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Streaming vs. Buffering

In NPipeline, understanding the concepts of streaming and buffering is crucial for designing efficient and performant data pipelines. These concepts dictate how data items are handled as they move between nodes, directly impacting memory consumption, latency, and overall throughput.

Visual Comparison: Streaming vs Buffering​

Figure 1: Side-by-side comparison of streaming and buffering data flow patterns. In streaming, items are processed individually as they arrive, while in buffering, items are collected into batches before processing.

Trade-offs Comparison​

AspectStreamingBuffering
Memory UsageLow - only a few items in memory at onceHigher - accumulates items before processing
LatencyLow - immediate processing of each itemHigher - waits for batch completion
Processing ModelContinuous, per-item processingBatch-oriented processing
OverheadHigher per-item processing costLower per-item cost due to batching
Use CasesReal-time analytics, continuous processingBatch processing, database writes, file I/O
Error HandlingErrors isolated to individual itemsErrors may affect entire batch

Table 1: Trade-offs between streaming and buffering approaches. Each approach has distinct advantages depending on your specific requirements.

Streaming​

Streaming refers to the processing of data items one by one, or in small, continuous batches, as they arrive. In a streaming pipeline, data is not accumulated in memory before being passed to the next stage. Instead, each item (or a small group of items) is processed and immediately forwarded.

Characteristics of Streaming​

  • Low Memory Footprint: Only a few data items are held in memory at any given time.
  • Low Latency: Data is processed and moved through the pipeline quickly, reducing delays.
  • Continuous Processing: Ideal for real-time data processing and long-running pipelines.
  • "Push" Model: Data is pushed from upstream nodes to downstream nodes as soon as it's ready.

Most NPipeline nodes inherently support streaming behavior, especially ISourceNode (which yields items) and ITransformNode (which also yields transformed items).

Example: A Pure Streaming Pipeline​

using NPipeline;

public sealed record EventData(int Id, string Message);

public sealed class StreamSource : SourceNode<EventData>
{
    public async IAsyncEnumerable<EventData> ExecuteAsync(CancellationToken cancellationToken = default)
    {
        for (int i = 0; i < 5; i++)
        {
            if (cancellationToken.IsCancellationRequested) yield break;
            var data = new EventData(i, $"Event {i}");
            Console.WriteLine($"Source: Producing {data.Id}");
            yield return data;
            await Task.Delay(10, cancellationToken); // Simulate fast stream
        }
    }
}

public sealed class StreamTransform : TransformNode<EventData, string>
{
    public async IAsyncEnumerable<string> ExecuteAsync(IAsyncEnumerable<EventData> input, CancellationToken cancellationToken = default)
    {
        await foreach (var item in input.WithCancellation(cancellationToken))
        {
            if (cancellationToken.IsCancellationRequested) yield break;
            var transformed = $"Processed: {item.Message.ToUpper()}";
            Console.WriteLine($"Transform: {item.Id} -> {transformed}");
            yield return transformed;
        }
    }
}

public sealed class StreamSink : SinkNode<string>
{
    public async Task ExecuteAsync(IAsyncEnumerable<string> input, CancellationToken cancellationToken = default)
    {
        await foreach (var item in input.WithCancellation(cancellationToken))
        {
            if (cancellationToken.IsCancellationRequested) break;
            Console.WriteLine($"Sink: Consumed '{item}'");
        }
    }
}

public static class Program
{
    public static async Task Main(string[] args)
    {
        var context = new PipelineContext();
        var runner = new PipelineRunner();

        Console.WriteLine("Starting streaming pipeline...");
        await runner.RunAsync<StreamingPipelineDefinition>(context);
        Console.WriteLine("Streaming pipeline finished.");
    }
}

public sealed class StreamingPipelineDefinition : IPipelineDefinition
{
    public void Define(PipelineBuilder builder, PipelineContext context)
    {
        var sourceHandle = builder.AddSource<StreamSource, EventData>("source");
        var transformHandle = builder.AddTransform<StreamTransform, EventData, string>("transform");
        var sinkHandle = builder.AddSink<StreamSink, string>("sink");

        builder.Connect(sourceHandle, transformHandle);
        builder.Connect(transformHandle, sinkHandle);
    }
}

In this example, each EventData item is produced by the StreamSource, immediately transformed by StreamTransform, and then immediately consumed by StreamSink. Data flows continuously without significant accumulation.

Buffering​

Buffering involves accumulating a certain amount of data in memory before processing or forwarding it to the next stage. This can be done explicitly by specific nodes (e.g., batching nodes) or implicitly by operations that require all data to be present before proceeding (e.g., certain aggregation operations).

Characteristics of Buffering​

  • Higher Memory Footprint: Data is temporarily stored in memory, potentially increasing memory usage.
  • Increased Latency: Processing is delayed until a buffer is full or a specific condition is met.
  • Batch Processing: Suitable for operations that benefit from processing data in chunks (e.g., database inserts, file writes, certain analytical functions).
  • "Pull" or "Batch-and-Push" Model: Data might be pulled into a buffer and then pushed as a batch.

While buffering can introduce latency and higher memory usage, it can also lead to increased throughput for certain operations by reducing overhead (e.g., fewer database transactions for batch inserts).

Example: Introducing Buffering with a Batching Node​

NPipeline provides specific nodes, like BatchingNode<T>, to introduce explicit buffering.

using NPipeline;
using NPipeline.Interfaces;
using NPipeline.Nodes;

public sealed record Message(int Id, string Content);

public sealed class CountingSource : SourceNode<Message>
{
    public async IAsyncEnumerable<Message> ExecuteAsync(CancellationToken cancellationToken = default)
    {
        for (int i = 0; i < 10; i++)
        {
            if (cancellationToken.IsCancellationRequested) yield break;
            var msg = new Message(i, $"Item {i}");
            Console.WriteLine($"Source: Producing {msg.Id}");
            yield return msg;
            await Task.Delay(10, cancellationToken);
        }
    }
}

public sealed class BatchSink : SinkNode<IReadOnlyList<Message>>
{
    public async Task ExecuteAsync(IAsyncEnumerable<IReadOnlyList<Message>> input, CancellationToken cancellationToken = default)
    {
        await foreach (var batch in input.WithCancellation(cancellationToken))
        {
            if (cancellationToken.IsCancellationRequested) break;
            Console.WriteLine($"Sink: Consumed a batch of {batch.Count} items.");
            foreach (var item in batch)
            {
                Console.WriteLine($"  - Processed item {item.Id}: {item.Content}");
            }
            await Task.Delay(50, cancellationToken); // Simulate batch processing time
        }
    }
}

public static class Program
{
    public static async Task Main(string[] args)
    {
        var context = new PipelineContext();
        var runner = new PipelineRunner();

        Console.WriteLine("Starting buffering pipeline...");
        await runner.RunAsync<BufferingPipelineDefinition>(context);
        Console.WriteLine("Buffering pipeline finished.");
    }
}

public sealed class BufferingPipelineDefinition : IPipelineDefinition
{
    public void Define(PipelineBuilder builder, PipelineContext context)
    {
        var sourceHandle = builder.AddSource<CountingSource, Message>("source");
        var batchHandle = builder.AddTransform<BatchingNode<Message>, Message, IReadOnlyList<Message>>("batcher");
        var sinkHandle = builder.AddSink<BatchSink, IReadOnlyList<Message>>("sink");

        builder.Connect(sourceHandle, batchHandle);
        builder.Connect(batchHandle, sinkHandle);
    }
}

In this example, the BatchingNode collects Message items until it has 3 items or 1 second passes, then it forwards the entire list (IReadOnlyList<Message>) to the BatchSink. This demonstrates explicit buffering.

Choosing Between Streaming and Buffering​

The choice between streaming and buffering depends on your specific use case and requirements:

FeatureStreamingBuffering
Memory UsageLowPotentially High
LatencyLowHigher
ThroughputHigh for continuous data, per-item overheadCan be higher for batch-optimized operations
Use CasesReal-time analytics, continuous processingBatch processing, database writes, file I/O
Fault ToleranceErrors can be handled per itemErrors might affect an entire batch

NPipeline is designed to support both paradigms, allowing you to combine streaming and buffering nodes to optimize your data flows for various scenarios.

Next Steps​