Most of the questions that somewhat relate to this link to old/deprecated resources, so I'm asking it once more.
I have a special tool that gets a lot of traffic and utilizes complex computations through R. We have opted to use Rserve to be able to intake a large influx of concurrent requests, but we cannot figure out how to communicate our C# ASP.NET web application with RServe. We have RServe up and running, but how can we actually communicate and make requests straight to Rserve from a C# application? The documentation for https://github.com/konne/RserveCLI2 isn't great, can someone help us understand how to call our functions?
Note: We have a plumber implementation up and running. It works great, but it seems to have issues with large amounts of concurrent requests as it simply queues them. The documentation talks about off-loading and creating parallel processes, but this may require a lot of parallel processes if each can only handle 1 request.
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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.
We are scraping an Web based API using Microsoft Azure. The issue is that there is SO much data to retrieve (there are combinations/permutations involved).
If we use a standard Web Job approach, we calculated it would take about 200 years to process all the data we want to get - and we would like our data to be refreshed every week.
Each request/response from the API takes about a 0.5-1.0 seconds to process. Request size is on average 20000 bytes and the average response is 35000 bytes. I believe the total number of requests is in the millions.
Another way to think about this question would be: how would you use Azure to Web scrape - and make sure you don't overload (in terms of memory + network) the VM it's running on? (I don't think you need too much CPU processing in this case).
What we have tried so far:
Used Service Bus Queues/Worker Roles scaled to 8 small VMs - but this caused a lot of network errors to occur (there must be some network limit to how much EACH worker role VM can handle).
Used Service Bus Queues/Continuous Web Job scaled to 8 small VMs - but this seems to work slower - and even scaled, doesn't give us too much control on what's happening behind the scenes. (We don't REALLY know how many VMs are up).
It seems that these things are built for CPU calculation - not for Web/API scraping.
Just to clarify: I throw my requests into a queue - which then get picked up by my multiple VMs for processing to get the responses. That's how I was using the queues. Each VM was using the ServiceBusTrigger class as prescribed by microsoft.
Is it better to have a lot small VMs or few massive VMs?
What C# classes should we be looking at?
What are the technical best practices when trying to do something like this on Azure?
Actually a web scraper is something that I have up and running, in Azure, for quite some time now :-)
AFAIK there is no 'magic bullet'. Scraping a lot of sources with deadlines is quite hard.
How it works (the most important things):
I use worker roles and C# code for the code itself.
For scheduling, I use the queue storage. I put crawling tasks on the queue with a timeout (e.g. 'when to crawl then') and have the scraper pull them off. You can put triggers on the queue size to ensure you meet deadlines in terms of speed -- personally I don't need them.
SQL Azure is slow, so I don't use that. Instead, I only use table storage for storing the scraped items. Note that updating data might be quite complex.
Don't use too much threading; instead, use async IO for all network traffic.
Also you might have to consider that extra threads require extra memory (parse trees can become quite big) - so there's a trade-off there... I do recall using some threads, but it's really just a few.
Note that probably this does require you to re-design and re-implement your complete web scraper if you're now using a threaded approach.. then again, there are some benefits:
Table storage and queue storage are cheap.
I currently use a single Extra Small VM to scrape well over a thousand web sources.
Inbound network traffic is for free.
As such, the result is quite cheap as well; I'm sure it's much less than the alternatives.
As for classes that I use... well, that's a bit of a long list. I'm using HttpWebRequest for the async HTTP requests and the Azure SDK -- but all the rest is hand crafted (and not open source).
P.S.: This doesn't just hold for Azure; most of this also holds for on-premise scrapers.
I have some experience with scraping so I will share my thoughts.
It seems that these things are built for CPU calculation - not for Web/API scraping.
They are built for dynamic scaling which given your task is not something you really need.
How to make sure you don't overload the VM?
Measure the response times and error rates and tune you code to lower them.
I don't think you need too much CPU processing in this case.
Depends on how much data is coming in each second and what you are doing with it. More complex parsing on quickly incoming data (if you decide to do it on the same machine) will eat up CPU pretty quickly.
8 small VMs caused a lot of network errors to occur (there must be some network limit)
The smaller the VMs the less shared resources they get. There are throughput limits and then there is an issue with your neighbors sharing the actual hardware with you. Often, the smaller your instance size the more trouble you run into.
Is it better to have a lot small VMs or few massive VMs?
In my experience, smaller VMs are too crippled. However, your mileage may vary and it all depends on the particular task and its solution implementation. Really, you have to measure yourself in your environment.
What C# classes should we be looking at?
What are the technical best practices when trying to do something like this on Azure?
With high throughput scraping you should be looking at infrastructure. You will have different latency in different Azure datacenters, and different experience with network latency/sustained throughput at different VM sizes, and depending on who in particular is sharing the hardware with you. The best practice is to try and find what works best for you - change datacenters, VM sizes and otherwise experiment.
Azure may not be the best solution to this problem (unless you are on a spending spree). 8 small VMs is $450 a month. It is enough to pay for an unmanaged dedicated server with 256Gb of RAM, 40 hardware threads and 500Mbps - 1Gbps (or even up to several Gbps bursts) of quality network bandwidth without latency issues.
For you budget, you will have a dedicated server that you cannot overload. You will have more than enough RAM to deal with async pinning (if you decide to go async), or enough hardware threads for multi-threaded synchronous IO which gives the best throughput (if you choose to go synchronous with a fixed-size threadpool).
On a sidenote, depending on the API specifics, it might turn out that your main issue will be the API owner simply throttling you down to a crawl when you start to put too much pressure on the API endpoints.
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!
As part of my constant learning curve into what you can do to make apps scale better, I am currently trying to get a direction to go with queuing, i.e. job queuing or workload processing whichever phrase you like.
In the distant past I used IBM MQ/Series - it worked for a financial app but quite heavy if I remember.
I know of MSMQ, and I have also heard of quite a few others.
But first, here is my context
I have a C#/.NET back-end web app which serves data etc to a Javascript (mostly jQuery etc) front-end via AJAX calls etc. I have a situation where a certain action involves uploading some files, setting up a few record entries in the database, emailing some users etc. So of course I don't want to make this process "online"/"real-time" due to the possible time delay and I am sure the overheads on the webserver/database etc.
So given the type of "messages" that I need to queue and process, what would be (I shouldn't just say easy here I guess!) a good start point? should I run with MSMQ and/or the SQL 2008 service broker stuff, or something like ZeroMQ - or should I simply create my own lightweight workload queue service?
I realise again without seeing the full picture it is hard to make full recommendations, however any start points gratefully received!
David
Don't try to make your own, please! There are so many things to take into account that you will spend more time on it than the rest of your project most probably.
I'd say go for MSMQ, it's very easy to use with WCF, the queues are transactional, have a retry mechanism, etc, and you benefit from the MSMQ UI to see the messages, move them and so on.
We're developing a .NET app that must make up to tens of thousands of small webservice calls to a 3rd party webservice. We would prefer a more 'chunky' call, but the 3rd party does not support it. We've designed the client to use a configurable number of worker threads, and through testing have code that is fairly well optimized for one multicore machine. However, we still want to improve the speed, and are looking at spreading the work accross multiple machines. We're well versed in typical client/server/database apps, but new to designing for multiple machines. So, a few questions related to that:
Is there any other client-side optimization, besides multithreading, that we should look at that could improve speed of a http request/response? (I should note this is a non-standard webservice, so is implemented using WebClient, not a WCF or SOAP client)
Our current thinking is to use WCF to publish chunks of work to MSMQ, and run clients on one or more machines to pull work off of the queue. We have experience with WCF + MSMQ, but want to be sure we're not missing better options. Are there other, better ways to do this today?
I've seen some 3rd party tools like DigiPede and Microsoft's HPC offerings, but these seem like overkill. Any experience with those products or reasons we should consider them over roll-our-own?
Sounds like your goal is to execute all these web service calls as quickly as you can, and get the results tabulated. Given that, your greatest efficiency control is going to be through scaling the number of concurrent requests you can make.
Be sure to look at your client-side connection limits. By default, I think the system default is 2 connections. I haven't tried this myself, but by upping the number of connections with this property, you should theoretically see a multiplier effect in terms of generating more requests by generating more connections from a single machine. There's more info on MS forums.
The MSMQ option works well. I'm running that configuration myself. ActiveMQ is also a fine solution, but MSMQ is already on the server.
You have a good starting point. Get that in operation, then move on to performance and throughput.
At CodeMash this year, Wesley Faler did an interesting presentation on this sort of problem. His solution was to store "jobs" in a DB, then use clients to pull down work and mark status when complete.
He then pushed the whole infrastructure up to Amazon's EC2.
Here's his slides from the presentation - they should give you the basic idea:
I've done something similar w/ multiple PC's locally - the basics of managing the workload were similar to Faler's approach.
If you have optimized the code, you could look into optimizing the network side to minimize the number of packets sent:
reuse HTTP sessions (i.e.: multiple transactions into one session by keeping the connection open, reduces TCP overhead)
reduce the number of HTTP headers to the minimum in the request to save bandwidth
if supported by server, use gzip to compress the body of the request (need to balance CPU usage to do the compression, and the bandwidth you save)
You might want to consider Rhino Service Bus instead of MSMQ. The source is available here.