Using Asynchronous programming to manage parallel processing

Background

There was a requirement to perform series of tasks, involving generation of output files, such that the required throughput is achieved. These tasks involve database read operation, external API invocation and file i/o. Generally, benchmarking showed that executing them in sequential way was not helpful. What if asynchronous programming be used to perform this task.

So Lets Start.

Approach

Lets assume that this typical use case requires,

• fetching data from database for the purpose of merging placeholders in a Template and perform mail merge

• Generate PDF file from mail-merged output of last step (say HTML to PDF)

• send notification to users via third party API.

The requirement is to perform these steps in such a way that 50 or more notifications (with file) are sent per minute.

For the purpose of simplicity, lets assume that,

• Database read operation and HTML generation basis template, takes upto 2 seconds per iteration
• We will use Puppeteer Sharp library for PDF Generation
• External API Integration takes up to 2 seconds per call

Since current approach of sequential execution is not helpful, lets try below (both the methods process 5 requests[i.e. generate 5 pdf files] per iteration),

• Using Task asynchronous programming model - This uses Task library to start tasks in parallel and subsequently process them as each completes.

• Using Dataflow - Task Parallel Library - This uses Dataflow Library to orchestrate each step in the process and use parallelism for performance.

Below is the Report from Benchmarkdotnet for both the approaches.

As one can see, using above techniques, It is straightforward to write asynchronous code that performs parallel execution and achieves better performance compared to sequential alternate approach.

References:

• BenchmarkDotNet
• Introduction to Benchmarking C# Code with Benchmark .NET

Happy Coding !!