Async Batching Strategies

In this lesson, you will learn how to optimize your asynchronous pipelines by grouping individual items into batches to minimize I/O overhead and improve overall throughput.

Why Batching Matters

When your application processes high-frequency tasks—such as writing log entries to a database or sending API requests—performing an individual I/O operation for every single item can overwhelm the system. Each call introduces latency due to network round-trips and thread synchronization. Instead of executing one task at a time, we can collect items in a temporary buffer and perform a single, bulk operation once a specific size or time threshold is met.

Implementing the Strategy

Using System.Threading.Channels is an excellent way to manage this flow. A ChannelReader acts as our source, while our logic consumes those items into a list until it reaches a "batch size."

async Task ProcessBatchesAsync(ChannelReader<WorkItem> reader, int batchSize)
{
    var batch = new List<WorkItem>();

    await foreach (var item in reader.ReadAllAsync())
    {
        batch.Add(item);

        if (batch.Count >= batchSize)
        {
            await ProcessBatchAsync(batch); // Perform one bulk I/O
            batch.Clear();
        }
    }
}

Balancing Throughput and Latency

The simple approach above has one flaw: if your pipeline produces items slowly, the batch might sit waiting for the batchSize to be filled, causing significant delays. To optimize this, you should combine your batching logic with a timer. You can use a CancellationTokenSource or a Task.Delay to "flush" the buffer if the batch isn't full after a certain period (e.g., 500 milliseconds).

This strategy prevents your system from idling while waiting for the next item, ensuring that you maintain both high efficiency through batching and low latency through periodic flushing. By decoupling the consumption rate from the arrival rate, you gain significant control over how your application interacts with external resources.

Key Takeaway: Batching reduces the overhead of individual I/O operations by grouping work, but always pair it with a timeout to prevent unnecessary latency for low-volume streams.