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# Friday, January 26, 2024

Introduction

We migrate code out of MF to Azure. Tool we use produces plain good functionally equivalent C# code. But it turns it's not enough!

So, what's the problem?

Converted code is very slow, especially for batch processing, where MF completes job, say in 30 minutes, while converted code finishes in 8 hours.

At this point usually someone appears and whispers in the ear:

Look, those old technologies are proven by the time. It worth to stick to old good Cobol, or better to Assembler if you want to do the real thing.

We're curious though: why is there a difference?

Turns out the issue lies in differences of network topology between MF and Azure solutions. On MF all programs, database and file storage virtually sit in a single box, thus network latency is negligible.

It's rather usual to see chatty SQL programs on MF that are doing a lot of small SQL queries.

In Azure - programs, database, file storage are different services most certainly sitting in different phisical boxes. You should be thankfull if they are co-located in a single datacenter. So, network latency immediately becomes a factor. Even if it just adds 1 millisecond per SQL roundtrip, it adds up in loops, and turns in the showstopper.

There is no easy workaround on the hardware level.

People advice to write programs differently: "Tune applications and databases for performance in Azure SQL Database".

That's a good advice for a new development but discouraging for migration done by a tool.

So, what is the way forward?

Well, there is one. While accepting weak sides of Azure we can exploit its strong sides.

Parallel refactoring

Before continuing let's consider a code demoing the problem:

  public void CreateReport(StringWriter writer)
  {
    var index = 0;

    foreach(var transaction in dataService.
      GetTransactions().
      OrderBy(item => (item.At, item.SourceAccountId)))
    {
      var sourceAccount = dataService.GetAccount(transaction.SourceAccountId);
      var targetAccount = transaction.TargetAccountId != null ?
        dataService.GetAccount(transaction.TargetAccountId) : null;

      ++index;

      if (index % 100 == 0)
      { 
        Console.WriteLine(index);
      }

      writer.WriteLine($"{index},{transaction.Id},{
        transaction.At},{transaction.Type},{transaction.Amount},{
        transaction.SourceAccountId},{sourceAccount?.Name},{
        transaction.TargetAccountId},{targetAccount?.Name}");
    }
  }

This cycle queries transactions, along with two more small queries to get source and target accounts for each transaction. Results are printed into a report.

If we assume query latency just 1 millisecond, and try to run such code for 100K transactions we easily come to 200+ seconds of execution.

Reality turns to be much worse. Program spends most of its lifecycle waiting for database results, and iterations don't advance until all work of previous iterations is complete.

We could do better even without trying to rewrite all code! Let's articulate our goals:

  1. To make code fast.
  2. To leave code recognizable.

The idea is to form two processing pipelines:

  • (a) one that processes data in parallel out of order;
  • (b) other that processes data serially, in original order;

Each pipeline may post sub-tasks to the other, so (a) runs its tasks in parallel unordered, while (b) runs its tasks as if everything was running serially.

So, parallel plan would be like this:

  1. Queue parallel sub-tasks (a) for each transaction.
  2. Parallel sub-task in (a) reads source and target accounts, and queues serial sub-task (b) passing transaction and accounts.
  3. Serial sub-task (b) increments index, and writes report record.
  4. Wait for all tasks to complete.

To reduce burden of task piplelines we use Dataflow (Task Parallel Library), and encapsulate everything in a small wrapper.

Consider refactored code:

  public void CreateReport(StringWriter writer)
  {
    using var parallel = new Parallel(options.Value.Parallelism); //     ⬅️ 1
    var index = 0;

    parallel.ForEachAsync( //     ⬅️ 2
      dataService.
        GetTransactions().
        OrderBy(item => (item.At, item.SourceAccountId)),
      transaction => //     ⬅️ 3
      {
        var sourceAccount = dataService.GetAccount(transaction.SourceAccountId);
        var targetAccount = transaction.TargetAccountId != null ?
          dataService.GetAccount(transaction.TargetAccountId) : null;

        parallel.PostSync(  //     ⬅️ 4
          (transaction, sourceAccount, targetAccount),  //     ⬅️ 5
          data =>
          {
            var (transaction, sourceAccount, targetAccount) = data; //     ⬅️ 6

            ++index;

            if (index % 100 == 0)
            {
              Console.WriteLine(index);
            }

            writer.WriteLine($"{index},{transaction.Id},{
              transaction.At},{transaction.Type},{transaction.Amount},{
              transaction.SourceAccountId},{sourceAccount?.Name},{
              transaction.TargetAccountId},{targetAccount?.Name}");
          });
      });
  }

Consider ⬅️ points:

  1. We create Parallel utility class passing degree of parallelism requested.
  2. We iterate transactions using parallel.ForEachAsync() that queues parallel sub-tasks for each transaction, and then waits until all tasks are complete.
  3. Each parallel sub-task recieves a transaction. It may be called from a different thread.
  4. Having recieved required accounts we queue a sub-task for synchronous execution using parallel.PostSync(), and
  5. Pass there data collected in parallel sub-task: transaction and accounts.
  6. We deconstruct data passed into variables, and then proceed with serial logic.

What we achieve with this refactoring:

  1. Top level query that brings transactions is done and iterated serially.
  2. But each iteration body is run in parallel. By default we set it up to allow up to 100 parallel executions. All those parallel sub-task do not wait on each other so their waitings do not add up.
  3. Sync sub-tasks are queued and executed in order of their serial appearance, so increments and report records are not a subject of race conditions, nor a subject of reordering of output records.

We think that such refactored code is still recognizible.

As for performance this is what log shows:

Serial test
100
...
Execution time: 00:01:33.8152540

Parallel test
100
...
Execution time: 00:00:05.8705468

Reference

Please take a look at project to understand implementation details, and in particular Parallel class implementing API to post parallel and serial tasks, run cycles and some more.

Please continue reading on GitHub.

Friday, January 26, 2024 1:27:16 PM UTC  #    Comments [0] -
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