposed difference in performance. If you are comfortable and familiar with threads, and know nothing about tasks, use threads. If you are familiar with both threads and tasks, use tasks because they are generally easier to use. You'd better invest your time to optimize the communication pattern between your service and its dependencies, than for doing benchmarks trying to find the best between mechanisms that for all intents and purposes are equally efficient.
I've read a lot about this topic, but still am not sure what to do.
First, the situation: I have software written in C# using .NET 4.5 that polls up to 64 devices on a CAN network that I communicate with via USB using a third party API from the device manufacturer. The purpose is to provide the user with realtime updates of temperature, pressure, and other values like that from some sensors.
Currently I create a System.Threading.Thread for every device which runs a while loop that queries the device for the relevant info, saves updates to SQL Server via Entity Framework, then sleeps for 1.25 seconds.
This runs ok on smaller systems with ~20 or fewer devices, but on a large install with 50+ devices it runs very slowly. I think that my problem is the overhead of creating so many threads. And it doesn't help that I'm stuck with a crappy Atom processor, although at least this one is quad core unlike the previous system I used that was dual core.
So, I've been trying to make the process more efficient. Everything I read seems to point to Task.Run() being the more effective way of doing something like this, but this software could potentially be running for weeks or months at a time, which I THINK means I would need to run it with TaskCreationOptions.LongRunning. But I've read conflicting things on this, so I'm not sure. But if that is the case, then my understanding is that TPL will just start up a new dedicated thread anyways, so it seems like that would still have the overhead I'm trying to avoid.
So, as you can see, I'm pretty lost on this topic. I don't know if I should just give Task.Run() a try, and see what happens, or if there's a whole different way I should do this.
Any help would be immensely appreciated.
Thank you.
I have a C# service application which interacts with a database. It was recently migrated from .NET 2.0 to .NET 4.0 so there are plenty of new tools we could use.
I'm looking for pointers to programming approaches or tools/libraries to handle defining tasks, configuring which tasks they depend on, queueing, prioritizing, cancelling, etc.
There are various types of services:
Data (for retrieving and updating)
Calculation (populate some table with the results of a calculation on the data)
Reporting
These services often depend on one another and are triggered on demand, i.e., a Reporting task, will probably have code within it such as
if (IsSomeDependentCalculationRequired())
PerformDependentCalculation(); // which may trigger further calculations
GenerateRequestedReport();
Also, any Data modification is likely to set the Required flag on some of the Calculation or Reporting services, (so the report could be out of date before it's finished generating). The tasks vary in length from a few seconds to a couple of minutes and are performed within transactions.
This has worked OK up until now, but it is not scaling well. There are fundamental design problems and I am looking to rewrite this part of the code. For instance, if two users request the same report at similar times, the dependent tasks will be executed twice. Also, there's currently no way to cancel a task in progress. It's hard to maintain the dependent tasks, etc..
I'm NOT looking for suggestions on how to implement a fix. Rather I'm looking for pointers to what tools/libraries I would be using for this sort of requirement if I were starting in .NET 4 from scratch. Would this be a good candidate for Windows Workflow? Is this what Futures are for? Are there any other libraries I should look at or books or blog posts I should read?
Edit: What about Rx Reactive Extensions?
I don't think your requirements fit into any of the built-in stuff. Your requirements are too specific for that.
I'd recommend that you build a task queueing infrastructure around a SQL database. Your tasks are pretty long-running (seconds) so you don't need particularly high throughput in the task scheduler. This means you won't encounter performance hurdles. It will actually be a pretty manageable task from a programming perspective.
Probably you should build a windows service or some other process that is continuously polling the database for new tasks or requests. This service can then enforce arbitrary rules on the requested tasks. For example it can detect that a reporting task is already running and not schedule a new computation.
My main point is that your requirements are that specific that you need to use C# code to encode them. You cannot make an existing tool fit your needs. You need the turing completeness of a programming language to do this yourself.
Edit: You should probably separate a task-request from a task-execution. This allows multiple parties to request a refresh of some reports while at the same time only one actual computation is running. Once this single computation is completed all task-requests are marked as completed. When a request is cancelled the execution does not need to be cancelled. Only when the last request is cancelled the task-execution is cancelled as well.
Edit 2: I don't think workflows are the solution. Workflows usually operate separately from each other. But you don't want that. You want to have rules which span multiple tasks/workflows. You would be working against the system with a workflow based model.
Edit 3: A few words about the TPL (Task Parallel Library). You mentioned it ("Futures"). If you want some inspiration on how tasks could work together, how dependencies could be created and how tasks could be composed, look at the Task Parallel Library (in particular the Task and TaskFactory classes). You will find some nice design patterns there because it is very well designed. Here is how you model a sequence of tasks: You call Task.ContinueWith which will register a continuation function as a new task. Here is how you model dependencies: TaskFactory.WhenAll(Task[]) starts a task that only runs when all its input tasks are completed.
BUT: The TPL itself is probably not well suited for you because its task cannot be saved to disk. When you reboot your server or deploy new code, all existing tasks are being cancelled and the process aborted. This is likely to be unacceptable. Please just use the TPL as inspiration. Learn from it what a "task/future" is and how they can be composed. Then implement your own form of tasks.
Does this help?
I would try to use the state machine package stateless to model the workflow. Using a package will provide a consistent way to advance the state of the workflow, across the various services. Each of your services would hold an internal statemachine implementation, and expose methods for advancing it. Stateless will be resposible for triggering actions based on the state of the workflow, and enforce you to explicitly setup the various states that it can be in - this will be particularly useful for maintenance, and it will probably help you understand the domain better.
If you want to solve this fundamental problem properly and in a scalable way, you should probably look as SOA architecture style.
Your services will receive commands and generate events you can handle in order to react on facts happen in your system.
And, yes, there are tools for it. For example NServiceBus is a wonderful tool to build SOA systems.
You can do a SQL data agent to run SQL queries in timed interval. You have to write the application yourself it looks like. Write like a long running program that checks the time and does something. I don't think there is clearcut tools out there to do what you are trying to do. Do C# application, WCF service. data automation can be done in the sql itself.
If I understand you right you want to cache the generated reports and do not the work again. As other commenters have pointed out this can be solved elegantly with multiple Producer/Consumer queues and some caches.
First you enqueue your Report request. Based on the report genration parameters you can check the cache first if a previously generated report is already available and simply return this one. If due to changes in the database the report becomes obsolete you need to take care that the cache is invalidated in a reliable manner.
Now if the report was not generated yet you need need to schedule the report for generation. The report scheduler needs to check if the same report is already beeing generated. If yes register an event to notify you when it is completed and return the report once it is finished. Make sure that you do not access the data via the caching layer since it could produce races (report is generated, data is changed and the finished report would be immediatly discared by the cache leaving noting for you to return).
Or if you do want to prevent to return outdated reports you can let the caching layer become your main data provider which will produce as many reports until one report is generated in time which was not outdated. But be aware that if you have constant changes in your database you might enter an endless loop here by constantly generating invalid reports if the report generation time is longer as the average time between to changes to your db.
As you can see you have plenty of options here without actually talking about .NET, TPL, SQL server. First you need to set your goals how fast/scalable and reliable your system should be then you need to choose the appropriate architecture-design as described above for your particular problem domain. I cannot do it for you because I do not have your full domain know how what is acceptable and what not.
The tricky part is the handover part between different queues with the proper reliability and correctness guarantees. Depending on your specific report generation needs you can put this logic into the cloud or use a single thread by putting all work into the proper queues and work on them concurrently or one by one or something in between.
TPL and SQL server can help there for sure but they are only tools. If used wrongly due to not sufficient experience with the one or the other it might turn out that a different approach (like the usage of only in memory queues and persisted reports on in the file system) is better suited for your problem.
From my current understanding I would not use SQL server to misuse it as a cache but if you want a database I would use something like RavenDB or RaportDB which look stable and much more light weight compared to a full blown SQL server.
But if you already have a SQL server running then go ahead and use it.
I am not sure if I understood you correctly, but you might want to have a look at JAMS Scheduler: http://www.jamsscheduler.com/. It's non-free, but a very good system for scheduling depending tasks and reporting. I have used it with success at my previous company. It's written in .NET and there is a .NET API for it, so you can write your own apps communicating with JAMS. They also have a very good support and are eager to implement new features.
I have a Windows Service which executes large number of tasks in parallel using the Task Parallel Library (TPL). This is about to be extended to handle tasks which interact with an SQL Server on an external server.
TPL is supposed to be good at measuring load and assigning the right number of parallel threads to the tasks. Is there a way to make it aware of load to the external SQL Server instance? The actual code to run for each task on the local server is quite small, but the calls to the database can be quite heavy.
Am I not likely to end up with my service bogging down the database with request because TPL sees that the local server has loads of free resources, or is there a known way to handle this?
There's is nothing native to TPL that will help you with this. TPL is about managing/maximizing the CPU load of your local application. It has no idea about SQL load, let alone on another machine.
That said, if you wanted to get crazy, there is an extensibility point called the TaskScheduler. You could theoretically implement a custom TaskScheduler that can watch the load on the SQL server and only schedule tasks to execute if that load is at some defined threshold.
Honestly though, I don't think it's the right solution to the problem. Managing load against a shared resource like a SQL server is a completely different beast than what TPL is designed to solve. You'd be much better off just making sure you design your application such that it doesn't abuse the SQL server in its own right by load testing, finding a sweet spot and configuring your application not go out outside those bounds. From there it would be up to your DBA to determine the right solution for the SQL server infrastructure itself to manage that application's needs along with any other external load.
If you parallelise the data access functionality in your client application, you will find that the next bottleneck is the SQL Server connection pool.
TPL is good at partitioning your data, as for measuring load, that task is for your OS to determine (and in fact its pretty good at it). Therefore this is more of a configuration question then a development question. (Does your SQL Server instance have a higher priority then your service?).
I've got a for loop I want to parallelize with something like PLINQ's Parallel.ForEach().
The key here is that the C++ library i'm calling to do the computation is decidedly not thread safe, therefore, any plans to parallelize this need to do so across multiple processes.
I was thinking about using WCF to create a "distributor" process to which the "client" and multiple "calculators" could both connect and add/remove items to/from a queue and then the "calculator" sends the results directly back to the client which could update the gui as it receives them. This architecture would allow me to bring as many "calculators" online as I have processors and as I see it even bring them up across multiple computers creating a potential farm of processing power to which all the clients could share.
I'm just wondering if anyone has had any experience doing this and if there are existing application blocks or frameworks that I can use to build this for me. PLINQ does it within the process. is there like a DPLINQ (distributed) or something?
Also if that doesn't exist, does anybody want to give an opinion on my proposed architecture? Any obvious pitfalls? Does anyone think it will work!?!?!?
Sounds like you could be looking for Dryad. It's a Microsoft research project right now, but they do have an "academic release" available. My understanding is that they are also in the process of better productizing it (probably some kind of integration with Azure) for RTM sometime near the end of 2011. Mary Jo Foley covers more about this here.
A long time standard for controlling/dispatching distributed work is MPI. I've only ever used it from C++, but implementations from many languages exist. A quick google suggests that MPI.Net could be a good implementation for .Net!