How to maximize process throughput (C#)?

throughput improvement process
improve throughput meaning
how to increase production capacity in manufacturing
how to increase production rate
manufacturing throughput
how to improve production efficiency
how to increase throughput in wireless network
how to increase throughput in a network

I want to process some files with maximum throughput. The paths to files are saved in a database. I need to get file paths from the database, change their status to processing, process them, then change their status to either completed or failed.

Currently, I get the files in batches (of 100 files) in order to decrease the number of queries done and process them in parallel (with a degree of parallelism of 10). But in this way, I am losing throughput towards the end of the batch. When there are less than 10 files remaining in the batch the degree of parallelism is not 10 anymore, it decreases.

Here is what I have:

private async Task CopyPendingFilesAsync(SourcePath sourcePath, Options options)
{
    var batchIndex = 0;
    while (true)
    {
        var fileBatch = _sourceFileService.GetSourceFileBatchBySourcePathId(
            sourcePath.Id, _dataSourceExportConfig.FileCopyBatchSize, Status.Pending);
        if (fileBatch.Count == 0)
            return;

        await SetInProgressStatusForBatch(fileBatch)
            .ConfigureAwait(false);

        fileBatch
            .AsParallel()
            .WithDegreeOfParallelism(_dataSourceExportConfig.FileCopyDegreeOfParallelism)
            .ForAll(file => ProcessFile(file, destinationBase, options));

        await _sourceFileService
            .UpdateSourceFilesStatusAsync(fileBatch)
            .ConfigureAwait(false);

        batchIndex++;
    }
}

private async Task SetInProgressStatusForBatch(IEnumerable<SourceFile> fileBatch)
{
    foreach (var file in fileBatch)
        file.Status = Status.InProgress;

    await _sourceFileService
        .UpdateSourceFilesStatusAsync(fileBatch)
        .ConfigureAwait(false);
}

private void ProcessFile(
    SourceFile file,
    string destinationBase,
    Options options)
{
    try
    {
        //do something ...

        file.Status = Status.Success;
        file.ExceptionMessage = null;
    }
    catch (Exception ex)
    {
        _logger.Error(ex);
        file.Status = Status.Failed;
        file.ExceptionMessage = ex.Message;
    }
}

How can I maximize the throughput? I read about the producer-consumer pattern with BlockingCollection, TPL Dataflow, and Rx and I am pretty sure that what I want to achieve can be implemented with any of the above, but I wasn't able to do it so far. With the producer-consumer pattern, my producer is extremely fast compared to the consumer, with TPL Dataflow I got stuck with the BatchBlock and I haven't tried Rx. Could someone please point me in the right direction?

Update: Here is a minimal, complete and verifiable example:

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Threading;

namespace ConsoleApp1
{
    internal static class Program
    {
        private static void Main()
        {
            Console.WriteLine("Processing files");

            var stopWatch = new Stopwatch();
            stopWatch.Start();

            var fileService = new FileService();
            fileService.ProcessPendingFiles();

            foreach (var sourceFile in fileService.SourceFiles)
            {
                Console.WriteLine($"{sourceFile.Id} {sourceFile.Status}");
            }

            Console.WriteLine(stopWatch.Elapsed);

            Console.ReadLine();
        }
    }

    public class FileService
    {
        private const int BatchSize = 100;
        private const int DegreeOfParallelism = 10;
        //this SourceFiles property replaces the Sqlite database where the data is actually stored
        public ICollection<SourceFile> SourceFiles =
            Enumerable
                .Range(0, 1000)
                .Select(i =>
                    new SourceFile
                    {
                        Id = i,
                        Path = "source file path",
                        Status = Status.Pending,
                    })
                .ToList();

        public void ProcessPendingFiles()
        {
            while (true)
            {
                var fileBatch = GetSourceFileBatch(BatchSize, Status.Pending);
                if (fileBatch.Count == 0)
                    return;

                SetInProgressStatusForBatch(fileBatch);

                fileBatch
                    .AsParallel()
                    .WithDegreeOfParallelism(DegreeOfParallelism)
                    .ForAll(ProcessFile);

                UpdateSourceFiles(fileBatch);
            }
        }

        private ICollection<SourceFile> GetSourceFileBatch(int batchSize, Status status)
            => SourceFiles
                .Where(sf => sf.Status == status)
                .Take(batchSize)
                .ToList();

        //set status to in progress for all files in the batch
        //and save the changes to database
        //in the application this is actually done with a bulk update and the method is async
        private void SetInProgressStatusForBatch(IEnumerable<SourceFile> fileBatch)
        {
            foreach (var file in fileBatch)
            {
                file.Status = Status.InProgress;

                var sourceFile = SourceFiles.First(sf => sf.Id == file.Id);
                sourceFile.Status = file.Status;
            }
        }

        //set status and exception messages for all files in the batch
        //and save the changes to database
        //in the application this is actually done with a bulk update and the method is async
        private void UpdateSourceFiles(IEnumerable<SourceFile> fileBatch)
        {
            foreach (var file in fileBatch)
            {
                var sourceFile = SourceFiles.First(sf => sf.Id == file.Id);
                sourceFile.Status = file.Status;
                sourceFile.ExceptionMessage = file.ExceptionMessage;
            }
        }

        private void ProcessFile(SourceFile file)
        {
            try
            {
                //do something ...
                Thread.Sleep(20);

                file.Status = Status.Success;
                file.ExceptionMessage = null;
            }
            catch (Exception ex)
            {
                file.Status = Status.Failed;
                file.ExceptionMessage = ex.Message;
            }
        }
    }

    public class SourceFile
    {
        public int Id { get; set; }

        public string Path { get; set; }

        public Status Status { get; set; }

        public string ExceptionMessage { get; set; }
    }

    public enum Status
    {
        Pending,

        InProgress,

        Success,

        Failed,
    }
}

I know you are probably going to hate this answer, but ultimately, it depends...

I'm not entirely sure what these files are, where they live or what processing them means. My answer assumes you are happy with the current processing at peak, you just need a better way of ensuring you get consistent performance here and it doesn't drop towards the tail of the operation. I'll try to stick to answering your more direct question in terms of using the producer-consumer pattern with a BlockingCollection rather than change the entire approach.

I do think you understand why the slowdown is happening, but you aren't sure how to deal with this since you are fetching the next batch of items only when the current batch completes. (Needless to say this is probably a good case for using a message queue rather than SQL but that's a somewhat separate discussion that avoids your primary question.)

This has been answered in quite a bit of detail on the following question:

classic producer consumer pattern using blockingcollection and tasks .net 4 TPL

public class YourCode
{
  private BlockingCollection<object> queue = new BlockingCollection<object>();

  public YourCode()
  {
    var thread = new Thread(StartConsuming);
    thread.IsBackground = true;
    thread.Start();
  }

  public void Produce(object item)
  {
    queue.Add(item);
  }

  private void StartConsuming()
  {
    while (true)
    {
      object item = queue.Take();
      // Add your code to process the item here.
      // Do not start another task or thread. 
    }
  }
}

You could then have multiple consumers with a single producer (since you do point out that you are producing much faster than you are consuming)

Temperature for Standard Lightpipe Optics = 1250°C Minimum Temperature better control during process ramp up, thus maximizing process throughput or  Lean Manufacturing focuses on customer value. And if you tell your employees you’re instituting Lean Manufacturing because you want to improve your bottom line, some may think improving throughput will mean their job will be eliminated. But less waste means more capacity, which helps increase the top line: revenue!

A+B→C r1=k1xAxB k1= 2 ×108exp(−6 ×104/RT) B+C→D r2=k2xBxC k2 DOFs are optimized for maximizing J. Since the desired process throughput can vary  While the process will start executing, the thread will be returned to the thread pool. After finishing the execution, one more thread is allocated for the request to bringing the request.So, in this way, the thread will not wait until the completion of process execution and come back to the thread pool for handling another Request.

A worker pattern should simplify things for you, and ensure you are always processing a consistent number of units of work in parallel.

If you create for example 10 tasks up front, and allow them to take a new job until there are none left, you no longer rely on waiting on a whole batch of threads or tasks to all complete before starting any more.

class WorkController
{
    private DataSourceExportConfig _dataSourceExportConfig;
    private SourceFileService _sourceFileService;
    private string destinationBase;

    public async Task CopyPendingFilesAsync(SourcePath sourcePath, Options options)
    {
        await Task.WhenAll(Enumerable.Range(0, 10).Select(x => Worker(sourcePath, options)));
    }

    public async Task Worker(SourcePath sourcePath, Options options)
    {
        SourceFile file = null;

        while (_sourceFileService.GetNextFile(out file))
        {
            ProcessFile(file, destinationBase, options);
        }
    }

    private void ProcessFile(SourceFile file, string destinationBase, Options options)
    {
    }
}

Thus, it is important in the process integration strategy that sufficient thermal budget in the 800 to 900°C range if the process is to have a reasonable throughput and be rate to be maximized and therefore improves the process throughput. To maximize throughput, try the following steps: Use asynchronous operations to take advantage of client-side batching. Use the default batching interval of 20 ms to reduce the number of Service Bus client protocol transmissions. Leave batched store access enabled. This access increases the overall

As there is demand for environment-friendly building, this process is widely injector heads in series has been developed to maximize process throughput, to grow non-oxide epitaxial films at low temperatures, 550°C–850°C, in several  I am facing a read throughput issue in my applicaion using informatica . My sql override is a big query . When I run it directly on the Oracle database using any tool , the output is quick . I get all the rows within 5 mins . But when I run the informatica session , it takes some 20 mins . The busy % for read thread is 100 % and almost all the

W. Marquardt, C. Pantelides BOTTLENECK For several plants, it is optimal to maximize the throughput (production rate), subject to achieving feasible  Are there any guides or examples on how to maximize receive throughput? When following guides and code examples we end up with really poor performance. For instance the MS example on receiving messages from a subscription doesn't break the 20-30 msg/s even though we increase the MaxConcurrentCalls property.

The first place to start when trying to increase your throughput is to review your existing workflow. c) Processes - Do you have clearly mapped processes? One strategy to increase efficiency in any company or organization is to calculate the throughput, or cycle time. Companies need to know how much time is taken to produce goods, from manufacturing until products leave for the stores. Throughput time will consist of many components, like process time, inspection time,

Finding effective ways to increase throughput helps manufacturers meet in manufacturing is to carefully analyze your production process for  1) Given that I'm using c# code and not a hex file (through BGAPI or BGScript), how am I supposed to change the xml files (specifically the throughput and connections parameters)? It seems that in the usbdc example (and many other ones) of the bluegiga software, these xml files exist and play a role in the connection.

Comments
  • 1) If you had a source of filenames, then you could have n workers which each takes a filename from the source, processes it, and repeats until there are no more filenames. 2) Depending on what processing a file entails, you might get more significant improvements by tuning the size of any disk I/O buffers that are used. 3) Make sure it doesn't run fastest with just one file being processed at a time.
  • just write some sudo "words" as to what you are doing, like a flow.. step1.. step 2... its hard to tell if you are interacting with the DB inside the ProcessFile, therefore hard to tell where the bottleneck is, like hard to tell the entry point
  • @Seabizkit I am not interacting with the DB inside ProcessFile.
  • Is it possible for you to provide a minimal reproducible example?
  • @MariusStănescu - You can change the SelectMany to Select + Merge to be able to specify the degree of parallelism.
  • What are those blocks supposed to do? In any case, instead of processing batches of files with a TransformBlock that processes/emits an entire IEnumerable at a time, use a TransformManyBlock which will emit each item to the next block, allowing multiple blocks to work in parallel. Otherwise, this code is no better than calling each function one at a time
  • That's what the dataflow block already does, with added queueing, backpresure support
  • Thank you for the answer. It doesn't answer my question though. Imagine that I am not processing files, but some random data entity. (In fact, the files are on different network shares)
  • @MariusStănescu: Network shares are unlikely to have Rate Throthling. So the limit is either the Network Transmission rate or the Disk I/O, wichever is lower. In either case this is hardly a programming problem. Loading the data will be the core limiter in 8/10 cases.
  • There could be network limits per connection. For example, even though the network might be 1Gbps, one connection could be limited to 1Mbps.
  • @MariusStănescu: Per connection limtis on a Windows share? Not sure if shares even have that feature. Or even have more then 1 connection/user for that mater. Of course it is not my area of expertise.
  • We are digressing. I am interested in a way to improve the throughput of the process just to learn how to do it better. As I said earlier, you can imagine the thing that I am processing is a random entity that does very CPU intensive work and is not related to I/O at all. So, the question is not about I/O vs CPU. But I appreciate that you noticed that, I thank you very much for the input. Maybe I'll ask a different question about the topic and then we can have that discussion there. :)