Go Pipelines: Rethink Backend Structure

The cursor blinks, accusingly. On screen, a tight, unforgiving loop. Inside, a jumble of strings.TrimSpace, strings.ReplaceAll, keyword checks, and a database Save call, all mashed together. It’s functional, sure. It works. But reading it feels like trying to decipher hieroglyphs etched into a single stone tablet.

This isn’t just about a job scraper. This is about a fundamental architectural quandary that plagues countless backend systems: how do you gracefully handle sequential, yet distinct, operations without creating an unmaintainable mess? The typical response? A monstrous for loop, rife with tightly coupled logic that mocks testability and scoffs at reusability.

And the concurrency aspect? Don’t even get me started. Trying to parallelize that tangled mess is like trying to untangle headphones while wearing them in the dark. It’s a recipe for bugs that’ll make your hair fall out.

The Problem with ‘One Big Loop’

Let’s break down the pain points of that initial, instinctual approach. In the job scraper example, the core tasks are simple: scrape raw data, normalize it (clean it up), score it based on relevance, and finally, save it to a database. Sounds straightforward, right? Except when you cram it all into a single iteration:

for _, raw := range rawJobs {
// normalize
raw.Title = strings.TrimSpace(raw.Title)
raw.Location = strings.ReplaceAll(raw.Location, "NYC", "New York")
// score
score := 0
for _, keyword := range keywords {
if strings.Contains(raw.Title, keyword) {
score++
}
}
// save
s.Repo.Create(raw.Title, raw.Location, score)
}

The code, as presented, technically works. But the inherent coupling is damning.

Obscured Stages: Where does normalization officially end and scoring begin? The lack of clear boundaries forces developers to perform mental gymnastics just to understand a single job’s processing flow.
Testing Hell: How do you isolate and test just the scoring logic? You can’t. It’s inextricably bound to the normalization and saving steps within that loop, making unit testing a Sisyphean task.
Maintenance Nightmares: Introducing a new scoring rule means wading into this dense loop, potentially disrupting normalization or, worse, the saving mechanism. Every change is a high-stakes gamble.
Code Duplication: Need that normalization logic elsewhere? Brace yourself for copy-paste, the arch-nemesis of maintainable software, and the inevitable drift as you try to keep those duplicates in sync.
Concurrency Impotence: Even thinking about parallel processing feels like a non-starter. The tangled dependencies make it nearly impossible to identify discrete, thread-safe units of work.

Enter the Pipeline: Explicit Stages, Explicit Flow

The fundamental insight here is that the original loop isn’t executing one complex problem; it’s executing a sequence of distinct steps for each item. Scrape. Normalize. Score. Store. Repeat. The breakthrough comes from making these stages explicit, rather than implicit within a monolithic function.

This is the essence of the Pipeline Pattern: breaking down a sequential process into discrete, interchangeable stages. Data flows from one stage to the next, undergoing transformation at each step. Think Scrape → Normalize → Score → Store.

Here’s how that looks in practice, using the Go code from the original project. The Pipeline struct itself becomes a container for these distinct functional components, injected via interfaces:

type Pipeline struct {\nscorer scoring.Scorer\njobService JobService\ncompanyService CompanyService\nlogger *slog.Logger\n}

func NewPipeline(
scorer scoring.Scorer,
jobService JobService,
companyService CompanyService,
logger *slog.Logger,
) *Pipeline {\nreturn &Pipeline{\nscorer: scorer,\njobService: jobService,\ncompanyService: companyService,\nlogger: logger,\n}
}

The constructor, NewPipeline, highlights a critical architectural decision: dependency injection. By accepting interfaces (scoring.Scorer, JobService, etc.), the Pipeline isn’t tied to any concrete implementation of these stages. This is where the magic of flexibility begins.

Then comes the Run() method, the orchestrator of the pipeline’s execution:

```go func (p *Pipeline) Run(ctx context.Context, scraper Scraper) error {\n// 1. Scrape\nrawJobs, err := scraper.Scrape(ctx)\nif err != nil {\nreturn fmt.Errorf(“scraping %s: %w”, scraper.Source(), err)\n}

for _